Knowledge graph-based customer service response methods, devices, electronic equipment, and media

Abstract

This application provides a customer service response method, apparatus, electronic device, and medium based on a knowledge graph, belonging to the field of artificial intelligence technology. The method includes: acquiring customer service demand data; extracting features from the customer service demand data to obtain customer service demand features; retrieving business operation knowledge features from a pre-constructed business knowledge graph to obtain target business operation knowledge features; wherein the business knowledge graph is constructed based on the customer service data; constructing target business operation instruction information based on the customer service demand features and the target business operation knowledge features; and inputting the target business operation instruction information into a pre-trained business service operation model to generate target response data matching the customer service demand data. This improves customer service quality.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a customer service response method, apparatus, electronic device, and medium based on knowledge graphs. Background Technology

[0002] Currently, customer service response methods typically use matching algorithms to match the business service content in the business service database with customer service request data to determine the final response. For example, in a body management service, the database stores standardized body management plans for clients. If a customer request is for a plan to guide a client through body management, the database will retrieve that plan. However, this method only matches the service content stored in the database, resulting in low accuracy. Furthermore, it only provides standardized service plans for different clients and cannot provide continuous tracking of customer service, leading to poor customer service quality. Therefore, improving customer service quality has become a pressing issue. Summary of the Invention

[0003] The main objective of this application is to provide a customer service response method, apparatus, electronic device, and medium based on knowledge graphs, aiming to improve customer service quality.

[0004] To achieve the above objectives, a first aspect of this application proposes a customer service response method based on a knowledge graph, the method comprising:

[0005] Obtain customer service demand data;

[0006] Feature extraction is performed on the customer service demand data to obtain customer service demand features;

[0007] The customer service demand features are retrieved from the pre-built business knowledge graph to obtain the target business operation knowledge features; wherein, the business knowledge graph is constructed based on customer service data.

[0008] Based on the customer service demand characteristics and the target business operation knowledge characteristics, target business operation instruction information is constructed;

[0009] The target business operation instruction information is input into a pre-trained business service operation model to generate target response data that matches the customer service demand data.

[0010] In some embodiments, the customer service demand feature is an icebreaking service feature, and the target business operation knowledge feature is an icebreaking operation knowledge feature. The step of constructing target business operation instruction information based on the customer service demand feature and the target business operation knowledge feature includes:

[0011] Obtain the icebreaking reception service features of the icebreaking service features, and obtain the reception operation knowledge features from the icebreaking operation knowledge features based on the icebreaking reception service features;

[0012] Obtain the icebreaking query service features of the icebreaking service features, and obtain the icebreaking query knowledge features from the icebreaking operation knowledge features based on the icebreaking query service features;

[0013] Obtain the icebreaking consumption behavior service features of the icebreaking service features, and obtain the icebreaking consumption behavior knowledge features from the icebreaking operation knowledge features based on the icebreaking consumption behavior service features;

[0014] The icebreaking reception service features, reception operation knowledge features, icebreaking inquiry service features, icebreaking inquiry knowledge features, icebreaking consumption behavior service features, and icebreaking consumption behavior knowledge features are integrated to obtain integrated icebreaking service features.

[0015] The integrated icebreaking service features are input into a preset instruction template to construct instruction information, thereby generating icebreaking service instruction information as the target business operation instruction information.

[0016] In some embodiments, the customer service demand feature is a customized service feature, the target business operation knowledge feature is a customized operation knowledge feature, and the step of constructing target business operation instruction information based on the customer service demand feature and the target business operation knowledge feature includes:

[0017] Obtain the customized customer guidance features of the customized service features, and obtain the customized guidance knowledge features from the customized operation knowledge features based on the customized customer guidance features;

[0018] Obtain the customized service scheme features of the customized service features, and obtain the customized scheme knowledge features from the customized operation knowledge features based on the customized service scheme features;

[0019] The customized customer guidance features, customized guidance knowledge features, customized service plan features, and customized plan knowledge features are integrated to obtain integrated customized service features.

[0020] The integrated customized service features are input into a preset instruction template to construct instruction information, thereby generating customized service instruction information as the target business operation instruction information.

[0021] In some embodiments, the customer service demand feature is a remote supervision service feature, the target business operation knowledge feature is a remote supervision knowledge feature, and the step of constructing target business operation instruction information based on the customer service demand feature and the target business operation knowledge feature includes:

[0022] Obtain the remote guidance service features of the remote supervision service features, and obtain the remote guidance knowledge features from the remote supervision knowledge features based on the remote guidance service features;

[0023] Obtain the remote supervision customer service features of the remote supervision service features, and obtain the remote supervision customer knowledge features from the remote supervision knowledge features based on the remote supervision customer service features;

[0024] The remote guidance service features, the remote guidance knowledge features, the remote supervision customer service features, and the remote supervision customer knowledge features are integrated to obtain the integrated remote supervision service features.

[0025] The integrated remote supervision service features are input into a preset instruction template to construct instruction information, thereby generating remote supervision service instruction information as the target business operation instruction information.

[0026] In some embodiments, the customer service demand feature is a plan revision service feature, the target business operation knowledge feature is a plan revision knowledge feature, and the step of constructing target business operation instruction information based on the customer service demand feature and the target business operation knowledge feature includes:

[0027] Obtain customer follow-up service features from the plan revision service features, and obtain follow-up service knowledge features from the plan revision knowledge features based on the customer follow-up service features;

[0028] Obtain the correction service scheme features of the plan correction service features, and obtain the correction scheme knowledge features from the plan correction knowledge features based on the correction service scheme features;

[0029] The customer follow-up service characteristics, the follow-up service knowledge characteristics, the revised service plan characteristics, and the revised plan knowledge characteristics are integrated to obtain the integrated plan revised service characteristics.

[0030] The features of the fusion plan correction service are input into a preset instruction template to construct instruction information, thereby generating plan correction service instruction information as the target business operation instruction information.

[0031] In some embodiments, the step of inputting the target business operation instruction information into a pre-trained business service operation model to generate target response data that matches the customer service demand data includes:

[0032] The business service operation model is used to identify the business operation intent of the target business operation instruction information to obtain a business operation instruction diagram.

[0033] Based on the business operation instruction diagram, a business operation instruction search is performed on the target business operation instruction information to obtain business operation instruction search information;

[0034] The search information for the business operation instructions is inferred and linked to obtain target response data that matches the customer service demand data.

[0035] In some embodiments, the step of retrieving target business operation knowledge features from the pre-built business knowledge graph by performing business operation retrieval on the customer service demand features includes:

[0036] Obtain the business knowledge features of the business knowledge graph, and match the customer service demand features with the business knowledge features to obtain candidate business knowledge features;

[0037] Obtain the similarity score of the candidate business knowledge features, and select the candidate business knowledge feature with the highest similarity score from the candidate business knowledge features as the first business operation knowledge feature based on the similarity score;

[0038] In response to a feature instruction selected by a preset terminal, a second service operation knowledge feature is obtained from the candidate service knowledge features;

[0039] The target business operation knowledge feature is selected from the candidate business knowledge features based on the first business operation knowledge feature and the second business operation knowledge feature.

