Financial service handling method and device and storage medium

Abstract

The invention discloses a financial service handling method and device and a storage medium. The method relates to the field of artificial intelligence and financial science and technology, and comprises the steps that first data information corresponding to a first object is acquired, and the first data information is used for representing description information of a financial service to be handled by the first object; multiple second objects in the target financial institution are determined, second data information corresponding to each second object is determined, and the second objects are objects for handling financial services; determining a target second object and target path information according to the first data information and the second data information; and pushing the target path information to the first object so as to guide the first object to handle the financial service to be handled to the target second object through the target path information. According to the method and the device, the problem of relatively low financial service handling efficiency caused by overlong waiting time when a customer handles the financial service in a financial institution in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the fields of artificial intelligence and fintech, and in particular, to a method, apparatus, and storage medium for handling financial services. Background Art

[0002] In the offline service handling of current financial institutions, the problem of excessive customer waiting time is a common one. With the increase in economic activities and the diversification of financial product services, customers' demands for financial institution services are constantly rising, while traditional service models, such as static service window settings and manual queuing management, are difficult to adapt to the rapidly changing business environment and customer traffic.

[0003] Regarding the problem in the related art that the waiting time of customers is too long when handling financial services in financial institutions, resulting in low efficiency in handling financial services, no effective solution has been proposed yet. Summary of the Invention

[0004] The main objective of the present application is to provide a method, apparatus, and storage medium for handling financial services, so as to solve the problem in the related art that the waiting time of customers is too long when handling financial services in financial institutions, resulting in low efficiency in handling financial services.

[0005] To achieve the above objective, according to one aspect of the present application, a method for handling financial services is provided. The method includes: obtaining first data information corresponding to a first object, where the first data information is used to represent description information of the financial service to be handled by the first object; determining a plurality of second objects in a target financial institution and determining second data information corresponding to each second object, where the second object is an object handling financial services, and the second data information at least includes: type information of the financial services that the second object can handle and location information of the second object; determining a target second object and target path information based on the first data information and the second data information; and pushing the target path information to the first object to guide the first object to the target second object to handle the financial service to be handled through the target path information.

[0006] Further, determining a plurality of second objects in a target financial institution includes: obtaining environmental parameter information of the target financial institution within a preset time period; obtaining service parameter information of the financial services processed by the target financial institution within a historical time period; processing the environmental parameter information and the service parameter information through a target prediction model to obtain the predicted financial service volume and predicted financial service types of the target financial institution within the preset time period; and determining a plurality of second objects based on the predicted financial service volume and the predicted financial service types.

[0007] Further, determining the target second object and the target path information based on the first data information and the second data information includes: determining the financial service to be handled based on the first data information; screening multiple second objects according to the financial service to be handled to obtain multiple candidate second objects; determining the target second object and the target path information based on the current financial service volume to be handled corresponding to the multiple candidate second objects and the second data information.

[0008] Further, determining the target second object and the target path information based on the current financial service volume to be handled corresponding to the multiple candidate second objects and the second data information includes: obtaining the location information of the first object, and generating multiple initial path information based on the location information of the first object and the location information of each candidate second object; scoring each candidate second object according to the current financial service volume to be handled corresponding to the multiple candidate second objects and the multiple initial path information to obtain the score value corresponding to each candidate second object; determining the target second object from the multiple candidate second objects according to the score value, and determining the initial path information corresponding to the target second object among the multiple initial path information as the target path information.

[0009] Further, generating multiple initial path information based on the location information of the first object and the location information of each candidate second object includes: obtaining the spatial layout information of the target financial institution; performing path planning based on the location information of the first object, the location information of each candidate second object and the spatial layout information to obtain multiple path information from the location information of the first object to the location information of each candidate second object; screening the multiple path information to obtain the initial path information corresponding to each candidate second object; obtaining multiple initial path information based on the initial path information corresponding to each candidate second object.

[0010] Further, screening multiple second objects according to the financial service to be handled to obtain multiple candidate second objects includes: obtaining the attribute information of the handling object corresponding to the historical financial services handled by the first object; determining the preference information of the first object according to the attribute information of the handling object; screening multiple second objects according to the financial service to be handled and the preference information to obtain multiple candidate second objects.

