技术领域Technical Field
本申请涉及自然语言处理技术领域，具体涉及一种客服诉求数据处理方法、装置、电子设备及存储介质。The present application relates to the technical field of natural language processing, and specifically to a method, device, electronic device and storage medium for processing customer service demand data.
背景技术Background Art
随着企业服务需求的增加和客户参与意识的增强，客服诉求日益成为企业管理的重要组成部分。客服诉求不仅反映了客户对服务质量的期望，也是企业了解客户需求、优化服务的重要渠道。然而，面对海量的客服诉求数据，如何有效感知、分析并预警潜在的服务问题，成为企业管理者面临的一项重大挑战。With the increase in corporate service demand and the enhancement of customer participation awareness, customer service demands are becoming an increasingly important part of corporate management. Customer service demands not only reflect customers' expectations for service quality, but are also an important channel for companies to understand customer needs and optimize services. However, faced with massive amounts of customer service demand data, how to effectively perceive, analyze and warn of potential service problems has become a major challenge for corporate managers.
当前，客服诉求态势智能感知及预警系统主要采用基于结构化的数据查询统计和可视化技术，能够实现数据的收集、存储、清洗和预处理，生成统计查询结果，帮助企业管理者对客服诉求有一个宏观的了解。然而，现有方法存在一些局限性：数据分析需要大量的人工介入，不仅耗时耗力，效率低下，且无法对海量的客服数据进行深度的精准分析，不能直观体现分析结果，从而无法为企业管理者提供高效、精准的数据支持和服务，局限性较大。At present, the intelligent perception and early warning system of customer service demand situation mainly adopts structured data query statistics and visualization technology, which can realize data collection, storage, cleaning and preprocessing, generate statistical query results, and help enterprise managers have a macro understanding of customer service demands. However, the existing methods have some limitations: data analysis requires a lot of manual intervention, which is not only time-consuming and labor-intensive, but also inefficient, and cannot conduct in-depth and accurate analysis of massive customer service data, and cannot intuitively reflect the analysis results, thus failing to provide efficient and accurate data support and services for enterprise managers, and the limitations are relatively large.
前面的叙述在于提供一般的背景信息，并不一定构成现有技术。The preceding description is intended to provide general background information and does not necessarily constitute prior art.
发明内容Summary of the invention
本申请实施例提供一种客服诉求数据处理方法、装置、电子设备以及存储介质，可以对海量的客服数据进行深度的精准分析，能够为企业管理者提供高效、精确的数据支持和服务。The embodiments of the present application provide a customer service demand data processing method, device, electronic device and storage medium, which can perform in-depth and accurate analysis of massive customer service data and provide efficient and accurate data support and services for enterprise managers.
第一方面，本申请实施例提供了一种客服诉求数据处理方法，包括：In a first aspect, an embodiment of the present application provides a method for processing customer service demand data, including:
获取客服诉求数据，并提取所述客服诉求数据中的诉求事件标题和诉求事件内容；Acquire customer service demand data, and extract demand event titles and demand event contents from the customer service demand data;
基于所述诉求事件标题和所述诉求事件内容，提取得到事件要素信息，并基于所述诉求事件内容生成事件摘要信息；Extracting event element information based on the appeal event title and the appeal event content, and generating event summary information based on the appeal event content;
基于所述事件要素信息和所述事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果；Performing event trend analysis on the target event based on the event element information and the event summary information to obtain an event trend analysis result;
基于所述事件趋势分析结果，生成对应的预警信息，并推送所述预警信息至已绑定的关联对象对应的关联终端。Based on the event trend analysis result, corresponding warning information is generated, and the warning information is pushed to the associated terminal corresponding to the bound associated object.
可选的，在本申请的一些实施例中，所述方法还包括：Optionally, in some embodiments of the present application, the method further includes:
基于所述事件趋势分析结果和所述预警信息，生成对应的分析报告；Based on the event trend analysis results and the warning information, generate a corresponding analysis report;
基于所述客服诉求数据和所述事件趋势分析结果，生成对应的诉求事件地图。Based on the customer service demand data and the event trend analysis results, a corresponding demand event map is generated.
可选的，在本申请的一些实施例中，所述方法还包括：Optionally, in some embodiments of the present application, the method further includes:
基于所述事件要素信息进行事件聚类处理，得到反复诉求事件；Performing event clustering processing based on the event element information to obtain repeated appeal events;
基于所述诉求事件内容进行感兴趣信息匹配，得到匹配结果，并基于所述匹配结果确定感兴趣事件；Matching the information of interest based on the content of the appeal event to obtain a matching result, and determining the event of interest based on the matching result;
基于所述诉求事件内容进行弱信号分析，确定弱信号事件。Weak signal analysis is performed based on the content of the appeal event to determine the weak signal event.
可选的，在本申请的一些实施例中，所述基于所述事件要素信息和所述事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果，包括：Optionally, in some embodiments of the present application, performing event trend analysis on the target event based on the event element information and the event summary information to obtain an event trend analysis result includes:
分别获取所述反复诉求事件、所述感兴趣事件和所述弱信号事件对应的事件要素信息和事件摘要信息；Respectively obtaining event element information and event summary information corresponding to the repeated appeal event, the event of interest, and the weak signal event;
基于所述事件要素信息和所述事件摘要信息分别对所述反复诉求事件、所述感兴趣事件和所述弱信号事件进行事件趋势分析，得到所述反复诉求事件、所述感兴趣事件和所述弱信号事件对应的事件趋势分析结果。Based on the event element information and the event summary information, event trend analysis is performed on the repeated demand events, the events of interest and the weak signal events respectively to obtain event trend analysis results corresponding to the repeated demand events, the events of interest and the weak signal events.
可选的，在本申请的一些实施例中，所述基于所述诉求事件标题和所述诉求事件内容，提取得到事件要素信息，包括：Optionally, in some embodiments of the present application, the extracting event element information based on the appeal event title and the appeal event content includes:
将所述诉求事件标题和所述诉求事件内容进行组合，得到对应的文本组合；Combining the appeal event title and the appeal event content to obtain a corresponding text combination;
采用分词器对所述文本组合进行分词处理，得到第一分词结果；Using a word segmenter to perform word segmentation processing on the text combination to obtain a first word segmentation result;
通过预训练模型对所述分词结果进行词嵌入，作为第一模型输入数据；Perform word embedding on the word segmentation result through a pre-trained model as input data for the first model;
基于编码器对所述第一模型输入数据进行编码，并基于编码后的第一模型输入数据提取对应的文本上下文相关特征；Encoding the first model input data based on an encoder, and extracting corresponding text context-related features based on the encoded first model input data;
通过多层感知器对每个分词进行分类标注，得到每个分词对应的实体类型；Each word is classified and labeled through a multi-layer perceptron to obtain the entity type corresponding to each word;
基于所述文本上下文相关特征和所述实体类型，将邻接的同一实体类型的分词进行合并，形成事件要素信息，所述事件要素信息包括事件发生地、涉诉主体、事件内容和事件处置结果。Based on the text context-related features and the entity type, adjacent word segments of the same entity type are merged to form event element information, and the event element information includes the place where the event occurred, the subject involved in the lawsuit, the event content and the event handling result.
可选的，在本申请的一些实施例中，基于所述诉求事件内容生成事件摘要信息，包括：Optionally, in some embodiments of the present application, generating event summary information based on the appeal event content includes:
采用分词器对所述诉求事件内容进行分词处理，得到第二分词结果；Using a word segmenter to perform word segmentation processing on the content of the appeal event to obtain a second word segmentation result;
通过预训练模型对所述分词结果进行词嵌入，作为第二模型输入数据；Perform word embedding on the word segmentation result through a pre-trained model as input data for the second model;
采用解码器对所述第二模型输入数据进行解码处理，并基于解码后的第二模型输入数据提取对应的文本上下文相关特征；Using a decoder to decode the second model input data, and extracting corresponding text context-related features based on the decoded second model input data;
通过线性变换和归一化指数函数生成所述第二模型输入数据对应的词概率分布结果；Generate a word probability distribution result corresponding to the second model input data through linear transformation and normalized exponential function;
通过集束搜索算法计算所述词概率分布结果中的每个候选队列对应的累计概率；Calculate the cumulative probability corresponding to each candidate queue in the word probability distribution result by a beam search algorithm;
选取所述累计概率最高的若干个候选队列作为事件摘要信息。A plurality of candidate queues with the highest cumulative probabilities are selected as event summary information.
可选的，在本申请的一些实施例中，所述基于所述事件要素信息和所述事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果，包括：Optionally, in some embodiments of the present application, performing event trend analysis on the target event based on the event element information and the event summary information to obtain an event trend analysis result includes:
通过预设业务规则确定符合分析条件的目标事件；Determine target events that meet the analysis criteria through preset business rules;
按照时间顺序对所述目标事件进行排列，并对所述目标事件对应的所述事件要素信息和所述事件摘要信息进行数值编码，得到序列分析输入数据；Arranging the target events in chronological order, and numerically encoding the event element information and the event summary information corresponding to the target events to obtain sequence analysis input data;
通过时序卷积网络模型对所述序列分析输入数据进行特征分析和事件趋势分析，得到所述目标事件对应的事件趋势分析结果。The sequence analysis input data is subjected to feature analysis and event trend analysis through a temporal convolutional network model to obtain an event trend analysis result corresponding to the target event.
可选的，在本申请的一些实施例中，所述基于所述事件要素信息进行事件聚类处理，得到反复诉求事件，包括：Optionally, in some embodiments of the present application, performing event clustering based on the event element information to obtain repeated appeal events includes:
将所述事件要素信息进行分词和词嵌入处理，得到第三模型输入数据；Perform word segmentation and word embedding processing on the event element information to obtain third model input data;
将所述第三模型输入数据输入至语义嵌入模型，通过所述语义嵌入模型的自注意力层和前馈神经网络输出对应的事件向量；Inputting the third model input data into the semantic embedding model, and outputting the corresponding event vector through the self-attention layer and feedforward neural network of the semantic embedding model;
使用余弦相似度函数计算所有所述事件向量之间的相似度，构建相似度矩阵；Using the cosine similarity function to calculate the similarity between all the event vectors, and construct a similarity matrix;
基于密度聚类算法和所述相似度矩阵对目标事件进行事件聚类处理，得到反复诉求事件。Based on the density clustering algorithm and the similarity matrix, event clustering processing is performed on the target event to obtain repeated appeal events.
