Business data processing method and device, equipment and medium

Abstract

The invention discloses a service data processing method and device, equipment and a medium. The method comprises the following steps: obtaining business data related to each business personnel, business data related to each customer and business data related to each product; establishing a relation network diagram corresponding to business personnel, clients and products; the relation network diagram is used for representing the incidence relation among the business personnel, the clients and the products; the relation network diagram comprises personnel nodes used for representing business personnel, client nodes used for representing clients, product nodes used for representing products and connection edges formed due to the incidence relation between the nodes; and determining label information of each customer according to a pre-trained label detection model. According to the embodiment of the invention, the association relationship among each business personnel, each customer and each product can be automatically analyzed and detected according to the business data, and the label information of each customer is automatically determined.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a business data processing method, device, equipment and medium. Background Art

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

[0003] In the related art, the commonly used business data processing scheme is: the business system stores the acquired business data in the corresponding business data table and records the acquired business data. The business data processing scheme in the related art cannot analyze and detect the relationship between each business person, each customer and each product based on the business data in the business system, and cannot determine the label information of each customer based on the business data in the business system, resulting in low efficiency in the use of business data. Summary of the invention

[0004] The present invention provides a business data processing method, device, equipment and medium to solve the problem that the business data processing scheme in the related art cannot analyze and detect the relationship between each business personnel, each customer and each product based on the business data in the business system, and cannot determine the label information of each customer based on the business data in the business system, resulting in low efficiency in the use of business data.

[0005] According to one aspect of the present invention, there is provided a service data processing method, comprising:

[0006] Obtain business data related to each business person, business data related to each customer, and business data related to each product;

[0007] Based on the business data related to each business person, the business data related to each customer, and the business data related to each product, a relationship network diagram corresponding to the business person, the customer, and the product is established;

[0008] The relationship network diagram is used to represent the association relationship between each business person, each customer and each product, and the relationship network diagram includes a person node for representing each business person, a customer node for representing each customer, a product node for representing each product, and connection edges formed by the association relationship between the nodes;

[0009] 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.

[0010] According to another aspect of the present invention, there is provided a service data processing device, comprising:

[0011] The business data acquisition module is used to acquire business data related to each business person, business data related to each customer, and business data related to each product;

[0012] A relationship network diagram establishment module is used to establish a relationship network diagram corresponding to business personnel, customers and products based on business data related to each business personnel, business data related to each customer and business data related to each product;

[0013] The relationship network diagram is used to represent the association relationship between each business person, each customer and each product, and the relationship network diagram includes a person node for representing each business person, a customer node for representing each customer, a product node for representing each product, and connection edges formed by the association relationship between the nodes;

[0014] The label information determination module is used to determine the label information of each customer based on a pre-trained label detection model, attribute data and portrait data of each customer.

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

[0016] at least one processor;

[0017] and a memory communicatively coupled to the at least one processor;

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the business data processing method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the business data processing method described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention obtains business data related to each business person, business data related to each customer, and business data related to each product; then, based on the business data related to each business person, business data related to each customer, and business data related to each product, a relationship network diagram corresponding to the business person, the customer, and the product is established; the relationship network diagram is used to characterize the association relationship between each business person, each customer, and each product, and the relationship network diagram includes a personnel node for characterizing each business person, a customer node for characterizing each customer, a product node for characterizing each product, and connection edges formed by the association relationship between the nodes; finally, based on a pre-trained label detection model, attribute data, and portrait data of each customer, label information of each customer is determined, thereby solving the problem that the business data processing solution in the related art cannot Based on the business data in the business system, the associations between each business personnel, each customer and each product are analyzed and detected. However, the label information of each customer cannot be determined based on the business data in the business system, resulting in low utilization efficiency of the business data. 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, a relationship network diagram can be established to characterize the associations between each business personnel, each customer and each product. Based on the pre-trained label detection model and the attribute data and portrait data of each customer, the label information of each customer can be determined. Therefore, based on the business data in the business system, the associations between each business personnel, each customer and each product can be automatically analyzed and detected, and the label information of each customer can be automatically determined, thereby improving the utilization efficiency of the business data.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flowchart of a business data processing method provided in Embodiment 1 of the present invention.

[0024] Figure 2 A flowchart of a business data processing method provided in Embodiment 2 of the present invention.

[0025] Figure 3 A schematic diagram of the structure of a business data processing device provided in Embodiment 3 of the present invention.

