Method and device for determining business consistency between customer and region, and storage medium

Abstract

The invention discloses a method for determining business consistency between a customer and a region, and the method comprises the steps: obtaining data information corresponding to a target customer, and determining a target region based on the data information, and the target region corresponds to the target customer; obtaining business information corresponding to the target customer, and determining a first business of the target customer based on the business information; collecting first text information of the target area in the first time period, and determining a second service included in the target area based on the first text information; determining a third service based on the first service and the second service, the third service being used for indicating the same service between the first service and the second service; and according to the occurrence frequency of the third business in the first text information, constructing a business consistency determination model, and based on the business consistency determination model, determining the business consistency of the target client and the target area.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device and storage medium for business consistency between customers and regions. Background Art

[0002] At present, most of the methods for determining the business consistency between customers and corresponding regions are to first obtain the registration location information and / or business implementation location information corresponding to the customer from the data center, and then determine the business consistency between the customer and the corresponding region based on the registration location information and / or business implementation location information. However, since the above method only determines the business consistency between the customer and the corresponding region from the customer's registration location information and / or business implementation location information, and does not determine the business consistency between the customer and the corresponding region based on the business supported by the customer itself, the accuracy of the above method for determining the business consistency between the customer and the corresponding region is low.

[0003] With respect to the technical problem in the above-mentioned prior art that the accuracy of determining the business consistency between a customer and a corresponding region is low based only on the customer's registration location information and / or business implementation location information, no effective solution has been proposed so far. Summary of the invention

[0004] The embodiments of the present disclosure provide a method, device and storage medium for determining the business consistency between a customer and a region, so as to at least solve the technical problem in the prior art of low accuracy in determining the business consistency between a customer and a corresponding region based only on the customer's registration location information and / or business implementation location information.

[0005] According to one aspect of an embodiment of the present disclosure, a method for determining business consistency between a customer and a region is provided, comprising: acquiring data information corresponding to a target customer, and determining a target area based on the data information, wherein the target area corresponds to the target customer; acquiring business information corresponding to the target customer, and determining a first business of the target customer based on the business information; collecting first text information of the target area within a first time period, and determining a second business included in the target area based on the first text information; determining a third business based on the first business and the second business, wherein the third business is used to indicate the same business between the first business and the second business; and constructing a business consistency determination model according to the number of occurrences of the third business in the first text information, and determining the business consistency between the target customer and the target area based on the business consistency determination model.

[0006] According to another aspect of an embodiment of the present disclosure, a storage medium is further provided, the storage medium including a stored program, wherein when the program is running, a processor executes any one of the methods described above.

[0007] According to another aspect of an embodiment of the present disclosure, there is also provided a device for determining the business consistency between a customer and a region, comprising: a data information acquisition module, used to acquire data information corresponding to a target customer, and based on the data information, determine a target area, wherein the target area corresponds to the target customer; a business information acquisition module, used to acquire business information corresponding to the target customer, and based on the business information, determine a first business of the target customer; a first text information acquisition module, used to acquire first text information of the target area within a first time period, and based on the first text information, determine a second business included in the target area; a third business determination module, used to determine a third business based on the first business and the second business, wherein the third business is used to indicate the same business between the first business and the second business; and a business consistency determination module, used to construct a business consistency determination model according to the number of occurrences of the third business in the first text information, and determine the business consistency between the target customer and the target area based on the business consistency determination model.

[0008] According to another aspect of an embodiment of the present disclosure, a method and apparatus for determining business consistency between a customer and a region is also provided, comprising: a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: obtaining data information corresponding to a target customer, and determining a target area based on the data information, wherein the target area corresponds to the target customer; obtaining business information corresponding to the target customer, and determining a first business of the target customer based on the business information; collecting first text information of the target area within a first time period, and determining a second business included in the target area based on the first text information; determining a third business based on the first business and the second business, wherein the third business is used to indicate the same business between the first business and the second business; and constructing a business consistency determination model based on the number of occurrences of the third business in the first text information, and determining the business consistency between the target customer and the target area based on the business consistency determination model.

