A service data processing method, apparatus, device, and medium

Abstract

The application discloses a kind of service data processing method, device, equipment and medium.Therein, method includes: obtaining the service data related to each service personnel, the service data related to each customer and the service data related to each product;Establish the relationship network graph corresponding to service personnel, customer and product;Relationship network graph is used to represent the association between each service personnel, each customer and each product, and relationship network graph includes personnel node for representing each service personnel, customer node for representing each customer, product node for representing each product and the connection edge formed by the association between nodes;According to pre-trained label detection model, determine the label information of each customer.The embodiment of the application can analyze and detect the association between each service personnel, each customer and each product according to service data, and automatically determine the label information of each customer.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and in particular, to a business data processing method and device, equipment and medium. BACKGROUND

[0002] The business system of an enterprise will usually obtain a large amount of business data in the running process. The business data obtained by the business system includes business data related to business personnel of the enterprise, business data related to customers of the enterprise, and business data related to products of the enterprise. The business system needs to process the obtained business data.

[0003] In related technologies, a commonly used business data processing scheme is that the business system stores the obtained business data into a corresponding business data table to record the obtained business data. The business data processing scheme in related technologies cannot analyze and detect the association relationship between each business personnel, each customer and each product according to the business data in the business system, and cannot determine the tag information of each customer according to the business data in the business system, resulting in low utilization efficiency of business data. SUMMARY

[0004] The present application provides a business data processing method, device, equipment and medium to solve the problem that the business data processing scheme in related technologies cannot analyze and detect the association relationship between each business personnel, each customer and each product according to the business data in the business system, and cannot determine the tag information of each customer according to the business data in the business system, resulting in low utilization efficiency of business data.

[0005] According to an aspect of the present application, a business data processing method is provided, comprising:

[0006] obtaining business data related to each business personnel, business data related to each customer and business data related to each product;

[0007] establishing a relationship network graph corresponding to the business personnel, the customers and the products according to the business data related to each business personnel, the business data related to each customer and the business data related to each product;

[0008] The relationship network graph is used to represent the association relationship between each business personnel, each customer and each product, and the relationship network graph includes personnel nodes representing each business personnel, customer nodes representing each customer, product nodes representing each product, and connection edges formed by the association relationship between the nodes.

[0009] determining the tag information of each customer according to a pre-trained label detection model, attribute data and portrait data of each customer.

[0010] According to another aspect of the present application, there is provided a business data processing apparatus, comprising:

[0011] a business data obtaining module, configured to obtain business data related to each business personnel, business data related to each customer, and business data related to each product;

[0012] a relationship network graph establishing module, configured to establish a relationship network graph corresponding to the business personnel, the customers, and the products according to the business data related to each business personnel, the business data related to each customer, and the business data related to each product;

[0013] wherein the relationship network graph is used to represent the association relationship between each business personnel, each customer, and each product, and the relationship network graph comprises personnel nodes used to represent each business personnel, customer nodes used to represent each customer, product nodes used to represent each product, and connecting edges formed between the nodes due to the association relationship between the nodes;

[0014] a label information determining module, configured to determine label information of each customer according to a pre-trained label detection model, attribute data, and portrait data of each customer.

[0015] According to another aspect of the present application, there is provided an electronic device, comprising:

[0016] at least one processor;

[0017] and a memory connected to the at least one processor in communication;

[0018] wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the business data processing method according to any one of the embodiments of the present application.

[0019] According to another aspect of the present application, there is provided a computer readable storage medium, which stores computer instructions for enabling a processor to execute the business data processing method according to any one of the embodiments of the present application when executed by the processor.