[0011] Further, after pushing the target path information to the first object to guide the first object to the target second object to handle the financial service to be handled, the method further includes: after the financial service to be handled is completed, obtaining the feedback data fed back by the first object, where the feedback data is used to describe the satisfaction of the first object with the financial service handling process; collecting the facial expression of the first object to obtain facial expression data; determining the optimization direction of the financial service handling strategy according to the facial expression data and the feedback data.

[0012] To achieve the above object, according to another aspect of the present application, there is provided an apparatus for handling financial services. The apparatus includes: a first acquisition unit configured to acquire first data information corresponding to a first object, where the first data information is used to characterize description information of a financial service to be handled by the first object; a first determination unit configured to determine a plurality of second objects in a target financial institution and determine second data information corresponding to each second object, where the second object is an object handling a financial service, and the second data information at least includes: type information of financial services that the second object can handle and location information of the second object; a second determination unit configured to determine a target second object and target path information based on the first data information and the second data information; and a push unit configured to push the target path information to the first object, so as to guide the first object to the target second object to handle the financial service to be handled through the target path information.

[0013] Further, the first determination unit includes: a first acquisition subunit configured to acquire environmental parameter information of the target financial institution within a preset time period; a second acquisition subunit configured to acquire service parameter information of financial services processed by the target financial institution within a historical time period; a processing subunit configured to process the environmental parameter information and the service parameter information through a target prediction model to obtain a predicted financial service volume and predicted financial service types of the target financial institution within the preset time period; and a first determination subunit configured to determine a plurality of second objects based on the predicted financial service volume and the predicted financial service types.

[0014] Further, the second determination unit includes: a second determination subunit configured to determine the financial service to be handled based on the first data information; a screening subunit configured to screen the plurality of second objects based on the financial service to be handled to obtain a plurality of candidate second objects; and a third determination subunit configured to determine the target second object and the target path information based on the current financial service volume to be handled corresponding to the plurality of candidate second objects and the second data information.

[0015] Further, the third determination subunit includes: a generation module configured to acquire location information of the first object and generate a plurality of initial path information based on the location information of the first object and the location information of each candidate second object; a scoring module configured to score each candidate second object based on the current financial service volume to be handled corresponding to the plurality of candidate second objects and the plurality of initial path information to obtain a score value corresponding to each candidate second object; and a first determination module configured to determine the target second object from the plurality of candidate second objects based on the score value and determine the initial path information corresponding to the target second object among the plurality of initial path information as the target path information.

[0016] Further, the generation module includes: an acquisition sub-module for acquiring the spatial layout information of the target financial institution; a path planning sub-module for performing path planning based on the position information of the first object, the position information of each candidate second object, and the spatial layout information to obtain multiple path information from the position information of the first object to the position information of each candidate second object; a screening sub-module for screening the multiple path information to obtain the initial path information corresponding to each candidate second object; and a determination sub-module for obtaining multiple initial path information based on the initial path information corresponding to each candidate second object.

[0017] Further, the screening sub-unit includes: an acquisition module for acquiring the attribute information of the handling object corresponding to the historical financial business handled by the first object; a second determination module for determining the preference information of the first object based on the attribute information of the handling object; and a screening module for screening multiple second objects based on the financial business to be handled and the preference information to obtain multiple candidate second objects.

[0018] Further, the device further includes: a second acquisition unit for acquiring the feedback data fed back by the first object after the financial business to be handled is completed, where the feedback data is used to describe the satisfaction of the first object with the financial business handling process; a collection unit for collecting the facial expression of the first object to obtain facial expression data; and a third determination unit for determining the optimization direction of the financial business handling strategy based on the facial expression data and the feedback data.