[0009] The present application discloses a method for determining the business consistency between a customer and a region. First, a processor obtains data information corresponding to a target customer, and determines a target area based on the data information. Then, the processor obtains business information corresponding to the target customer, and determines a first business of the target customer based on the business information. Further, the processor collects first text information of the target area within a first time period, and determines a second business included in the target customer based on the first text information. Thereafter, the processor determines a third business based on the first business and the second business. Finally, the processor constructs a business consistency determination model based on the number of occurrences of the third business in the first text information, and determines the business consistency between the target customer and the target area based on the business consistency determination model.

[0010] This application can further determine the third business (i.e., the same business between the first business and the second business) by determining the first business corresponding to the target customer and the second business corresponding to the target area. Thus, the consistency of the business between the target customer and the target area can be roughly determined in the above manner.

[0011] In addition, the first text information of the target area is further collected, and the number of occurrences of the third business in the first text information is determined. Then, the number of occurrences of the third business in the first text information and the business consistency determination model are combined to accurately determine the business consistency between the target customer and the target area.

[0012] Therefore, unlike the prior art, the present application does not merely determine the business consistency between the target customer and the target region by obtaining the registration location information and / or business implementation location information corresponding to the target customer, but determines the business consistency between the target customer and the target region based on the business supported by the target customer itself, objective data information, and a business consistency determination model. Therefore, it is more accurate than the existing method of determining the business consistency between the target customer and the target region.

[0013] This solves the technical problem in the prior art of low accuracy in determining the business consistency between a customer and a corresponding region based only on the customer's registration location information and / or business implementation location information. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0015] Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of the present application;

[0016] Figure 2 is a schematic diagram of a system for determining business consistency between customers and regions according to Example 1 of the present application;

[0017] Figure 3 It is a flowchart of a method for determining the business consistency between a customer and a region according to Example 1 of the present application;

[0018] Figure 4 is a schematic diagram of a weight relationship determination model according to Example 1 of the present application;

[0019] Figure 5 is a schematic diagram of a device for determining the business consistency between a customer and a region according to Embodiment 2 of the present application; and

[0020] Figure 6 It is a schematic diagram of a device for determining the business consistency between customers and regions according to Example 3 of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present disclosure.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure 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 disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof 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 that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0023] Example 1

[0024] According to this embodiment, a method embodiment for determining the business consistency between customers and regions is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] The method embodiment provided in this embodiment can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computing device for determining the business consistency between customers and regions. Figure 1As shown, the computing device may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, the transmission device, and the input / output interface are connected to the processor via a bus. In addition, it may also include: a display, a keyboard, and a cursor control device connected to the input / output interface. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0026] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computing device. As involved in the embodiments of the present disclosure, the data processing circuitry acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0027] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the determination of the business consistency between customers and regions in the embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, to achieve the above-mentioned application program to determine the business consistency between customers and regions. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computing device. In one example, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0029] The display may be, for example, a touch screen liquid crystal display (LCD) that may enable a user to interact with a user interface of the computing device.

[0030] It should be noted that, in some optional embodiments, the above Figure 1 The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing devices described above.

[0031] Figure 2 is a schematic diagram of a system for determining the business consistency between customers and regions according to this embodiment. Figure 2 As shown, the system includes: a data collection module, a model building module, a business consistency determination module and a judgment module.

[0032] The data acquisition module is in communication with the model building module, and is used to obtain data information and services corresponding to the target customer from the data center. The data information corresponding to the target customer includes the area where the target customer is located (i.e., the target area). The service information corresponding to the target customer includes the services provided by the target customer (i.e., the first service).

[0033] The model building module is in communication connection with the business consistency determination module and is used to build a business consistency determination model and a weight relationship determination model.

[0034] The business consistency determination module is connected to the determination module and is used to determine the first weight relationship between each business in the third business based on the second text information and the weight relationship determination model. The business consistency determination module is also used to determine the business consistency between the target customer and the target area based on the first weight relationship and the business consistency determination model.

[0035] The determination module is used to determine the size between the normalized function value and the preset threshold value, wherein the normalized function value corresponds to the output result of the business consistency determination model.