可选的，在本申请的一些实施例中，所述基于所述诉求事件内容进行感兴趣信息匹配，得到匹配结果，并基于所述匹配结果确定感兴趣事件，包括：Optionally, in some embodiments of the present application, matching the information of interest based on the content of the appeal event to obtain a matching result, and determining the event of interest based on the matching result includes:
基于领域知识和经验构建感兴趣词库；Build a vocabulary of interest based on domain knowledge and experience;
基于所述感兴趣词库，采用关键词匹配方式查找所述诉求事件内容中的关键词，作为第一感兴趣信息；Based on the interest word library, a keyword matching method is used to search for keywords in the content of the appeal event as the first interest information;
采用分词器对所述诉求事件内容进行分词后去除停用词，通过关键词匹配方式查找去除停用词后的分词结果中的关键词；Using a word segmenter to segment the content of the appeal event and remove stop words, and searching for keywords in the segmentation results after removing the stop words by keyword matching;
通过语义编码模型对所述关键词进行特征编码，并通过余弦相似度函数计算特征编码后的关键词和所述感兴趣词库的语义相似度；The keyword is feature-encoded by a semantic coding model, and the semantic similarity between the feature-encoded keyword and the vocabulary of interest is calculated by a cosine similarity function;
基于所述语义相似度确定所述诉求事件内容中的第二感兴趣信息；Determining second information of interest in the content of the appeal event based on the semantic similarity;
基于所述第一感兴趣信息和所述第二感兴趣信息，确定目标事件是否为感兴趣事件，并确定目标事件是否包含感兴趣信息及命中的感兴趣关键词。Based on the first information of interest and the second information of interest, it is determined whether the target event is an event of interest, and whether the target event contains information of interest and a hit keyword of interest.
第二方面，本申请实施例提供了一种客服诉求数据处理装置，包括：In a second aspect, an embodiment of the present application provides a customer service demand data processing device, including:
数据获取模块，用于获取客服诉求数据，并提取所述客服诉求数据中的诉求事件标题和诉求事件内容；A data acquisition module, used to acquire customer service demand data and extract demand event titles and demand event contents from the customer service demand data;
数据分析模块，用于基于所述诉求事件标题和所述诉求事件内容，提取得到事件要素信息，并基于所述诉求事件内容生成事件摘要信息；A data analysis module, configured to extract event element information based on the appeal event title and the appeal event content, and generate event summary information based on the appeal event content;
事件分析模块，用于基于所述事件要素信息和所述事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果；An event analysis module, used to perform event trend analysis on a target event based on the event element information and the event summary information to obtain an event trend analysis result;
事件预警模块，用于基于所述事件趋势分析结果，生成对应的预警信息，并推送所述预警信息至已绑定的关联对象对应的关联终端。The event warning module is used to generate corresponding warning information based on the event trend analysis result, and push the warning information to the associated terminal corresponding to the bound associated object.
相应的，本申请还提供一种电子设备，包括存储器，处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述程序时如第一方面所述方法的步骤。Correspondingly, the present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program according to the steps of the method described in the first aspect.
本申请还提供一种可读存储介质，所述存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如第一方面所述方法的计算机程序。The present application also provides a readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the method described in the first aspect.
本申请提供一种客服诉求数据处理方法、装置、电子设备以及存储介质，该客服诉求数据处理方法包括：获取客服诉求数据，并提取所述客服诉求数据中的诉求事件标题和诉求事件内容；基于所述诉求事件标题和所述诉求事件内容，提取得到事件要素信息，并基于所述诉求事件内容生成事件摘要信息；基于所述事件要素信息和所述事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果；基于所述事件趋势分析结果，生成对应的预警信息，并推送所述预警信息至已绑定的关联对象对应的关联终端。在本申请提供的客服诉求数据处理方案中，通过对客服诉求数据的全面获取和分析，利用自然语言处理技术，从诉求事件的标题和内容中提取关键要素信息生成事件摘要，提高了对客户问题的理解和分析的精准度；基于事件要素和摘要信息进行趋势分析，能够实时监测和预测潜在的服务问题，预测可能出现的服务问题，有助于企业提前准备和应对，及时生成预警信息，帮助企业快速响应潜在的服务危机，减少负面影响；最后通过自动化的数据处理和预警信息推送，为企业管理者提供高效、精准的数据支持，从而做出更加及时和准确的决策；另外，通过对多个事件系统进行多维度的数据分析，为决策者提供了全面的视角；从而实现对客服诉求的全面感知、精准分析和实时预警，进而提高企业竞争力。The present application provides a customer service demand data processing method, device, electronic device and storage medium, the customer service demand data processing method comprising: obtaining customer service demand data, and extracting the demand event title and demand event content in the customer service demand data; extracting event element information based on the demand event title and the demand event content, and generating event summary information based on the demand event content; performing event trend analysis on a target event based on the event element information and the event summary information to obtain an event trend analysis result; generating corresponding warning information based on the event trend analysis result, and pushing the warning information to an associated terminal corresponding to a bound associated object. In the customer service complaint data processing solution provided in this application, by comprehensively acquiring and analyzing customer service complaint data and utilizing natural language processing technology, key element information is extracted from the title and content of the complaint event to generate an event summary, thereby improving the accuracy of understanding and analyzing customer issues; trend analysis based on event elements and summary information can monitor and predict potential service problems in real time, predict possible service problems, help companies prepare and respond in advance, and generate early warning information in a timely manner to help companies quickly respond to potential service crises and reduce negative impacts; finally, through automated data processing and early warning information push, efficient and accurate data support is provided to company managers, so that they can make more timely and accurate decisions; in addition, through multi-dimensional data analysis of multiple event systems, a comprehensive perspective is provided to decision makers, thereby achieving comprehensive perception, accurate analysis and real-time early warning of customer service complaints, thereby improving corporate competitiveness.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单的介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域技术人员来讲，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 embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
图1是本申请实施例提供的客服诉求数据处理方法的应用环境图；FIG1 is an application environment diagram of a customer service demand data processing method provided in an embodiment of the present application;
图2是本申请实施例提供的客服诉求数据处理方法的流程示意图；FIG2 is a flow chart of a method for processing customer service demand data provided in an embodiment of the present application;
图3是本申请实施例提供的客服诉求数据处理方法的另一流程示意图；FIG3 is another flow chart of a method for processing customer service demand data provided by an embodiment of the present application;
图4是本申请实施例提供的客服诉求数据处理装置的结构示意图；FIG4 is a schematic diagram of the structure of a customer service demand data processing device provided in an embodiment of the present application;
图5是本申请实施例提供的电子设备的结构示意图。FIG. 5 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
具体实施方式DETAILED DESCRIPTION
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本申请相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的例子，或者如本申请的一些方面相一致的系统和方法的例子。Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of systems and methods consistent with the examples detailed in the attached claims, or with some aspects of the present application.
需要说明的是，在本文中，通过术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含等之类的描述，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素，此外，本申请不同实施例中具有同样命名的部件、特征、要素可能具有相同含义，也可能具有不同含义，其具体含义需以其在该具体实施例中的解释或者进一步结合该具体实施例中上下文进行确定。It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover descriptions such as non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
在后续的描述中，使用用于表示元件的诸如“模块”、“部件”或者“单元”的后缀仅为了有利于本申请的说明，其本身没有特定的意义。因此，“模块”、“部件”或者“单元”可以混合地使用。In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.
目前现有的客服诉求态势智能感知及预警系统主要基于结构化的数据查询统计和可视化技术，数据分析需要大量的人工介入，无法对海量的基于自然语言的客服数据进行深度的精准分析，不能自动生成主题或专题报告，不能对客服风险事件做出及时的预警，无法为企业提供高效、精准的数据支持和服务。The existing customer service demand situation intelligent perception and early warning system is mainly based on structured data query statistics and visualization technology. Data analysis requires a lot of manual intervention, and it is impossible to conduct in-depth and accurate analysis of massive natural language-based customer service data, and it cannot automatically generate thematic or special reports. It cannot make timely warnings for customer service risk events and cannot provide enterprises with efficient and accurate data support and services.
为了解决上述技术问题，本申请实施例提供了一种客服诉求数据处理方法、装置、电子设备及存储介质，通过处理和分析大量的客服诉求数据，实现对客服诉求的全面感知、精准分析和实时预警，从而为企业管理提供更加高效、精准的决策支持，提高企业竞争力。In order to solve the above technical problems, the embodiments of the present application provide a customer service demand data processing method, device, electronic device and storage medium. By processing and analyzing a large amount of customer service demand data, comprehensive perception, accurate analysis and real-time warning of customer service demands are achieved, thereby providing more efficient and accurate decision-making support for enterprise management and improving enterprise competitiveness.
图1为一个实施例中客服诉求数据处理方法的应用环境图。参照图1，该客服诉求数据处理方法应用于客服诉求数据处理系统。该客服诉求数据处理系统包括终端110和服务器120。终端110和服务器120通过网络连接，终端110具体可以是台式终端或移动终端，移动终端具体可以是手机、平板电脑、笔记本电脑等中的至少一种。服务器120可以用独立的服务器或者是多个服务器组成的服务器集群来实现。终端110用于获取客服诉求数据，服务器120用于提取客服诉求数据中的诉求事件标题和诉求事件内容；基于诉求事件标题和诉求事件内容，提取得到事件要素信息，并基于诉求事件内容生成事件摘要信息；基于事件要素信息和事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果；基于事件趋势分析结果，生成对应的预警信息，并推送预警信息至已绑定的关联对象对应的关联终端。FIG1 is an application environment diagram of a customer service demand data processing method in an embodiment. Referring to FIG1 , the customer service demand data processing method is applied to a customer service demand data processing system. The customer service demand data processing system includes a terminal 110 and a server 120. The terminal 110 and the server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal. The mobile terminal can be at least one of a mobile phone, a tablet computer, a laptop computer, etc. The server 120 can be implemented as an independent server or a server cluster composed of multiple servers. The terminal 110 is used to obtain customer service demand data, and the server 120 is used to extract the demand event title and demand event content in the customer service demand data; based on the demand event title and the demand event content, the event element information is extracted, and the event summary information is generated based on the demand event content; based on the event element information and the event summary information, the target event is analyzed for event trend, and the event trend analysis result is obtained; based on the event trend analysis result, the corresponding warning information is generated, and the warning information is pushed to the associated terminal corresponding to the bound associated object.
请参阅图2，图2是本申请一实施例提供的客服诉求数据处理方法的流程示意图，本实施例主要以该客服诉求数据处理方法应用于计算机设备为例来举例说明，本申请一实施例提供的客服诉求数据处理方法包括以下步骤：Please refer to FIG. 2 , which is a flow chart of a method for processing customer service demand data provided by an embodiment of the present application. This embodiment mainly uses the application of the method for processing customer service demand data to a computer device as an example to illustrate. The method for processing customer service demand data provided by an embodiment of the present application includes the following steps:
S1.获取客服诉求数据，并提取客服诉求数据中的诉求事件标题和诉求事件内容。S1. Obtain customer service demand data, and extract the demand event title and demand event content in the customer service demand data.