[0040] To achieve the above objectives, a second aspect of this application proposes a knowledge graph-based customer service response device, the device comprising:

[0041] The customer service demand data acquisition module is used to acquire customer service demand data.

[0042] The feature extraction module is used to extract features from the customer service demand data to obtain customer service demand features;

[0043] The business operation retrieval module is used to retrieve the customer service demand features from the pre-built business knowledge graph to obtain the target business operation knowledge features; wherein, the business knowledge graph is constructed based on customer service data;

[0044] The instruction information construction module is used to construct target business operation instruction information based on the customer service demand characteristics and the target business operation knowledge characteristics;

[0045] The customer service request response generation module is used to input the target business operation instruction information into a pre-trained business service operation model, so as to generate target response data that matches the customer service request data through the business service operation model.

[0046] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0047] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0048] This application proposes a knowledge graph-based customer service response method, apparatus, electronic device, and medium. It acquires customer service demand data; extracts features from the customer service demand data to obtain customer service demand features; retrieves business operation knowledge features from a pre-constructed business knowledge graph to obtain target business operation knowledge features; wherein the business knowledge graph is constructed based on the customer service data; and constructs target business operation instruction information based on the customer service demand features and target business operation knowledge features. The target business operation instruction information is then input into a pre-trained business service operation model to generate target response data that matches the customer service demand data. This improves customer service quality. Attached Figure Description

[0049] Figure 1 This is a flowchart of a knowledge graph-based customer service response method provided in an embodiment of this application;

[0050] Figure 2 yes Figure 1 The flowchart of step S103 in the process;

[0051] Figure 3 yes Figure 1 The flowchart of step S104 in the process;

[0052] Figure 4 yes Figure 1 Another flowchart of step S104 in the process;

[0053] Figure 5 yes Figure 1 Another flowchart of step S104 in the process;

[0054] Figure 6 yes Figure 1 Another flowchart of step S104 in the process;

[0055] Figure 7 yes Figure 1 The flowchart of step S105 in the process;

[0056] Figure 8 This is a schematic diagram of the structure of the knowledge graph-based customer service response device provided in an embodiment of this application;

[0057] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0061] First, let's analyze some of the terms used in this application:

[0062] Artificial intelligence (AI) is a new branch of computer science that studies, develops, and applies theories, methods, technologies, and systems to simulate, extend, and expand human intelligence. It aims to understand the essence of intelligence and produce intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thought. Furthermore, AI utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results.

[0063] Based on this, embodiments of this application provide a customer service response method, apparatus, electronic device, and medium based on knowledge graphs, aiming to improve customer service quality.

[0064] The knowledge graph-based customer service response method, apparatus, electronic device, and medium provided in this application are specifically described through the following embodiments. First, the knowledge graph-based customer service response method in this application is described.

[0065] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0066] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0067] The knowledge graph-based customer service response method provided in this application relates to the field of artificial intelligence technology. This knowledge graph-based customer service response method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the knowledge graph-based customer service response method, but is not limited to the above forms.

[0068] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0069] Figure 1 This is an optional flowchart of the knowledge graph-based customer service response method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0070] Step S101: Obtain customer service demand data.

[0071] Step S102: Extract features from customer service demand data to obtain customer service demand features.

[0072] Step S103: Retrieve business operation features of customer service demand features from the pre-built business knowledge graph to obtain target business operation knowledge features; wherein, the business knowledge graph is constructed based on customer service data.

[0073] Step S104: Based on the characteristics of customer service needs and the characteristics of target business operation knowledge, construct target business operation instruction information.

[0074] Step S105: Input the target business operation instruction information into the pre-trained business service operation model to generate target response data that matches the customer service demand data through the business service operation model.

[0075] Steps S101 to S105 as shown in this embodiment involve: acquiring customer service demand data; extracting features from the customer service demand data to obtain customer service demand features; retrieving business operation knowledge features from a pre-built business knowledge graph to obtain target business operation knowledge features; wherein the business knowledge graph is constructed based on the customer service data; constructing target business operation instruction information based on the customer service demand features and the target business operation knowledge features; and inputting the target business operation instruction information into a pre-trained business service operation model to generate target response data that matches the customer service demand data. This improves customer service quality.

[0076] In step S101 of some embodiments, specifically, customer service demand data refers to customer service business operation demand data, which can be at least one of the following: icebreaking service business operation demand data, customized service business operation demand data, remote supervision business operation demand data, and plan correction business operation demand data.

[0077] For example, in the field of body management services, operational data for icebreaker services could include: what icebreaker scripts are needed for initial customer reception and understanding of the customer's weight and background information; operational data for customized services could include: how to customize a corresponding weight loss plan based on the input customer identity information; operational data for remote monitoring services could include: what group messages are needed when remotely monitoring a customer's body management; operational data for plan revision services could include: how to update the customized weight loss plan based on the input customer's weight and body fat changes; and it could also include: how to conduct icebreaker consultations on the customer's physical health status and customize a corresponding weight loss plan based on the consultation results.

[0078] In step S102 of some embodiments, specifically, the customer service demand feature refers to the vector information of the customer service demand data in the semantic space. The customer service demand feature is the key information extracted from the customer service demand data. Therefore, the customer service demand feature can also be at least one of the following customer service demand features: icebreaking service feature, customized service feature, remote supervision service feature, and plan correction service feature.

[0079] Specifically, the customer service request data is first preprocessed (including stop word removal, stemming, and word form restoration) to help eliminate noise and improve data quality. Secondly, the preprocessed data is tokenized to obtain multiple tokenized customer service request data. Finally, the tokenized data is embedded to generate a vector representation for each token; these vectors are the customer service request features.

[0080] For example, in the field of body management services, if the operational needs data for icebreaker services involve understanding the basic information of clients (such as age, height, weight, weight loss history, etc.) during initial customer interactions, then embedding can extract vectorized icebreaker service features such as age, height, weight, weight loss history, icebreaker, and dialogue. If the operational needs data for customized services involve how to create a personalized weight loss plan based on the input client's basic information, then embedding can extract vectorized customized service features such as client's basic information, personalization, and weight loss plan. If the operational needs data for remote monitoring services involve sending mass messages about the diet composition during weight loss, then embedding can extract vectorized remote monitoring service features such as remote monitoring, weight loss period, diet composition, and mass messages. If the operational needs data for plan revision services involve updating the customized weight loss plan based on the input client's weight and body fat changes, then embedding can extract vectorized plan revision service features such as client weight changes, body fat changes, updates, customization, weight loss, and plan.