[0019] In the embodiments of the present application, by obtaining first data information corresponding to a first object, where the first data information is used to represent description information of a financial service to be handled by the first object; determining a plurality of second objects in a target financial institution and determining second data information corresponding to each second object, where the second object is an object handling a financial service, and the second data information at least includes: type information of financial services that the second object can handle and location information of the second object; determining a target second object and target path information based on the first data information and the second data information; and pushing the target path information to the first object to guide the first object to the target second object through the target path information to handle the to-be-handled financial service, the problem in the related art that the waiting time is too long when a customer handles a financial service in a financial institution, resulting in low efficiency in handling financial services, is solved. In the present application, by predicting the business volume and predicted business types of the target financial institution within a preset time period, configuring a plurality of second objects and screening a plurality of candidate second objects capable of handling the target to-be-handled services of the target customer, and determining the target second object and target path information according to the target to-be-handled service and the current volume of financial services to be handled by the candidate second objects, and pushing the target path information to the target customer to guide the target customer to quickly reach the optimal position of the second object, the waiting time of the customer is significantly shortened, thereby achieving the technical effect of improving the efficiency of handling financial services. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0021] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for handling financial services is shown;

[0022] Figure 2 is a flowchart of a method for handling financial services according to an embodiment of the present application;

[0023] Figure 3 is a flowchart of a method for determining a plurality of second objects in a target financial institution according to an embodiment of the present application;

[0024] Figure 4 is a flowchart of a method for determining a target second object and target path information according to an embodiment of the present application;

[0025] Figure 5 is a flowchart of a method for generating a plurality of initial path information according to an embodiment of the present application;

[0026] Figure 6 is a schematic diagram of a device for handling financial services according to an embodiment of the present application;

[0027] Figure 7 It is a structural block diagram of an electronic device according to an embodiment of the present application. Specific embodiments

[0028] In order to enable those skilled in the art to better understand the solution of the present application, 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 only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily need to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] It should be noted that the information collected in the present application (including but not limited to the initial path information, the target path information, the financial business to be handled, the environmental parameter information, the service parameter information, the spatial layout information, the attribute information of the handling object, etc.) and data (including but not limited to the first data information, the second data information, the facial expression data, the feedback data, etc.) are information and data authorized by the user or fully authorized by all parties. And the processing of the relevant data, such as collection, storage, use, processing, transmission, provision, disclosure and application, etc., all comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse. For example, an interface is set between the present system and relevant users or institutions to provide corresponding operation entrances for users to choose to agree or refuse the automated decision-making results; if the user chooses to refuse, the expert decision-making process will be entered.

[0031] Embodiment 1

[0032] According to an embodiment of the present application, an embodiment of a method for handling financial services is further 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 the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0033] The method embodiment provided by the first embodiment of the present application can be executed on a mobile terminal, a computer terminal or a similar computing device. Figure 1 The following shows a hardware structure block diagram of a computer terminal (or mobile device) for implementing the method for handling financial services. As Figure 1 shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b,..., 102n in the figure) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only illustrative and does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may further include more or fewer components than Figure 1 shown, or have a different configuration from Figure 1 shown.

[0034] It should be noted that the above one or more processors 102 and / or other data processing circuits are generally referred to as "data processing circuits" in this article. The data processing circuit can be embodied in software, hardware, firmware or any combination thereof, in whole or in part. In addition, the data processing circuit can be a single independent processing module, or be incorporated in whole or in part into any one of the other elements in the computer terminal 10 (or mobile device). As involved in the embodiments of the present application, the data processing circuit is used for processor control (such as the selection of a variable resistor terminal path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the financial business handling method in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the above-mentioned financial business handling method. The memory 104 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 memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the computer terminal 10 through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include the wireless network provided by the communication provider of the computer terminal 10. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0038] Under the above operating environment, the present application provides a financial business handling method as Figure 2 shown. Figure 2 It is a flowchart of the financial business handling method according to Embodiment 1 of the present application.

[0039] Step S201, obtain first data information corresponding to a first object, where the first data information is used to characterize the description information of the financial business to be handled by the first object.

[0040] Optionally, when the user (i.e., the above-mentioned first object) needs to handle relevant financial business, the first data information of the user can be obtained through a front-end device. It should be noted that the first data information is used to characterize the description information of the financial business to be handled by the first object. For example, the first data information can be "I want to transfer 5000 yuan from my savings account to my son's education fund account, and at the same time, I also want to transfer 2000 yuan from my salary account to the account of my friend so-and-so".

[0041] It should be noted that the front-end device can be a relevant device in the target financial institution for handling financial services.