[0020] The technical scheme of the embodiment of the present application comprises the following steps: obtaining the business data related to each business personnel, the business data related to each customer and the business data related to each product; then, according to the business data related to each business personnel, the business data related to each customer and the business data related to each product, a relationship network graph corresponding to the business personnel, the customer and the product is established; the relationship network graph is used to represent the association relationship among each business personnel, each customer and each product, and the relationship network graph comprises personnel nodes used to represent each business personnel, customer nodes used to represent each customer, product nodes used to represent each product and connecting edges formed by the association relationship among the nodes; finally, according to the pre-trained label detection model, the attribute data and the portrait data of each customer, the label information of each customer is determined, and the problem that the business data processing scheme in the related art cannot analyze and detect the association relationship among each business personnel, each customer and each product according to the business data in the business system, cannot determine the label information of each customer according to the business data in the business system, and the utilization efficiency of the business data is low is solved, the relationship network graph used to represent the association relationship among each business personnel, each customer and each product can be established based on the business data related to each business personnel, the business data related to each customer and the business data related to each product, and the label information of each customer can be determined based on the pre-trained label detection model, the attribute data and the portrait data of each customer, so that the association relationship among each business personnel, each customer and each product can be automatically analyzed and detected according to the business data in the business system, the label information of each customer can be automatically determined, and the utilization efficiency of the business data is improved.

[0021] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0023] Figure 1 A flowchart of a business data processing method provided for the first embodiment of the present application.

[0024] Figure 2 A flowchart of a business data processing method provided for the second embodiment of the present application.

[0025] Figure 3 A structural schematic diagram of a service data processing device provided for the third embodiment of the present application.

[0026] Figure 4 A structural schematic diagram of an electronic device for implementing the service data processing method of the embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to enable persons skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the protection scope of the present application.

[0028] It should be noted that the terms “target”, “first”, “second” and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms “comprise”, “include” and “have” and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices. In the technical solutions of the present application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in the technical solutions comply with the relevant legal regulations and do not violate public order and good customs.

[0029] Embodiment One

[0030] Figure 1 A flowchart of a service data processing method provided for the first embodiment of the present application. The present embodiment can be applicable to the case of processing service data in a service system. The method can be executed by a service data processing device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device. As shown in the figure, the method comprises: Figure 1

[0031] Step 101, obtaining service data related to each service personnel, service data related to each customer and service data related to each product.

[0032] ​Optionally, the business system of the enterprise can be a server set up in the enterprise for managing various products of the enterprise. A large amount of business data is usually stored in the business system. The business data can be data acquired by the business system in the process of managing various products of the enterprise. The business data in the business system includes business data related to various business personnel, business data related to various customers, and business data related to various products. The business personnel can be personnel in the enterprise responsible for managing products of the enterprise. The customer can be a user using the products of the enterprise. The product can refer to a product that the enterprise can provide for the user to use. The product can be an insurance product, but can also be a software product or a hardware product for realizing a specified function.

[0033] Optionally, the business data related to various business personnel can refer to data related to various business personnel acquired by the business system in the process of managing various products of the enterprise. For each business personnel, the business data related to the business personnel can include attribute data of the business personnel. The attribute data of the business personnel can include identification data of the business personnel, identification data of the product managed by the business personnel. The identification data of the business personnel can be a digital number or text for uniquely identifying the business personnel. The identification data of the product can be a digital number or text for uniquely identifying the product. The business data related to various business personnel is provided by various business personnel to the business system in the process of interacting with the business system. The business system stores the business data related to various business personnel provided by various business personnel in a business personnel data table. The business personnel data table can be a list set up in the business system for storing the business data related to various business personnel.

[0034] Optionally, the business data related to various customers can refer to data related to various customers acquired by the business system in the process of managing various products of the enterprise. For each customer, the business data related to the customer can include attribute data of the customer, business personnel association data, product association data, and portrait data. The business data related to various customers is provided by various customers to the business system in the process of interacting with the business system. The business system stores the business data related to various customers provided by various customers in a customer data table. The customer data table can be a list set up in the business system for storing the business data related to various customers.

[0035] Optionally, the attribute data of the customer can include identification data of the customer, use frequency, and contact time interval. The identification data of the customer can be a digital number or text for uniquely identifying the customer. The use frequency can be the number of times the customer uses the products of the enterprise. The contact time interval can be the average time interval of the customer contacting the business personnel of the enterprise.

[0036] Optionally, the business personnel association data can include identification data of a business personnel responsible for managing the product used by the customer, the number of times the customer uses the product managed by the business personnel, and the amount of money paid by the customer when using the product managed by the business personnel.

[0037] Optionally, the business personnel association data can include identification data of a business personnel who has contacted the customer through an online or offline manner, and the number of times the customer contacts the business personnel through the online or offline manner.