具体的，对于步骤S1，主要是获取来自多方面的客服诉求数据，然后提取出客服诉求数据中对应的诉求事件标题和诉求事件内容。其中，获取途径可以是获取来源于客户服务热线、电子邮件、社交媒体、在线客服系统、移动应用、反馈表单、社区论坛、产品评价和客户调研等途径获取到的客服诉求数据，在实际的实施例中，客户通过拨打客服热线进行咨询或投诉；客户通过发送电子邮件提出问题或反馈意见；客户可能通过社交媒体平台公开或私信的方式表达诉求；客户可以直接在企业网站上通过在线聊天工具与客服代表进行实时交流；一些企业通过移动应用程序提供客服支持，客户可以在应用内提交问题或反馈；客户可以在企业网站或产品内通过填写在线表单提交他们的诉求；客户可能在企业运营的或第三方的社区论坛中发布问题或讨论；客户在产品购买页面留下评价，其中可能包含对产品或服务的反馈；企业通过问卷调查、电话访谈等方式主动收集客户的意见和建议。Specifically, for step S1, the main purpose is to obtain customer service demand data from multiple aspects, and then extract the corresponding demand event title and demand event content in the customer service demand data. Among them, the acquisition method can be to obtain customer service demand data from customer service hotlines, emails, social media, online customer service systems, mobile applications, feedback forms, community forums, product evaluations, and customer surveys. In actual embodiments, customers make inquiries or complaints by calling the customer service hotline; customers raise questions or feedback by sending emails; customers may express their demands through social media platforms in public or private messages; customers can communicate with customer service representatives directly on the company's website through online chat tools in real time; some companies provide customer service support through mobile applications, and customers can submit questions or feedback within the application; customers can submit their demands by filling out online forms on the company's website or product; customers may post questions or discussions in community forums operated by the company or third parties; customers leave comments on the product purchase page, which may include feedback on the product or service; companies actively collect customer opinions and suggestions through questionnaires, telephone interviews, etc.
获取方式可以是通过web（World WideWeb，全球广域网）接口进行获取，在此不进行具体限制，例如使用客服软件或CRM系统自动记录和整理客户的互动和服务请求；通过应用程序编程接口（API）从不同的平台和渠道集成客服数据；利用数据挖掘技术分析社交媒体、论坛和网站等公开渠道的客户反馈；客服代表在与客户交流后手动记录客户的诉求和相关信息；将电话客服的语音通话转换为文本数据，便于进一步分析；对电子邮件、在线聊天、社交媒体帖子等文本数据进行内容分析；评估客户反馈的情感倾向，识别正面或负面的表达；将不同来源的客户反馈进行聚合，以便于统一管理和分析；从各个平台定期导出客服数据，进行备份和深入分析；实时监控客服渠道的动态，快速响应客户的诉求。The acquisition method can be through a web (World Wide Web) interface, which is not specifically limited here, such as using customer service software or CRM systems to automatically record and organize customer interactions and service requests; integrating customer service data from different platforms and channels through application programming interfaces (APIs); using data mining technology to analyze customer feedback from public channels such as social media, forums, and websites; customer service representatives manually recording customer requests and related information after communicating with customers; converting voice calls from telephone customer service to text data for further analysis; performing content analysis on text data such as emails, online chats, and social media posts; evaluating the emotional tendency of customer feedback and identifying positive or negative expressions; aggregating customer feedback from different sources for unified management and analysis; regularly exporting customer service data from various platforms for backup and in-depth analysis; and monitoring the dynamics of customer service channels in real time to quickly respond to customer requests.
在具体的实施例中，可以通过web接口获取来自客服服务热线、在线客服系统和移动应用的客服诉求信息，提取出事件标题、内容、时间、区域、来源渠道等信息后进行数据融合，对融合后的信息进行有效性检查、去除错误及重复数据，提升数据质量。In a specific embodiment, customer service demand information from a customer service hotline, online customer service system, and mobile application can be obtained through a web interface, and information such as event title, content, time, region, source channel, etc. can be extracted and then data fusion can be performed. The fused information can be checked for validity, and errors and duplicate data can be removed to improve data quality.
S2.基于诉求事件标题和诉求事件内容，提取得到事件要素信息，并基于诉求事件内容生成事件摘要信息。S2. Based on the appeal event title and appeal event content, event element information is extracted, and event summary information is generated based on the appeal event content.
具体的，对于步骤S2，在提取客服诉求数据中的诉求事件标题和诉求事件内容之后，根据诉求事件标题和诉求事件内容进行组合，提取得到对应的事件要素信息，并根据诉求事件内容自动生成对应的事件摘要信息。Specifically, for step S2, after extracting the claim event title and claim event content in the customer service claim data, the claim event title and claim event content are combined to extract corresponding event element information, and corresponding event summary information is automatically generated according to the claim event content.
S3.基于事件要素信息和事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果。S3. Perform event trend analysis on the target event based on the event element information and event summary information to obtain event trend analysis results.
具体的，对于步骤S3，通过业务规则筛选出符合分析条件的目标事件，从而根据提取得到的事件要素信息和自动生成的事件摘要信息对目标事件进行实时趋势分析处理，预测目标事件未来的事件发生趋势，实时趋势分析处理可以是基于时序卷积网络模型进行趋势分析，最终输出得到事件趋势分析结果。Specifically, for step S3, the target events that meet the analysis conditions are screened out through business rules, so that the target events are subjected to real-time trend analysis and processing based on the extracted event element information and the automatically generated event summary information to predict the future event trends of the target events. The real-time trend analysis and processing can be based on a temporal convolutional network model to perform trend analysis, and finally output the event trend analysis results.
S4.基于事件趋势分析结果，生成对应的预警信息，并推送预警信息至已绑定的关联对象对应的关联终端。S4. Based on the event trend analysis results, generate corresponding warning information, and push the warning information to the associated terminal corresponding to the bound associated object.
具体的，对于步骤S4，在得到事件趋势分析结果之后，根据事件趋势分析结果自动生成对应的预警信息，通过短信、邮箱或者微信等渠道，实时推送预警信息至已经绑定的关联对象对应的关联终端，例如企业管理者的计算机或者移动智能终端，确保及时响应和处置。Specifically, for step S4, after obtaining the event trend analysis results, the corresponding warning information is automatically generated according to the event trend analysis results, and the warning information is pushed in real time to the associated terminal corresponding to the bound associated object through channels such as SMS, email or WeChat, such as the computer or mobile smart terminal of the enterprise manager, to ensure timely response and disposal.
可见，本实施例提供的客服诉求数据处理方法，通过对海量的客服诉求数据进行深度的精准分析，提取出事件要素信息并自动生成事件摘要，从而根据事件要素信息和事件摘要信息对目标事件进行趋势分析和预警，实现对客户诉求的全面感知、精准分析和实时预警，从而为企业管理提供更加高效、精准的决策支持。It can be seen that the customer service demand data processing method provided in this embodiment, through in-depth and accurate analysis of massive customer service demand data, extracts event element information and automatically generates event summaries, thereby performing trend analysis and early warning on target events based on event element information and event summary information, achieving comprehensive perception, accurate analysis and real-time early warning of customer demands, thereby providing more efficient and accurate decision-making support for enterprise management.
可选的，如图3所示，在一些实施例中，所述客服诉求数据处理方法具体还可以包括：Optionally, as shown in FIG3 , in some embodiments, the customer service demand data processing method may further include:
S5.基于事件趋势分析结果和预警信息，生成对应的分析报告；S5. Generate corresponding analysis reports based on event trend analysis results and warning information;
S6.基于客服诉求数据和事件趋势分析结果，生成对应的诉求事件地图。S6. Generate a corresponding demand event map based on customer service demand data and event trend analysis results.
在具体的实施例中，在得到事件趋势分析结果和预警信息之后，本实施例提供的客服诉求数据处理方法还包括自动生成分析报告、专题报告和诉求事件地图的步骤，可以基于事件趋势分析结果及其对应的预警信息，自动生成对应日报、周报和月报，以提供详细的分析报告，还可以根据特定事件生成专题报告，为决策提供支持，另外，还可以通过可视化的形式，基于客服诉求数据和事件趋势分析结果，生成对应的诉求事件地图，在诉求事件地图上展示数据分布情况，还可以展示事件处置结果和事件趋势分析结果，并支持数据的形式交互，本实施例利用模型分析后的事件趋势分析结果和预警信息，进行多维度的可视化分析，为管理者提供全面直观的分析结果。In a specific embodiment, after obtaining the event trend analysis results and warning information, the customer service complaint data processing method provided in this embodiment also includes the steps of automatically generating analysis reports, special reports and complaint event maps. Based on the event trend analysis results and their corresponding warning information, corresponding daily, weekly and monthly reports can be automatically generated to provide detailed analysis reports. Special reports can also be generated based on specific events to provide support for decision-making. In addition, a corresponding complaint event map can be generated in a visual form based on the customer service complaint data and event trend analysis results, and the data distribution can be displayed on the complaint event map. The event handling results and event trend analysis results can also be displayed, and data interaction is supported. This embodiment uses the event trend analysis results and warning information after model analysis to conduct multi-dimensional visualization analysis to provide managers with comprehensive and intuitive analysis results.
可选的，在一些实施例中，步骤S2中“基于诉求事件标题和诉求事件内容，提取得到事件要素信息”，具体可以包括：Optionally, in some embodiments, in step S2, “extracting event element information based on the appeal event title and the appeal event content” may specifically include:
S211.将诉求事件标题和诉求事件内容进行组合，得到对应的文本组合；S211. Combine the appeal event title and the appeal event content to obtain a corresponding text combination;
S212.采用分词器对文本组合进行分词处理，得到第一分词结果；S212. Use a word segmenter to perform word segmentation processing on the text combination to obtain a first word segmentation result;
S213.通过预训练模型对分词结果进行词嵌入，作为第一模型输入数据；S213. embed the word segmentation results through the pre-trained model as the first model input data;
S214.基于编码器对第一模型输入数据进行编码，并基于编码后的第一模型输入数据提取对应的文本上下文相关特征；S214. Encode the first model input data based on the encoder, and extract corresponding text context-related features based on the encoded first model input data;
S215.通过多层感知器对每个分词进行分类标注，得到每个分词对应的实体类型；S215. Classify and label each participle through a multi-layer perceptron to obtain the entity type corresponding to each participle;
S216.基于文本上下文相关特征和实体类型，将邻接的同一实体类型的分词进行合并，形成事件要素信息，事件要素信息包括事件发生地、涉诉主体、事件内容和事件处置结果。S216. Based on the text context-related features and entity types, adjacent word segments of the same entity type are merged to form event element information. The event element information includes the place where the event occurred, the subject involved in the lawsuit, the event content and the event handling result.