[0026] Figure 4 A schematic diagram of the structure of an electronic device for implementing the business data processing method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "target", "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention 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 of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved shall comply with the provisions of relevant laws and regulations and shall not violate public order and good morals.

[0029] Embodiment 1

[0030] Figure 1 This is a flow chart of a business data processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to the case of processing business data in a business system. The method can be executed by a business data processing device, which can be implemented in the form of hardware and / or software, and the business data processing device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] Step 101: Acquire business data related to each business person, business data related to each customer, and business data related to each product.

[0032] Optionally, the business system of an enterprise may refer to a server set up within the enterprise for managing the various products of the enterprise. A large amount of business data is usually stored in the business system. The business data may be data obtained by the business system in the process of managing the 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. Business personnel may be personnel in the enterprise who are responsible for managing the products of the enterprise. Customers may be users who use the products of the enterprise. Products may refer to products that the enterprise can provide to users for use. Products may be insurance products, or they may be software products or hardware products used to implement specified functions.

[0033] Optionally, the business data related to each business person may refer to the data related to each business person obtained by the business system in the process of managing each product of the enterprise. For each business person, the business data related to the business person may include the attribute data of the business person. The attribute data of the business person may include the identification data of the business person and the identification data of the product managed by the business person. The identification data of the business person may be a digital number or text for uniquely identifying the business person. The identification data of the product may be a digital number or text for uniquely identifying the product. The business data related to each business person is provided to the business system by each business person in the process of interacting with the business system. The business system will store the business data related to each business person provided by each business person in the business person data table. The business person data table may be a list set in the business system for storing business data related to each business person.

[0034] Optionally, the business data related to each customer may refer to the data related to each customer acquired by the business system in the process of managing each product of the enterprise. For each customer, the business data related to the customer may include the customer's attribute data, business personnel related data, product related data and portrait data. The business data related to each customer is provided to the business system by each customer in the process of interacting with the business system. The business system will store the business data related to each customer provided by each customer in the customer data table. The customer data table may be a list set in the business system for storing business data related to each customer.

[0035] Optionally, the attribute data of the customer may include the identification data of the customer, the number of times used, and the contact time interval. The identification data of the customer may be a digital number or text used to uniquely identify the customer. The number of times used may be the number of times the customer uses the product of the enterprise. The contact time interval may be the average time interval between the customer and the business personnel of the enterprise.

[0036] Optionally, the business personnel-related data may include identification data of the business personnel who is 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 paid by the customer when using the products managed by the business personnel.

[0037] Optionally, the business personnel-related data may include identification data of the business personnel who have contacted the customer online or offline, and the number of times the customer has contacted the business personnel online or offline.

[0038] Optionally, the product-related data may include identification data of the product used by the customer, the number of times the customer used the product, and the amount the customer paid when using the product. The portrait data may include the customer's gender, age, income, and related data of the products used by the customer.

[0039] Optionally, the business data related to each product may refer to the data related to each product acquired by the business system in the process of managing each product of the enterprise. For each product, the business data related to the product may include the attribute data of the product. The attribute data of the product may include the identification data, category data and user population data of the product. Category data may be data used to characterize the category of the product. User population data may be data used to characterize the characteristics of users who use the product. The business data related to each product is provided to the business system by the business personnel responsible for managing each product in the process of interacting with the business system. The business system can store the business data related to each product in a product data table, thereby recording the business data related to each product. The product data table may be a list set in the business system for storing business data related to each product.

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

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

[0042] Step 102: Establish a relationship network diagram corresponding to the business personnel, customers and products 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.

[0043] Among them, the relationship network diagram is used to characterize the association relationship between each business personnel, each customer and each product. The relationship network diagram includes personnel nodes for characterizing each business personnel, customer nodes for characterizing each customer, product nodes for characterizing each product, and connecting edges formed by the association relationship between the nodes.

[0044] Optionally, the relationship network diagram corresponding to business personnel, customers and products is a structural diagram for representing the relationship between each business personnel, each customer and each product, which is composed of personnel nodes for representing each business personnel, customer nodes for representing each customer, product nodes for representing each product, and connecting edges between nodes with associated relationships.