[0038] Optionally, the product association data can include identification data of a product used by the customer, the number of times the customer uses the product, and the amount of money paid by the customer when using the product. The portrait data can include gender, age, income of the customer, and related data of the product used by the customer.

[0039] Optionally, the business data related to each product can refer to data related to each product obtained by the business system in the process of managing each product of the enterprise. For each product, the business data related to the product can include attribute data of the product. The attribute data of the product can include identification data, category data, and user group data of the product. The category data can be data for representing the category of the product. The user group data can be data for representing the characteristics of the user using the product. The business data related to each product is provided by the business personnel responsible for managing each product to the business system in the process of interacting with the business system. The business system can store the business data related to each product into a product data table, thereby recording the business data related to each product. The product data table can be a list set in the business system for storing the business data related to each product.

[0040] Optionally, obtaining the business data related to each business personnel, the business data related to each customer, and the business data related to each product includes: obtaining the business data related to each business personnel, the business data related to each customer, and the business data related to each product from a business data table. The business data table includes a business personnel data table, a customer data table, and a product data table set in the business system. The electronic device can access the business system, and obtain the business data related to each business personnel from the business personnel data table set in the business system, the business data related to each customer from the customer data table set in the business system, and the business data related to each product from the product data table set in the business system.

[0041] Optionally, the business data related to each business personnel, the business data related to each customer and the business data related to each product are obtained from the business data table, including: obtaining the business data related to each business personnel from the business personnel data table; obtaining the business data related to each customer from the customer data table; and obtaining the business data related to each product from the product data table.

[0042] In step 102, a relationship network graph corresponding to the business personnel, the customer and the product is established according to the business data related to each business personnel, the business data related to each customer and the business data related to each product.

[0043] The relationship network graph is used to represent the association relationship between each business personnel, each customer and each product, and the relationship network graph includes personnel nodes used to represent each business personnel, customer nodes used to represent each customer, product nodes used to represent each product and connection edges formed between the nodes with the association relationship.

[0044] Optionally, the relationship network graph corresponding to the business personnel, the customer and the product is a structure graph used to represent the association relationship between each business personnel, each customer and each product, and is composed of the personnel nodes used to represent each business personnel, the customer nodes used to represent each customer, the product nodes used to represent each product and the connection edges between the nodes with the association relationship.

[0045] Optionally, a relationship network graph corresponding to the business personnel, the customers and the products is established according to the business data related to the business personnel, the business data related to the customers and the business data related to the products, including: extracting attribute data of each business personnel from the business data related to the business personnel, taking the attribute data of each business personnel as a personnel node for representing each business personnel; extracting attribute data of each customer from the business data related to the customers, taking the attribute data of each customer as a customer node for representing each customer; extracting attribute data of each product from the business data related to the products, taking the attribute data of each product as a product node for representing each product; determining a personnel node having an association relationship with each customer node according to business personnel association data in the business data related to the customers, establishing a connection line between each customer node and the personnel node having the association relationship with the customer node, obtaining a connection edge between each customer node and the personnel node having the association relationship with the customer node, and determining a weight of the connection edge between each customer node and the personnel node having the association relationship with the customer node; determining a product node having an association relationship with each customer node according to product association data in the business data related to the customers, establishing a connection line between each customer node and the product node having the association relationship with the customer node, obtaining a connection edge between each customer node and the product node having the association relationship with the customer node, and determining a weight of the connection edge between each customer node and the product node having the association relationship with the customer node; and converging all nodes and the connection edges between the nodes to obtain the relationship network graph corresponding to the business personnel, the customers and the products.

[0046] Optionally, the personnel node is a node for representing a business personnel. The customer node is a node for representing a customer. The product node is a node for representing a product. For each business personnel, attribute data of the business personnel is extracted from the business data related to the business personnel, and the attribute data of the business personnel is taken as a personnel node for representing the business personnel. For each customer, attribute data of the customer is extracted from the business data related to the customer, and the attribute data of the customer is taken as a customer node for representing the customer. For each product, attribute data of the product is extracted from the business data related to the product, and the attribute data of the product is taken as a product node for representing the product.