[0081] In this embodiment, feature extraction from customer service demand data is a multi-dimensional and multi-level process. It involves extracting key demand information from customer service demand data and transforming the key demand information into structured data that can be used for analysis, which helps to improve the efficiency of obtaining relevant knowledge features from the business knowledge graph.

[0082] Please see Figure 2 In some embodiments, step S103 includes, but is not limited to, steps S201 to S204:

[0083] Step S201: Obtain the business knowledge features of the business knowledge graph, and perform feature matching between the customer service demand features and the business knowledge features to obtain candidate business knowledge features.

[0084] Step S202: Obtain the similarity score of the candidate business knowledge features, and select the candidate business knowledge feature with the highest similarity score as the first business operation knowledge feature based on the similarity score.

[0085] Step S203: In response to the feature instruction selected by the preset terminal, the second service operation knowledge feature is obtained from the candidate service knowledge features.

[0086] Step S204: Select the target business operation knowledge feature from the candidate business knowledge features based on the first business operation knowledge feature and the second business operation knowledge feature.

[0087] In step S201 of some embodiments, specifically, before retrieving the target business operation knowledge features from the pre-built business knowledge graph by performing business operation retrieval on the customer service demand features, the knowledge graph-based customer service response method further includes: extracting business operation step entities from the customer service data to obtain business operation step entities; extracting operation step entity relationships from the business operation step entities to obtain business operation entity relationships; and constructing a business knowledge graph based on the business operation step entities and business operation entity relationships.

[0088] Furthermore, named entity recognition technology can be used to identify the entity references of each business operation step in the customer service data, and the entity references can be classified into entities through the BERT network to obtain the business operation step entities.

[0089] Specifically, in the field of weight loss, customer service data can be obtained from the weight loss customization operation manual stored in the enterprise database. This customer service data includes, but is not limited to, ice-breaking service operation steps for customers in the weight loss process, operation steps for customized weight loss plans, operation steps for remote weight loss monitoring, and operation steps for revising customized weight loss plans.

[0090] For example, in the weight loss field, ice-breaking operation steps data may include, but are not limited to, guiding customers to their seats and greeting them, asking about the distance of their residence / workplace, asking about their origin, and asking customers based on information (such as age, weight, and height). Then, through entity extraction, entity of customer reception operation steps, entity of weight loss store origin operation steps, and entity of operation steps of asking customers basic information can be generated.

[0091] Furthermore, in the process of extracting entity relations for operational steps, it is first necessary to identify the semantic relationships between entities in the business operational steps, and then, based on these semantic relationships, identify the subject-verb-object structure and modification relationships in the sentence through dependency parsing and semantic role labeling, so as to extract multi-level business operational entity relations.

[0092] For example, in the field of weight loss, the business operation step entity can include icebreaking service operation step entity, customized service operation step entity, remote supervision operation step entity, and modified customized service operation step entity, which have a sequential relationship. Moreover, there is also a sequential relationship between the two icebreaking service operation sub-step entities of the icebreaking service operation step entity, namely, "inquiring about the source of the customer" is usually done after "introducing the customer". There is also a corresponding sequential relationship between the specific operation cases corresponding to the operation sub-step entities. The semantic relationship between entities can be determined by entity relation extraction, and the entity relations of the business operation sub-steps and the specific case entities contained in the business operation sub-steps can be extracted by dependency parsing to obtain the entity relations between the multi-level weight loss service operation step entities.

[0093] Specifically, a business knowledge graph refers to storing relevant knowledge about customer service data through a graph network structure. In this graph, business operation step entities serve as nodes, and relationships between them are the edges connecting those nodes. This allows all business operation step entities and their relationships within the customer service data to be stored using the Neo4j graph structure.

[0094] Furthermore, the node types of the entities in the business operation steps are obtained, as well as the relationship types of the relationships between these entities. A business knowledge graph is then constructed based on the node types and relationship types.

[0095] For example, in the field of weight reduction, the node type of the entity that introduces the seat and greets can be the node type of the icebreaking service operation step entity, the node type of the customization service operation step entity, and the relationship type between them is the follow type, that is, the operation step that follows the icebreaking service operation step entity is the customization service operation type. All entities and entity relationships are stored in the Neo4j graph structure to generate a business knowledge graph.

[0096] In this embodiment, by constructing a corresponding business knowledge graph from customer service data, it becomes easier to quickly find relevant business operation steps when responding to customer service questions by querying the business knowledge graph, thus providing accurate guidance for customer service responses and helping to improve the efficiency and accuracy of customer service responses.

[0097] Specifically, business knowledge features refer to vector representations used in the business knowledge graph for quick retrieval and location of business operation knowledge features. These business knowledge features include, but are not limited to, icebreaking service operation step features, customized service operation step features, remote supervision operation step features, and modified customized service operation step features, and each feature contains corresponding operation step sub-features.

[0098] For example, in the weight loss industry, if the customer service needs feature is what the icebreaker process is for first-time customers, then the business knowledge features are guiding customers to sit down and greeting them, asking customers about the distance between their residence and the weight loss store, and how customers learn about the store, etc.

[0099] Specifically, target business operation knowledge characteristics refer to specific business operation characteristics that match customer service needs.

[0100] Furthermore, similarity scores between customer service demand features and business knowledge features can be calculated using similarity algorithms such as cosine similarity, Euclidean distance, or Manhattan distance. The top k business knowledge features with the highest similarity scores can then be selected as candidate business knowledge features to search for business operation steps that may match customer service demand features in the business knowledge graph.

[0101] In this embodiment, by matching customer service demand features with business knowledge features, candidate business knowledge features are obtained. By comparing the similarity between vectors, candidate business knowledge features that may match customer service demand features can be quickly retrieved, which helps to improve the efficiency of subsequent business operation retrieval.

[0102] In step S202 of some embodiments, specifically, the similarity score can be determined by calculating the similarity between customer service demand features and business knowledge features based on the cosine similarity algorithm.

[0103] Specifically, the first business operation knowledge feature is the business operation knowledge feature that best matches the customer service needs feature, selected based on similarity scores.

[0104] Specifically, the similarity scores are sorted from high to low, and the candidate business knowledge feature with the highest similarity score is selected as the first business operation knowledge feature based on the sorted scores.

[0105] In this embodiment, the candidate business knowledge feature with the highest similarity score is selected as the first business operation knowledge feature based on the similarity score. The similarity score can quantify the relationship between different candidate business knowledge features and customer service demand features to determine the degree of matching between features, thereby selecting the candidate knowledge feature with the highest degree of matching as the first business operation knowledge feature, which helps to improve the accuracy of business operation retrieval.

