AI big data analysis method for assisting in optimizing bank outlet layout

Abstract

The invention particularly relates to an AI big data analysis method for assisting in optimizing bank outlet layout, and the method comprises the steps: carrying out the data mining and analysis based on a multi-source data set, determining a mining analysis result, enabling the data mining and analysis to conveniently and accurately recognize the influence of a multi-dimensional factor on bank outlet service, and improving the accuracy of the data mining and analysis. Scientific basis is provided for optimizing bank outlet layout, and the rationality of bank outlet resource allocation is improved. And then, based on the matched population distribution characteristics, the matched enterprise distribution characteristics and the traffic dynamic characteristics in the mining analysis result, carrying out potential network site selection, and based on the positions of a plurality of potential network sites, and the customer group distribution characteristics and the economic activity distribution characteristics in the mining analysis result, carrying out potential network site evaluation. Potential website evaluation is helpful for the bank to realize optimal configuration of resources, and economic benefits and market competitiveness are improved. And finally, performing optimization adjustment based on the target outlet position and the existing bank outlet layout to obtain an optimized bank outlet layout.

Description

Technical Field

[0001] This application relates to the technical field of data analysis, and particularly to an AI big data analysis method for assisting in optimizing the layout of bank branches. Background Art

[0002] In the financial industry, the layout of bank branches has always been a key element in bank strategic planning and customer service. Traditional bank branch layout strategies mostly rely on historical data and manual judgment. Although this method reflects the value of experience and intuition to a certain extent, it is particularly lagging and lacks accuracy in dealing with the rapidly changing market environment and evolving customer needs, and it is difficult to capture the latest trends in market changes and customer needs.

[0003] To overcome this limitation, some financial institutions have begun to try to introduce statistical models and simple data analysis tools to assist in branch planning. Although these data analysis tools can improve the scientific nature and efficiency of the layout to a certain extent, they still do not fully tap the potential of big data, resulting in unreasonable allocation of branch resources, and there may be situations of over-service or under-service in some areas.

[0004] Therefore, how to solve the above technical defects is an urgent problem for those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide an AI big data analysis method for assisting in optimizing the layout of bank branches to solve at least one of the above technical problems.

[0006] The above invention purpose of this application is achieved through the following technical solutions: In the first aspect, this application provides an AI big data analysis method for assisting in optimizing the layout of bank branches, adopting the following technical solutions: An AI big data analysis method for assisting in optimizing the layout of bank branches includes: Obtain a multi-source data set, conduct data mining and analysis based on the multi-source data set, and determine the mining and analysis results, where the mining and analysis results include: customer group distribution characteristics, economic vitality distribution characteristics, matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics; Based on the matching population distribution characteristics, the matching enterprise distribution characteristics, and the traffic dynamic characteristics in the mining and analysis results, conduct potential branch location selection to determine multiple potential branch locations, and based on the multiple potential branch locations, the customer group distribution characteristics and the economic vitality distribution characteristics in the mining and analysis results, conduct potential branch evaluation to determine the target branch location; Obtain the existing bank branch layout, and optimize and adjust it based on the target branch location and the existing bank branch layout to obtain the optimized bank branch layout.

[0007] By adopting the above technical solution, data mining and analysis are carried out based on the multi-source data set to determine the mining and analysis results. Among them, the mining and analysis results include: customer group distribution characteristics, economic vitality distribution characteristics, matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics. By carrying out data mining and analysis on the multi-source data set in multiple dimensions, it is convenient to accurately identify the impacts of multi-dimensional factors on bank branch services, providing a scientific basis for optimizing the bank branch layout and helping to improve the rationality of bank branch resource allocation. Furthermore, based on the matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics in the mining and analysis results, potential branch locations are selected to determine multiple potential branch locations, and based on the multiple potential branch locations, the customer group distribution characteristics and economic vitality distribution characteristics in the mining and analysis results, potential branch evaluation is carried out to determine the target branch location. Potential branch evaluation helps the bank achieve the optimal allocation of resources, improve economic benefits and market competitiveness. Finally, based on the target branch location and the existing bank branch layout, optimization and adjustment are carried out to obtain the optimized bank branch layout, which can adapt to market changes, meet customer needs and promote business development.

[0008] In a preferred example of the present application, it can be further configured as: the data mining and analysis based on the multi-source data set to determine the mining and analysis results includes: Carry out customer group analysis based on the customer behavior data in the multi-source data set to determine the customer group distribution characteristics, where the customer group distribution characteristics are used to characterize the dispersion of various attributes and behavior patterns corresponding to different customer groups in the spatial dimension; Carry out business environment analysis based on the commercial opening data in the multi-source data set to determine the economic vitality distribution characteristics, where the economic vitality distribution characteristics are used to characterize the active degree of different regions in terms of business activities, economic growth, and market potential; Obtain the target customer group characteristics, carry out population matching analysis based on the target customer group characteristics and the demographic data in the multi-source data set to determine the matching population distribution characteristics, where the matching population distribution characteristics are used to characterize the distribution of the target customer group in the geographical space; Obtain the target enterprise group characteristics, carry out enterprise matching analysis based on the target enterprise group characteristics and the enterprise distribution data in the multi-source data set to determine the matching enterprise distribution characteristics, where the matching enterprise distribution characteristics are used to characterize the distribution of the target enterprise in the geographical space; Perform traffic flow analysis based on the traffic flow data in the multi-source data set to determine the traffic dynamic characteristics, where the traffic dynamic characteristics are used to characterize the variation law of traffic flow with time and spatial factors.

[0009] In a preferred example, this application can be further configured as follows: The optimization adjustment based on the target network point location and the existing bank network layout to obtain the optimized bank network layout includes: Draw an isochrone for each bank network point in the existing bank network layout to obtain a distribution map of network point isochrones; Perform isochrone overlay addition based on the target network point location and the distribution map of network point isochrones to obtain a distribution map of added network point isochrones; Perform isochrone overlap analysis based on the distribution map of added network point isochrones to determine network point optimization information, and optimize and adjust the existing bank network layout based on the network point optimization information to obtain the optimized bank network layout.

[0010] In a preferred example, this application can be further configured as follows: The potential network point evaluation based on multiple potential network point locations, the customer group distribution characteristics and the economic vitality distribution characteristics in the mining analysis result to determine the target network point location includes: Perform potential network point benefit evaluation based on the target potential network point location, the customer group distribution characteristics and the economic vitality distribution characteristics in the mining analysis result to determine the estimated network point benefit data corresponding to the target potential network point location, where the target potential network point location is any one of the potential network point locations; Perform potential network point evaluation based on the estimated network point benefit data corresponding to each target potential network point location to determine the target network point location, where the target network point location is the target potential network point location with the highest network point benefit.