[0042] Step S202: Determine multiple second objects in the target financial institution and determine the second data information corresponding to each second object. Here, the second object is an object handling financial services, and the second data information at least includes: the type information of the financial services that the second object can handle and the location information of the second object.

[0043] Optionally, clarify multiple second objects in the target financial institution that can currently handle financial services for customers, and determine the second data information corresponding to the second objects. It should be noted that the second data information is data information such as the service types that the second objects can handle, the current business volume, and the specific locations. The second object can be a service staff or a self-service device for financial services.

[0044] It should be noted that the number of multiple second objects in the target financial institution that can currently handle financial services for customers can be determined based on the business in the historical time period.

[0045] Step S203: Determine the target second object and the target path information based on the first data information and the second data information.

[0046] Optionally, after determining the above-mentioned multiple second objects, determine the target second object from the multiple second objects according to the first data information and the second data information, and the target path information from the first object to the target second object.

[0047] For example, the type of financial service that the first object needs to handle can be determined according to the first data information, and the second object that can handle the type of financial service can be determined from the second data information, and the second object is determined as the above-mentioned target second object. Then, obtain the location information of the target second object from the second data information and obtain the location information of the first object according to the location of the front-end device. Finally, perform path planning based on the location information of the target second object and the location information of the first object to obtain the above-mentioned target path information.

[0048] It should be noted that the target second object can be the second object that can provide the best service for the first object, and the target path information is the best moving path from the location information of the first object to the location information of the target second object.

[0049] Step S204: Push the target path information to the first object to guide the first object to the target second object to handle the financial service to be handled through the target path information.

[0050] Optionally, after obtaining the above-mentioned target path information, the target path information is pushed to the first object, so that the first object can be guided by the target path information to the location of the target second object to handle the financial business to be handled. For example, the target path information can be pushed to the above-mentioned front-end device interface, or the target path information can also be pushed to the user's mobile APP, and AR navigation information such as the service window, the real-time location of self-service devices, waiting time, and the optimal path indication arrow is superimposed on the APP interface, so that the customer can intuitively see the location of the target second object in the actual environment and how to move to reach the target location. Customers can quickly find the location of the target second object according to the pushed optimal path information, avoiding blind queuing and ineffective waiting, and improving the customer experience and the service efficiency of financial institutions.

[0051] For example, a financial institution APP uses augmented reality technology to integrate voice navigation technology to push target path information to customer A in real time and broadcast navigation in real time. The target path information can be: turn left from the entrance of the financial institution, go straight to the stairs, go up the stairs to the first floor, then turn right, and go straight to service window B.

[0052] Through the above technical solution, the reasonable allocation of the second object in the financial institution is realized, the optimal resources are allocated to customers, the shortest path is provided, the customer waiting time is shortened, and the technical effect of improving the efficiency of financial business handling is achieved.

[0053] In an optional embodiment, as Figure 3 shown, determining multiple second objects in the target financial institution includes:

[0054] Step S301, obtaining the environmental parameter information of the target financial institution within a preset time period.

[0055] Optionally, the environmental parameter information of the target financial institution within a preset time period is obtained by calling the third-party system API. For example, the preset time period can be one day, one week, one month or a specific peak period. The environmental parameter information can be weather information, traffic condition information, and promotional activity information of the target financial institution, etc. For example, the environmental parameter information can be: light rain, normal operation of public transportation, and a promotional activity starts at 3 pm at financial institution A, etc.

[0056] For example, based on the geographical location of the target financial institution, a third-party weather forecast API can be called to obtain weather forecast data for a specific future time period, including temperature, humidity, rainfall probability, wind speed, etc.; an integrated traffic data API can be called to obtain the traffic conditions around the target financial institution, including road congestion, public transportation adjustment information, usage of nearby parking lots, etc.; by accessing a holiday database, legal holiday information for a preset time period can be obtained; and by accessing a database that usually maintains preferential information within the financial institution, preferential activity information for a preset time period can be obtained, etc.

[0057] Step S302: Obtain the business parameter information of the financial operations processed by the target financial institution within a historical time period.