[0036] Under the above operating environment, according to the first aspect of this embodiment, a method for determining the service consistency between a customer and a region is provided. The method comprises: Figure 2 The system implementation shown in . Figure 3 A schematic diagram showing the process of the method is shown in FIG. Figure 3 As shown, the method includes:

[0037] S302: Acquire data information corresponding to the target customer, and determine a target area based on the data information, wherein the target area corresponds to the target customer;

[0038] S304: Acquire business information corresponding to the target customer, and determine the first business of the target customer based on the business information;

[0039] S306: collecting first text information of the target area within the first time period, and determining a second service included in the target area based on the first text information;

[0040] S308: Determine a third service based on the first service and the second service, where the third service is used to indicate the same service between the first service and the second service; and

[0041] S310: constructing a business consistency determination model according to the number of occurrences of the third business in the first text information, and determining the business consistency between the target customer and the target area based on the business consistency determination model.

[0042] Specifically, first, the data acquisition module obtains data information corresponding to the target customer, and based on the data information, determines the target area where the target customer is located (S302). The data acquisition module can, for example, obtain data information corresponding to the target customer from the data center. And the data information corresponding to the target customer includes the address of the target customer, the name of the target customer, the station area, the cell name corresponding to the target customer, and the cell address corresponding to the cell name, etc.

[0043] Then, the data collection module obtains the business information corresponding to the target customer, and determines the first business of the target customer based on the business information (S304). The data collection module can, for example, obtain the business information corresponding to the target customer from the data center. The business information corresponding to the target customer collected by the data collection module includes: the amount of electricity purchased, the frequency of electricity purchase, the electricity usage scenario, the electricity payment method, and the electricity purchase transaction cycle.

[0044] For example, the business information corresponding to target customer 1 collected by the data collection module includes the amount of electricity purchased, the frequency of electricity purchased, and the scene of electricity use. The business information corresponding to target customer 1 indicates that target customer 1 purchases electricity mainly for the production of various chemical raw materials, purchases electricity worth xx yuan, and the target customer purchases electricity once every month. Therefore, based on the business information corresponding to target customer 1 collected, the data collection module can determine the first business corresponding to target customer 1.

[0045] Afterwards, the data collection module collects the first text information of the target area within the first time period, and determines the second business of the target area based on the first text information (S306). For example, the data collection module collects N pieces of first text information within the first time period T1, and extracts K pieces of first text information used to describe business information from the collected N pieces of first text information. Thus, the data collection module determines that the target customer's area involves m second businesses based on the extracted K pieces of first text information used to describe business information.

[0046] Furthermore, the model building module receives the first business information and the second business information sent by the data acquisition module, and based on the first business M1 to M2 in the first business information, n and the second services P1 to P2 in the second service information m , determine the third business W1~W c (S308). Among them, the third services W1~W c Used to indicate the first service M1~M n With the second business P1~P m The same service between them. And since the first service M1~M n Including the third business W1~W c , the second service P1~P m The third business W1~W c Therefore, based on the third service W1~W c The number of services W1 to W2 can roughly determine the consistency between the services supported by the target customers and the services developed in the corresponding areas. c The more the number of , the stronger the consistency between the target customers and the business of the corresponding area; c The smaller the number of W1, the weaker the consistency between the target customer and the corresponding area. c The number of customers can roughly determine the business consistency between the target customers and the region.

[0047] Finally, the model building module builds a business consistency determination model according to the number of occurrences of the third business in the first text information, and determines the business consistency between the target customer and the target area based on the business consistency determination model (S310). The business consistency determination model can be, for example, a Dirichlet distribution model. Specifically, first, the model building module determines the third business W1~W c The number of occurrences of each business in the first text information F1~F c For example, the third service W1 appears in the first text message a number of times F1, the third service W2 appears in the first text message a number of times F2, ..., the third service Wc The number of occurrences in the first text message is F c .