[0011] In a preferred example, this application can be further configured as follows: After determining the target network point location by performing potential network point evaluation based on the network point benefit data corresponding to each target potential network point location, it further includes: When a bank network point verification instruction is detected, obtain the actual network point benefit data corresponding to the target network point location, perform actual benefit evaluation based on the actual network point benefit data and the estimated network point benefit data, and determine the network point benefit evaluation result; When the network point benefit evaluation result is abnormal benefit, perform operation optimization analysis based on the actual network point benefit data to determine the operation optimization information corresponding to the target network point location, where the operation optimization information is used to guide the network point improvement strategy and improve the benefit.

[0012] In a preferred example, the present application can be further configured as follows: After optimizing and adjusting based on the target network point location and the existing bank network layout to obtain the optimized bank network layout, it further includes: When a network point service optimization instruction is detected, obtain the service benefits corresponding to each target bank network point in the optimized bank network layout, and obtain the competitive network point service structure corresponding to the competitive bank network points; Conduct service optimization analysis based on the service benefits corresponding to each target bank network point and the competitive network point service structure, and determine the optimized network point service structure corresponding to each target bank network point.

[0013] In a second aspect, the present application provides an electronic device, adopting the following technical solution: At least one processor; A memory; At least one application program, where at least one application program is stored in the memory and is configured to be executed by at least one processor. The at least one application program is configured to: execute the above-mentioned AI big data analysis method for assisting in optimizing the bank network layout.

[0014] In a third aspect, the present application provides a computer-readable storage medium, adopting the following technical solution: A computer-readable storage medium, on which a computer program is stored. When the computer program is executed on a computer, the computer is made to execute the above-mentioned AI big data analysis method for assisting in optimizing the bank network layout.

[0015] In a fourth aspect, the present application provides a computer program product, adopting the following technical solution: A computer program product, including a computer program, where the computer program, when executed by a processor, implements the above-mentioned AI big data analysis method for assisting in optimizing the bank network layout.

[0016] In summary, the present application includes at least one of the following beneficial technical effects: Data mining and analysis are performed based on a multi-source data set to determine the mining and analysis results, where the mining and analysis results include: customer group distribution characteristics, economic vitality distribution characteristics, matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics. By performing data mining and analysis on the multi-source data set in multiple dimensions, it is convenient to accurately identify the impacts of multi-dimensional factors on bank branch services, providing a scientific basis for optimizing the layout of bank branches and helping to improve the rationality of bank branch resource allocation. Furthermore, based on the matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics in the mining and analysis results, potential branch locations are selected to determine multiple potential branch positions, and based on the multiple potential branch positions, the customer group distribution characteristics and economic vitality distribution characteristics in the mining and analysis results, potential branch evaluation is carried out to determine the target branch position. The potential branch evaluation helps the bank achieve the optimal allocation of resources, improve economic efficiency and market competitiveness. Finally, based on the target branch position and the existing bank branch layout, optimization and adjustment are carried out to obtain the optimized bank branch layout, which can adapt to market changes, meet customer needs and promote business development.

[0017] Based on each bank branch in the existing bank branch layout, an isochrone map is drawn to obtain the isochrone map distribution of the branches. Then, based on the target branch position and the isochrone map distribution of the branches, isochrone overlay of the branches is added to obtain the isochrone map distribution of the added branches. Furthermore, based on the isochrone map distribution of the added branches, isochrone overlap analysis is carried out to determine the branch optimization information, and based on the branch optimization information, the existing bank branch layout is optimized and adjusted to obtain the optimized bank branch layout. Implementing branch optimization helps to improve the rationality of bank branch resource allocation, reduce excessive competition and internal friction among branches, and improve the overall operation efficiency. Brief Description of the Drawings

[0018] Figure 1 is a flowchart of an AI big data analysis method for assisting in optimizing the layout of bank branches according to an embodiment of the present application; Figure 2 is a structural diagram of an AI big data analysis system for assisting in optimizing the layout of bank branches according to an embodiment of the present application; Figure 3 is a structural diagram of an electronic device according to an embodiment of the present application. Detailed Description of the Embodiment

[0019] The following Figures 1 to 3 is a further detailed description of the present application.

[0020] This specific embodiment is only an interpretation of the present application and does not limit the present application. After reading this specification, those skilled in the art can make modifications to this embodiment without creative contributions as needed, but as long as it is within the scope of the present application, it is protected by the patent law.

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application. It should be noted that in the alternative embodiments of the present application, for relevant data such as object information, when the embodiments of the present application are applied to specific products or technologies, permission or consent from the object needs to be obtained, and the collection, use, and processing of the relevant data need to comply with the relevant laws, regulations, and standards of the relevant countries and regions. That is to say, if the embodiments of the present application involve data related to an object, it needs to be obtained under the authorization and consent of the object, the authorization and consent of the relevant department, and compliance with the relevant laws, regulations, and standards of the relevant countries and regions. If personal information is involved in the embodiments, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained, and the embodiments also need to be implemented under the authorization and consent of the object.

[0022] In addition, the term "and / or" in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0023] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings of the specification.

[0024] The embodiments of the present application provide an AI big data analysis method for assisting in optimizing the layout of bank branches, which is executed by an electronic device. The electronic device can be a server or a terminal device. Among them, the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected through wired or wireless communication methods. The embodiments of the present application do not limit this here. As Figure 1 shown, the method includes step S101, step S102, and step S103, where: Step S101: Obtain a multi-source data set, perform data mining and analysis based on the multi-source data set, and determine the mining and analysis results. The mining and analysis results include: customer group distribution characteristics, economic vitality distribution characteristics, matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics.

[0025] For the embodiments of the present application, most traditional bank branch layout strategies rely on historical data and manual judgment, which are particularly lagging and lack accuracy in coping with the rapidly changing market environment and evolving customer needs, and it is difficult to capture the latest dynamics of market changes and customer needs. Although statistical models and simple data analysis tools are tried to be introduced in related technologies to assist branch planning, the potential of big data has not been fully explored, resulting in unreasonable allocation of branch resources. In the actual service process of bank branches, factors such as different customer groups, economic vitality levels, population distribution, enterprise distribution, and traffic dynamics will affect the service efficiency and profitability of bank branches. In the embodiments of the present application, by performing data mining and analysis on a multi-source data set in multiple dimensions, it is convenient to accurately identify the impacts of multi-dimensional factors on bank branch services, providing a scientific basis for optimizing the layout of bank branches and helping to improve the rationality of bank branch resource allocation.