[0045] Optionally, a relationship network diagram corresponding to the business personnel, customers and products is 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, including: extracting the attribute data of each business personnel from the business data related to each business personnel, and using the attribute data of each business personnel as a personnel node for characterizing each business personnel; extracting the attribute data of each customer from the business data related to each customer, and using the attribute data of each customer as a customer node for characterizing each customer; extracting the attribute data of each product from the business data related to each product, and using the attribute data of each product as a product node for characterizing each product; determining the personnel nodes that have an association relationship with each customer node based on the business personnel-related data in the business data related to each customer, and establishing each The customer nodes and the personnel nodes associated with each customer node are connected to obtain 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 personnel nodes associated with each customer node are determined; according to the product-related data in the business data related to each customer, the product nodes associated with each customer node are determined, and the connections between each customer node and the product nodes associated with each customer node are established to obtain the connection edges between each customer node and the product 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 are determined; all nodes and the connection edges between nodes are aggregated to obtain a relationship network diagram corresponding to business personnel, customers and products.

[0046] Optionally, a personnel node is a node used to characterize a business personnel. A customer node is a node used to characterize a customer. A product node is a node used to characterize 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 used as a personnel node used to characterize 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 used as a customer node used to characterize 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 used as a product node used to characterize the product.

[0047] Optionally, the business personnel association data may 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 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 customers and business personnel with an associated relationship. The customer node used to characterize the customer and the personnel node used to characterize the business personnel responsible for managing the products used by the customer are customer nodes and personnel nodes with an associated relationship. The connection edge between the customer node used to characterize the customer and the personnel node used to characterize the business personnel responsible for managing the products used by the customer can be used to characterize 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 to characterize the customer and the personnel node used to characterize the business personnel responsible for managing the products used by the customer can be a numerical value used to characterize the strength of the association relationship between the customer and the business personnel responsible for managing the products used by the customer. The larger the weight, the stronger the association relationship. The smaller the weight, the weaker the association relationship.

[0048] Optionally, the following operations are performed for each customer node: extracting the 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 paid by the customer represented by the customer node when using the products managed by the business personnel from the business personnel-related data in the business data related to the customer represented by the customer node; determining 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 in each personnel node as the personnel node associated with the customer node; establishing a connection between the customer node and the personnel node associated with the customer node to obtain a connection edge between the customer node and the personnel node associated with the customer node; determining the number of times the customer represented by the customer node uses the products managed by the business personnel or the amount paid by the customer represented by the customer node when using the products managed by the business personnel as the weight of the connection edge between the customer node and the personnel node associated with the customer node. 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 to represent the business personnel responsible for managing the products used by the customer.

[0049] Optionally, the business personnel association data may include identification data of the business personnel who have contacted the customer online or offline, and the number of times the customer has contacted the business personnel online or offline. The customer and the business personnel who have contacted the customer online or offline are customers and business personnel with an association relationship. The customer node used to represent the customer and the personnel node used to represent the business personnel who have contacted the customer online or offline are customer nodes and personnel nodes with an association relationship. The connection edge between the customer node used to represent the customer and the personnel node used to represent the business personnel who have contacted the customer online or offline can be used to represent the association relationship between the customer and the business personnel who have contacted the customer online or offline. The weight of the connection edge between the customer node used to represent the customer and the personnel node used to represent the business personnel who have contacted the customer online or offline 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 online or offline. The larger the weight, the stronger the association relationship. The smaller the weight, the weaker the association relationship.

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

[0051] Optionally, the product association data may include identification data of the product used by the customer, the number of times the customer uses the product, and the amount paid by the customer when using the product. The customer and the product used by the customer are customers and products with an associated relationship. The customer node used to characterize the customer and the product node used to characterize the product used by the customer are customer nodes and product nodes with an associated relationship. The connecting edge between the customer node used to characterize the customer and the product node used to characterize the product used by the customer can be used to characterize the associated relationship between the customer and the product used by the customer. The weight of the connecting edge between the customer node used to characterize the customer and the product node used to characterize the product used by the customer can be a numerical value used to characterize the strength of the associated relationship between the customer and the product used by the customer. The larger the weight, the stronger the associated relationship. The smaller the weight, the weaker the associated relationship.

[0052] Optionally, the following operations are performed for each customer node: 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 uses the product, and the amount paid by the customer represented by the customer node when using the product from the product-related data in the business data related to the customer represented by the customer node; determine the product node containing the identification data of the product used by the customer represented by the customer node in each product node as the product node associated with the customer node; establish a connection between the customer node and the product node associated with the customer node, and obtain a connection edge between the customer node and the product node associated with the customer node; determine the number of times the customer represented by the customer node uses 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 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 connecting edges between each customer node and the personnel node associated with each customer node, and the connecting edges between each customer node and the product nodes associated with each customer node, and determining the weights of the connecting edges between each customer node and the personnel node associated with each customer node, and the weights of the connecting edges between each customer node and the product nodes associated with each customer node, all nodes and the connecting edges between nodes are aggregated to obtain a relationship network diagram corresponding to business personnel, customers, and products.