[0047] Optionally, the business personnel association data can include identification data of the business personnel responsible for managing the products used by the customer, the number of times the customer uses the products managed by the business personnel, and the amount of money paid by the customer when using the products managed by the business personnel. The customer and the business personnel responsible for managing the products used by the customer are in an association relationship. The customer node used for representing the customer and the personnel node used for representing the business personnel responsible for managing the products used by the customer are in an association relationship. The connection edge between the customer node used for representing the customer and the personnel node used for representing the business personnel responsible for managing the products used by the customer can be used to represent the association relationship between the customer and the business personnel responsible for managing the products used by the customer. The weight of the connection edge between the customer node used for representing the customer and the personnel node used for representing the business personnel responsible for managing the products used by the customer can be a numerical value used to represent the strength of the association relationship between the customer and the business personnel responsible for managing the products used by the customer. The greater the weight, the stronger the association relationship. The smaller the weight, the weaker the association relationship.

[0048] Optionally, for each customer node, the following operations are performed: extracting, from the business personnel association data in the business data related to the customer represented by the customer node, identification data of the business personnel responsible for managing the products used by the customer represented by the customer node, the number of times the customer represented by the customer node uses the products managed by the business personnel, and the amount of money paid by the customer represented by the customer node when using the products managed by the business personnel; determining, as the personnel nodes in association with the customer node, the personnel nodes containing the identification data of the business personnel responsible for managing the products used by the customer represented by the customer node; establishing a connection between the customer node and the personnel nodes in association with the customer node, obtaining a connection edge between the customer node and the personnel nodes in association with the customer node; and determining, as the weight of the connection edge between the customer node and the personnel nodes in association with the customer node, the number of times the customer represented by the customer node uses the products managed by the business personnel or the amount of money paid by the customer represented by the customer node when using the products managed by the business personnel. The personnel node containing the identification data of the business personnel responsible for managing the products used by the customer represented by the customer node is the personnel node used for representing the business personnel responsible for managing the products used by the customer.

[0049] Optionally, the business personnel association data can include identification data of the business personnel who have contacted the customer through online or offline manners, and the number of times the customer has contacted the business personnel through online or offline manners. The customer and the business personnel who have contacted the customer through online or offline manners are in an association relationship. The customer node used for representing the customer and the personnel node used for representing the business personnel who have contacted the customer through online or offline manners are in an association relationship. The connection edge between the customer node used for representing the customer and the personnel node used for representing the business personnel who have contacted the customer through online or offline manners can be used to represent the association relationship between the customer and the business personnel who have contacted the customer through online or offline manners. The weight of the connection edge between the customer node used for representing the customer and the personnel node used for representing the business personnel who have contacted the customer through online or offline manners can be a numerical value used to represent the strength of the association relationship between the customer and the business personnel who have contacted the customer through online or offline manners. The greater the weight, the stronger the association relationship. The smaller the weight, the weaker the association relationship.

[0050] Optionally, for each customer node, the following operations are performed: extracting, from the business personnel association data in the business data related to the customer represented by the customer node, identification data of the business personnel who have contacted the customer represented by the customer node through online or offline manners, and the number of times the customer represented by the customer node has contacted the business personnel through online or offline manners; determining, as the personnel nodes in association with the customer node, the personnel nodes containing the identification data of the business personnel who have contacted the customer represented by the customer node through online or offline manners; establishing a connection between the customer node and the personnel nodes in association with the customer node, to obtain a connection edge between the customer node and the personnel nodes in association with the customer node; and determining the number of times the customer represented by the customer node has contacted the business personnel through online or offline manners as the weight of the connection edge between the customer node and the personnel nodes in association with the customer node. The personnel nodes containing the identification data of the business personnel who have contacted the customer represented by the customer node through online or offline manners are the personnel nodes used for representing the business personnel who have contacted the customer through online or offline manners.

[0051] Optionally, product association data may include the identification data of the products used by the customer, the number of times the customer used the product, and the amount paid by the customer when using the product. The customer and the product used by the customer are related. The customer node used to represent the customer and the product node used by the customer are related. The connection edge between the customer node and the product node can be used to represent the association between the customer and the product used by the customer. The weight of the connection edge between the customer node and the product node can be a numerical value representing the strength of the association between the customer and the product used by the customer. A larger weight indicates a stronger association, and a smaller weight indicates a weaker association.