[0048] Then, the third business W1~W is determined in the model building module c The number of occurrences of each business in the first text information F1~F c In the case of , further construct a business consistency determination model, and determine the business consistency of the target customer and the target area. Specifically, referring to the above content, it can be known that the model construction module can determine the third business W1~W c The number of times each service in the first text message appears F1 to F c . Then the model building module takes: α1 = F1, α2 = F2, ..., α c =F c . Thus, the model building module can generate the corresponding business consistency determination model:

[0049]

[0050] Among them, x1,x2,x3,...,x c are both greater than or equal to 0, and

[0051] When the model building module builds the corresponding business consistency determination model, the business consistency determination module further normalizes the output result of the business consistency determination model. That is, the output result of the business consistency determination model is mapped in the space of 0 to 1, so as to determine the corresponding normalization function g(x1,x2,x3,...,x c ).

[0052] Then, the service consistency determination module inputs the second text information into a preset weight relationship determination model, so that the weight relationship determination model can determine the third services W1 to W c The first weight relationship between each business in the business consistency determination model. The second text information is used to indicate the first text information corresponding to the target customer. Further, the business consistency determination module inputs the first weight relationship into the normalization function g(x1, x2, x3, ..., x c ), and output the normalized function value, so that the business consistency determination module can determine the business consistency between the target customer and the target area based on the normalized function value. The above content will be described in detail later, so it will not be repeated here.

[0053] As described in the background technology, the current method for determining the business consistency between a customer and a corresponding region is mostly to obtain the registration location information and / or business implementation location information corresponding to the customer from the data center, and then determine the business consistency between the customer and the corresponding region based on the registration location information and / or business implementation location information. However, since the above method only determines the business consistency between the customer and the corresponding region based on the customer's registration location information and / or business implementation location information, and does not determine the business consistency between the customer and the corresponding region based on the business supported by the customer itself, the accuracy of the above method for determining the business consistency between the customer and the corresponding region is low.

[0054] In view of this, the present application provides a method for determining the consistency of services between customers and regions. The present application can further determine the third service (i.e., the same service between the first service and the second service) by determining the first service corresponding to the target customer and the second service corresponding to the target region. Thus, the consistency of services between the target customer and the target region can be roughly determined in the above manner.

[0055] In addition, the first text information of the target area is further collected, and the number of occurrences of the third business in the first text information is determined. Then, the number of occurrences of the third business in the first text information and the business consistency determination model are combined to accurately determine the business consistency between the target customer and the target area.

[0056] Therefore, unlike the prior art, the present application does not merely determine the business consistency between the target customer and the target region by obtaining the registration location information and / or business implementation location information corresponding to the target customer, but determines the business consistency between the target customer and the target region based on the business supported by the target customer itself, objective data information, and a business consistency determination model. Therefore, it is more accurate than the existing method of determining the business consistency between the target customer and the target region.

[0057] This solves the technical problem in the prior art of low accuracy in determining the business consistency between a customer and a corresponding region based only on the customer's registration location information and / or business implementation location information.

[0058] Optionally, based on the number of occurrences of the third business in the first text information, a business consistency determination model is constructed, and the business consistency between the target customer and the target area is determined based on the business consistency determination model, including: inputting the second text information into a preset weight relationship determination model, and determining the first weight relationship between each business in the third business, wherein the second text information is used to indicate the first text information corresponding to the target customer; and determining the business consistency between the target customer and the target area based on the first weight relationship and the business consistency determination model.

[0059] Further optionally, the weight relationship determination model includes a Bert model, a fully connected layer and a sofamx classifier, wherein the second text information is input into a preset weight relationship determination model, and the operation of determining the first weight relationship between the respective services in the third service includes: classifying the second text information using the Bert model and the fully connected layer, and determining the second text information containing the third service and the second text information not containing the third service; and respectively inputting the second text information containing the third service and the second text information not containing the third service into the sofamx classifier to determine the first weight relationship.

[0060] Further optionally, based on the first weight relationship and the business consistency determination model, the operation of determining the business consistency between the target customer and the target area includes: normalizing the output result of the business consistency determination model to determine the corresponding normalization function; based on the first weight relationship and the normalization function, determining the corresponding normalization function value; judging whether the normalization function value is greater than a preset threshold; and when the normalization function value is greater than the preset threshold, it indicates that the business consistency between the target customer and the target area is strong.