[0106] In step S203 of some embodiments, the preset terminal can be a mobile phone, iPad, or other terminal.

[0107] Specifically, the selected feature instruction can be the option button instruction clicked by the customer service representative.

[0108] Specifically, after obtaining candidate business knowledge features, the candidate business knowledge features are converted into candidate business knowledge text, and the candidate business knowledge text is displayed to the terminal through a pop-up window. Customer service personnel can select at least one candidate business knowledge text by clicking the option button corresponding to the candidate business knowledge text related to the customer service needs features, and use the candidate business knowledge features corresponding to the selected candidate business knowledge text as the second business operation knowledge feature.

[0109] In this embodiment, by responding to the feature command selected by the preset terminal, the second business operation knowledge feature is obtained from the candidate business knowledge features. Customer service personnel can combine their work experience to select business operation knowledge features that match the customer service needs, so as to provide targeted and high-quality services to different customers and help improve the quality of subsequent customer service.

[0110] In step S204 of some embodiments, specifically, the target business operation knowledge feature refers to the feature that best matches the customer service demand feature.

[0111] Specifically, if the first business operation knowledge feature is consistent with the second business operation knowledge feature, then the first business operation knowledge feature or the second business operation knowledge feature shall be used as the target business operation knowledge feature; if the first business operation knowledge feature is inconsistent with the second business operation knowledge feature, then the second business operation knowledge feature shall be used as the target business operation knowledge feature.

[0112] For example, in the weight loss industry, if the customer service needs feature is "what is the ice-breaking process?", the first business operation knowledge feature is "guiding customers to sit down and greeting them", and the second business operation knowledge feature is "inquiring about the customer's origin and how the customer learned about the store", then the target business operation knowledge feature is "inquiring about the customer's origin and how the customer learned about the store".

[0113] In this embodiment, the target business operation knowledge feature is selected from the candidate business knowledge features based on the first business operation knowledge feature and the second business operation knowledge feature. The candidate business knowledge feature can be quickly selected through the business knowledge graph. The final target business operation knowledge feature is also selected by combining the experience of customer service personnel. This enables the selection of the most matching knowledge feature for different needs, rather than simply matching features. This facilitates the provision of personalized and high-quality services to different customers and helps improve the quality of subsequent customer service.

[0114] Please see Figure 3 In some embodiments, step S104 includes, but is not limited to, steps S301 to S305:

[0115] Step S301: Obtain the icebreaking reception service features of the icebreaking service features, and obtain the reception operation knowledge features from the icebreaking operation knowledge features based on the icebreaking reception service features.

[0116] Step S302: Obtain the icebreaking query service features of the icebreaking service features, and obtain the icebreaking query knowledge features from the icebreaking operation knowledge features based on the icebreaking query service features.

[0117] Step S303: Obtain the icebreaking consumption behavior service features of the icebreaking service features, and obtain the icebreaking consumption behavior knowledge features from the icebreaking operation knowledge features based on the icebreaking consumption behavior service features.

[0118] Step S304: The ice-breaking reception service characteristics, reception operation knowledge characteristics, ice-breaking inquiry service characteristics, ice-breaking inquiry knowledge characteristics, ice-breaking consumption behavior service characteristics, and ice-breaking consumption behavior knowledge characteristics are integrated to obtain integrated ice-breaking service characteristics.

[0119] Step S305: Input the integrated icebreaking service features into the preset instruction template to construct instruction information, so as to generate icebreaking service instruction information as target business operation instruction information.

[0120] In step S301 of some embodiments, specifically, the customer service demand feature can be an icebreaking service feature.

[0121] Specifically, the target business operation knowledge characteristics can be icebreaking operation knowledge characteristics.

[0122] Furthermore, the icebreaking service feature can be the icebreaking reception service feature, which is used to represent the reception guidance feature for customers meeting for the first time.

[0123] For example, in the weight loss industry, icebreaker reception services can include how to guide customers to their seats and greet them when they first meet them, how to ask customers how far their home / workplace is from the weight loss store, and how to ask customers how they learned about the weight loss store.

[0124] Specifically, icebreaking operation knowledge features are extracted from the business knowledge graph, corresponding to reception operation knowledge features. These reception operation knowledge features are used to guide customer service staff in their initial customer reception actions and conversations.

[0125] For example, in the weight loss industry, the reception skills required for guiding customers to their seats and greeting them could include smiling, nodding, and asking questions to guide them to their seats. Similarly, the skills required for asking customers about the distance between their home / workplace and the weight loss store could include asking, "Are you close to our store?" And the skills required for asking customers about their knowledge of the weight loss store could include asking, "How did you learn about weight loss brand B and the store?"

[0126] Furthermore, if a customer appears nervous during the reception process, customer service representatives can adopt a gentler and more patient attitude to soothe the customer, based on the guidance in the reception operation knowledge. This personalized reception approach helps alleviate the customer's anxiety, making them more willing to share personal information and specific weight loss needs, thus laying the foundation for providing personalized services in the future.

[0127] In step S302 of some embodiments, specifically, the icebreaking service feature can be an icebreaking query service feature.

[0128] For example, in the field of weight loss, a key feature of icebreaker inquiry services could be how to understand a client's basic information and medical history.

[0129] Specifically, icebreaking operation knowledge features are extracted from the business knowledge graph, and these icebreaking inquiry knowledge features are used to express the dialogue for understanding the customer's basic body shape.

[0130] For example, in the field of weight loss, would it be convenient to know your height, weight, body fat percentage, and medical history? Or would it be convenient to know your weight loss goals and health status?

[0131] In this embodiment, by acquiring icebreaking inquiry service features and obtaining icebreaking consultation knowledge features from icebreaking operation knowledge features based on these features, it is possible to ensure that customer service representatives can more accurately understand the customer's weight loss goals and weight loss plan needs during the initial communication with the customer. This helps to quickly identify the customer's specific needs and expectations, thereby laying the foundation for providing personalized services in the future.

[0132] In step S303 of some embodiments, specifically, the icebreaking service feature can be the icebreaking consumption behavior service feature.

[0133] For example, in the weight loss industry, breaking the ice in consumer behavior services can involve understanding how customers have a budget for weight loss and their desire to purchase customized weight loss products.

[0134] Specifically, ice-breaking operational knowledge features are extracted from the business knowledge graph to represent ice-breaking consumer behavior knowledge features. These ice-breaking consumer behavior knowledge features are used to understand customer consumption patterns and to formulate sales pitches for customized weight loss products.

[0135] For example, in the field of weight loss, would it be helpful to understand your weight loss budget based on an assessment of your body data?

[0136] In this embodiment, by acquiring icebreaking consumption behavior service characteristics and icebreaking operation knowledge characteristics, it is possible to better understand customers' purchasing habits and preferences, thereby helping customer service to make customized weight loss product recommendations based on customers' consumption history and budget.