具体的，对于步骤S2中的事件要素信息的提取，是客服诉求数据处理方法中的一个关键步骤，确保了从原始文本中准确抽取出有用的信息，以便于后续的分析和处理，具体流程如下：Specifically, the extraction of event element information in step S2 is a key step in the customer service demand data processing method, which ensures that useful information is accurately extracted from the original text to facilitate subsequent analysis and processing. The specific process is as follows:
数据组合：将客服诉求事件的标题和内容进行组合，形成统一的文本数据，以确保上下文的完整性。分词处理：使用分词器对组合后的文本进行分词，将连续的文本切分成独立的词汇或短语，为后续处理打下基础。词嵌入：通过预训练模型将分词结果转换为词向量，这些向量能够捕捉词的语义信息，为深度学习模型提供输入。编码器应用：利用基于Transformer架构（Attention is All You Need模型，一种基于自注意力机制的深度学习模型）的编码器对词向量进行编码，提取文本中的上下文相关特征。特征提取：编码器能够捕捉词与词之间的关系，生成包含丰富语义信息的特征表示。多层感知器（MultilayerPerceptron，MLP）：使用MLP对每个词进行分类标注，识别其对应的实体类型，如地点、主体、事件内容等。实体合并：对于相同类型的邻接分词，进行合并处理，形成完整的事件要素，如事件发生地、涉诉主体等。核心信息形成：通过上述步骤，形成事件的核心信息，包括事件发生地、涉诉主体、事件内容和事件处置结果等。Data combination: Combine the title and content of customer service appeal events to form a unified text data to ensure the integrity of the context. Word segmentation: Use a word segmenter to segment the combined text and divide the continuous text into independent words or phrases to lay the foundation for subsequent processing. Word embedding: Convert the word segmentation results into word vectors through a pre-trained model. These vectors can capture the semantic information of the words and provide input for the deep learning model. Encoder application: Encode the word vectors using an encoder based on the Transformer architecture (Attention is All You Need model, a deep learning model based on the self-attention mechanism) to extract context-related features in the text. Feature extraction: The encoder can capture the relationship between words and generate feature representations containing rich semantic information. Multilayer Perceptron (MLP): Use MLP to classify and label each word and identify its corresponding entity type, such as location, subject, event content, etc. Entity merging: For adjacent word segments of the same type, merge them to form complete event elements, such as the place where the event occurred and the subject involved in the lawsuit. Formation of core information: Through the above steps, the core information of the incident is formed, including the place where the incident occurred, the parties involved in the lawsuit, the content of the incident and the results of the incident handling.
可以理解的是，Transformer架构的优势在于其自注意力机制，能够更好地理解文本中的长距离依赖关系，增强了模型对上下文的理解能力。另外，除了文本数据，还可以考虑将图像、声音等多模态数据融合进来，以获取更全面的事件信息。随着时间的推移，模型可以通过增量学习不断更新其知识库，以适应新的事件类型和表达方式。在提取关键要素的过程中，模型的决策过程应该是可解释的，以便用户能够理解模型的判断依据。该技术不仅可以应用于客服诉求处理，还可以扩展到其他领域，如法律文档分析、医疗记录整理等。随着技术的发展，该过程可以实现实时处理，为事件响应提供更快的支持。引入用户反馈机制，对模型识别的结果进行人工校正，不断优化模型的准确性。扩展模型以支持多语言处理能力，以适应使用不同语言用户的需求。Understandably, the advantage of the Transformer architecture lies in its self-attention mechanism, which can better understand long-distance dependencies in text and enhance the model's ability to understand context. In addition, in addition to text data, you can also consider integrating multimodal data such as images and sounds to obtain more comprehensive event information. Over time, the model can continuously update its knowledge base through incremental learning to adapt to new event types and expressions. In the process of extracting key elements, the model's decision-making process should be explainable so that users can understand the basis for the model's judgment. This technology can not only be applied to customer service complaint processing, but can also be extended to other fields, such as legal document analysis, medical record organization, etc. With the development of technology, this process can be processed in real time, providing faster support for event response. Introduce a user feedback mechanism to manually correct the results of model recognition and continuously optimize the accuracy of the model. Expand the model to support multilingual processing capabilities to meet the needs of users who use different languages.
可选的，在一些实施例中，步骤S2中的“基于诉求事件内容生成事件摘要信息”，具体可以包括：Optionally, in some embodiments, the “generating event summary information based on the appeal event content” in step S2 may specifically include:
S221.采用分词器对诉求事件内容进行分词处理，得到第二分词结果；S221. Use a word segmenter to segment the content of the appeal event to obtain a second word segmentation result;
S222.通过预训练模型对分词结果进行词嵌入，作为第二模型输入数据；S222. embed the word segmentation results through the pre-trained model as the input data of the second model;
S223.采用解码器对第二模型输入数据进行解码处理，并基于解码后的第二模型输入数据提取对应的文本上下文相关特征；S223. Decoding the second model input data using a decoder, and extracting corresponding text context-related features based on the decoded second model input data;
S224.通过线性变换和归一化指数函数生成第二模型输入数据对应的词概率分布结果；S224. Generate a word probability distribution result corresponding to the second model input data through linear transformation and normalized exponential function;
S225.通过集束搜索算法计算词概率分布结果中的每个候选队列对应的累计概率；S225. Calculate the cumulative probability corresponding to each candidate queue in the word probability distribution result by a beam search algorithm;
S226.选取累计概率最高的若干个候选队列作为事件摘要信息。S226. Select several candidate queues with the highest cumulative probability as event summary information.
具体的，对于步骤S2中的事件摘要信息的生成，是将长篇的客服诉求事件内容文本转换为简洁摘要的过程，这些摘要能够快速传达事件的核心信息，具体流程如下：分词处理：使用分词器将事件内容文本分解成单独的词汇或短语，为后续处理提供基础。词嵌入：通过预训练模型将分词结果转换为词向量，这些向量能够捕捉词的语义信息。位置编码：为模型输入数据添加位置编码，以保持序列中词汇的顺序信息，这对于捕捉上下文关系至关重要。Transformer解码器：利用基于Transformer架构的解码器处理输入数据，解码器能够理解文本的上下文特征。特征表示：解码器提取文本中的特征表示，这些特征包含了文本的语义和语法信息。线性变换与Softmax（Softmax Function，一种在机器学习和深度学习中常用的激活函数）：通过线性变换和Softmax函数生成词概率分布，为每个词汇分配一个概率值，表示其在摘要中出现的可能性。Beam Search算法（束搜索算法，一种启发式图搜索算法）：使用Beam Search算法在候选摘要中进行选择，它通过累积概率来评估候选摘要的质量。摘要输出：选择概率最大的k个候选摘要作为最终输出，这些摘要能够准确反映事件的主要内容。Specifically, the generation of event summary information in step S2 is the process of converting the long text of customer service appeal event content into concise summaries. These summaries can quickly convey the core information of the event. The specific process is as follows: Word segmentation: Use a word segmenter to decompose the event content text into separate words or phrases to provide a basis for subsequent processing. Word embedding: Convert the word segmentation results into word vectors through a pre-trained model. These vectors can capture the semantic information of the words. Position encoding: Add position encoding to the model input data to maintain the order information of the words in the sequence, which is crucial for capturing contextual relationships. Transformer decoder: Use a decoder based on the Transformer architecture to process the input data. The decoder can understand the contextual features of the text. Feature representation: The decoder extracts feature representations from the text. These features contain the semantic and grammatical information of the text. Linear transformation and Softmax (Softmax Function, a commonly used activation function in machine learning and deep learning): Generate word probability distribution through linear transformation and Softmax function, assign a probability value to each word, indicating the possibility of its appearance in the summary. Beam Search algorithm (a heuristic graph search algorithm): Use the Beam Search algorithm to select from candidate summaries, which evaluates the quality of candidate summaries by cumulative probability. Summary output: Select the k candidate summaries with the highest probability as the final output, which can accurately reflect the main content of the event.
可以理解的是，本实施例通过调整Beam Search的参数，可以生成多样化的摘要，以提供事件的不同视角。能够同时处理多篇相关文档，生成涵盖多个文本的综合摘要。开发交互式摘要系统，允许用户指定摘要的长度或关键信息，以定制摘要内容。使模型能够适应不同领域的文本，通过领域特定的预训练或微调来提高摘要的相关性。随着事件的发展，实时更新摘要内容，以反映最新的事件进展。引入自动评估机制，以量化摘要的质量，并提供反馈给模型进行优化。扩展模型以支持多语言文本的摘要生成，以适应全球化的需求。允许用户根据需要定制摘要的风格和重点，例如，侧重于事件的影响或原因。结合文本摘要和数据可视化，提供更丰富的信息呈现方式。在生成摘要时，确保不泄露个人隐私或感兴趣信息。It is understandable that this embodiment can generate diversified summaries to provide different perspectives on the event by adjusting the parameters of Beam Search. It is possible to process multiple related documents at the same time and generate a comprehensive summary covering multiple texts. An interactive summary system is developed to allow users to specify the length or key information of the summary to customize the summary content. The model is enabled to adapt to texts in different fields and improve the relevance of the summary through field-specific pre-training or fine-tuning. As the event develops, the summary content is updated in real time to reflect the latest progress of the event. An automatic evaluation mechanism is introduced to quantify the quality of the summary and provide feedback to the model for optimization. The model is expanded to support summary generation of multilingual texts to meet the needs of globalization. Allow users to customize the style and focus of the summary as needed, for example, focusing on the impact or cause of the event. Combine text summarization and data visualization to provide a richer way of presenting information. When generating summaries, ensure that personal privacy or information of interest is not disclosed.
可选的，在一些实施例中，步骤S3“基于事件要素信息和事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果”，具体可以包括：Optionally, in some embodiments, step S3 of "performing an event trend analysis on the target event based on the event element information and the event summary information to obtain an event trend analysis result" may specifically include:
S31.通过预设业务规则确定符合分析条件的目标事件；S31. Determine the target event that meets the analysis conditions through preset business rules;
S32.按照时间顺序对目标事件进行排列，并对目标事件对应的事件要素信息和事件摘要信息进行数值编码，得到序列分析输入数据；S32. Arrange the target events in chronological order, and numerically encode the event element information and event summary information corresponding to the target events to obtain sequence analysis input data;
S33.通过时序卷积网络模型对序列分析输入数据进行特征分析和事件趋势分析，得到目标事件对应的事件趋势分析结果。S33. Perform feature analysis and event trend analysis on the sequence analysis input data through the temporal convolutional network model to obtain the event trend analysis results corresponding to the target event.