[0061] Specifically, first, the business consistency determination module determines the second text information. The second text information is used to indicate the first text information corresponding to the target customer. For example, among N first text information, N1 first text information contains the name of the target customer and the third business W1 to W1 corresponding to the target customer. c , the business consistency determination module can determine the target customer name and the third business W1~W corresponding to the target customer. c The N1 first text messages are the second text messages.

[0062] Then, the service consistency determination module inputs the determined second text information into a preset weight relationship determination model, and determines the third services W1 to W c The first weight relationship between each business in θ1~W c . Specifically, Figure 4 is a schematic diagram of a weight relationship determination model according to an embodiment of the present application. Figure 4 As shown, the weight relationship determination model includes a Bert model, a fully connected layer, and a softmax classifier. Thus, first, the business consistency determination module inputs the second text information into the preset Bert model and the fully connected layer, so as to classify the second text information and determine the third business W1~W c The second text information and does not include the third service W1~W c The second text message.

[0063] After that, the Bert model and the fully connected layer will determine the third business W1~W c The second text information and does not include the third service W1~W c The second text information is input to the sofamx classifier, so that the sofamx classifier can output the second text information for indicating the target area, and the third service W1~W c The weight ratios θ1 to θ1 corresponding to the number of occurrences of each business in c (i.e., the first weight relationship). c belongs to [0,1], and θ1+θ2+...+θ c =1. For example, the first weight relationship corresponding to the third service W1 is θ1, the first weight relationship corresponding to the third service W2 is θ2, ..., the third service W c The corresponding first weight relationship is θ c .

[0064] It is also worth noting that since different target customers support different businesses, the above-mentioned Sofamx classifier is a general classifier. Before training the weight relationship determination model, the model construction module collects the third text information of the target area in the second time period. Among them, the third text information contains all the businesses in the target area. Then, all the businesses in the third text information are annotated by manual annotation, and then the model construction module uses the annotated third text information to train the weight relationship determination model, so that the Sofamx classifier can determine the probabilities corresponding to all the businesses in the target area.

[0065] For example, the business consistency determination module determines the second text information corresponding to the target customer 1, and inputs the second text information corresponding to the target customer 1 into the Bert model and the fully connected layer, so that the fully connected layer outputs the third business W1~W c The second text information and does not include the third service W1~W c The second text information. Then the sofamx classifier is based on the third service w1~W c The second text information and does not include the third service W1~W c The second text information determines the probability corresponding to all services in the target area, including the third services W1~W c After that, the sofamx classifier only retains the third service W1~W c The corresponding probability is calculated, and the probability corresponding to the third service is normalized, so as to finally output the first weight relationship θ1~θ c .

[0066] The business consistency determination module determines the first weight relationship θ1~θc In the case of further third business W1~W c The first weight relationship between each business in the business consistency determination model is input into a normalization function corresponding to the business consistency determination model, so that the corresponding normalization function value can be output.

[0067] When the business consistency determination module inputs the corresponding normalized function value, the normalized function value is sent to the determination module. Thus, the determination module can determine the business consistency between the target customer and the region based on the normalized function value and the preset threshold. That is, the larger the normalized function value, the stronger the business consistency between the target customer and the region; the smaller the normalized function value, the weaker the business consistency between the target customer and the region. Among them, the preset threshold can be set by experts based on experience or based on historical data, which is not limited here.

[0068] Therefore, when the business consistency determination module determines the normalized function value, the determination module can accurately determine the business consistency between the target customer and the target area based on the normalized function value and the preset threshold, thereby facilitating providing business adjustment suggestions for the target customer based on the business consistency information between the target customer and the target area.

[0069] Optionally, the output result of the business consistency determination model is normalized to determine the operation of the corresponding normalization function, including: mapping the maximum value of the output result of the business consistency determination model to the value 1, thereby mapping the output result of the business consistency determination model to the interval range of [0,1].