[0058] Optionally, query the financial business handling information within a certain historical time period in the business database of the target financial institution. For example, through technologies such as SQL query and NoSQL database operation, extract the business types and quantities from transaction logs or business processing systems, extract the start time and end time of the business from the core system, extract the customer satisfaction scores or comments from the customer relationship management system or online feedback system, and obtain the available quantities of service windows, service personnel, and self-service devices at specific time points from the internal resource management system. Among them, the above historical time period can be one year, half a year, or a more specific time period, aiming to collect sufficient data for the training of subsequent prediction models; the above business information can include business types (such as deposits, loan applications, credit card services), business processing time, customer waiting time, customer satisfaction evaluation, the quantity of the second object, etc.

[0059] For example, the business parameter information of the financial operations processed from 8 am to 12 pm every day in the past month can include: on the first day, the business type distribution is 40 loan operations, 30 deposit operations, 20 credit card services, and 10 other operations; the business processing time is that the average time for a loan is 30 minutes, the average time for a deposit is 10 minutes, and the average time for a credit card service is 20 minutes; the average customer queuing waiting time is 15 minutes; the customer satisfaction evaluation is that the loan satisfaction is 85%, the deposit satisfaction is 90%, and the credit card service satisfaction is 80%; the distribution of the second object is: 2 credit card service windows, 3 deposit windows, 4 loan windows, 1 loan self-service device, 2 deposit self-service devices, and so on, to obtain the business parameter information of the financial operations processed at the same time period every day in a month.

[0060] Step S303: Process the environmental parameter information and business parameter information through the target prediction model to obtain the predicted financial business volume and predicted financial business types of the target financial institution within a preset time period.

[0061] Optionally, after obtaining the environmental parameter information and business parameter information, the above parameter information can be preprocessed, and then the preprocessed parameter information is input into the target prediction model for prediction processing to obtain the predicted financial business volume and predicted financial business types within a preset time period. Among them, the above preset time period can be one day, half a day or a more specific time period; the above target prediction model can be established using machine learning (such as time series analysis, neural network), deep learning technology, etc., and is obtained through the following training and optimization: First, construct a data set, use feature selection technology to extract features from the above parameter information, select features highly relevant to the prediction of financial business volume and business types, construct a feature data set, and divide the feature data set into a training set, a validation set and a test set. For example, the first 80% of the data can be used as the training set, and the last 20% as the validation set; secondly, perform model training and validation, use the training set data to train the model, use the validation set to evaluate the prediction accuracy of the model, and perform multiple rounds of training until a satisfactory prediction effect is achieved to obtain the target prediction model.

[0062] For example, the prediction model combining ARIMA (time series analysis) and XGBoost (deep learning) can predict the financial business volume and types as follows: The predicted customer flow from 8 am to 12 pm will reach 100 people; the predicted business type distribution is 50 loans, 30 deposits, 15 credit card services, and 5 other services.

[0063] Step S304, determine multiple second objects based on the predicted financial business volume and predicted financial business types.

[0064] Optionally, based on the predicted financial business volume and predicted business types generated by the prediction model, analyze the expected demand of different business types within a preset time period, evaluate the matching degree between the capabilities of existing second objects (such as the number of service windows, the type and number of self-service devices, and the service staff configuration) and the predicted demand, and determine the number and configuration of the second objects based on this matching degree. For example, if it is predicted that the main business of a financial institution from 10 am to 11 am is loan consultation, then multiple service windows or self-service devices good at handling loan consultation can be used as the second objects. The configuration and status of these second objects can also be dynamically adjusted, such as adjusting one or more of the deposit business windows to loan consultation windows to ensure that there are sufficient human and physical resources to handle the peak business demand during the predicted peak period, thereby reducing the customer waiting time and improving the service efficiency.