[0137] In step S304 of some embodiments, specifically, the icebreaking reception service features, reception operation knowledge features, icebreaking inquiry service features, icebreaking inquiry knowledge features, icebreaking consumption behavior service features, and icebreaking consumption behavior knowledge features are spliced ​​together, and the interactions and common meanings between the icebreaking features are learned, and finally, the fused icebreaking service features are output.

[0138] In this embodiment, by integrating icebreaking reception service features, reception operation knowledge features, icebreaking inquiry service features, icebreaking inquiry knowledge features, icebreaking consumption behavior service features, and icebreaking consumption behavior knowledge features, it is possible to comprehensively integrate information from multiple dimensions such as customers' weight loss needs, weight loss plans, and weight loss consumption behaviors. This helps customer service personnel to fully understand customer needs, thereby providing more accurate and personalized services and facilitating the improvement of customer service quality in the future.

[0139] In step S305 of some embodiments, specifically, the instruction template is used to integrate icebreaking service features and icebreaking operation knowledge features to generate icebreaking service requirement data that can be understood by the subsequent business service operation model.

[0140] For example, in the weight loss field, the instruction template could be: [Customer Needs Stage], where a question was asked about [Icebreaker Weight Loss Services]. Based on the business knowledge graph, icebreaker service guidance was provided: [Weight Loss Service Guidance].

[0141] For example, when customer service inquired about how to communicate with clients about weight loss during the icebreaker phase, they received icebreaker service guidance based on the business knowledge graph: First, smile, nod, and greet the client to guide them to a seat. Ask if the client is near the store, and inquire about their weight loss goals and health status (e.g., would it be convenient to know their height, weight, body fat percentage, and medical history). Further, based on the client's body data assessment, inquire about their weight loss budget (e.g., based on the assessment of your body data, would it be convenient to know your weight loss budget?). Based on the client's answer, guide them to purchase a customized weight loss plan.

[0142] In this embodiment, by inputting the integrated icebreaking service features into a preset instruction template to construct instruction information, icebreaking service instruction information is generated as target business operation instruction information. This allows customer service personnel to take action quickly based on the instruction information without having to reconsider how to interact with customers in each communication. This helps to quickly build customer trust and lays the foundation for subsequent personalized weight loss solutions, significantly improving customer service quality.

[0143] Please see Figure 4 In some embodiments, step S104 may include, but is not limited to, steps S401 to S404:

[0144] Step S401: Obtain the customized customer guidance feature of the customized service feature, and obtain the customized guidance knowledge feature from the customized operation knowledge feature based on the customized customer guidance feature.

[0145] Step S402: Obtain the customized service scheme features of the customized service features, and obtain the customized scheme knowledge features from the customized operation knowledge features based on the customized service scheme features.

[0146] Step S403: The customized customer guidance features, customized guidance knowledge features, customized service plan features, and customized plan knowledge features are integrated to obtain integrated customized service features.

[0147] Step S404: Input the integrated customized service features into the preset instruction template to construct instruction information, so as to generate customized service instruction information as target business operation instruction information.

[0148] In step S401 of some embodiments, specifically, the customer service demand feature can be a customized service feature.

[0149] Specifically, the target business operation knowledge characteristics can be customized operation knowledge characteristics.

[0150] Furthermore, customized service features can include customized customer guidance features.

[0151] For example, in the weight loss field, customized client guidance features can provide personalized diet and exercise advice based on the client's health condition.

[0152] Specifically, the customization guidance knowledge features corresponding to the customization operation knowledge features are extracted from the business knowledge graph.

[0153] For example, in the field of weight loss, customized guidance is characterized by specific guidance programs for weight loss diet plans and weight loss exercise arrangements on a weekly basis.

[0154] In this embodiment, by acquiring customized customer guidance features and obtaining customized guidance knowledge features from customized operation knowledge features based on customized customer guidance features, it is possible to ensure that customer service personnel provide targeted weight loss guidance in the initial communication with customers, which helps to build customer trust and improve the professionalism of the service.

[0155] In step S402 of some embodiments, specifically, the customized service scheme feature can be the icebreaker consumption behavior service feature.

[0156] For example, in the field of weight loss, the characteristics of a customized service plan can be the specific details of how to provide customized weight loss services to customers.

[0157] Specifically, the customization scheme knowledge features corresponding to the customization operation knowledge features are extracted from the business knowledge graph.

[0158] For example, in the field of weight loss, a customized one-month program was created for client A, which included weight loss products, a weight loss diet plan, a weight loss exercise plan, and a body care plan.

[0159] In this embodiment, by acquiring the characteristics of customized service plans and obtaining the knowledge characteristics of customized plans from the knowledge characteristics of customized operations based on these characteristics, customer service personnel can provide personalized weight loss service plans that meet the needs and budgets of different customers, thereby satisfying the personalized weight loss needs of different customers and significantly improving the quality of customer service.

[0160] In step S403 of some embodiments, specifically, the customized customer guidance features, customized guidance knowledge features, customized service plan features, and customized plan knowledge features are vectorized and concatenated to achieve feature fusion.

[0161] In this embodiment, by integrating customized customer guidance features, customized guidance knowledge features, customized service plan features, and customized plan knowledge features, the personalized weight loss needs and personalized weight loss plan customization of different customers are combined, thereby improving the quality of customer service during the customized service process.

[0162] In step S404 of some embodiments, specifically, a clear customization guide is provided to customer service personnel through an instruction template.

[0163] For example, the instruction template could be: [Customer Service Needs Stage], which asks about [Customized Weight Loss Service]. Based on the business knowledge graph, the following guidance is provided for customized services: [Customized Weight Loss Plan Guidance].

[0164] For example, in the weight loss field, instruction templates can guide customer service personnel to first understand the customer's basic information, weight loss goals, and weight loss plan, then provide personalized diet and exercise advice based on this information, and finally guide the customer to choose a customized weight loss plan that suits them.

[0165] In this embodiment, by inputting the integrated customized service features into a preset instruction template to construct instruction information, customized service instruction information is generated as target business operation instruction information. This enables customer service personnel to accurately and quickly guide customers to select suitable customized weight loss solutions, further improving customer service quality.

[0166] Please see Figure 5 In some embodiments, step S104 may include, but is not limited to, steps S501 to S504:

[0167] Step S501: Obtain the remote guidance service features of the remote supervision service features, and obtain the remote guidance knowledge features from the remote supervision knowledge features based on the remote guidance service features.

[0168] Step S502: Obtain remote supervision customer service features from remote supervision service features, and obtain remote supervision customer knowledge features from remote supervision knowledge features based on remote supervision customer service features.

[0169] Step S503: The remote guidance service features, remote guidance knowledge features, remote supervision customer service features, and remote supervision customer knowledge features are integrated to obtain the integrated remote supervision service features.