[0070] Optionally, it also includes: in the case of determining the business consistency between the target customer and the target area, determining the business relevance between the target customer and the target area. Further optionally, the operation of determining the business relevance between the target customer and the target area includes: determining a first business volume of the third business in the target area, and a second business volume corresponding to the third business of the target customer; determining a second weight relationship based on the first business volume and the second business volume; and determining the business relevance between the target customer and the target area based on the first weight relationship and the second weight relationship using a preset logistic regression model.

[0071] Specifically, when the determination module determines the business consistency between the target customer and the target area, it is necessary to further determine the business relevance between the target customer and the target area, wherein the business consistency corresponds to the number of occurrences of each business, and the business relevance corresponds to the number of occurrences of each business and the business volume of each business.

[0072] Therefore, first, the determination module determines the third services w1-W in the target area. cThe first business volume Q1~Q corresponding to each business in c , and determine the third business W1~W with the target customers c The second service volume P1~P corresponding to each service in c For example, the third business W1 in the target area corresponds to the first business volume Q1, and the third business W1 of the target customer corresponds to the first business volume P1; the third business W1 in the target area corresponds to the first business volume Q2, and the third business W1 of the target customer corresponds to the first business volume P2; and so on; the third business W1 in the target area corresponds to the first business volume Q c , the third business W1 of the target customer corresponds to the first business volume P c .

[0073] Then, the determination module determines the third services W1 to W2 in the target area respectively. c The first business volume Q1~Q corresponding to each business in c , and the third business W1~W of the target customers c The second service volume P1~P corresponding to each service in c The second weight relationship between β1 and β c The calculation formula of the second weight relationship β1 corresponding to the third service W1 is as follows:

[0074]

[0075] Wherein, β1 represents the second weight relationship corresponding to the third business W1, P1 represents the second business volume corresponding to the third business W1 of the target customer, and Q1 represents the first business volume corresponding to the third business W1 of the target area.

[0076] The calculation formula of the second weight relationship β2 corresponding to the third service W2 is as follows:

[0077]

[0078] Among them, β2 represents the second weight relationship corresponding to the third business W2, P2 represents the second business volume corresponding to the third business W2 of the target customer, and Q2 represents the first business volume corresponding to the third business W2 of the target area.

[0079] And so on.

[0080] With the third business W c The corresponding second weight relationship β c The calculation formula is as follows:

[0081]

[0082] Among them, β c Indicates the third service Wc The corresponding second weight relationship, P c Indicates the third business W with the target customer c The corresponding second service volume, Q c Indicates the third business W with the target area c The corresponding first business volume.

[0083] Finally, the determination module determines the first weight relationship θ1 to θ c The second weight relationship β1 to β c The data are input into a preset logistic regression model, so that the logistic regression model finally outputs a business relevance score corresponding to each business in the third business. If the business relevance score is greater than a preset threshold, it means that the business relevance between the target customer's business (corresponding to any business in the third business) and the business in the target area (corresponding to any business in the third business) is relatively large; if the business relevance score is less than the preset threshold, it means that the business relevance between the target customer's business and the business in the target area is relatively small.

[0084] It is worth noting that, when the logistic regression model is trained, the first weight relationship sample and the second weight relationship sample can be determined by statistical means, thereby determining the input sample of the logistic regression model. In addition, the business corresponding to the first weight relationship sample and the second weight relationship sample can be scored in advance by manual scoring, thereby determining the output sample of the logistic regression model. Since the sample determination method is the same as the above-mentioned application stage, it will not be repeated here.

[0085] Therefore, according to the first aspect of this embodiment, the technical effect of being able to more accurately determine the business consistency between the target customer and the target area is achieved.

[0086] In addition, reference Figure 1 As shown, according to the second aspect of this embodiment, a storage medium is provided, wherein the storage medium includes a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0087] Therefore, according to this embodiment, the technical effect of being able to more accurately determine the business consistency between the target customer and the target area is achieved.