[0052] Optionally, for each customer node, perform the following operations: extract the identification data of the product used by the customer represented by the customer node, the number of times the customer represented by the customer node used the product, and the amount paid by the customer represented by the customer node when using the product from the product association data in the business data related to the customer represented by the customer node; identify the product nodes containing the identification data of the product used by the customer represented by the customer node as product nodes that are associated with the customer node; establish connections between the customer node and the product nodes that are associated with the customer node to obtain the connection edges between the customer node and the product nodes that are associated with the customer node; determine the number of times the customer represented by the customer node used the product or the amount paid by the customer represented by the customer node when using the product as the weight of the connection edge between the customer node and the product node that is associated with the customer node. The product node containing the identification data of the product used by the customer represented by the customer node is the product node used to represent the product used by the customer.

[0053] Optionally, after obtaining the connection edges between each customer node and the personnel nodes associated with each customer node, the connection edges between each customer node and the product nodes associated with each customer node, and determining the weights of the connection edges between each customer node and the personnel nodes associated with each customer node, and the weights of the connection edges between each customer node and the product nodes associated with each customer node, all nodes and the connection edges between nodes are aggregated to obtain a relationship network diagram corresponding to business personnel, customers, and products.

[0054] Step 103: Determine the tag information for each customer based on the pre-trained tag detection model, the attribute data and profile data of each customer.

[0055] Optionally, the electronic device is provided with a pre-trained label detection model. The pre-trained label detection model can be a machine learning model pre-trained for analyzing and detecting the attribute data and portrait data of the customer to determine the label information of the customer. The label information of the customer can be a word used to represent the product preference and / or product use intention of the user. The input of the pre-trained label detection model is the attribute data and portrait data of the customer, and the output of the pre-trained label detection model is the label information of the customer. The electronic device can input the attribute data and portrait data of the customer into the pre-trained label detection model, the pre-trained label detection model can analyze and detect the attribute data and portrait data of the customer to determine the label information of the customer, and then output the label information of the customer. The electronic device can obtain the label information of the customer output by the label detection model.

[0056] Optionally, according to the pre-trained label detection model, the attribute data and portrait data of each customer, the label information of each customer is determined, including: obtaining the attribute data of each customer from the relationship network graph; determining the portrait data of each customer; inputting the attribute data and portrait data of each customer into the pre-trained label detection model respectively to obtain the label information of each customer output by the label detection model; wherein the input of the label detection model is the attribute data and portrait data of the customer, and the output of the label detection model is the label information of the customer.

[0057] Optionally, each customer node in the relationship network graph is the attribute data of each customer. The attribute data of each customer can be obtained by obtaining each customer node in the relationship network graph.

[0058] Optionally, the portrait data of each customer is determined, including: extracting the portrait data of each customer from the business data related to each customer.

[0059] Optionally, for each customer, the attribute data and portrait data of the customer are input into the pre-trained label detection model, the pre-trained label detection model analyzes and detects the attribute data and portrait data of the customer to determine the label information of the customer, and then outputs the label information of the customer. The electronic device obtains the label information of the customer output by the label detection model, thereby obtaining the label information of the customer output by the label detection model.

[0060] Optionally, after the attribute data and the portrait data of the customer are input into the pre-trained label detection model, the pre-trained label detection model can convert the attribute data of the customer into a vector representation to obtain a vector corresponding to the attribute data of the customer, convert the portrait data of the customer into a vector representation to obtain a vector corresponding to the portrait data of the customer, then concatenate the vector corresponding to the attribute data of the customer and the vector corresponding to the portrait data of the customer into one vector, analyze and detect the concatenated vector, determine the label information of the customer, and output the label information of the customer.

[0061] Optionally, after the label information of each customer is determined according to the pre-trained label detection model, the attribute data and the portrait data of each customer, the method further includes: storing the relationship network graph and the label information of each customer into a local database. The local database can be a database provided in the electronic device. The relationship network graph is stored in the local database, and the identification data and the label information of each customer are stored in the local database.

[0062] Optionally, after the label information of each customer is determined according to the pre-trained label detection model, the attribute data and the portrait data of each customer, the method further includes: after receiving a label information query request sent by a target user, providing the label information of each customer to the target user.