[0026] Specifically, a multi-source data set is obtained. The multi-source data set includes, but is not limited to, customer behavior data, commercial open data, demographic data, enterprise distribution data, and traffic flow data. Among them, the electronic device is connected to the management systems of different data sources through API interfaces, so that the electronic device can quickly and accurately obtain the multi-source data set. Of course, the user can add or adjust the data dimensions in the multi-source data set according to actual needs, and the embodiments of the present application do not limit the dimensions of the data sources. Furthermore, data mining and analysis are performed based on the multi-source data set to determine the mining and analysis results. The mining and analysis results include: customer group distribution characteristics, economic vitality distribution characteristics, matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics. The customer group distribution characteristics are used to characterize the dispersion of various attributes and behavior patterns corresponding to different customer groups in the spatial dimension; the economic vitality distribution characteristics are used to characterize the activity level and distribution of different regions in terms of business activities, economic growth, and market potential; the matching population distribution characteristics are used to characterize the distribution of the target customer group in the geographical space; the matching enterprise distribution characteristics are used to characterize the distribution of the target enterprise in the geographical space; the traffic dynamic characteristics are used to characterize the variation law of traffic flow with time and space factors. Since different factors such as customer groups, economic vitality levels, population distribution, enterprise distribution, and traffic dynamics will affect the service efficiency and profitability of bank branches, performing multi-dimensional data mining and analysis helps to analyze useful characteristics of bank branch layouts from the multi-source data set, thereby assisting in optimizing bank branch layouts to improve the rationality of bank branch resource allocation and the service efficiency of bank branches.

[0027] Step S102: Based on the matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics in the mining and analysis results, potential branch locations are selected to determine multiple potential branch locations, and based on the multiple potential branch locations, the customer group distribution characteristics and economic vitality distribution characteristics in the mining and analysis results, potential branch evaluations are performed to determine the target branch location.

[0028] For the embodiments of the present application, during the process of bank branch location selection, the population density, enterprise concentration, and traffic convenience are key factors to be considered in bank branch location selection. To facilitate the bank's subsequent expansion of bank customers and promote the rapid development of banking business, areas with high population density, concentrated enterprises, and convenient traffic are usually used as potential branch locations. Therefore, based on the matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics in the mining and analysis results, potential branch locations are selected to determine multiple potential branch locations, and potential branch location selection is performed by integrating various characteristics, which improves the scientificity and accuracy of bank branch location selection.

[0029] There are various implementation processes for potential branch location selection, which are not limited in the embodiments of this application. In one implementable manner, a map visualization tool is used to visually display the matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics respectively, obtaining visual maps corresponding to different dimensional characteristics. Different regions with different characteristics will be classified and represented in the visual maps. For example, regions with different population densities will be distinguished by different colors in the visual map. Then, the visual maps corresponding to the matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics are overlaid as different layers, and key regions are selected from the overlaid visual map. The regions that meet the conditions of dense population, concentrated enterprises, and convenient transportation are recorded as key regions. Finally, the central position is selected from the key regions and recorded as the potential branch location. In another implementable manner, a potential branch location selection prediction model is pre-stored in the electronic device. This potential branch location selection prediction model can automatically synthesize the factors of population distribution, enterprise distribution, and traffic flow to select a branch location that can cover the target customer group and enterprise-intensive area and has good traffic convenience. Therefore, the matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics are input into the potential branch location selection prediction model, and the model is controlled to automatically select the branch location and output multiple potential branch locations.

[0030] To improve the accuracy and scientificity of the bank branch location selection decision, after determining the potential branch location, potential branch evaluation is carried out based on the customer group distribution characteristics and economic vitality distribution characteristics in the mining analysis results, to understand the economic benefits that can be brought after setting up a bank branch at each potential branch location, and the potential branch location with the best benefits is selected as the target branch location ultimately, which helps the bank achieve the optimal allocation of resources, improve economic benefits and market competitiveness. There are various specific implementation processes for determining the target branch location through potential branch evaluation, which are not limited in the embodiments of this application. In one implementable manner, potential branch benefit evaluation is carried out based on the target potential branch location, the customer group distribution characteristics and economic vitality distribution characteristics in the mining analysis results, to determine the estimated branch benefit data corresponding to the target potential branch location, where the target potential branch location is any one of the potential branch locations; potential branch evaluation is carried out based on the estimated branch benefit data corresponding to each target potential branch location to determine the target branch location, where the target branch location is the target potential branch location with the highest branch benefit.

[0031] Step S103: Obtain the existing bank branch layout, and optimize and adjust it based on the target branch location and the existing bank branch layout to obtain the optimized bank branch layout.

[0032] For the embodiments of the present application, in order to improve the service efficiency and quality of bank branches and reduce the operating costs of bank branches, the existing bank branch layout is optimized and adjusted based on the target branch location determined after branch evaluation, so that the optimized bank branch layout can adapt to market changes, meet customer needs and promote business development. Therefore, the existing bank branch layout is obtained, which records factors such as the location information and business data of existing bank branches, and is optimized and adjusted based on the target branch location and the existing bank branch layout to obtain an optimized bank branch layout. Among them, the optimization and adjustment are used to adjust some inefficient or redundant branches to improve the overall operating efficiency of bank branches. There are various specific implementation methods for the optimization and adjustment, which are not limited in the embodiments of the present application. In one feasible method, isochrones are drawn for each bank branch in the existing bank branch layout to obtain an isochrone distribution map of branches; based on the target branch location and the isochrone distribution map of branches, isochrones of branches are added and superimposed to obtain an added isochrone distribution map of branches; based on the added isochrone distribution map of branches, isochrone overlap analysis is performed to determine branch optimization information, and based on the branch optimization information, the existing bank branch layout is optimized and adjusted to obtain an optimized bank branch layout.