具体的，对于步骤S3，是一个综合利用数据提取、特征编码和深度学习技术来预测事件发展趋势的高级分析过程。具体过程如下：Specifically, step S3 is an advanced analysis process that comprehensively utilizes data extraction, feature encoding, and deep learning techniques to predict the development trend of events. The specific process is as follows:
业务规则筛选：根据预定义的业务规则，从大量事件数据中筛选出符合特定分析条件的事件信息，确保分析的针对性和准确性。关键要素提取：利用从筛选后的事件中提取关键要素，包括时间、地点、主体、类型等，为深入分析打下基础。摘要信息利用：结合生成的事件摘要，将事件的主要内容和要点纳入分析，增加分析的全面性。特征要素排列与编码：将提取的特征要素按照时间顺序排列，并对这些要素进行数值编码，转换为适合机器学习模型处理的格式。序列分析输入：形成序列分析的输入数据集，这些数据集将被用于时序预测模型的训练和预测。时序卷积网络（TCN）应用：采用TCN模型对序列数据进行特征分析，利用卷积神经网络捕捉时间序列中的局部模式和趋势。扩张卷积：通过扩张卷积操作，TCN能够扩大感受野，捕捉更长期的依赖关系，提高模型对时间序列的预测能力。因果卷积层：利用因果卷积层保证预测的因果性，确保未来事件的预测不会受到未来数据的影响。事件频次和概率预测：模型输出对未来事件发生的频次和概率的预测，为企业决策者提供量化的风险评估。Business rule screening: According to predefined business rules, event information that meets specific analysis conditions is screened from a large amount of event data to ensure the pertinence and accuracy of the analysis. Key element extraction: Extract key elements from the screened events, including time, location, subject, type, etc., to lay the foundation for in-depth analysis. Summary information utilization: Combine the generated event summary to include the main content and key points of the event in the analysis to increase the comprehensiveness of the analysis. Feature element arrangement and encoding: Arrange the extracted feature elements in chronological order, encode these elements numerically, and convert them into a format suitable for machine learning model processing. Sequence analysis input: Form the input data set for sequence analysis, which will be used for training and prediction of the time series prediction model. Time series convolutional network (TCN) application: Use the TCN model to perform feature analysis on sequence data, and use convolutional neural networks to capture local patterns and trends in time series. Dilated convolution: Through the dilated convolution operation, TCN can expand the receptive field, capture longer-term dependencies, and improve the model's prediction ability for time series. Causal convolution layer: Use the causal convolution layer to ensure the causality of the prediction and ensure that the prediction of future events will not be affected by future data. Event frequency and probability prediction: The model outputs predictions on the frequency and probability of future events, providing quantitative risk assessment for corporate decision makers.
可以理解的是，可以结合更多维度的特征，如事件的情感倾向、影响力等，以获得更全面的事件视图。采用模型融合技术，结合多个不同模型的预测结果，提高预测的准确性和鲁棒性。根据事件类型和历史数据，动态调整模型参数，以适应不同事件特性的分析需求。将实时数据流集成到分析流程中，实现对事件发展趋势的实时监控和即时预测。开发交互式可视化工具，使用户能够直观地探索和理解事件趋势分析的结果。It is understandable that more dimensional features, such as the sentiment tendency and influence of events, can be combined to obtain a more comprehensive view of events. Use model fusion technology to combine the prediction results of multiple different models to improve the accuracy and robustness of predictions. Dynamically adjust model parameters according to event types and historical data to adapt to the analysis needs of different event characteristics. Integrate real-time data streams into the analysis process to achieve real-time monitoring and instant prediction of event development trends. Develop interactive visualization tools to enable users to intuitively explore and understand the results of event trend analysis.
可选的，在一些实施例中，所述客服诉求数据处理方法具体还可以包括：Optionally, in some embodiments, the customer service demand data processing method may further include:
基于事件要素信息进行事件聚类处理，得到反复诉求事件；Perform event clustering based on event element information to obtain repeated appeal events;
基于诉求事件内容进行感兴趣信息匹配，得到匹配结果，并基于匹配结果确定感兴趣事件；Matching the information of interest based on the content of the appeal event, obtaining a matching result, and determining the event of interest based on the matching result;
基于诉求事件内容进行弱信号分析，确定弱信号事件。Weak signal analysis is performed based on the content of the appeal event to determine the weak signal event.
具体的，在对目标事件进行事件趋势分析步骤之前，还会对目标事件进行分类，例如反复诉求事件、感兴趣事件以及弱信号事件。例如，通过反复投诉事件检测模型，检测重复投诉的事件并进行标注；通过提取事件内容中的感兴趣信息，从而输出时间是否含有感兴趣信息以及命中的感兴趣关键词，以确定感兴趣事件；通过并发弱信号分析模型分析并发事件中的弱信号，识别潜在投诉风险，确定弱信号事件。Specifically, before the event trend analysis step is performed on the target event, the target event will also be classified, such as repeated complaint events, events of interest, and weak signal events. For example, through the repeated complaint event detection model, repeated complaint events are detected and annotated; by extracting the information of interest in the event content, the output time contains the information of interest and the hit keywords of interest, so as to determine the event of interest; through the concurrent weak signal analysis model, the weak signals in concurrent events are analyzed, the potential complaint risks are identified, and the weak signal events are determined.
其中，本实施例中的感兴趣事件可以是客户投诉事件、数据泄露事件、产品召回事件、服务中断事件、负面宣传事件、法律问题事件、欺诈行为事件、供应链问题事件、客户安全问题事件、知识产权侵权事件和环境事件中的一种或多种。具体的，客户投诉事件指的是对产品质量、服务水平或企业条款的正式投诉；数据泄露事件指的是客户个人信息或交易数据的泄露；产品召回事件指的是由于安全问题或其他原因导致的大规模产品召回；服务中断事件指的是服务或产品的中断，可能影响大量用户；负面宣传事件指的是通过社交媒体或其他渠道传播的负面信息或评价；法律问题事件指的是涉及法律诉讼或第三方机构调查的事件；欺诈行为事件指的是信用卡欺诈、服务滥用或其他类型的欺诈行为；供应链问题事件指的是供应链中断或质量问题，影响产品交付；客户安全问题事件指的是产品或服务导致的客户安全问题；知识产权侵权事件指的是涉及知识产权的侵权行为；环境问题事件指的是对环境造成负面影响的事件，如污染或不合规排放。Among them, the events of interest in this embodiment can be one or more of customer complaint events, data leakage events, product recall events, service interruption events, negative publicity events, legal problem events, fraudulent behavior events, supply chain problem events, customer safety problem events, intellectual property infringement events and environmental events. Specifically, customer complaint events refer to formal complaints about product quality, service levels or corporate terms; data leakage events refer to the leakage of customer personal information or transaction data; product recall events refer to large-scale product recalls due to safety issues or other reasons; service interruption events refer to interruptions in services or products that may affect a large number of users; negative publicity events refer to negative information or comments spread through social media or other channels; legal problem events refer to events involving legal proceedings or investigations by third-party agencies; fraudulent behavior events refer to credit card fraud, service abuse or other types of fraud; supply chain problem events refer to supply chain interruptions or quality issues that affect product delivery; customer safety problem events refer to customer safety issues caused by products or services; intellectual property infringement events refer to infringements involving intellectual property rights; environmental problem events refer to events that have a negative impact on the environment, such as pollution or non-compliant emissions.
其中，本实施例中的并发事件可以是产品缺陷问题事件、服务中断事件、价格变动争议事件、促销活动问题事件、更新或更改问题事件、供应链问题事件、数据安全事件、虚假广告事件、客户服务失败事件、法律问题事件、技术故障事件、品牌声誉危机事件中的一种或多种。具体的，产品缺陷问题事件指的是大量客户报告同一产品存在缺陷或功能故障；服务中断事件指的是服务或产品出现大规模中断，影响广泛用户群体；价格变动争议事件指的是价格突然变动或计费错误，引发大量客户投诉；促销活动问题事件指的是促销活动规则不明确或执行不一致，导致客户不满；更新或更改问题事件指的是软件或服务更新后出现普遍问题，或更改服务条款引起客户反对；供应链问题事件指的是供应链问题导致产品交付延迟，影响大量订单；数据安全事件指的是数据泄露或安全漏洞影响大量客户；虚假广告事件指的是广告宣传与实际产品或服务不符，引发客户并发投诉；客户服务失败事件指的是客服响应不及时或处理不当，导致批量客户不满；法律问题事件指的是企业操作违反法律，引起客户关注或诉讼；技术故障事件指的是技术问题导致服务不可用，影响大量用户；品牌声誉危机事件指的是由于负面新闻或事件，品牌形象受损，引起客户群体反应。Among them, the concurrent events in this embodiment can be one or more of product defect problem events, service interruption events, price change dispute events, promotional activity problem events, update or change problem events, supply chain problem events, data security events, false advertising events, customer service failure events, legal problem events, technical failure events, and brand reputation crisis events. Specifically, a product defect problem incident refers to a large number of customers reporting defects or functional failures in the same product; a service interruption incident refers to a large-scale interruption of services or products, affecting a wide range of user groups; a price change dispute incident refers to a sudden price change or billing error, which triggers a large number of customer complaints; a promotion problem incident refers to unclear promotion rules or inconsistent implementation, leading to customer dissatisfaction; an update or change problem incident refers to widespread problems after the software or service is updated, or changes to the terms of service cause customer opposition; a supply chain problem incident refers to supply chain problems causing product delivery delays, affecting a large number of orders; a data security incident refers to data leaks or security vulnerabilities affecting a large number of customers; a false advertising incident refers to advertising that is inconsistent with the actual product or service, triggering concurrent customer complaints; a customer service failure incident refers to customer service's untimely response or improper handling, resulting in dissatisfaction among a large number of customers; a legal problem incident refers to an enterprise's operation violating the law, causing customer concern or litigation; a technical failure incident refers to technical problems causing service unavailability, affecting a large number of users; a brand reputation crisis incident refers to the damage to the brand image due to negative news or events, causing reactions from the customer group.
可选的，在一些实施例中，步骤S3“基于事件要素信息和事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果”，具体可以包括：Optionally, in some embodiments, step S3 of "performing an event trend analysis on the target event based on the event element information and the event summary information to obtain an event trend analysis result" may specifically include:
分别获取反复诉求事件、感兴趣事件和弱信号事件对应的事件要素信息和事件摘要信息；Obtain event element information and event summary information corresponding to repeated appeal events, events of interest, and weak signal events respectively;
基于事件要素信息和事件摘要信息分别对反复诉求事件、感兴趣事件和弱信号事件进行事件趋势分析，得到反复诉求事件、感兴趣事件和弱信号事件对应的事件趋势分析结果。Based on the event element information and event summary information, event trend analysis is performed on repeated demand events, events of interest and weak signal events respectively, and event trend analysis results corresponding to repeated demand events, events of interest and weak signal events are obtained.