[0063] Optionally, the target user can be a technical personnel responsible for managing the business system. The label information query request can be a request for requesting to obtain the label information of each customer determined by the electronic device. The target user can send the label information query request to the electronic device through a terminal device. After the electronic device receives the label information query request sent by the target user through the terminal device, the identification data and the label information of each customer are obtained from the local database, and the identification data and the label information of each customer are sent to the terminal device of the target user, so as to provide the label information of each customer to the target user. The terminal device of the target user is a terminal device used by the target user.

[0064] The technical scheme of the embodiment of the present application comprises the following steps: obtaining the business data related to each business personnel, the business data related to each customer and the business data related to each product; then, according to the business data related to each business personnel, the business data related to each customer and the business data related to each product, a relationship network graph corresponding to the business personnel, the customer and the product is established; the relationship network graph is used to represent the association relationship among each business personnel, each customer and each product, and the relationship network graph comprises personnel nodes used to represent each business personnel, customer nodes used to represent each customer, product nodes used to represent each product and connecting edges formed by the association relationship among the nodes; finally, according to the pre-trained label detection model, the attribute data and the portrait data of each customer, the label information of each customer is determined, and the problem that the business data processing scheme in the related art cannot analyze and detect the association relationship among each business personnel, each customer and each product according to the business data in the business system, cannot determine the label information of each customer according to the business data in the business system, and the utilization efficiency of the business data is low is solved, the relationship network graph used to represent the association relationship among each business personnel, each customer and each product can be established based on the business data related to each business personnel, the business data related to each customer and the business data related to each product, the label information of each customer can be determined based on the pre-trained label detection model, the attribute data and the portrait data of each customer, and thus the association relationship among each business personnel, each customer and each product can be automatically analyzed and detected according to the business data in the business system, the label information of each customer can be automatically determined, and the utilization efficiency of the business data is improved.

[0065] Embodiment two

[0066] Figure 2 A flowchart of a business data processing method provided by the embodiment two of the present application. The embodiment of the present application can be combined with each optional scheme in one or more of the above embodiments. As shown in the figure, the method comprises the following steps: Figure 2

[0067] Step 201: obtaining the business data related to each business personnel, the business data related to each customer and the business data related to each product from a business data table.

[0068] Step 202: according to the business data related to each business personnel, the business data related to each customer and the business data related to each product, a relationship network graph corresponding to the business personnel, the customer and the product is established.

[0069] ​The relationship network diagram is used for representing the association relationship among the business personnel, the customers and the products, and includes personnel nodes representing the business personnel, customer nodes representing the customers, product nodes representing the products and connection edges formed by the association relationship among the nodes.

[0070] In step 203, attribute data of each customer is obtained from the relationship network diagram.

[0071] In step 204, portrait data of each customer is determined.

[0072] In step 205, the attribute data and the portrait data of each customer are respectively input into a pre-trained label detection model to obtain label information of each customer output by the label detection model.

[0073] The input of the label detection model is the attribute data and the portrait data of the customer, and the output of the label detection model is the label information of the customer.

[0074] The technical scheme of the embodiment of the application can obtain the business data related to each business personnel, the business data related to each customer and the business data related to each product from the business data table, establish a relationship network diagram for representing the association relationship among the business personnel, the customers and the products based on the obtained business data, input the attribute data and the portrait data of each customer into a pre-trained label detection model, determine the label information of each customer based on the pre-trained label detection model, and thus can automatically analyze and detect the association relationship among the business personnel, the customers and the products according to the business data in the business system, automatically determine the label information of each customer, and improve the utilization efficiency of the business data.

[0075] Embodiment Three

[0076] Figure 3 A structural schematic diagram of a business data processing device provided by the embodiment three of the application is shown. The device can be configured in an electronic device. As shown in the figure, the device includes a business data obtaining module 301, a relationship network diagram establishing module 302 and a label information determining module 303. Figure 3

[0077] ​The business data acquisition module 301 is configured to acquire business data related to each business personnel, business data related to each customer, and business data related to each product.