[0065] Suppose a financial institution expects to handle 200 transactions during the morning session. The demand for deposit and withdrawal transactions is the highest, each accounting for 50 transactions. The total number of transactions for loan consultations, credit card applications, foreign exchange transactions, card opening, etc. is 100. The financial institution may allocate 10 comprehensive service windows. Among them, 5 windows are dedicated to handling deposit and withdrawal transactions to ensure fast processing; 2 windows handle loan consultations. Since more detailed information exchange is required, the number of windows is relatively small, but it is ensured that the service staff have high professionalism; 2 windows handle credit card applications, maintaining the same configuration as the loan consultation windows; 1 window handles foreign exchange transactions. And 15 service staff are allocated. Among them, 8 are responsible for quickly handling deposit and withdrawal transactions, 2 are dedicated to loan consultations, 2 handle credit card applications, 1 handles foreign exchange transactions, and 2 are mobile staff for filling in positions or handling emergencies, etc. At the same time, 10 ATMs are deployed inside the branch. 5 ATMs mainly handle withdrawals and small deposits, considering the highest demand for withdrawals; 3 ATMs provide deposit and check deposit services to meet the deposit demand; 1 ATM provides loan inquiries and cash advances for loan demand; 1 ATM is configured as an intelligent teller machine that can handle more complex operations, such as querying the status of credit card applications and foreign exchange conversions.

[0066] Through the above technical solution, the second object is dynamically allocated based on the prediction result, and the service process is optimized, thereby achieving the technical effect of improving the service efficiency of business handling in financial institutions.

[0067] In an optional embodiment, determining the target second object and the target path information based on the first data information and the second data information includes:

[0068] In the first step, based on the first data information, determine the financial business to be handled.

[0069] Optionally, after collecting the description information of the financial business to be handled by the customer, methods such as API interfaces, databases, natural language processing, speech recognition, machine learning, etc. can be used to obtain and understand the financial business description information input by the customer. For example, for the business requirements input by voice, the system first uses speech recognition technology to convert the voice into text, and then uses natural language recognition technology to semantically understand the text of the business description information, ensuring that the system can accurately capture the customer's intentions and demand details, so as to accurately identify the specific business needs of the customer.

[0070] For example, a customer A expressed the following needs through a conversation with an intelligent robot of a financial institution: "I need to pay our company's annual supplier payment this year, the total amount is 100,000 yuan; in addition, I also want to transfer 2,000 yuan to my sister to help her pay the credit card bill that is about to expire." After the system obtains this description information, it first converts the voice into text, and then uses natural language processing technology to perform part-of-speech tagging and feature extraction on the text, and converts the words in the text into vector representations containing semantic information. The system recognizes key information such as "company", "annual supplier payment", and "100,000 yuan", indicating that the customer intends to make a public transfer related to the company's business; the system also recognizes information such as "sister", "2,000 yuan" and "credit card bill", indicating that the customer intends to make a small amount of personal transfer to help others pay bills. Therefore, it is recognized that the business needs of customer A include private transfers and public transfers.

[0071] In the second step, the plurality of second objects are screened according to the financial business to be processed to obtain a plurality of candidate second objects.

[0072] Optionally, after clarifying the specific needs of the customer, screening is performed from multiple second objects within the target financial institution based on the specific needs. The screening condition may be whether the second object supports the type of financial business to be handled by the customer. If the second object can handle the target customer's pending business, the second object is listed as a candidate second object.

[0073] For example, according to the first step, the financial services to be handled by customer A are personal transfer and public transfer, and the service windows A and B, and self-service devices C and D of the current target financial institution that can handle personal transfer and public transfer services are searched from multiple second objects, and A, B, C, and D constitute multiple candidate second objects.

[0074] The third step is to determine the target second object and target path information according to the current financial business volume and second data information corresponding to the plurality of candidate second objects that need to be processed.

[0075] Optionally, the target second object can be determined by combining the current business volume of each candidate second object, the spatial layout of the target financial institution, customer preferences and other factors to make a comprehensive prediction, obtain the estimated arrival window time, the estimated waiting time, etc., and then sort the candidate second objects according to the estimated waiting time, etc., select the second object with the highest ranking as the target selection, and obtain the initial path information of the target second object as the target path information. The above-mentioned financial business volume includes the number of businesses being processed and waiting to be processed by the current service window or self-service device.

[0076] For example, among the two service windows A and B that can handle personal and corporate transfer services screened in the second step, window A is located on the first floor with 5 pending business items and an estimated waiting time of 30 minutes, while window B is located on the second floor with 3 pending business items and an estimated waiting time of 20 minutes. From the perspective of sorting by the estimated waiting time, it can be determined that window B is the target second object, and the initial path information leading to window B is determined as the target path information.