具体的，目标事件可以包括反复诉求事件、感兴趣事件和弱信号事件，则步骤S3具体为：分别获取反复诉求事件、感兴趣事件和弱信号事件对应的事件要素信息和事件摘要信息；基于反复诉求事件的事件要素信息和事件摘要信息对反复诉求事件进行事件趋势分析，得到反复诉求事件对应的事件趋势分析结果；基于感兴趣事件的事件要素信息和事件摘要信息对感兴趣事件进行事件趋势分析，得到感兴趣事件对应的事件趋势分析结果；基于弱信号事件的事件要素信息和事件摘要信息对弱信号事件进行事件趋势分析，得到弱信号事件对应的事件趋势分析结果。Specifically, the target event may include a repeated demand event, an event of interest, and a weak signal event, and step S3 is specifically as follows: respectively obtaining event element information and event summary information corresponding to the repeated demand event, the event of interest, and the weak signal event; performing event trend analysis on the repeated demand event based on the event element information and event summary information of the repeated demand event, and obtaining an event trend analysis result corresponding to the repeated demand event; performing event trend analysis on the event of interest based on the event element information and event summary information of the event of interest, and obtaining an event trend analysis result corresponding to the event of interest; performing event trend analysis on the weak signal event based on the event element information and event summary information of the weak signal event, and obtaining an event trend analysis result corresponding to the weak signal event.
可选的，在一些实施例中，步骤“基于事件要素信息进行事件聚类处理，得到反复诉求事件”，具体可以包括：Optionally, in some embodiments, the step of “performing event clustering processing based on event element information to obtain repeated appeal events” may specifically include:
将事件要素信息进行分词和词嵌入处理，得到第三模型输入数据；Perform word segmentation and word embedding processing on the event element information to obtain input data for the third model;
将第三模型输入数据输入至语义嵌入模型，通过语义嵌入模型的自注意力层和前馈神经网络输出对应的事件向量；Input the third model input data into the semantic embedding model, and output the corresponding event vector through the self-attention layer and feedforward neural network of the semantic embedding model;
使用余弦相似度函数计算所有事件向量之间的相似度，构建相似度矩阵；Use the cosine similarity function to calculate the similarity between all event vectors and construct a similarity matrix;
基于密度聚类算法和相似度矩阵对目标事件进行事件聚类处理，得到反复诉求事件。The target events are clustered based on the density clustering algorithm and similarity matrix to obtain repeated appeal events.
具体的，对于反复诉求事件的获取方法，具体流程如下：采用基于对比学习的语义嵌入模型SIMCSE抽取事件要素信息的特征向量，对特征向量进行聚类，SIMCSE（Semantically Interpretable Matching of Contextualized Embeddings）是一种基于对比学习的语义嵌入模型，通过预训练和微调阶段学习文本的高质量语义表示。在预训练阶段，SIMCSE首先在大量无标注文本上进行预训练，这有助于模型学习通用的语言模式和语义特征。在微调阶段，使用人工标注的相似文本对进行微调，通过批次内数据对比，进一步提升模型在文本匹配任务上的性能。通过SIMCSE模型，将事件要素信息相关文本转换为特征向量，这些向量能够捕捉文本的深层语义信息。SIMCSE模型中的自注意力层和前馈神经网络协同工作，为每个事件生成一个综合的向量表示。使用余弦相似度函数计算所有事件向量之间的相似度，构建起一个相似度矩阵。在相似度矩阵中，根据预设的阈值将相似度高于该阈值的项目设置为0，其他项目设为1，转换为距离矩阵。根据业务场景，如同一投诉人的多次投诉或同一地点的重复事件，对距离矩阵进行修正，以更准确地反映事件之间的相似性。使用DBSCAN算法（Density-Based Spatial Clustering of Applications withNois，基于密度的带噪声的空间聚类算法）对修正后的距离矩阵进行聚类处理，识别并合并相同或相似的投诉事件。最终，DBSCAN算法输出聚类结果，将相似的投诉事件聚集在一起，为进一步的分析和处理提供依据。Specifically, for the acquisition method of repeated appeal events, the specific process is as follows: the semantic embedding model SIMCSE based on contrastive learning is used to extract the feature vectors of event element information and cluster the feature vectors. SIMCSE (Semantically Interpretable Matching of Contextualized Embeddings) is a semantic embedding model based on contrastive learning. It learns high-quality semantic representations of texts through pre-training and fine-tuning stages. In the pre-training stage, SIMCSE is first pre-trained on a large amount of unlabeled text, which helps the model learn common language patterns and semantic features. In the fine-tuning stage, fine-tuning is performed using similar text pairs with manual annotations, and the performance of the model on text matching tasks is further improved by comparing data within batches. Through the SIMCSE model, the text related to event element information is converted into feature vectors, which can capture the deep semantic information of the text. The self-attention layer and feedforward neural network in the SIMCSE model work together to generate a comprehensive vector representation for each event. The cosine similarity function is used to calculate the similarity between all event vectors and construct a similarity matrix. In the similarity matrix, according to the preset threshold, the items with similarity higher than the threshold are set to 0, and the other items are set to 1, and converted into a distance matrix. According to the business scenario, such as multiple complaints from the same complainant or repeated events at the same location, the distance matrix is modified to more accurately reflect the similarity between events. The DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Nois) is used to cluster the modified distance matrix to identify and merge the same or similar complaint events. Finally, the DBSCAN algorithm outputs the clustering results, clustering similar complaint events together to provide a basis for further analysis and processing.
可选的，在一些实施例中，步骤“基于诉求事件内容进行感兴趣信息匹配，得到匹配结果，并基于匹配结果确定感兴趣事件”，具体可以包括：Optionally, in some embodiments, the step of “matching the information of interest based on the content of the appeal event, obtaining a matching result, and determining the event of interest based on the matching result” may specifically include:
基于领域知识和经验构建感兴趣词库；Build a vocabulary of interest based on domain knowledge and experience;
基于感兴趣词库，采用关键词匹配方式查找诉求事件内容中的关键词，作为第一感兴趣信息；Based on the word library of interest, a keyword matching method is used to search for keywords in the content of the appeal event as the first information of interest;
采用分词器对诉求事件内容进行分词后去除停用词，通过关键词匹配方式查找去除停用词后的分词结果中的关键词；Use a word segmenter to segment the content of the appeal event and remove stop words, and use keyword matching to find keywords in the segmentation results after removing stop words;
通过语义编码模型对关键词进行特征编码，并通过余弦相似度函数计算特征编码后的关键词和感兴趣词库的语义相似度；The keywords are feature encoded by the semantic encoding model, and the semantic similarity between the feature-encoded keywords and the vocabulary of interest is calculated by the cosine similarity function;
基于语义相似度确定诉求事件内容中的第二感兴趣信息；Determining second information of interest in the content of the appeal event based on semantic similarity;
基于第一感兴趣信息和第二感兴趣信息，确定目标事件是否为感兴趣事件，并确定目标事件是否包含感兴趣信息及命中的感兴趣关键词。Based on the first information of interest and the second information of interest, it is determined whether the target event is an event of interest, and whether the target event contains the information of interest and the hit keyword of interest.
具体的，对于感兴趣信息的提取和感兴趣事件的确认，是客服诉求事件分析中的一个关键环节，旨在识别和处理可能涉及感兴趣话题或信息的事件，具体过程如下：首先构建感兴趣词库，基于对特定领域深入的知识和丰富的经验，构建一个包含感兴趣词汇的词库，感兴趣词库是识别感兴趣信息的基础。利用感兴趣词库，通过关键词匹配技术在客服诉求事件内容中查找包含的感兴趣信息，实现初步的感兴趣信息筛选。对事件内容进行分词处理，并去除停用词，以净化文本数据，为关键词提取做准备。应用TF-IDF（TermFrequency-Inverse Document Frequency）和TextRank（基于图的文本摘要技术）等算法提取文本中的重要关键词，这些关键词可能指示感兴趣信息。使用语义编码模型SIMCSE对提取的关键词进行特征编码，将文本中的关键词转换为向量形式，以便于进行语义比较。利用余弦相似度函数计算文本关键词与感兴趣词库中词汇的语义相似度，确定它们之间的接近程度。根据相似度阈值，识别文本中与感兴趣词库匹配的感兴趣词，这些词汇可能指示感兴趣信息的存在。将关键词匹配和语义匹配的结果进行融合，综合判断事件内容是否含有感兴趣信息，并确定具体命中的感兴趣关键词。最终输出包含感兴趣信息的事件，并列出命中的感兴趣关键词，为后续的感兴趣信息处理和决策提供依据。Specifically, the extraction of interesting information and the confirmation of interesting events are key links in the analysis of customer service appeal events, which aim to identify and process events that may involve interesting topics or information. The specific process is as follows: First, a vocabulary of interest is constructed. Based on in-depth knowledge and rich experience in a specific field, a vocabulary containing interesting words is constructed. The vocabulary of interest is the basis for identifying information of interest. Using the vocabulary of interest, the information of interest contained in the content of customer service appeal events is searched through keyword matching technology to achieve preliminary screening of information of interest. The event content is segmented and stop words are removed to purify the text data in preparation for keyword extraction. Algorithms such as TF-IDF (TermFrequency-Inverse Document Frequency) and TextRank (graph-based text summarization technology) are applied to extract important keywords in the text, which may indicate interesting information. The semantic encoding model SIMCSE is used to encode the extracted keywords and convert the keywords in the text into vector form for semantic comparison. The cosine similarity function is used to calculate the semantic similarity between the text keywords and the words in the vocabulary of interest to determine the degree of proximity between them. According to the similarity threshold, the interesting words in the text that match the interesting word library are identified. These words may indicate the existence of interesting information. The results of keyword matching and semantic matching are integrated to comprehensively judge whether the event content contains interesting information and determine the specific interesting keywords that are hit. Finally, the events containing interesting information are output and the hit interesting keywords are listed to provide a basis for subsequent interesting information processing and decision-making.