[0078] The technical scheme of the embodiment of the present application acquires business data related to each business personnel, business data related to each customer, and business data related to each product, and then establishes a relationship network graph corresponding to the business personnel, the customer, and the product according to the business data related to each business personnel, the business data related to each customer, and the business data related to each product. The relationship network graph is used to represent the association relationship between each business personnel, each customer, and each product, and includes personnel nodes representing each business personnel, customer nodes representing each customer, product nodes representing each product, and connecting edges formed by the association relationship between the nodes. Finally, the label information determination module 303 is configured to determine the label information of each customer according to the pre-trained label detection model, the attribute data, and the portrait data of each customer. The technical scheme solves the problem that the business data processing scheme in the related art cannot analyze and detect the association relationship between each business personnel, each customer, and each product according to the business data in the business system, cannot determine the label information of each customer according to the business data in the business system, and leads to low utilization efficiency of the business data. The technical scheme can establish a relationship network graph representing the association relationship between each business personnel, each customer, and each product based on the business data related to each business personnel, the business data related to each customer, and the business data related to each product, and can determine the label information of each customer based on the pre-trained label detection model, the attribute data, and the portrait data of each customer. Therefore, the technical scheme can automatically analyze and detect the association relationship between each business personnel, each customer, and each product according to the business data in the business system, automatically determine the label information of each customer, and improve the utilization efficiency of the business data.

[0079] In an optional implementation of the embodiment of the present application, the business data obtaining module 301 is specifically configured to: obtain the business data related to each business personnel, the business data related to each customer, and the business data related to each product from the business data table.

[0080] In an optional implementation of the embodiment of the present application, the relationship network graph establishing module 302 is specifically configured to: extract attribute data of each business personnel from the business data related to each business personnel, and take the attribute data of each business personnel as a personnel node for representing each business personnel; extract attribute data of each customer from the business data related to each customer, and take the attribute data of each customer as a customer node for representing each customer; extract attribute data of each product from the business data related to each product, and take the attribute data of each product as a product node for representing each product; determine, according to the business personnel association data in the business data related to each customer, the personnel nodes having an association relationship with each customer node, establish a connection line between each customer node and the personnel nodes having an association relationship with each customer node, obtain a connection edge between each customer node and the personnel nodes having an association relationship with each customer node, and determine a weight of the connection edge between each customer node and the personnel nodes having an association relationship with each customer node; determine, according to the product association data in the business data related to each customer, the product nodes having an association relationship with each customer node, establish a connection line between each customer node and the product nodes having an association relationship with each customer node, obtain a connection edge between each customer node and the product nodes having an association relationship with each customer node, and determine a weight of the connection edge between each customer node and the product nodes having an association relationship with each customer node; and aggregate all nodes and the connection edges between the nodes to obtain a relationship network graph corresponding to the business personnel, the customer, and the product.

[0081] In an optional implementation of the embodiment of the present application, the label information determining module 303 is specifically configured to: obtain the attribute data of each customer from the relationship network graph; determine the portrait data of each customer; and input the attribute data and the portrait data of each customer into a pre-trained label detection model respectively to obtain the label information of each customer output by the label detection model; wherein the input of the label detection model is the attribute data and the portrait data of the customer, and the output of the label detection model is the label information of the customer.

[0082] In an optional implementation of the embodiment of the present application, when performing the operation of determining the portrait data of each customer, the label information determining module 303 is specifically configured to: extract the portrait data of each customer from the business data related to each customer.

[0083] In an optional embodiment of the present invention, the business data processing device may further include: a storage module for storing the relationship network diagram and the tag information of each customer in a local database.

[0084] In an optional embodiment of the present invention, the business data processing device may further include: a query module, configured to provide the tag information of each customer to the target user after receiving a tag information query request sent by the target user.

[0085] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0086] The aforementioned business data processing apparatus can execute the business data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the business data processing method.

[0087] Example 4

[0088] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement the business data processing method of embodiments of the present invention is shown. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0089] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executed by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or a computer program constructed from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0090] A plurality of components in the electronic device 10 are connected to an input / output (I / O) interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0091] The processor 11 can be various general and / or special purpose processing components having processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the business data processing method.

[0092] In some embodiments, the business data processing method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the read-only memory (ROM) 12 and / or the communication unit 19. When the computer program is built into the random access memory (RAM) 13 and executed by the processor 11, one or more steps of the business data processing method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the business data processing method by any other appropriate means, such as by means of firmware.