[0033] It can be seen that in the embodiments of the present application, data mining and analysis are performed based on a multi-source data set to determine the mining and analysis results. Among them, the mining and analysis results include: customer group distribution characteristics, economic vitality distribution characteristics, matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics. By performing data mining and analysis on the multi-source data set in multiple dimensions, it is convenient to accurately identify the impacts of multi-dimensional factors on bank branch services, providing a scientific basis for optimizing the bank branch layout and helping to improve the rationality of bank branch resource allocation. Furthermore, based on the matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics in the mining and analysis results, potential branch locations are selected to determine multiple potential branch locations, and based on the multiple potential branch locations, the customer group distribution characteristics and economic vitality distribution characteristics in the mining and analysis results, potential branch evaluation is performed to determine the target branch location. The potential branch evaluation helps the bank achieve the optimal allocation of resources, improve economic benefits and market competitiveness. Finally, based on the target branch location and the existing bank branch layout, optimization and adjustment are performed to obtain an optimized bank branch layout, which can adapt to market changes, meet customer needs and promote business development.

[0034] Further, in order to accurately identify the impacts of multi-dimensional factors on bank branch services, in the embodiments of the present application, data mining and analysis are performed based on a multi-source data set to determine the mining and analysis results, including steps SA - SE (not shown in the drawings), where: Step SA: Conduct customer group analysis based on customer behavior data in the multi-source data set to determine the distribution characteristics of customer groups, where the distribution characteristics of customer groups are used to characterize the dispersion of various attributes and behavior patterns corresponding to different customer groups in the spatial dimension.

[0035] For the embodiments of the present application, by performing data mining and analysis on the multi-source data set in multiple dimensions, it is convenient to accurately identify the impacts of multi-dimensional factors on bank branch services, providing a scientific basis for optimizing the layout of bank branches and helping to improve the rationality of bank branch resource allocation.

[0036] Specifically, customer behavior data can reflect customers' daily activity patterns, consumption habits, preferences, and needs, which helps to identify high-value customer groups and potential market areas. Therefore, customer group analysis is conducted based on customer behavior data in the multi-source data set to determine the dispersion of different customer groups in the spatial dimension. The specific implementation process of customer group analysis is as follows: Generate corresponding customer portraits through the basic customer information and consumption habit data in the customer behavior data; at the same time, determine customers' interests and needs through browsing behaviors and purchase behaviors in the customer behavior data, and evaluate customer value through consumption ability and purchase willingness in the customer behavior data to identify high-value customers and potential customers. Then, use geographic information system tools to visually display the customer portraits in the spatial dimension, and mark interests, needs, and customer labels (including: ordinary customers, high-value customers, or potential customers) for each customer in the visual map of the customer portraits. Finally, record the visual map of the customer portraits marked with attributes as the distribution characteristics of customer groups.

[0037] Step SB: Conduct business environment analysis based on commercial open data in the multi-source data set to determine the distribution characteristics of economic vitality, where the distribution characteristics of economic vitality are used to characterize the activity levels of different regions in terms of business activities, economic growth, and market potential.

[0038] For the embodiments of the present application, commercial open data is used to evaluate the economic environment in different regions. The commercial open data includes, but is not limited to, data such as enterprise registration information, business activity records, market transaction data, per capita income, and industrial structure, which helps to identify regions with strong economic vitality and provides key guidance for optimizing the layout of bank branches. Therefore, based on the commercial open data in the multi-source data set, business environment analysis is carried out to determine the distribution characteristics of economic vitality. The specific implementation process for business environment analysis is as follows: Based on the enterprise registration information and business activity records in the commercial open data, analyze the quantity, frequency, and type of business activities in different regions to identify hotspots and sparse areas of business activities. At the same time, based on the per capita income, industrial structure, and market transaction data in the commercial open data, evaluate the economic growth potential and market potential to divide the overall region into different types of small regions. The types of small regions include: economically prosperous regions, emerging growth regions, potential exploration regions, and economically lagging regions. Finally, use geographic information system tools to visually display the business activity regions and different types of small regions to achieve a visual presentation of the division results of the overall region according to business activities and economic development.

[0039] Step SC: Obtain the characteristics of the target customer group, and perform population matching analysis based on the characteristics of the target customer group and the demographic data in the multi-source data set to determine the distribution characteristics of the matching population, where the distribution characteristics of the matching population are used to represent the distribution of the target customer group in the geographical space.

[0040] For the embodiments of the present application, as an important channel for financial services, the positioning of the target customer group for bank branches is crucial. By comprehensively understanding the distribution of the target customer group in the streets set in different regions, it helps the bank to more reasonably plan the number and location of branches to ensure that the branches can cover the target customer group to the greatest extent and improve the market penetration rate. Therefore, perform population matching analysis based on the characteristics of the target customer group and the demographic data in the multi-source data set to determine the distribution characteristics of the matching population, which are used to represent the distribution of the target customer group in the geographical space. The specific implementation process for population matching analysis is as follows: Obtain the characteristics of the target customer group, which are used to record information such as the age, gender, income level, occupation, and education level of interested customers. Then, use a matching algorithm to perform population matching analysis based on the multi-faceted group characteristics in the target customer group characteristics and the demographic data in the multi-source data set, screen out the population data that matches the target customer group characteristics from the demographic data, and use geographic information system tools to map the population data with data matching to the geographical space to obtain the distribution characteristics of the matching population in the form of a visual map.

[0041] Step SD: Obtain the characteristics of the target enterprise group, perform enterprise matching analysis based on the characteristics of the target enterprise group and the enterprise distribution data in the multi-source data set, and determine the matching enterprise distribution characteristics, where the matching enterprise distribution characteristics are used to characterize the distribution of target enterprises in the geographical space.

[0042] For the embodiments of this application, the primary goal of bank branch layout is to serve the target customer group. Therefore, setting up branches near enterprise groups that are in line with the development of banking business helps attract more potential customers, thereby promoting the development and innovation of banking business. Thus, enterprise matching analysis is performed based on the characteristics of the target enterprise group and the enterprise distribution data in the multi-source data set to determine the matching enterprise distribution characteristics, where the matching enterprise distribution characteristics are used to characterize the distribution of target enterprises in the geographical space. The specific implementation process of enterprise matching analysis is as follows: Obtain the characteristics of the target enterprise group, which are used to record the enterprise name, enterprise type, enterprise scale, enterprise operating conditions, industry where the enterprise is located, etc. of the interested enterprises. Then, using a matching algorithm, perform enterprise matching analysis based on the multi-faceted group characteristics in the target enterprise group characteristics and the enterprise distribution data in the multi-source data set, screen out the enterprise data that matches the target enterprise group characteristics from the enterprise distribution data, and use a geographic information system tool to map the enterprise data with matching data onto the geographical space to obtain the matching enterprise distribution characteristics in the form of a visual map.