综上所述，本实施例提供的客服诉求数据处理方法，综合利用基于深度学习的自然语言处理（NLP）、时序分析预测、数据分析挖掘等技术，对海量的客服投诉数据进行分析，及时全面准确地发现影响企业管理、企业产品或企业服务的事件，解决了各类客服诉求数据量大，人工分析耗时耗力，且分析结果不及时的痛点问题。本实施例基于统一文本结构生成框架UIE（Unified Textual Structure Generation Framework），从非结构化的文本中抽取投诉事件发生地、涉诉主体、事件内容、事件处置结果等核心信息；基于chatGPT（ChatGenerative Pre-trained Transformer，用于聊天的生成式预训练变换器模型）同架构的Transformer Decoder（Transformer 编码器-解码器）文本生成模型，对投诉的主要内容进行归纳整理自动生成事件摘要；基于对比学习的语义嵌入模型SIMCSE，对投诉关键信息进行语义编码和相似度比较；基于密度聚类的DBSCAN算法并配合专家规则，对重复投诉事件进行自动的识别及合并；通过基于BI-GRU（Bidirectional Gated Recurrent Unit，双向门控循环单元）架构的词法分析模型，对诉求内容中关键信息、感兴趣信息提取和统计；通过多维数据分析及基于TCN时序预测模型，对感兴趣事件，多发事件、弱信号事件等进行实时的趋势分析，及时形成预警信息并通过APP或电子邮箱等渠道推送给企业管理者重点关注与优先处置，提高诉求处理的效率和实时性。To summarize, the customer service demand data processing method provided in this embodiment comprehensively utilizes natural language processing (NLP) based on deep learning, time series analysis and prediction, data analysis and mining and other technologies to analyze massive customer service complaint data, and timely, comprehensively and accurately discover events that affect enterprise management, enterprise products or enterprise services, thereby solving the pain points of large amounts of various customer service demand data, time-consuming and labor-intensive manual analysis, and untimely analysis results. This embodiment is based on the unified textual structure generation framework UIE (Unified Textual Structure Generation Framework), which extracts core information such as the location of the complaint incident, the subject involved in the complaint, the event content, and the event handling results from the unstructured text; based on the Transformer Decoder (Transformer encoder-decoder) text generation model with the same architecture as chatGPT (ChatGenerative Pre-trained Transformer, a generative pre-trained transformer model for chat), the main content of the complaint is summarized and automatically generated. The semantic embedding model SIMCSE based on contrastive learning is used to semantically encode and compare the similarity of the key information of the complaint; based on the DBSCAN algorithm based on density clustering and in conjunction with expert rules, repeated complaint events are automatically identified and merged; through the BI-GRU (Bidirectional Gated Recurrent Unit, bidirectional gated recurrent unit) architecture, extracts and counts key information and information of interest in the content of the appeal; through multi-dimensional data analysis and TCN time series prediction model, real-time trend analysis is performed on events of interest, multiple events, weak signal events, etc., and early warning information is generated in a timely manner and pushed to enterprise managers through APP or e-mail and other channels for key attention and priority treatment, thereby improving the efficiency and real-time nature of appeal processing.
为了便于理解本实施例提供的客服诉求数据处理方法，本实施例还提供了一种执行客服诉求数据处理方法的客服诉求数据处理系统，该客服诉求数据处理系统包括基础支撑层、采集汇聚层、数据管理层、数据服务层和展示层，其中，基础支撑层包括分布式计算与存储单元、大规模并行处理数据库、分布式数据库和云平台基础设施；采集汇聚层包括批量采集单元、互联网采集单元、增量采集单元、流式采集单元、实时采集单元、数据填报单元、数据资源目录单元和数据交换单元，数据管理层包括客服数据分析模型和数据资源库，其中客服数据分析模型包括多维数据分析模型、不当退单模型、感兴趣事件分析模型、客服数据专用分类模型、投诉人情绪识别模型、预警事件分析模型、统一地址归并模型、地址抽取模型、反复投诉事件检测模型、被投诉主题抽取模型、并发弱信号分析模型，数据资源库包括客服数据库、同构客服数据库、客服数据分析结果库；数据服务层包括服务目录单元和服务API单元，展示层包括APP对接单元、短信通知单元、预警消息推送单元、地图及块数据形式交互单元、日报/周报/月报生成单元、专题报告生成单元。以客服诉求数据处理系统执行数据处理分析为例，具体流程如下：In order to facilitate understanding of the customer service demand data processing method provided in this embodiment, this embodiment also provides a customer service demand data processing system for executing the customer service demand data processing method, and the customer service demand data processing system includes a basic support layer, a collection and aggregation layer, a data management layer, a data service layer and a display layer, wherein the basic support layer includes a distributed computing and storage unit, a large-scale parallel processing database, a distributed database and a cloud platform infrastructure; the collection and aggregation layer includes a batch collection unit, an Internet collection unit, an incremental collection unit, a streaming collection unit, a real-time collection unit, a data reporting unit, a data resource directory unit and a data exchange unit, and the data management layer includes a customer service data analysis model and a data resource library , where the customer service data analysis model includes a multidimensional data analysis model, an improper return order model, an event of interest analysis model, a customer service data-specific classification model, a complainant emotion recognition model, an early warning event analysis model, a unified address merging model, an address extraction model, a repeated complaint event detection model, a complaint subject extraction model, and a concurrent weak signal analysis model. The data resource library includes a customer service database, a homogeneous customer service database, and a customer service data analysis result library; the data service layer includes a service catalog unit and a service API unit, and the display layer includes an APP docking unit, a SMS notification unit, an early warning message push unit, a map and block data form interaction unit, a daily/weekly/monthly report generation unit, and a special report generation unit. Taking the customer service demand data processing system to perform data processing and analysis as an example, the specific process is as follows:
步骤1.数据采集；Step 1. Data collection;
1.1）批量采集：从历史数据中批量获取客服投诉数据。1.1) Batch collection: Batch obtain customer service complaint data from historical data.
1.2）互联网采集：通过互联网接口实时获取相关投诉数据。1.2) Internet collection: Obtain relevant complaint data in real time through the Internet interface.
1.3）增量采集：对已有数据进行增量更新，确保数据的实时性和完整性。1.3) Incremental collection: Incrementally update existing data to ensure the real-time and integrity of the data.
步骤2.数据清洗、治理、融合；Step 2. Data cleaning, governance, and integration;
2.1）数据清洗：对采集到的原始数据进行清洗，去除噪声和冗余信息，标准化数据格式。2.1) Data cleaning: Clean the collected raw data, remove noise and redundant information, and standardize the data format.
2.2）数据治理：应用数据治理规则对数据进行校验和修正，确保数据的一致性和准确性。2.2) Data governance: Apply data governance rules to verify and correct data to ensure data consistency and accuracy.
2.3）数据融合：将来自不同渠道的数据进行整合，形成统一的数据视图。2.3) Data fusion: Integrate data from different channels to form a unified data view.
步骤3.数据建模与分析；Step 3. Data modeling and analysis;
3.1）热线数据分析：使用基于深度学习的自然语言处理（NLP）模型，对热线投诉数据进行分类及内容摘要。3.1) Hotline data analysis: Use a deep learning-based natural language processing (NLP) model to classify and summarize hotline complaint data.
3.2）统一地址归并模型：将不同格式的地址信息进行归并和标准化。3.2) Unified address merging model: merge and standardize address information in different formats.
3.3）地址抽取模型：从文本中抽取出具体的地址信息。3.3) Address extraction model: extract specific address information from the text.
3.4）反复投诉事件检测模型：检测重复投诉的事件并进行标注。3.4) Repeated complaint event detection model: Detect and label repeated complaint events.
3.5）被投诉主题抽取模型：抽取投诉的具体主题和对象。3.5) Complaint topic extraction model: extract the specific topic and object of the complaint.
3.6）并发弱信号分析模型：分析并发事件中的弱信号，识别潜在风险。3.6) Concurrent weak signal analysis model: Analyze weak signals in concurrent events and identify potential risks.
3.7）投诉人情绪识别模型：通过情绪识别技术，分析投诉人的情绪状态。3.7) Complainant emotion recognition model: Analyze the complainant’s emotional state through emotion recognition technology.
步骤4.多维数据分析；Step 4. Multidimensional data analysis;
4.1）多维数据分析模型：对清洗后的数据进行多维分析，挖掘数据中的隐含关系和模式。4.1) Multidimensional data analysis model: Perform multidimensional analysis on the cleaned data to explore the implicit relationships and patterns in the data.
4.2）感兴趣事件分析模型：对感兴趣事件进行分析，及时发现潜在的客户投诉风险。4.2) Event of Interest Analysis Model: Analyze events of interest to promptly identify potential customer complaint risks.
4.3）预警事件分析模型：对潜在的预警事件进行分析和预测。4.3) Warning event analysis model: Analyze and predict potential warning events.
步骤5.时序分析与预测；Step 5. Timing analysis and prediction;
基于TCN的时序预测模型：使用时序卷积网络（TCN）模型，对感兴趣事件、多发事件、弱信号事件等进行实时的趋势分析，预测未来的事件发生趋势。TCN-based time series prediction model: Use the time series convolutional network (TCN) model to perform real-time trend analysis on events of interest, multiple events, weak signal events, etc., and predict future event trends.
步骤6.数据展示与预警；Step 6. Data display and early warning;
6.1）预警消息推送：通过短信、微信等渠道，实时推送预警消息，确保及时响应和处置。6.1) Warning message push: Warning messages are pushed in real time through SMS, WeChat and other channels to ensure timely response and disposal.
6.2）地图及数据形式交互：在地图上展示数据分布情况，支持数据的形式交互。6.2) Map and data form interaction: Display data distribution on the map and support data form interaction.
6.3）日报/周报/月报生成：自动生成日报、周报和月报，提供详细的分析报告。6.3) Daily/weekly/monthly report generation: Automatically generate daily, weekly and monthly reports and provide detailed analysis reports.
6.4）专题报告生成：根据特定事件生成专题报告，为企业提供数据支持。6.4) Special report generation: Generate special reports based on specific events to provide data support for enterprises.
本实施例的应用场景主要包括：The application scenarios of this embodiment mainly include:
反复投诉分析：通过利用AI算法模型分析归并多发事件、反诉事件，按涉事事件数量排序展示，发现找出某一客户投诉问题扩大、发酵的态势。及时预警到企业的分管部门或者管理者，为企业客户事件处置的优先级提供重要参考。Repeated complaint analysis: By using AI algorithm models to analyze and merge multiple incidents and counter-complaints, and sorting and displaying them by the number of incidents involved, we can find out the trend of a customer's complaint expanding and fermenting. Timely warnings are sent to the company's responsible departments or managers, providing important reference for the priority of handling customer incidents.
企业办公（AI写报告）：利用大语言模型对大量的非结构化客服数据的自然语言理解分析，定时自动的给不同的企业部门写日常报告。解决基层员工花费大量人力在各类用途、各类格式报告的制作上，全面快速地提高企业工作人员工作效率。Enterprise office (AI report writing): Use the large language model to understand and analyze the natural language of a large amount of unstructured customer service data, and automatically write daily reports for different enterprise departments on a regular basis. This solves the problem of grassroots employees spending a lot of manpower on the production of reports of various purposes and formats, and comprehensively and quickly improves the work efficiency of enterprise staff.