[0093] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0094] A computer program for implementing the business data processing method of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0095] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0096] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0097] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0098] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0099] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in different orders, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0100] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A business data processing method, characterized in that, include: Acquire business data related to each business person, each customer, and each product; Based on business data related to each business person, business data related to each customer, and business data related to each product, establish a relationship network diagram corresponding to business people, customers, and products. The relationship network diagram is used to represent the relationships between various business personnel, customers, and products. The relationship network diagram includes personnel nodes representing various business personnel, customer nodes representing various customers, product nodes representing various products, and connecting edges formed by the relationships between the nodes. Based on a pre-trained tag detection model, the attribute data and profile data of each customer, the tag information of each customer is determined; wherein, the attribute data of each customer is obtained from the relationship network graph; the profile data of each customer is extracted from the business data related to each customer; the attribute data and profile data of each customer are respectively input into the pre-trained tag detection model to obtain the tag information of each customer output by the tag detection model; the input of the tag detection model is the attribute data and profile data of the customer, and the output of the tag detection model is the tag information of the customer.

2. The business data processing method according to claim 1, characterized in that, Acquire business data related to each business person, each customer, and each product, including: Retrieve business data related to each business person, each customer, and each product from the business data table.

3. The business data processing method according to claim 1, characterized in that, Based on business data related to each salesperson, each customer, and each product, establish a relationship network diagram corresponding to salespersons, customers, and products, including: Extract the attribute data of each business person from the business data related to each business person, and use the attribute data of each business person as the personnel node to represent each business person. Extract the attribute data of each customer from the business data related to each customer, and use the attribute data of each customer as customer nodes to represent each customer. Extract the attribute data of each product from the business data related to each product, and use the attribute data of each product as product nodes to represent each product. Based on the business personnel association data in the business data related to each customer, identify the personnel nodes that are associated with each customer node, establish the connection between each customer node and the personnel nodes that are associated with each customer node, obtain the connection edges between each customer node and the personnel nodes that are associated with each customer node, and determine the weight of the connection edges between each customer node and the personnel nodes that are associated with each customer node. Based on the product association data in the business data related to each customer, identify the product nodes that are associated with each customer node, establish the connection between each customer node and the product nodes that are associated with each customer node, obtain the connection edges between each customer node and the product nodes that are associated with each customer node, and determine the weight of the connection edges between each customer node and the product nodes that are associated with each customer node. By aggregating all nodes and the connections between them, a relationship network diagram corresponding to business personnel, customers, and products is obtained.

4. The business data processing method according to claim 1, characterized in that, After determining the tag information for each customer based on the pre-trained tag detection model, the attribute data of each customer, and the profile data, the process also includes: The relationship network diagram and the tag information of each customer are stored in a local database.

5. The business data processing method according to claim 1, characterized in that, After determining the tag information for each customer based on the pre-trained tag detection model, the attribute data of each customer, and the profile data, the process also includes: After receiving a tag information query request from the target user, the tag information of each customer is provided to the target user.

6. A business data processing device, characterized in that, include: The business data acquisition module is used to acquire business data related to various business personnel, business data related to various customers, and business data related to various products. The relationship network diagram building module is used to build a relationship network diagram corresponding to business personnel, customers, and products based on business data related to each business person, business data related to each customer, and business data related to each product. The relationship network diagram is used to represent the relationships between various business personnel, customers, and products. The relationship network diagram includes personnel nodes representing various business personnel, customer nodes representing various customers, product nodes representing various products, and connecting edges formed by the relationships between the nodes. The tag information determination module is used to determine the tag information of each customer based on a pre-trained tag detection model, the attribute data and profile data of each customer; wherein, the attribute data of each customer is obtained from the relationship network graph; the profile data of each customer is extracted from the business data related to each customer; the attribute data and profile data of each customer are respectively input into the pre-trained tag detection model to obtain the tag information of each customer output by the tag detection model; the input of the tag detection model is the attribute data and profile data of the customer, and the output of the tag detection model is the tag information of the customer.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that is executed by the at least one processor, which enables the at least one processor to perform the business data processing method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the business data processing method according to any one of claims 1-5.

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