[0043] Step SE: Perform traffic flow analysis based on the traffic flow data in the multi-source data set to determine the traffic dynamic characteristics, where the traffic dynamic characteristics are used to characterize the variation law of traffic flow with time and space factors.

[0044] For the embodiments of this application, traffic flow is an important parameter of the urban traffic system. Performing traffic flow analysis helps screen out areas with large traffic flow and frequent customer flow, providing a more scientific basis for bank branch layout. Thus, traffic flow analysis is performed based on the traffic flow data in the multi-source data set to determine the traffic dynamic characteristics, where the traffic dynamic characteristics are used to characterize the variation law of traffic flow with time and space factors. The specific implementation process of traffic flow analysis is as follows: Statistically analyze the flow indicators based on the traffic flow data to determine indicators such as the number of vehicles, vehicle speed, and traffic density corresponding to different road sections; at the same time, also use machine learning algorithms to extract traffic dynamic characteristics from the traffic flow data, which include but are not limited to: periodic changes in traffic flow, traffic congestion conditions, etc. Finally, use a geographic information system tool to map the flow indicators and traffic dynamic characteristics onto the geographical space to obtain the traffic dynamic characteristics in the form of a visual map, which helps intuitively present the variation law and trend of traffic flow.

[0045] It can be seen that in the embodiments of the present application, by performing data mining and analysis on multi-source data sets in multiple dimensions, it is convenient to accurately identify the impacts of multi-dimensional factors on bank branch services, providing a scientific basis for optimizing the layout of bank branches and helping to improve the rationality of bank branch resource allocation. Therefore, customer group analysis, business environment analysis, population matching analysis, enterprise matching analysis, and traffic flow analysis are performed on the multi-source data sets to obtain customer group distribution characteristics, economic vitality distribution characteristics, matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics.

[0046] Furthermore, in order to improve the rationality of bank branch resource allocation, in the embodiments of the present application, based on the target branch location and the existing bank branch layout, optimization and adjustment are performed to obtain an optimized bank branch layout, including: Drawing an isochrone circle for each bank branch in the existing bank branch layout to obtain a distribution map of branch isochrone circles; Performing superposition of added branch isochrone circles based on the target branch location and the distribution map of branch isochrone circles to obtain a distribution map of added branch isochrone circles; Performing isochrone circle overlap analysis based on the distribution map of added branch isochrone circles to determine branch optimization information, and based on the branch optimization information, optimizing and adjusting the existing bank branch layout to obtain an optimized bank branch layout.

[0047] For the embodiments of the present application, during the process of optimizing and adjusting the existing bank branch layout, it is preset to add a bank branch at the target branch location, further analyzing the overlap situation between the area covered after adding the new bank branch and the existing bank branches, and performing a closing operation on the branches with overlapping coverage or low efficiency, which helps to improve the rationality of bank branch resource allocation, reduce excessive competition and internal friction between branches, and improve the overall operation efficiency.

[0048] Specifically, the geographical locations of each existing bank branch are recorded in the existing bank branch layout. In order to intuitively see the service coverage area of each existing branch, isochrones are drawn based on each bank branch in the existing bank branch layout to obtain an isochrone distribution map of branches. This isochrone distribution map of branches intuitively shows the coverage area of each bank branch. For an isochrone, it refers to the area covered by the distance that can be reached within a specific time by a certain means of transportation with an existing bank branch as the center. Therefore, during the process of drawing isochrones, an online map software is used to set parameters such as travel speed and time with each branch as the center, and the online map software is controlled to automatically generate the area that can be reached within a specific time with the branch as the center. Furthermore, the location of the target branch is imported into the online map software, and the same parameters such as travel speed and time are set, and the online map software is controlled to automatically generate the area that can be reached within a specific time with the new branch as the center. Finally, the isochrone layer corresponding to the location of the target branch is overlaid on the isochrone distribution map of branches to obtain an isochrone distribution map of added branches.

[0049] Furthermore, isochrone overlap analysis is carried out based on the isochrone distribution map of added branches to determine branch optimization information. Among them, the branch optimization information is used to characterize the optimization methods for existing branches. For example, branch relocation, merger, expansion, addition of self-service facilities, provision of remote banking services, etc. For isochrone overlap analysis, an online map software is used to identify the overlapping areas between isochrones. This overlapping area is an area with redundant services or high competition pressure, and the operating conditions, business conditions, and competition conditions of existing bank branches within the overlapping area are obtained. Based on the multi-dimensional information of existing bank branches, the types of bank branches within the overlapping area are determined. For example, branches with redundant service scopes and branches with excessive competition pressure. Then, based on the types of bank branches, optimization methods are selected for the existing bank branches within the overlapping area to determine the branch optimization information. For example, for branches with redundant service scopes, the branch optimization information is determined to be merger, relocation, or adjustment of service time to optimize resource allocation; for branches with excessive competition pressure, the branch optimization information is determined to be improvement of service quality, increase in product variety, or innovation of service models to increase market competitiveness. Finally, based on the branch optimization information, the existing bank branch layout is optimized and adjusted to obtain an optimized bank branch layout.

[0050] It can be seen that in the embodiment of the present application, based on each bank branch in the existing bank branch layout, an isochrone is drawn to obtain a distribution map of branch isochrones. Then, based on the target branch location and the distribution map of branch isochrones, an isochrone overlay of the branch is added to obtain a distribution map of the added branch isochrones. Furthermore, based on the distribution map of the added branch isochrones, an isochrone overlap analysis is performed to determine branch optimization information, and based on the branch optimization information, the existing bank branch layout is optimized and adjusted to obtain an optimized bank branch layout. Implementing branch optimization helps improve the rationality of bank branch resource allocation, reduce excessive competition and internal friction among branches, and improve overall operational efficiency.

[0051] Further, in order to improve the accuracy and scientific nature of bank branch location decision-making, and enhance economic benefits and market competitiveness, in the embodiment of the present application, based on multiple potential branch locations, the customer group distribution characteristics and economic vitality distribution characteristics in the mining analysis results are used to evaluate potential branches to determine the target branch location, including: Based on the target potential branch location, the customer group distribution characteristics and economic vitality distribution characteristics in the mining analysis results are used to evaluate the benefits of potential branches to determine the estimated branch benefit data corresponding to the target potential branch location, where the target potential branch location is any one of the potential branch locations; Based on the estimated branch benefit data corresponding to each target potential branch location, potential branches are evaluated to determine the target branch location, where the target branch location is the target potential branch location with the highest branch benefit.