[0088] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0089] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0090] Example 2

[0091] Figure 5 The device 500 for determining the service consistency between a customer and a region according to this embodiment is shown, and the device 500 corresponds to the method according to embodiment 1. Figure 5 As shown, the device 500 includes: a data information acquisition module 510, which is used to acquire data information corresponding to the target customer, and determine the target area based on the data information, wherein the target area corresponds to the target customer; a business information acquisition module 520, which is used to acquire business information corresponding to the target customer, and determine the first business of the target customer based on the business information; a first text information acquisition module 530, which is used to acquire the first text information of the target area within a first time period, and determine the second business included in the target area based on the first text information; a third business determination module 540, which is used to determine the third business based on the first business and the second business, wherein the third business is used to indicate the same business between the first business and the second business; and a business consistency determination module 550, which is used to construct a business consistency determination model according to the number of occurrences of the third business in the first text information, and determine the business consistency between the target customer and the target area based on the business consistency determination model.

[0092] Optionally, the business consistency determination module 550 includes: a first weight relationship determination module, used to input the second text information into a pre-set weight relationship determination model, and determine the first weight relationship between each business in the third business, wherein the second text information is used to indicate the first text information corresponding to the target customer; and a business consistency determination sub-module, used to determine the business consistency between the target customer and the target area based on the first weight relationship and the business consistency determination model.

[0093] Optionally, the weight relationship determination model includes a Bert model, a fully connected layer and a sofamx classifier, wherein the first weight relationship determination module includes: a second text information determination module, used to classify the second text information using the Bert model and the fully connected layer, and determine the second text information containing the third business and the second text information not containing the third business; and a first weight relationship determination sub-module, used to input the second text information containing the third business and the second text information not containing the third business into the sofamx classifier respectively, so as to determine the first weight relationship.

[0094] Optionally, the business consistency determination submodule includes: a normalization processing unit, used to normalize the output results of the business consistency determination model and determine the corresponding normalization function; a normalization function value determination unit, used to determine the corresponding normalization function value based on the first weight relationship and the normalization function; a comparison unit, used to determine whether the normalization function value is greater than a preset threshold; and a business consistency determination unit, used to indicate that the business consistency between the target customer and the target area is strong when the normalization function value is greater than a preset threshold.

[0095] Optionally, the normalization processing unit includes: a mapping unit, configured to map the maximum value of the output result of the business consistency determination model to a value 1, thereby mapping the output result of the business consistency determination model to an interval range of [0, 1].

[0096] Optionally, the apparatus 500 further includes: a business relevance determination module, configured to determine the business relevance between the target customer and the target area when the business consistency between the target customer and the target area is determined.

[0097] Optionally, the business relevance determination module includes: a business volume determination module, used to determine a first business volume of a third business in a target area, and a second business volume corresponding to the third business of a target customer; a second weight relationship determination module, used to determine a second weight relationship based on the first business volume and the second business volume; and a business relevance determination sub-module, used to determine the business relevance between the target customer and the target area by using a preset logistic regression model and based on the first weight relationship and the second weight relationship.

[0098] Therefore, according to this embodiment, the technical effect of being able to more accurately determine the business consistency between the target customer and the target area is achieved.

[0099] Example 3

[0100] Figure 6 FIG. 6 shows an apparatus 600 for determining the service consistency between a customer and a region according to this embodiment, and the apparatus 600 corresponds to the method according to embodiment 1. Figure 6 As shown, the device 600 includes: a processor 610; and a memory 620, which is connected to the processor 610 and is used to provide the processor 620 with instructions for processing the following processing steps: obtaining data information corresponding to the target customer, and determining the target area based on the data information, wherein the target area corresponds to the target customer; obtaining business information corresponding to the target customer, and determining the first business of the target customer based on the business information; collecting first text information of the target area within a first time period, and determining the second business included in the target area based on the first text information; determining a third business based on the first business and the second business, wherein the third business is used to indicate the same business between the first business and the second business; and constructing a business consistency determination model according to the number of occurrences of the third business in the first text information, and determining the business consistency between the target customer and the target area based on the business consistency determination model.

[0101] Therefore, according to this embodiment, the technical effect of being able to more accurately determine the business consistency between the target customer and the target area is achieved.