[0077] Through the above technical solution, the second object is matched according to information such as the specific business needs and service preferences of the customer, and the shortest path to the target second object that has been planned is obtained, effectively shortening the customer's waiting time and improving the efficiency of financial business handling.

[0078] In an alternative embodiment, as Figure 4 shown, determining the target second object and the target path information based on the current financial business volume to be handled corresponding to multiple candidate second objects and the second data information includes:

[0079] Step S401, obtain the location information of the first object, and generate multiple initial path information based on the location information of the first object and the location information of each candidate second object.

[0080] Optionally, according to the customer's current real-time location information, the location information of the candidate second object, and the spatial layout information of the financial institution, a path planning algorithm is used to calculate multiple path information from the customer's current location to the location of each candidate second object. Among them, the path planning algorithm can be the Dijkstra algorithm, the A* algorithm, etc.

[0081] For example, customer XX is currently located at the entrance of the financial institution branch. It is known that he needs to handle credit card services. The screened candidate second objects are service windows D and E. Among them, service window D is located somewhere on the first floor, and service window E is located somewhere on the second floor. Two initial path information are generated through the path planning algorithm: Initial path information D is to go straight from the entrance to the T-junction, turn left into the left corridor, go straight to the end of the corridor, and then turn right into the service window D area. Considering the real-time pedestrian flow density and obstacle situation in the branch, it is estimated that the time required to reach service window D from the entrance through this path is 6 minutes; Initial path information E is to go straight from the entrance to the elevator, take the elevator to the second floor, and go straight to the service window E area after getting out of the elevator. Considering the waiting time of the elevator and the pedestrian flow situation on the second floor, it is estimated that the time required to reach service window E from the entrance through this path is 8 minutes.

[0082] Step S402, score each candidate second object based on the current financial business volume to be handled corresponding to multiple candidate second objects and the multiple initial path information, and obtain the score value corresponding to each candidate second object.

[0083] Optionally, considering factors such as the financial business volume of the candidate second object, the length of the initial path information, and the estimated arrival time, a comprehensive evaluation is conducted on each candidate second object to obtain the above-mentioned score value. Through a scoring mechanism, the service efficiency of each candidate second object and the convenience for customers to reach this second object are quantitatively evaluated, and finally a score value is obtained for the subsequent selection of the target second object. Among them, the scoring methods can be the weighted scoring method, fuzzy comprehensive evaluation method, grey relational analysis, principal component analysis, deep reinforcement learning scoring, etc. For example, using the deep reinforcement learning scoring mechanism, the training model can consider the following factors: the less the current business volume, the faster the service, and a higher score can be obtained; the shorter the path, the less the customer's moving time, and the higher the score; the higher the customer satisfaction, the higher the score; the higher the business processing speed, the higher the score, etc.

[0084] It should be noted that in order to more comprehensively evaluate the comprehensive service capabilities of each candidate second object, the index system referred to by the scoring mechanism may include business volume, business processing speed, customer satisfaction, path information, skill matching degree, operation permissions of business personnel, etc.

[0085] For example, the current business volume of service window D is 3, and it is expected to wait for 15 minutes; the current business volume of service window E is 1, and it is expected to wait for 5 minutes. Assuming that the scoring rule is that the less the business volume, the higher the score, the score value of service window D can be obtained as 85, and the score value of service window E can be obtained as 90.

[0086] Step S403, based on the score value, determine the target second object from multiple candidate second objects, and determine the initial path information corresponding to the target second object among multiple initial path information as the target path information.

[0087] Optionally, after comprehensively scoring each candidate second object, compare the scores of each candidate second object, and select the candidate object with the highest score as the target second object. When there are multiple candidate objects with equal scores, the target second object can be further determined by random selection, and then obtain the corresponding initial path information as the final target path information to guide the customer to the location of the target second object.

[0088] For example, based on the scoring results in step S402, the score value of service window E is the highest, so service window E is taken as the target second object, and the initial path E is taken as the target path information.

[0089] Through the above technical solution, the optimal service window and path are determined, providing an efficient customer service experience and improving the efficiency of financial business handling.