[0052] For the embodiment of the present application, in order to improve the accuracy and scientific nature of bank branch location decision-making, after determining the potential branch location, based on the customer group distribution characteristics and economic vitality distribution characteristics in the mining analysis results, potential branches are evaluated to understand the economic benefits that can be brought after setting up a bank branch at each potential branch location, and the potential branch location with the best benefits is selected as the final target branch location, which helps the bank achieve the optimal allocation of resources and enhance economic benefits and market competitiveness.

[0053] Specifically, based on the target potential outlet location, the customer group distribution characteristics and economic vitality distribution characteristics in the mining and analysis results, a potential outlet benefit evaluation is carried out to determine the estimated outlet benefit data corresponding to the target potential outlet location. There are various implementation methods for the potential outlet benefit evaluation, which are not limited in the embodiments of the present application. In a feasible method, similar outlets are screened based on the customer group distribution characteristics and economic vitality distribution characteristics to determine the opened outlets with an environment similar to that of the target potential outlet location. Through analogical reasoning, the estimated outlet benefit data corresponding to the target potential outlet location is estimated based on the benefit data of the similar outlets. In another feasible method, the historical outlet benefit data corresponding to multiple opened outlets is used as a training set to train the model, and a benefit evaluation model is obtained. This benefit evaluation model can automatically perform benefit evaluation based on the customer group distribution characteristics and economic vitality distribution characteristics near the outlet. Therefore, the target potential outlet location, the customer group distribution characteristics and economic vitality distribution characteristics in the corresponding range of the target potential outlet location are input into the benefit evaluation model, and the benefit evaluation model is controlled to perform estimation analysis to determine the estimated outlet benefit data corresponding to the target potential outlet location. Based on the estimated outlet benefit data corresponding to each target potential outlet location, a potential outlet evaluation is carried out, and the target potential outlet location with the highest outlet benefit is selected as the target outlet location.

[0054] It can be seen that in the embodiments of the present application, in order to improve the accuracy and scientificity of the bank outlet location decision-making, a potential outlet benefit evaluation is carried out based on the target potential outlet location, the customer group distribution characteristics and economic vitality distribution characteristics in the mining and analysis results, and the estimated outlet benefit data corresponding to the target potential outlet location is determined. Then, based on the estimated outlet benefit data corresponding to each target potential outlet location, a potential outlet evaluation is carried out, and the potential outlet location with the optimal benefit is finally selected as the target outlet location, which helps the bank achieve the optimal allocation of resources and improve economic benefits and market competitiveness.

[0055] Further, in order to improve the utilization efficiency of outlet resources, in the embodiments of the present application, after a potential outlet evaluation is carried out based on the outlet benefit data corresponding to each target potential outlet location and the target outlet location is determined, it further includes: When a bank outlet verification instruction is detected, the actual outlet benefit data corresponding to the target outlet location is obtained, and an actual benefit evaluation is carried out based on the actual outlet benefit data and the estimated outlet benefit data to determine the outlet benefit evaluation result; When the outlet benefit evaluation result is an abnormal benefit, an operation optimization analysis is carried out based on the actual outlet benefit data to determine the operation optimization information corresponding to the target outlet location, where the operation optimization information is used to guide the outlet improvement strategy and improve the benefit.

[0056] For the embodiments of this application, after the bank branch at the target branch location has been actually put into use for a preset duration, benefit evaluation will be performed based on the actual branch benefit data of the newly added bank branch to determine whether the newly added bank branch has achieved the expected business conditions. If there are abnormal benefits, operation optimization analysis can be quickly executed to guide branch improvement strategies and enhance benefits, thereby improving the utilization efficiency of branch resources to a certain extent.

[0057] Specifically, after the newly added bank branch has been put into use for a preset duration, a bank branch verification instruction is automatically generated. The length of this preset duration can be set by the user according to needs. For example, it can be 3 months, 6 months, etc. When the bank branch verification instruction is detected, the actual branch benefit data corresponding to the target branch location is obtained. This actual branch benefit data is obtained from the data warehouse of the bank branch through an allowed interface connection method and includes, but is not limited to, key indicators such as deposit amount, loan amount, and transaction volume. Then, based on the actual branch benefit data and the estimated branch benefit data, actual benefit evaluation is performed to determine the branch benefit evaluation result, that is, calculate the overall percentage of the actual branch benefit data reaching the estimated branch benefit data, and obtain the actual benefit percentage threshold. This percentage threshold is determined based on the benefit growth trend of the bank branch and can be adjusted by the user according to the actual situation. When the calculated overall percentage is less than the percentage threshold, the branch benefit evaluation result is determined to be abnormal benefits; otherwise, the branch benefit evaluation result is determined to be normal benefits.

[0058] When the branch benefit evaluation result is normal benefits, no further operations are performed, and only the branch benefits of the newly added bank branch at the target branch location need to be continuously monitored. When the branch benefit evaluation result is abnormal benefits, based on the abnormal data dimension in the actual branch benefit data, the operation optimization information for improving this abnormal data dimension is determined. The corresponding relationship between this abnormal data dimension and the operation optimization information is pre-stored in the electronic device. For example, when the deposit amount of the bank branch does not meet the expected standard, the determined operation optimization information includes: optimizing deposit products and services, and strengthening the publicity of deposit business.

[0059] It can be seen that in the embodiments of this application, when the bank branch verification instruction is detected, actual benefit evaluation is performed based on the actual branch benefit data and the estimated branch benefit data to determine the branch benefit evaluation result. When the branch benefit evaluation result is abnormal benefits, operation optimization analysis is performed based on the actual branch benefit data to determine the operation optimization information corresponding to the target branch location. If there are abnormal benefits, operation optimization analysis is quickly executed to guide branch improvement strategies and enhance benefits, thereby improving the utilization efficiency of branch resources to a certain extent.

[0060] Further, in order to improve the business efficiency of each bank branch and optimize resource allocation, in the embodiment of the present application, after optimizing and adjusting based on the target branch location and the existing bank branch layout to obtain the optimized bank branch layout, it further includes: When a branch business optimization instruction is detected, obtain the branch business efficiency corresponding to each target bank branch in the optimized bank branch layout, and obtain the competitive branch business structure corresponding to the competing bank branches; Based on the branch business efficiency corresponding to each target bank branch and the competitive branch business structure, conduct business optimization analysis to determine the optimized branch business structure corresponding to each target bank branch.