[0054] Step 103: 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.

[0055] Optionally, a pre-trained label detection model is provided in the electronic device. The pre-trained label detection model may be a pre-trained machine learning model for analyzing and detecting the customer's attribute data and portrait data to determine the customer's label information. The customer's label information may be words used to characterize the user's product preferences and / or product usage intentions. The input of the pre-trained label detection model is the customer's attribute data and portrait data, and the output of the pre-trained label detection model is the customer's label information. The electronic device may input the customer's attribute data and portrait data into the pre-trained label detection model, and the pre-trained label detection model will analyze and detect the customer's attribute data and portrait data, determine the customer's label information, and then output the customer's label information. The electronic device may obtain the customer's label information output by the label detection model.

[0056] Optionally, the label information of each customer is determined based on a pre-trained label detection model, the attribute data and the portrait data of each customer, including: obtaining the attribute data of each customer from the relationship network diagram; determining the portrait data of each customer; respectively inputting the attribute data and the portrait data of each customer into the pre-trained label detection model, and obtaining 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.

[0057] Optionally, each customer node in the relationship network diagram 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 diagram.

[0058] Optionally, determining the portrait data of each customer includes: extracting the portrait data of each customer from business data related to each customer.

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

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

[0061] Optionally, after determining 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 method further includes: storing the relationship network diagram and the label information of each customer in a local database. The local database may be a database set in an electronic device. The relationship network diagram is stored in the local database, and the identification data and label information of each customer are correspondingly stored in the local database.

[0062] Optionally, after determining the label information of each customer based on a pre-trained label detection model, attribute data and portrait data of each customer, it also includes: after receiving a label information query request sent by the target user, providing the label information of each customer to the target user.

[0063] Optionally, the target user may be a technician responsible for managing the business system. The tag information query request may be a request for obtaining tag information of each customer determined by the electronic device. The target user may send the tag information query request to the electronic device through a terminal device. After receiving the tag information query request sent by the target user through the terminal device, the electronic device obtains the identification data and tag information of each customer from the local database, and sends the identification data and tag information of each customer to the terminal device of the target user, thereby providing the tag 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 solution of the embodiment of the present invention obtains business data related to each business person, business data related to each customer, and business data related to each product; then, based on the business data related to each business person, business data related to each customer, and business data related to each product, a relationship network diagram corresponding to the business person, the customer, and the product is established; the relationship network diagram is used to characterize the association relationship between each business person, each customer, and each product, and the relationship network diagram includes a personnel node for characterizing each business person, a customer node for characterizing each customer, a product node for characterizing each product, and connection edges formed by the association relationship between the nodes; finally, based on a pre-trained label detection model, attribute data, and portrait data of each customer, label information of each customer is determined, thereby solving the problem that the business data processing solution in the related art cannot Based on the business data in the business system, the associations between each business personnel, each customer and each product are analyzed and detected. However, the label information of each customer cannot be determined based on the business data in the business system, resulting in low utilization efficiency of the business data. 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, a relationship network diagram can be established to characterize the associations between each business personnel, each customer and each product. Based on the pre-trained label detection model and the attribute data and portrait data of each customer, the label information of each customer can be determined. Therefore, based on the business data in the business system, the associations between each business personnel, each customer and each product can be automatically analyzed and detected, and the label information of each customer can be automatically determined, thereby improving the utilization efficiency of the business data.

[0065] Embodiment 2

[0066] Figure 2 This is a flow chart of a business data processing method provided by Embodiment 2 of the present invention. This embodiment of the present invention can be combined with each optional solution in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0067] Step 201: Acquire business data related to each business person, business data related to each customer, and business data related to each product from a business data table.

[0068] Step 202: Establish a relationship network diagram corresponding to the business personnel, customers and products 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.

[0069] Among them, the relationship network diagram is used to characterize the association relationship between each business personnel, each customer and each product. The relationship network diagram includes personnel nodes for characterizing each business personnel, customer nodes for characterizing each customer, product nodes for characterizing each product, and connecting edges formed by the association relationship between the nodes.

[0070] Step 203: Acquire attribute data of each customer from the relationship network diagram.

[0071] Step 204: Determine the profile data of each customer.