[0170] Step S504: Input the integrated remote supervision service features into the preset instruction template to construct instruction information, so as to generate remote supervision service instruction information as target business operation instruction information.

[0171] In step S501 of some embodiments, specifically, the customer service demand feature can be a remote supervision service feature.

[0172] Specifically, the target business operation knowledge characteristics can be remote supervision knowledge characteristics.

[0173] Furthermore, the remote supervision service feature can be the remote guidance service feature.

[0174] For example, in the field of weight loss, remote guidance services can feature how to provide real-time guidance via video call, or how to send customized exercise and diet plans through an app.

[0175] Specifically, remote supervision knowledge features corresponding to remote guidance service features are extracted from the business knowledge graph to represent specific remote guidance strategies and methods.

[0176] For example, in the field of weight loss, remote monitoring knowledge features can include asking clients about their hunger and providing corresponding dietary analysis and guidance, reminding clients of their sleep time, and guiding clients to exercise via video.

[0177] In this embodiment, by identifying the characteristics of remote supervision services and obtaining the corresponding remote guidance knowledge characteristics, customer service personnel can provide customers with specific guidelines and action steps, enabling continuous tracking of customer service and further improving customer service quality.

[0178] In step S502 of some embodiments, specifically, the remote supervision service feature can be the remote supervision service feature.

[0179] For example, in the field of weight loss, remote monitoring of customer service features could be used to generate online message reminders about a customer's weight loss journey.

[0180] Specifically, remote supervision customer knowledge features are extracted from the business knowledge graph.

[0181] For example, in the field of weight loss, remote monitoring of customer knowledge characteristics on a weekly basis can be used to encourage customer A, regardless of whether they have gained weight or maintained their original weight, to engage in in-person exercise. Because of the cooler weather, fat is most active, storing energy for winter, which is the rhythm of weight gain. We hope you continue to persevere in losing weight and achieve a great figure! Furthermore, tomorrow, an instructor from store D will be visiting to conduct a phase-by-phase weight loss summary, and we hope you can participate if you are available tomorrow.

[0182] In this embodiment, by acquiring remote supervision customer service characteristics and obtaining remote supervision customer knowledge characteristics from remote supervision knowledge characteristics based on remote supervision customer service characteristics, it is helpful to monitor the customer's weight loss situation, give the customer timely encouragement, and thus improve the customer's satisfaction with the weight loss service.

[0183] In step S503 of some embodiments, specifically, feature fusion can be achieved by vector concatenating the remote guidance service features, remote guidance knowledge features, remote supervision customer service features, and remote supervision customer knowledge features.

[0184] In this embodiment, by integrating remote guidance service features, remote guidance knowledge features, remote supervision customer service features, and remote supervision customer knowledge features, the real-time execution and satisfaction of customers in the customized weight loss plan are comprehensively considered, thereby improving the quality of customer service during the remote supervision process.

[0185] In step S504 of some embodiments, specifically, a clear remote supervision guide is provided to customer service personnel through an instruction template.

[0186] For example, the instruction template could be: [Customer Service Needs Stage], which inquired about [Remote Supervision Weight Reduction Service]. Based on the business knowledge graph, remote supervision weight reduction guidance was provided: [Remote Supervision Weight Reduction Guidance].

[0187] For example, in the weight loss field, instruction templates can guide customer service personnel to first understand the customer's current health status and weight loss goals through video calls, and then provide personalized diet and exercise advice based on this information, and track the customer's progress and feedback regularly through the application.

[0188] In this embodiment, by inputting the integrated remote supervision service features into a preset instruction template to construct instruction information, remote supervision service instruction information is generated as target business operation instruction information. This provides customer service personnel with clear remote supervision action guidelines, including how to use remote tools to communicate with customers, how to adjust service plans based on customer feedback, and how to ensure customer satisfaction and participation when using remote services, thereby further improving the quality of customer weight loss services.

[0189] Please see Figure 6 In some embodiments, step S104 may include, but is not limited to, steps S601 to S604:

[0190] Step S601: Obtain customer follow-up service features from the plan revision service features, and obtain follow-up service knowledge features from the plan revision knowledge features based on the customer follow-up service features.

[0191] Step S602: Obtain the correction service scheme features of the plan correction service features, and obtain the correction scheme knowledge features from the plan correction knowledge features based on the correction service scheme features.

[0192] Step S603: Integrate the customer follow-up service characteristics, follow-up service knowledge characteristics, revised service plan characteristics, and revised plan knowledge characteristics to obtain the integrated revised service characteristics.

[0193] Step S604: Input the features of the integrated plan correction service into the preset instruction template to construct instruction information, so as to generate plan correction service instruction information as target business operation instruction information.

[0194] In step S601 of some embodiments, specifically, the customer service demand feature can be the planned modification service feature.

[0195] Specifically, the target business operation knowledge characteristics can be used to modify the plan's knowledge characteristics.

[0196] Furthermore, the plan is to modify the service features to include customer follow-up service features.

[0197] For example, in the weight loss industry, customer follow-up service features could include how to follow up on a customer's weight loss progress when they visit the store for management purposes.

[0198] Specifically, the customer follow-up service knowledge features are extracted from the business knowledge graph.

[0199] For example, in the field of weight loss, the knowledge characteristics of follow-up services can include asking customers about their weight gain or loss, their diet and exercise habits, and the difficulties they encounter in implementing their weight loss plan.

[0200] In this embodiment, by identifying the characteristics of remote supervision services and obtaining the corresponding remote guidance knowledge characteristics, continuous tracking of customers' weight loss services can be achieved, and the customer's satisfaction with the weight loss effect can be understood, thereby improving the quality of customer service.

[0201] In step S602 of some embodiments, specifically, the planned service feature can be a service scheme feature.

[0202] For example, in the field of weight loss, the characteristics of a modified service plan can be how to combine the client's current weight loss results, weight loss mood, and weight loss goals to provide modified service plan features.

[0203] Specifically, the knowledge features of the correction scheme corresponding to the features of the correction service scheme are extracted from the business knowledge graph.

[0204] For example, in the field of weight loss, the knowledge characteristics of a modified weight loss plan can help clients who have encountered a plateau in their weight loss plan. By combining the client's current weight loss results, weight loss mood, weight loss goals, and providing new dietary advice, exercise plans, and weight loss products, a modified weight loss plan can be offered.

[0205] In this embodiment, by obtaining the correction service scheme features from the plan correction service features and obtaining the correction scheme knowledge features from the plan correction knowledge features based on these features, personalized weight loss update plans can be provided to customers. This enables customers to match suitable weight loss plans at different weight loss stages, thereby helping customers achieve their weight loss goals and improving customer satisfaction with weight loss services.