[0102] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0103] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0108] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining business consistency between customers and regions, characterized in that include: Acquire data information corresponding to the target customer, and determine a target area based on the data information, wherein the target area corresponds to the target customer; Acquire business information corresponding to the target customer, and determine a first business of the target customer based on the business information; Acquire first text information corresponding to the target area in a first time period from the data center, and determine a second business included in the target area based on the first text information; Determine a third service based on the first service and the second service, wherein the third service is used to indicate the same service as the first service and the second service; as well as A business consistency determination model is constructed according to the number of occurrences of the third business in the first text information, and the business consistency between the target customer and the target area is determined based on the business consistency determination model.

2. The method according to claim 1, characterized in that The operation of constructing a business consistency determination model according to the number of occurrences of the third business in the first text information, and determining the business consistency between the target customer and the target area based on the business consistency determination model includes: Inputting the second text information into a preset weight relationship determination model, and determining a first weight relationship between the respective businesses in the third business, wherein the second text information is used to indicate the first text information corresponding to the target customer; and Based on the first weight relationship and the business consistency determination model, the business consistency between the target customer and the target area is determined.

3. The method according to claim 2, characterized in that The weight relationship determination model includes a Bert model, a fully connected layer, and a sofamx classifier, wherein the second text information is input into a preset weight relationship determination model, and an operation of determining a first weight relationship between each business in the third business includes: Using the Bert model and the fully connected layer to classify the second text information, and determine the second text information containing the third business and the second text information not containing the third business; and The second text information including the third service and the second text information not including the third service are respectively input into the sofamx classifier, so as to determine the first weight relationship.

4. The method according to claim 2, characterized in that: The operation of determining the business consistency between the target customer and the target area based on the first weight relationship and the business consistency determination model includes: Normalizing the output result of the business consistency determination model to determine a corresponding normalization function; Based on the first weight relationship and the normalization function, determining a corresponding normalization function value; Determining whether the normalized function value is greater than a preset threshold; and When the normalized function value is greater than a preset threshold, it indicates that the business consistency between the target customer and the target area is strong.

5. The method according to claim 4, characterized in that Normalizing the output result of the business consistency determination model to determine the operation of the corresponding normalization function includes: The maximum value of the output result of the business consistency determination model is mapped to the value 1, so that the output result of the business consistency determination model is mapped to the interval range of [0, 1].

6. The method according to claim 5, characterized in that Also includes: When the business consistency between the target customer and the target area is determined, the business relevance between the target customer and the target area is determined.

7. The method according to claim 6, characterized in that The operation of determining the business relevance between the target customer and the target area includes: Determine a first business volume of the third business in the target area, and a second business volume corresponding to the third business of the target customer; Determining a second weight relationship based on the first traffic volume and the second traffic volume; and The business relevance between the target customer and the target area is determined by using a preset logistic regression model and based on the first weight relationship and the second weight relationship.

8. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the processor executes the method according to any one of claims 1 to 4.

9. A device for determining the business consistency of a customer and a region, characterized in that include: A data information acquisition module, used to acquire data information corresponding to a target customer, and determine a target area based on the data information, wherein the target area corresponds to the target customer; A business information acquisition module, used to acquire business information corresponding to the target customer, and determine the first business of the target customer based on the business information; A first text information collection module, used to collect first text information of the target area within a first time period, and determine a second service included in the target area based on the first text information; A third service determination module, configured to determine a third service based on the first service and the second service, wherein the third service is used to indicate the same service as the first service and the second service; as well as The business consistency determination module is used to construct a business consistency determination model according to the number of occurrences of the third business in the first text information, and determine the business consistency between the target customer and the target area based on the business consistency determination model.

10. A method and apparatus for determining the business consistency between a customer and a region, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Acquire data information corresponding to the target customer, and determine a target area based on the data information, wherein the target area corresponds to the target customer; Acquire business information corresponding to the target customer, and determine a first business of the target customer based on the business information; Collecting first text information of the target area within a first time period, and determining a second service included in the target area based on the first text information; Determine a third service based on the first service and the second service, wherein the third service is used to indicate the same service as the first service and the second service; as well as A business consistency determination model is constructed according to the number of occurrences of the third business in the first text information, and the business consistency between the target customer and the target area is determined based on the business consistency determination model.

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