[0072] Step 205: Input the attribute data and portrait data of each customer into the pre-trained label detection model respectively, and obtain the label information of each customer output by the label detection model.

[0073] Among them, the input of the label detection model is the customer's attribute data and portrait data, and the output of the label detection model is the customer's label information.

[0074] The technical solution of the embodiment of the present invention can obtain business data related to each business personnel, business data related to each customer, and business data related to each product from the business data table, and establish a relationship network diagram for characterizing the relationship between each business personnel, each customer, and each product based on the acquired business data. The attribute data and portrait data of each customer can be input into a pre-trained label detection model, and the label information of each customer can be determined based on the pre-trained label detection model. In this way, according to the business data in the business system, the relationship between each business personnel, each customer, and each product can be automatically analyzed and detected, and the label information of each customer can be automatically determined, thereby improving the utilization efficiency of business data.

[0075] Embodiment 3

[0076] Figure 3 This is a schematic diagram of the structure of a business data processing device provided in Embodiment 3 of the present invention. The device may be configured in an electronic device. Figure 3 As shown, the device includes: a business data acquisition module 301, a relationship network diagram establishment module 302 and a label information determination module 303.

[0077] Among them, the business data acquisition module 301 is used to acquire business data related to each business personnel, business data related to each customer, and business data related to each product; the relationship network diagram establishment module 302 is used to establish a relationship network diagram corresponding to the business personnel, customers, and products based on the business data related to each business personnel, business data related to each customer, and business data related to each product; wherein the relationship network diagram is used to characterize the relationship between each business personnel, each customer, and each product, and the relationship network diagram includes a personnel node for characterizing each business personnel, a customer node for characterizing each customer, a product node for characterizing each product, and connecting edges formed by the relationship between the nodes; the label information determination module 303 is used to determine the label information of each customer based on a pre-trained label detection model, the attribute data of each customer, and the portrait data.

[0078] The technical solution of the embodiment of the present invention obtains business data related to each business person, business data related to each customer, and business data related to each product; then, based on the business data related to each business person, business data related to each customer, and business data related to each product, a relationship network diagram corresponding to the business person, the customer, and the product is established; the relationship network diagram is used to characterize the association relationship between each business person, each customer, and each product, and the relationship network diagram includes a personnel node for characterizing each business person, a customer node for characterizing each customer, a product node for characterizing each product, and connection edges formed by the association relationship between the nodes; finally, based on a pre-trained label detection model, attribute data, and portrait data of each customer, label information of each customer is determined, thereby solving the problem that the business data processing solution in the related art cannot Based on the business data in the business system, the associations between each business personnel, each customer and each product are analyzed and detected. However, the label information of each customer cannot be determined based on the business data in the business system, resulting in low utilization efficiency of the business data. 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, a relationship network diagram can be established to characterize the associations between each business personnel, each customer and each product. Based on the pre-trained label detection model and the attribute data and portrait data of each customer, the label information of each customer can be determined. Therefore, based on the business data in the business system, the associations between each business personnel, each customer and each product can be automatically analyzed and detected, and the label information of each customer can be automatically determined, thereby improving the utilization efficiency of the business data.

[0079] In an optional implementation of the embodiment of the present invention, optionally, the business data acquisition module 301 is specifically used to: acquire business data related to each business person, business data related to each customer, and business data related to each product from the business data table.

[0080] In an optional implementation of an embodiment of the present invention, optionally, the relationship network diagram establishment module 302 is specifically used to: extract the attribute data of each business personnel from the business data related to each business personnel, and use the attribute data of each business personnel as a personnel node for characterizing each business personnel; extract the attribute data of each customer from the business data related to each customer, and use the attribute data of each customer as a customer node for characterizing 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 a product node for characterizing each product; determine the personnel node that has an association relationship with each customer node based on the business personnel-related data in the business data related to each customer, and establish each customer node and the personnel node that has an association relationship with each customer node The connection between the personnel nodes is obtained by connecting each customer node and the personnel node associated with each customer node, and the weight of the connection between each customer node and the personnel node associated with each customer node is determined; according to the product association data in the business data related to each customer, the product node associated with each customer node is determined, and the connection between each customer node and the product node associated with each customer node is established to obtain the connection between each customer node and the product node associated with each customer node, and the weight of the connection between each customer node and the product node associated with each customer node is determined; all nodes and the connection edges between nodes are aggregated to obtain a relationship network diagram corresponding to business personnel, customers and products.