[0206] In step S603 of some embodiments, specifically, feature fusion can be achieved by vector concatenating the customer follow-up service features, follow-up service knowledge features, modified service plan features, and modified plan knowledge features.

[0207] In this embodiment, by integrating customer follow-up service characteristics, follow-up service knowledge characteristics, correction service plan characteristics, and correction plan knowledge characteristics, customer service personnel can fully understand the real-time situation of customer weight loss, so as to formulate more accurate correction weight loss plan measures and improve the quality of customer weight loss service.

[0208] In step S604 of some embodiments, specifically, a clear guide to revising the solution is provided to customer service personnel through an instruction template.

[0209] For example, the instruction template could be: [Customer Service Needs Stage], which inquired about [Weight Reduction and Weight Optimization Service]. Based on the business knowledge graph, guidance on a weight reduction and optimization plan is provided: [Weight Reduction and Weight Optimization Plan Guidance].

[0210] For example, in the field of weight loss, instruction templates can guide customer service personnel to first inquire about the customer's weight gain or loss, as well as the difficulties the customer encounters during the weight loss process, then pay attention to the customer's emotional changes during the weight loss process, and finally generate a revised weight loss plan based on the customer's latest weight loss goals, weight loss results, and weight loss tolerance.

[0211] In this embodiment, by inputting the features of the integrated plan correction service into a preset instruction template to construct instruction information, the plan correction service instruction information is generated as the target business operation instruction information, which facilitates the subsequent generation of accurate correction and weight reduction schemes by the instruction business service operation model, thereby improving the accuracy of customer service responses.

[0212] Please see Figure 7 In some embodiments, step S105 includes, but is not limited to, steps S701 to S703:

[0213] Step S701: Use the business service operation model to identify the business operation intent of the target business operation instruction information to obtain a business operation instruction diagram.

[0214] Step S702: Based on the business operation instruction diagram, perform a business operation instruction search on the target business operation instruction information to obtain business operation instruction search information.

[0215] Step S703: Perform reasoning and linking on the business operation instruction search information to obtain target response data that matches the customer service demand data.

[0216] In step S701 of some embodiments, specifically, the business service operation model can be LLMs (Large Language Models), which are mainly composed of Transformer networks.

[0217] Specifically, Transformer can be used to extract key words from the target business operation instructions and perform intent analysis on the content of the key words to determine the specific needs of customer service.

[0218] For example, in the field of weight loss, if the model identifies a customer's need to adjust a specific diet plan or exercise program, then the business operation diagram is identified as an intention to modify the weight loss program.

[0219] In this embodiment, by using a business service operation model to identify the business operation intent of the target business operation instruction information, the instruction information can be deeply understood through a large language model to identify the core intent of the knowledge information and provide an accurate response, which helps to improve the accuracy of subsequent customer service responses.

[0220] In step S702 of some embodiments, specifically, the business operation instruction search information refers to a series of business operation service steps that may be related to the business operation instruction diagram.

[0221] Specifically, the purpose of searching for target business operation instructions based on the business operation instruction diagram is to find specific business operation instructions that match the diagram in the business knowledge graph.

[0222] For example, in the weight loss field, the target business operation instructions are for questions about how to implement remotely monitored weight loss services. Regarding remotely monitored weight loss services, customer service personnel can first understand the customer's current health condition and weight loss goals through video calls, then provide personalized diet and exercise advice based on this information, and regularly track the customer's progress and feedback through the application.

[0223] In step S703 of some embodiments, specifically, since the target response data is response data generated based on customer service demand data, the target response data also includes at least one response content from the four stages of icebreaking, customization, remote monitoring, and correction.

[0224] Specifically, the step of reasoning and linking search information for business operation instructions involves matching the searched instructions with the customer's specific needs and generating the final target response data. In this process, the model may consider the customer's personal preferences, health status, historical feedback, and other relevant factors to ensure that the provided response not only meets the standards of business operations but also satisfies the customer's personalized needs.

[0225] For example, if a client has specific dietary restrictions or health issues, the model will ensure that the recommended weight loss plan takes these factors into account, thereby generating a safe and effective personalized weight loss program.

[0226] In this embodiment, by reasoning and linking the business operation instruction search information, target response data matching customer service demand data is obtained. This data is then used to respond to the instruction information generated based on knowledge features and demand features, resulting in accurate business operation step response content and improving the accuracy of customer service responses.

[0227] In one optional embodiment of this application, after reasoning and linking the business operation instruction search information to obtain target response data that matches customer service needs data, the target response data is displayed on the terminal device (such as a mobile phone or iPad) through a pop-up window. Customer service personnel can combine the customer's weight loss needs, weight loss goals, and weight loss plans with the target response data by using control keys (such as input boxes) to input targeted customer service responses, thereby generating more accurate customer service response data based on customer needs and further improving customer service quality.

[0228] Please see Figure 8 This application also provides a knowledge graph-based customer service response device, which can implement the above-mentioned knowledge graph-based customer service response method. The device includes:

[0229] The customer service demand data acquisition module is used to acquire customer service demand data.

[0230] The feature extraction module is used to extract features from customer service demand data to obtain customer service demand features;

[0231] The business operation retrieval module is used to retrieve business operation features from customer service demand features in a pre-built business knowledge graph to obtain target business operation knowledge features; the business knowledge graph is built based on customer service data.

[0232] The instruction information construction module is used to construct target business operation instruction information based on customer service demand characteristics and target business operation knowledge characteristics;

[0233] The customer service request response generation module is used to input target business operation instruction information into a pre-trained business service operation model, so as to generate target response data that matches the customer service request data through the business service operation model.

[0234] The specific implementation of this knowledge graph-based customer service response device is basically the same as the specific implementation of the knowledge graph-based customer service response method described above, and will not be repeated here.

[0235] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned knowledge graph-based customer service response method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0236] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0237] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0238] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the processing system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and called by the processor 901 to execute the knowledge graph-based customer service response method of the embodiments of this application.

[0239] The input / output interface 903 is used to implement information input and output;

[0240] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0241] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0242] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0243] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described knowledge graph-based customer service response method.

[0244] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0245] The customer service response method, device, electronic device, and storage medium based on knowledge graphs provided in this application embodiment acquire customer service demand data; extract features from the customer service demand data to obtain customer service demand features; retrieve business operation knowledge features from a pre-constructed business knowledge graph to obtain target business operation knowledge features; wherein the business knowledge graph is constructed based on customer service data; construct target business operation instruction information based on customer service demand features and target business operation knowledge features; input the target business operation instruction information into a pre-trained business service operation model to generate target response data that matches the customer service demand data through the business service operation model. This improves customer service quality.

[0246] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0247] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0248] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0249] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0250] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0251] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0252] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.