[0061] For the embodiment of the present application, a business optimization cycle is preset in the electronic device. Whenever the business optimization cycle is reached, a branch business optimization instruction is automatically generated. Therefore, when a branch business optimization instruction is detected, automatically obtain the branch business efficiency corresponding to each target bank branch in the optimized bank branch layout. At the same time, obtain the competitive branch business structure corresponding to the competing bank branches, where the competitive branch business structure includes, but is not limited to, information such as business types, business policies, and market shares. Furthermore, based on the comparison between the business structure of the target bank branch and the competitive branch business structure, determine the branch business to be improved corresponding to the target bank branch, and based on the comparison between each business efficiency of the target bank branch and the corresponding business efficiency in the competing bank branches, determine the branch business to be enhanced corresponding to the target bank branch. Finally, based on the branch business to be improved and the branch business to be enhanced corresponding to the target bank branch, determine the optimized branch business structure corresponding to each target bank branch. Through business optimization analysis, it helps to identify the gap between the target bank branch and the competing bank branches and formulate targeted optimization measures, which helps to improve the business efficiency of each bank branch and optimize resource allocation.

[0062] It can be seen that in the embodiment of the present application, when a branch business optimization instruction is detected, obtain the branch business efficiency corresponding to each target bank branch in the optimized bank branch layout, and obtain the competitive branch business structure corresponding to the competing bank branches. Then, based on the branch business efficiency corresponding to each target bank branch and the competitive branch business structure, conduct business optimization analysis to determine the optimized branch business structure corresponding to each target bank branch. Through business optimization analysis, it helps to identify the gap between the target bank branch and the competing bank branches and formulate targeted optimization measures, which helps to improve the business efficiency of each bank branch and optimize resource allocation.

[0063] The above embodiment introduces an AI big data analysis method for assisting in optimizing the bank branch layout from the perspective of the method flow. The following embodiment introduces an AI big data analysis system for assisting in optimizing the bank branch layout from the perspective of virtual modules or virtual units. For details, see the following embodiment.

[0064] An embodiment of the present application provides an AI big data analysis system for assisting in optimizing the layout of bank branches. As Figure 2 shown, the AI big data analysis system for assisting in optimizing the layout of bank branches may specifically include: A mining and analysis module 210, configured to obtain a multi-source data set, perform data mining and analysis based on the multi-source data set, and determine a mining and analysis result. The mining and analysis result includes: customer group distribution characteristics, economic vitality distribution characteristics, matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics; A branch location selection module 220, configured to perform potential branch location selection based on the matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics in the mining and analysis result, determine multiple potential branch locations, and perform potential branch evaluation based on the multiple potential branch locations, the customer group distribution characteristics and economic vitality distribution characteristics in the mining and analysis result, and determine the target branch location; An optimization and adjustment module 230, configured to obtain the existing bank branch layout, perform optimization and adjustment based on the target branch location and the existing bank branch layout, and obtain the optimized bank branch layout.

[0065] In a possible implementation manner of the embodiment of the present application, when the mining and analysis module 210 performs data mining and analysis based on the multi-source data set to determine the mining and analysis result, it is configured to: Perform customer group analysis based on the customer behavior data in the multi-source data set to determine the customer group distribution characteristics, where the customer group distribution characteristics are used to characterize the dispersion of various attributes and behavior patterns corresponding to different customer groups in the spatial dimension; Perform business environment analysis based on the commercial opening data in the multi-source data set to determine the economic vitality distribution characteristics, where the economic vitality distribution characteristics are used to characterize the active degree of different regions in terms of business activities, economic growth, and market potential; Obtain the target customer group characteristics, perform population matching analysis based on the target customer group characteristics and the demographic data in the multi-source data set, and determine the matching population distribution characteristics, where the matching population distribution characteristics are used to characterize the distribution of the target customer group in the geographical space; Obtain the target enterprise group characteristics, perform enterprise matching analysis based on the target enterprise group characteristics and the enterprise distribution data in the multi-source data set, and determine the matching enterprise distribution characteristics, where the matching enterprise distribution characteristics are used to characterize the distribution of the target enterprise in the geographical space; Perform traffic flow analysis based on the traffic flow data in the multi-source data set to determine the traffic dynamic characteristics, where the traffic dynamic characteristics are used to characterize the change law of traffic flow with time and space factors.

[0066] In a possible implementation manner of the embodiment of the present application, when the optimization and adjustment module 230 performs optimization and adjustment based on the target branch location and the existing bank branch layout to obtain the optimized bank branch layout, it is used for: Drawing an isochrone circle for each bank branch in the existing bank branch layout to obtain a distribution map of branch isochrone circles; Performing isochrone circle superposition addition based on the target branch location and the distribution map of branch isochrone circles to obtain a distribution map of added branch isochrone circles; Performing isochrone circle overlap analysis based on the distribution map of added branch isochrone circles to determine branch optimization information, and optimizing and adjusting the existing bank branch layout based on the branch optimization information to obtain the optimized bank branch layout.

[0067] In a possible implementation manner of the embodiment of the present application, when the branch location selection module 220 performs potential branch evaluation based on multiple potential branch locations, the customer group distribution characteristics and economic vitality distribution characteristics in the mining and analysis results to determine the target branch location, it is used for: Performing potential branch benefit evaluation based on the target potential branch location, the customer group distribution characteristics and economic vitality distribution characteristics in the mining and analysis results to determine the estimated branch benefit data corresponding to the target potential branch location, where the target potential branch location is any one of the potential branch locations; Performing potential branch evaluation based on the estimated branch benefit data corresponding to each target potential branch location to determine the target branch location, where the target branch location is the target potential branch location with the highest branch benefit.

[0068] In a possible implementation manner of the embodiment of the present application, it further includes: An operation optimization module, which is used to obtain the actual branch benefit data corresponding to the target branch location when detecting a bank branch verification instruction, perform actual benefit evaluation based on the actual branch benefit data and the estimated branch benefit data, and determine the branch benefit evaluation result; When the branch benefit evaluation result is abnormal benefit, perform operation optimization analysis based on the actual branch benefit data to determine the operation optimization information corresponding to the target branch location, where the operation optimization information is used to guide the branch improvement strategy and improve the benefit.

[0069] In a possible implementation manner of the embodiment of the present application, it further includes: A service optimization module, which is used to obtain the branch service benefit corresponding to each target bank branch in the optimized bank branch layout and obtain the competitive branch service structure corresponding to the competitive bank branches when detecting a branch service optimization instruction; Performing service optimization analysis based on the branch service benefit corresponding to each target bank branch and the competitive branch service structure to determine the optimized branch service structure corresponding to each target bank branch.