可见，本实施例采用自研AI（Artificial Intelligence，人工智能）大语言模型KuaiGPT（Kuai Generative Pre-trained Transformer）大模型深度分析挖掘企业风险事件，及时发出预警。通过模型自动分析数据，自动生成专题报告。利用模型分析后的结果数据进行多维度、多信息交叉、细颗粒的可视化分析，为企业管理者提供全面直观的分析结果。It can be seen that this embodiment uses the self-developed AI (Artificial Intelligence) large language model KuaiGPT (Kuai Generative Pre-trained Transformer) to deeply analyze and mine enterprise risk events and issue early warnings in a timely manner. The model automatically analyzes data and automatically generates special reports. The result data after model analysis is used for multi-dimensional, multi-information cross-cutting, and fine-grained visual analysis to provide comprehensive and intuitive analysis results for enterprise managers.
应该理解的是，虽然图2和图3的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图2和图3中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。It should be understood that, although the various steps in the flowcharts of Fig. 2 and Fig. 3 are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in Fig. 2 and Fig. 3 may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.
为便于更好的实施本申请实施例的客服诉求数据处理方法，本发明实施例还提供一种基于上述客服诉求数据处理方法的客服诉求数据处理装置。其中名词的含义与上述客服诉求数据处理方法中相同，具体实现细节可以参考方法实施例中的说明。In order to better implement the customer service demand data processing method of the embodiment of the present application, the embodiment of the present invention also provides a customer service demand data processing device based on the above customer service demand data processing method. The meanings of the terms are the same as those in the above customer service demand data processing method, and the specific implementation details can refer to the description in the method embodiment.
请参阅图4，图4为本申请实施例提供的客服诉求数据处理装置的结构示意图，该客服诉求数据处理装置包括数据获取模块201、数据分析模块202、事件分析模块203和事件预警模块204，其中：Please refer to FIG. 4 , which is a schematic diagram of the structure of a customer service demand data processing device provided in an embodiment of the present application. The customer service demand data processing device includes a data acquisition module 201, a data analysis module 202, an event analysis module 203, and an event warning module 204, wherein:
数据获取模块201，用于获取客服诉求数据，并提取客服诉求数据中的诉求事件标题和诉求事件内容；The data acquisition module 201 is used to acquire customer service demand data and extract the demand event title and demand event content in the customer service demand data;
数据分析模块202，用于基于诉求事件标题和诉求事件内容，提取得到事件要素信息，并基于诉求事件内容生成事件摘要信息；The data analysis module 202 is used to extract event element information based on the appeal event title and the appeal event content, and generate event summary information based on the appeal event content;
事件分析模块203，用于基于事件要素信息和事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果；An event analysis module 203 is used to perform event trend analysis on a target event based on event element information and event summary information to obtain an event trend analysis result;
事件预警模块204，用于基于事件趋势分析结果，生成对应的预警信息，并推送预警信息至已绑定的关联对象对应的关联终端。The event warning module 204 is used to generate corresponding warning information based on the event trend analysis result, and push the warning information to the associated terminal corresponding to the bound associated object.
关于客服诉求数据处理装置的具体限定可以参见上文中对于客服诉求数据处理方法的限定，在此不再赘述。上述客服诉求数据处理装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。For the specific definition of the customer service demand data processing device, please refer to the definition of the customer service demand data processing method above, which will not be repeated here. Each module in the above-mentioned customer service demand data processing device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
本实施例提供的客服诉求数据处理装置，通过对海量的客服诉求数据进行深度的精准分析，提取出事件要素信息并自动生成事件摘要，从而根据事件要素信息和事件摘要信息对目标事件进行趋势分析和预警，实现对客户诉求的全面感知、精准分析和实时预警，从而为企业管理提供更加高效、精准的决策支持。The customer service demand data processing device provided in this embodiment performs in-depth and accurate analysis on massive customer service demand data, extracts event element information and automatically generates event summaries, thereby performing trend analysis and early warning on target events based on the event element information and event summary information, achieving comprehensive perception, accurate analysis and real-time early warning of customer demands, thereby providing more efficient and accurate decision support for enterprise management.
此外，本申请实施例还提供一种电子设备，如图5所示，其示出了本申请实施例所涉及的电子设备的结构示意图，具体来讲：In addition, an embodiment of the present application further provides an electronic device, as shown in FIG5 , which shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically:
该电子设备可以包括一个或者一个以上处理核心的处理器301、一个或一个以上计算机可读存储介质的存储器302、电源303和输入单元304等部件。本领域技术人员可以理解，图5中示出的电子设备结构并不构成对电子设备的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。其中：The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will appreciate that the electronic device structure shown in FIG5 does not limit the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them:
处理器301是该电子设备的控制中心，利用各种接口和线路连接整个电子设备的各个部分，通过运行或执行存储在存储器302内的软件程序和/或模块，以及调用存储在存储器302内的数据，执行电子设备的各种功能和处理数据，从而对电子设备进行整体监控。可选的，处理器301可包括一个或多个处理核心；优选的，处理器301可集成应用处理器和调制解调处理器，其中，应用处理器主要处理操作系统、用户界面和应用程序等，调制解调处理器主要处理无线通信。可以理解的是，上述调制解调处理器也可以不集成到处理器301中。The processor 301 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and/or modules stored in the memory 302 and calling data stored in the memory 302, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.
存储器302可用于存储软件程序以及模块，处理器301通过运行存储在存储器302的软件程序以及模块，从而执行各种功能应用以及客服诉求数据处理方法。存储器302可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序（比如声音播放功能、图像播放功能等）等；存储数据区可存储根据电子设备的使用所创建的数据等。此外，存储器302可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。相应地，存储器302还可以包括存储器控制器，以提供处理器301对存储器302的访问。The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and customer service demand data processing methods by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.
电子设备还包括给各个部件供电的电源303，优选的，电源303可以通过电源管理系统与处理器301逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。电源303还可以包括一个或一个以上的直流或交流电源、再充电系统、电源故障检测电路、电源转换器或者逆变器、电源状态指示器等任意组件。The electronic device also includes a power supply 303 for supplying power to each component. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so as to manage charging, discharging, power consumption and other functions through the power management system. The power supply 303 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.
该电子设备还可包括输入单元304，该输入单元304可用于接收输入的数字或字符信息，以及产生与用户设置以及功能控制有关的键盘、鼠标、操作杆、光学或者轨迹球信号输入。The electronic device may further include an input unit 304, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.
尽管未示出，电子设备还可以包括显示单元等，在此不再赘述。具体在本实施例中，电子设备中的处理器301会按照如下的指令，将一个或一个以上的应用程序的进程对应的可执行文件加载到存储器302中，并由处理器301来运行存储在存储器302中的应用程序，从而实现各种功能，如下：Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302, thereby realizing various functions, as follows:
获取客服诉求数据，并提取客服诉求数据中的诉求事件标题和诉求事件内容；基于诉求事件标题和诉求事件内容，提取得到事件要素信息，并基于诉求事件内容生成事件摘要信息；基于事件要素信息和事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果；基于事件趋势分析结果，生成对应的预警信息，并推送预警信息至已绑定的关联对象对应的关联终端。Acquire customer service request data, and extract the request event title and request event content in the customer service request data; extract event element information based on the request event title and request event content, and generate event summary information based on the request event content; perform event trend analysis on the target event based on the event element information and event summary information to obtain event trend analysis results; generate corresponding warning information based on the event trend analysis results, and push the warning information to the associated terminal corresponding to the bound associated object.
以上各个操作的具体实施可参见前面的实施例，在此不再赘述。The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.
本申请实施例通过对海量的客服诉求数据进行深度的精准分析，提取出事件要素信息并自动生成事件摘要，从而根据事件要素信息和事件摘要信息对目标事件进行趋势分析和预警，实现对客户诉求的全面感知、精准分析和实时预警，从而为企业管理提供更加高效、精准的决策支持。The embodiments of the present application perform in-depth and accurate analysis on massive amounts of customer service demand data, extract event element information and automatically generate event summaries, thereby performing trend analysis and early warning on target events based on the event element information and event summary information, achieving comprehensive perception, accurate analysis and real-time early warning of customer demands, thereby providing more efficient and accurate decision support for enterprise management.
本领域普通技术人员可以理解，上述实施例的各种方法中的全部或部分步骤可以通过指令来完成，或通过指令控制相关的硬件来完成，该指令可以存储于一计算机可读存储介质中，并由处理器进行加载和执行。A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.
为此，本申请实施例提供一种存储介质，其中存储有多条指令，该指令能够被处理器进行加载，以执行本申请实施例所提供的任一种客服诉求数据处理中的步骤。例如，该指令可以执行如下步骤：To this end, an embodiment of the present application provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute any step in the customer service demand data processing provided in the embodiment of the present application. For example, the instructions can execute the following steps:
获取客服诉求数据，并提取客服诉求数据中的诉求事件标题和诉求事件内容；基于诉求事件标题和诉求事件内容，提取得到事件要素信息，并基于诉求事件内容生成事件摘要信息；基于事件要素信息和事件摘要信息对目标事件进行事件趋势分析，得到事件趋势分析结果；基于事件趋势分析结果，生成对应的预警信息，并推送预警信息至已绑定的关联对象对应的关联终端。Acquire customer service request data, and extract the request event title and request event content in the customer service request data; extract event element information based on the request event title and request event content, and generate event summary information based on the request event content; perform event trend analysis on the target event based on the event element information and event summary information to obtain event trend analysis results; generate corresponding warning information based on the event trend analysis results, and push the warning information to the associated terminal corresponding to the bound associated object.
以上各个操作的具体实施可参见前面的实施例，在此不再赘述。The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.
其中，该存储介质可以包括：只读存储器（ROM，Read Only Memory）、随机存取记忆体（RAM，Random Access Memory）、磁盘或光盘等。The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.
由于该存储介质中所存储的指令，可以执行本申请实施例所提供的任一种客服诉求数据处理方法中的步骤，因此，可以实现本申请实施例所提供的任一种客服诉求数据处理方法所能实现的有益效果，详见前面的实施例，在此不再赘述。Since the instructions stored in the storage medium can execute the steps in any customer service demand data processing method provided in the embodiments of the present application, the beneficial effects that can be achieved by any customer service demand data processing method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.
以上对本申请实施例所提供的一种客服诉求数据处理方法、装置、电子设备以及存储介质进行了详细介绍，本文中应用了具体个例对本申请的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本申请的方法及其核心思想；同时，对于本领域的技术人员，依据本申请的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本申请的限制。The above is a detailed introduction to a customer service demand data processing method, device, electronic device and storage medium provided in an embodiment of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the idea of the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.
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