[0253] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0254] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0255] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0256] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A customer service response method based on knowledge graphs, characterized in that, The method includes: Obtain customer service demand data; Feature extraction is performed on the customer service demand data to obtain customer service demand features; The customer service demand features are retrieved from the pre-built business knowledge graph to obtain the target business operation knowledge features; wherein, the business knowledge graph is constructed based on customer service data. Based on the customer service demand characteristics and the target business operation knowledge characteristics, target business operation instruction information is constructed; The target business operation instruction information is input into a pre-trained business service operation model to generate target response data that matches the customer service demand data. The step of retrieving target business operation knowledge features from the pre-constructed business knowledge graph by performing business operation retrieval on the customer service demand features includes: Obtain the business knowledge features of the business knowledge graph, and match the customer service demand features with the business knowledge features to obtain candidate business knowledge features; Obtain the similarity score of the candidate business knowledge features, and select the candidate business knowledge feature with the highest similarity score from the candidate business knowledge features as the first business operation knowledge feature based on the similarity score; In response to a feature instruction selected by a preset terminal, a second service operation knowledge feature is obtained from the candidate service knowledge features; The target business operation knowledge feature is selected from the candidate business knowledge features based on the first business operation knowledge feature and the second business operation knowledge feature.

2. The method according to claim 1, characterized in that, The customer service demand feature is an icebreaking service feature, and the target business operation knowledge feature is an icebreaking operation knowledge feature. The step of constructing target business operation instruction information based on the customer service demand feature and the target business operation knowledge feature includes: Obtain the icebreaking reception service features of the icebreaking service features, and obtain the reception operation knowledge features from the icebreaking operation knowledge features based on the icebreaking reception service features; Obtain the icebreaking query service features of the icebreaking service features, and obtain the icebreaking query knowledge features from the icebreaking operation knowledge features based on the icebreaking query service features; Obtain the icebreaking consumption behavior service features of the icebreaking service features, and obtain the icebreaking consumption behavior knowledge features from the icebreaking operation knowledge features based on the icebreaking consumption behavior service features; The icebreaking reception service features, reception operation knowledge features, icebreaking inquiry service features, icebreaking inquiry knowledge features, icebreaking consumption behavior service features, and icebreaking consumption behavior knowledge features are integrated to obtain integrated icebreaking service features. The integrated icebreaking service features are input into a preset instruction template to construct instruction information, thereby generating icebreaking service instruction information as the target business operation instruction information.

3. The method according to claim 1, characterized in that, The customer service demand characteristics are customized service characteristics, and the target business operation knowledge characteristics are customized operation knowledge characteristics. The step of constructing target business operation instruction information based on the customer service demand characteristics and the target business operation knowledge characteristics includes: Obtain the customized customer guidance features of the customized service features, and obtain the customized guidance knowledge features from the customized operation knowledge features based on the customized customer guidance features; Obtain the customized service scheme features of the customized service features, and obtain the customized scheme knowledge features from the customized operation knowledge features based on the customized service scheme features; The customized customer guidance features, customized guidance knowledge features, customized service plan features, and customized plan knowledge features are integrated to obtain integrated customized service features. The integrated customized service features are input into a preset instruction template to construct instruction information, thereby generating customized service instruction information as the target business operation instruction information.

4. The method according to claim 1, characterized in that, The customer service demand feature is a remote supervision service feature, and the target business operation knowledge feature is a remote supervision knowledge feature. The step of constructing target business operation instruction information based on the customer service demand feature and the target business operation knowledge feature includes: Obtain the remote guidance service features of the remote supervision service features, and obtain the remote guidance knowledge features from the remote supervision knowledge features based on the remote guidance service features; Obtain the remote supervision customer service features of the remote supervision service features, and obtain the remote supervision customer knowledge features from the remote supervision knowledge features based on the remote supervision customer service features; The remote guidance service features, the remote guidance knowledge features, the remote supervision customer service features, and the remote supervision customer knowledge features are integrated to obtain the integrated remote supervision service features. The integrated remote supervision service features are input into a preset instruction template to construct instruction information, thereby generating remote supervision service instruction information as the target business operation instruction information.

5. The method according to claim 1, characterized in that, The customer service demand feature is a plan revision service feature, and the target business operation knowledge feature is a plan revision knowledge feature. The step of constructing target business operation instruction information based on the customer service demand feature and the target business operation knowledge feature includes: Obtain customer follow-up service features from the plan revision service features, and obtain follow-up service knowledge features from the plan revision knowledge features based on the customer follow-up service features; Obtain the correction service scheme features of the plan correction service features, and obtain the correction scheme knowledge features from the plan correction knowledge features based on the correction service scheme features; The customer follow-up service characteristics, the follow-up service knowledge characteristics, the revised service plan characteristics, and the revised plan knowledge characteristics are integrated to obtain the integrated plan revised service characteristics. The features of the fusion plan correction service are input into a preset instruction template to construct instruction information, thereby generating plan correction service instruction information as the target business operation instruction information.

6. The method according to any one of claims 1 to 5, characterized in that, The step of inputting the target business operation instruction information into a pre-trained business service operation model to generate target response data that matches the customer service demand data includes: The business service operation model is used to identify the business operation intent of the target business operation instruction information to obtain a business operation instruction diagram. Based on the business operation instruction diagram, a business operation instruction search is performed on the target business operation instruction information to obtain business operation instruction search information; The search information for the business operation instructions is inferred and linked to obtain target response data that matches the customer service demand data.

7. A customer service response device based on a knowledge graph, characterized in that, The device includes: The customer service demand data acquisition module is used to acquire customer service demand data. The feature extraction module is used to extract features from the customer service demand data to obtain customer service demand features; The business operation retrieval module is used to retrieve the customer service demand features from the pre-built business knowledge graph to obtain the target business operation knowledge features; wherein, the business knowledge graph is constructed based on customer service data; The instruction information construction module is used to construct target business operation instruction information based on the customer service demand characteristics and the target business operation knowledge characteristics; The customer service request response generation module is used to input the target business operation instruction information into a pre-trained business service operation model, so as to generate target response data that matches the customer service request data through the business service operation model; The business operation retrieval module also includes: Obtain the business knowledge features of the business knowledge graph, and match the customer service demand features with the business knowledge features to obtain candidate business knowledge features; Obtain the similarity score of the candidate business knowledge features, and select the candidate business knowledge feature with the highest similarity score from the candidate business knowledge features as the first business operation knowledge feature based on the similarity score; In response to a feature instruction selected by a preset terminal, a second service operation knowledge feature is obtained from the candidate service knowledge features; The target business operation knowledge feature is selected from the candidate business knowledge features based on the first business operation knowledge feature and the second business operation knowledge feature.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the knowledge graph-based customer service response method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the knowledge graph-based customer service response method as described in any one of claims 1 to 6.

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