[0081] In an optional implementation of an embodiment of the present invention, optionally, the label information determination module 303 is specifically used to: obtain the attribute data of each customer from the relationship network diagram; determine the portrait data of each customer; input the attribute data and portrait data of each customer into a pre-trained label detection model respectively, and 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.

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

[0083] In an optional implementation of the embodiment of the present invention, optionally, the business data processing device further includes: a storage module, which is used to store the relationship network diagram and label information of each customer in a local database.

[0084] In an optional implementation of the embodiment of the present invention, optionally, the business data processing device further includes: a query module, which is used to provide the tag information of each customer to the target user after receiving the tag information query request sent by the target user.

[0085] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0086] The above-mentioned business data processing device can execute the business data processing method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the business data processing method.

[0087] Embodiment 4

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

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

[0090] A number 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 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 may be a variety of general and / or special processing components with 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 special 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 executes the various methods and processes described above, such as a business data processing method.

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

[0093] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0094] The computer program for implementing the business data processing method of the present invention 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, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0095] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0096] To provide interaction with a user, the systems and techniques described herein may 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 trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0097] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may 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] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may 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 difficult management and weak business scalability in traditional physical hosts and VPS services.

[0099] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0100] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A business data processing method, characterized in that: include: Obtain business data related to each business person, business data related to each customer, and business data related to each product; Based on the business data related to each business person, the business data related to each customer, and the business data related to each product, a relationship network diagram corresponding to the business person, the customer, and the product is established; The relationship network diagram is used to represent the association relationship between each business person, each customer and each product, and the relationship network diagram includes a person node for representing each business person, a customer node for representing each customer, a product node for representing each product, and connection edges formed by the association relationship between the nodes; 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.

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

3. The business data processing method according to claim 1, characterized in that: Based on the business data related to each business person, the business data related to each customer, and the business data related to each product, a relationship network diagram corresponding to the business person, customer, and product is established, including: Extracting attribute data of each business person from business data related to each business person, and using the attribute data of each business person as a person node for characterizing each business person; Extract attribute data of each customer from business data related to each customer, and use the attribute data of each customer as a customer node for characterizing each customer; Extract attribute data of each product from business data related to each product, and use the attribute data of each product as a product node for characterizing each product; According to the business personnel associated data in the business data related to each customer, the personnel nodes associated with each customer node are determined, and the connection between each customer node and the personnel node associated with each customer node is established to obtain the connection edge between each customer node and the personnel node associated with each customer node, and determine the weight of the connection edge between each customer node and the personnel node associated with each customer node; According to the product association data in the business data related to each customer, the product nodes associated with each customer node are determined, the connection between each customer node and the product node associated with each customer node is established, the connection edge between each customer node and the product node associated with each customer node is obtained, and the weight of the connection edge between each customer node and the product node associated with each customer node is determined; Gather all nodes and the connecting edges between nodes to obtain a relationship network diagram corresponding to business personnel, customers, and products.

4. The business data processing method according to claim 3, characterized in that: Based on the pre-trained label detection model, the attribute data and profile data of each customer, the label information of each customer is determined, including: Acquire attribute data of each customer from the relationship network diagram; Determine the profile data of each customer; Inputting the attribute data and the portrait data of each customer into a pre-trained label detection model respectively, and obtaining the label information of each customer output by the label detection model; Among them, the input of the label detection model is the customer's attribute data and portrait data, and the output of the label detection model is the customer's label information.

5. The business data processing method according to claim 4, characterized in that: Determine the profile data of each customer, including: Extract profile data of each customer from the business data related to each customer.

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

7. The business data processing method according to claim 1, characterized in that: After determining the label information of each customer based on the pre-trained label detection model, the attribute data and portrait data of each customer, it also includes: After receiving the tag information query request sent by the target user, the tag information of each customer is provided to the target user.

8. A business data processing device, characterized in that: include: The business data acquisition module is used to acquire business data related to each business person, business data related to each customer, and business data related to each product; A relationship network diagram establishment module is used to establish a relationship network diagram corresponding to business personnel, customers and products based on business data related to each business personnel, business data related to each customer and business data related to each product; The relationship network diagram is used to represent the association relationship between each business person, each customer and each product, and the relationship network diagram includes a person node for representing each business person, a customer node for representing each customer, a product node for representing each product, and connection edges formed by the association relationship between the nodes; The label information determination module is used to determine the label information of each customer based on a pre-trained label detection model, attribute data and portrait data of each customer.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the business data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the business data processing method according to any one of claims 1 to 7 when executed.

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