[0070] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process of the above-described AI big data analysis system for assisting in optimizing the layout of bank branches can refer to the corresponding process in the foregoing method embodiments and will not be elaborated herein.

[0071] An embodiment of the present application provides an electronic device, such as Figure 3 shown. Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. Among them, the processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0072] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in conjunction with the disclosure of the present application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0073] The bus 302 may include a path for transmitting information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0074] The memory 303 can be a ROM (Read Only Memory), or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0075] The memory 303 is used to store the application program code for executing the solution of this application, and is controlled by the processor 301 for execution. The processor 301 is used to execute the application program code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0076] Among them, the electronic device includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The shown electronic device is only an example and should not bring any limitation to the functions and usage scope of the embodiments of this application.

[0077] The embodiments of this application provide a computer-readable storage medium, on which a computer program is stored. When it runs on a computer, it enables the computer to execute the corresponding content in the foregoing method embodiments.

[0078] The embodiments of this application provide a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the method in any of the above embodiments.

[0079] It should be understood that although the steps in the flowchart of the accompanying drawings are shown sequentially as indicated by the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise clearly stated in this document, there is no strict order restriction for the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0080] The above are only some embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. An AI big data analysis method for assisting in optimizing bank branch layout, characterized in that: include: Acquire a multi-source data set, perform data mining and analysis based on the multi-source data set, and determine mining and analysis results, wherein the mining and analysis results include: customer group distribution characteristics, economic vitality distribution characteristics, matching population distribution characteristics, matching enterprise distribution characteristics, and traffic dynamic characteristics; Based on the matching population distribution characteristics, the matching enterprise distribution characteristics and the traffic dynamic characteristics in the mining and analysis results, potential network point locations are selected to determine multiple potential network point locations, and based on the multiple potential network point locations, the customer group distribution characteristics and the economic vitality distribution characteristics in the mining and analysis results, potential network point evaluation is performed to determine the target network point location; The existing bank branch layout is obtained, and optimization and adjustment are performed based on the target branch location and the existing bank branch layout to obtain an optimized bank branch layout.

2. The AI ​​big data analysis method for assisting in optimizing bank branch layout according to claim 1 is characterized in that: The performing data mining and analysis based on the multi-source data set to determine the mining and analysis results includes: Performing customer group analysis based on the customer behavior data in the multi-source data set to determine the customer group distribution characteristics, wherein the customer group distribution characteristics are used to characterize the dispersion of multiple attributes and behavior patterns corresponding to different customer groups in the spatial dimension; Performing business environment analysis based on the commercial open data in the multi-source data set to determine the economic vitality distribution characteristics, wherein the economic vitality distribution characteristics are used to characterize the degree of activity of different regions in terms of business activities, economic growth and market potential; Acquire target customer group characteristics, perform population matching analysis based on the target customer group characteristics and demographic data in the multi-source data set, and determine the matching population distribution characteristics, wherein the matching population distribution characteristics are used to characterize the distribution of the target customer group in geographic space; Acquire target enterprise group characteristics, perform enterprise matching analysis based on the target enterprise group characteristics and enterprise distribution data in the multi-source data set, and determine the matching enterprise distribution characteristics, wherein the matching enterprise distribution characteristics are used to characterize the distribution of target enterprises in geographic space; Traffic flow analysis is performed based on the traffic flow data in the multi-source data set to determine the traffic dynamic characteristics, wherein the traffic dynamic characteristics are used to characterize the changing patterns of traffic flow with time and space factors.

3. The AI ​​big data analysis method for assisting in optimizing bank branch layout according to claim 1 is characterized in that: The optimizing and adjusting based on the target branch location and the existing bank branch layout to obtain an optimized bank branch layout includes: Based on the existing bank branch layout, isochronous circles are drawn for each bank branch to obtain a branch isochronous circle distribution map; Adding isochronous circles of network dots based on the target network dot position and the network dot isochronous circle distribution map to obtain an added network dot isochronous circle distribution map; Based on the added branch isochronal circle distribution map, isochronal circle overlap analysis is performed to determine branch optimization information, and based on the branch optimization information, the existing bank branch layout is optimized and adjusted to obtain an optimized bank branch layout.

4. The AI ​​big data analysis method for assisting in optimizing bank branch layout according to claim 1 is characterized in that: The step of evaluating potential outlets based on the plurality of potential outlet locations, the customer group distribution characteristics and the economic vitality distribution characteristics in the mining analysis results, and determining the target outlet location includes: Based on the target potential outlet location, the customer group distribution characteristics and the economic vitality distribution characteristics in the mining analysis results, potential outlet benefit evaluation is performed to determine the estimated outlet benefit data corresponding to the target potential outlet location, wherein the target potential outlet location is any one of the potential outlet locations; A potential network point evaluation is performed based on the estimated network point efficiency data corresponding to each target potential network point location to determine a target network point location, wherein the target network point location is the target potential network point location with the highest network point efficiency.

5. The AI ​​big data analysis method for assisting in optimizing bank branch layout according to claim 4 is characterized in that: After performing potential network point evaluation based on the network point efficiency data corresponding to each target potential network point location and determining the target network point location, the method further includes: When a bank branch verification instruction is detected, actual branch benefit data corresponding to the target branch location is obtained, actual benefit evaluation is performed based on the actual branch benefit data and the estimated branch benefit data, and a branch benefit evaluation result is determined; When the network point benefit evaluation result is abnormal, an operation optimization analysis is performed based on the actual network point benefit data to determine the operation optimization information corresponding to the target network point location, wherein the operation optimization information is used to guide network point improvement strategies and enhance benefits.

6. The AI ​​big data analysis method for assisting in optimizing bank branch layout according to claim 1 is characterized in that: After optimizing and adjusting the target bank outlet location and the existing bank outlet layout to obtain an optimized bank outlet layout, the method further includes: When a branch business optimization instruction is detected, the branch business benefits corresponding to each target bank branch in the optimized bank branch layout are obtained, and the competing bank branch business structure corresponding to the competing bank branch is obtained; Based on the branch business benefits corresponding to each of the target bank branches and the business structure of the competing branches, a business optimization analysis is performed to determine the optimized branch business structure corresponding to each of the target bank branches.

7. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the AI ​​big data analysis method for assisting in optimizing the layout of bank branches as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the AI ​​big data analysis method for assisting in optimizing the layout of bank outlets as described in any one of claims 1 to 6.

9. A computer program product, characterized in that It includes a computer program, and the processor executes the AI ​​big data analysis method for assisting in optimizing the layout of bank branches as described in any one of claims 1 to 6.

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