[0090] In an alternative embodiment, such asFigure 5 As shown, multiple initial path information is generated based on the position information of the first object and the position information of each candidate second object, including:

[0091] Step S501, obtain the spatial layout information of the target financial institution.

[0092] Optionally, obtaining the spatial layout information not only includes static layouts but also dynamic area adjustments (such as temporarily added queuing areas). Among them, the static layout can be obtained by using geographic information system software to mark the specific positions and identifications of service facilities (such as service window numbers, self-service equipment types, emergency exit positions, etc.) on the electronic map of the target financial institution; the dynamic adjustment information can be obtained by using Internet of Things devices deployed within the financial institution to monitor environmental changes and facility status in real time, such as high-definition cameras, temperature sensors, pedestrian flow monitors, Wi-Fi probes, Bluetooth beacons, and RFID sensors. By using the data collected in real time by the Internet of Things devices and combining with the geographic information system, the electronic map data is updated in real time to ensure the accuracy and timeliness of the map information. Among them, the above-mentioned spatial layout information refers to the detailed layout inside the target financial institution, which can include the specific positions of each functional area (such as service windows, self-service equipment areas, rest areas), the relative position relationships between each area, the positions of obstacles (such as columns, obstacle lines), real-time congestion areas, and safety information such as emergency exits.

[0093] Step S502, perform path planning based on the position information of the first object, the position information of each candidate second object, and the spatial layout information to obtain multiple path information from the position information of the first object to the position information of each candidate second object.

[0094] Optionally, according to the current real-time position information of the target customer, the position information of each candidate second object, and the spatial layout information of the financial institution, use a path planning algorithm to plan multiple path information from the customer's current position to the position of each candidate second object. Among them, the path planning algorithm can be the Dijkstra algorithm, the A* algorithm, etc. When generating the path, information such as real-time pedestrian flow density, obstacle conditions, and real-time congestion areas needs to be considered for route planning, and the estimated arrival time is generated.

[0095] For example, the current location of customer XX is at the entrance, and the location of a candidate second object service window E is somewhere on the second floor. Using a path planning algorithm, two path information may be generated. One path information E1 is to go straight from the entrance to the elevator, take the elevator to the second floor, and go straight to the service window E area after getting out of the elevator. Considering the elevator waiting time, the flow of people, etc., the estimated arrival time is 8 minutes. The other path information E2 is to go straight forward to the right from the entrance to the staircase, walk up the stairs to the second floor, and go straight forward to the left after getting out of the stairwell to the service window E area. Considering the flow of people, etc., the estimated arrival time is 4 minutes.

[0096] Step S503: Screen multiple path information to obtain the initial path information corresponding to each candidate second object.

[0097] Optionally, after generating multiple path information for each candidate second object, select an optimal path as the initial path information for each candidate second object. Among them, the screening method can be a path scoring method based on customer preferences, path selection based on cost-benefit analysis, etc. The screening of path information helps to maximize the resource utilization efficiency while meeting customer preferences.

[0098] For example, for the two path information generated in step S502, if the customer prefers to take the elevator, select path information E1 as the initial path information; if the customer prefers to walk, select path information E2 as the initial path information.

[0099] Step S504: Obtain multiple initial path information based on the initial path information corresponding to each candidate second object.

[0100] Optionally, after determining the initial path information of each candidate second object, that is, obtaining multiple initial path information, it provides a basis for quickly pushing the target path information after further selecting the optimal second object, so as to enable customers to handle financial business quickly and efficiently.

[0101] For example, there are 4 service windows for the candidate second object of customer XX, namely A, B, C, and D, and the corresponding initial path information are A1, B2, C3, and D4 respectively. Then A1, B2, C3, and D4 form multiple initial path information for subsequent selection.

[0102] Through the above technical solution, an optimized service experience is provided for customers, effectively shortening the customer waiting time and improving the efficiency of handling financial business.

[0103] In an optional embodiment, screening multiple second objects according to the financial business to be handled, and obtaining multiple candidate second objects includes:

[0104] Step 1: Obtain the attribute information of the handling object corresponding to the historical financial services handled by the first object.

[0105] Optionally, by querying the historical transaction database of the financial institution, extract the financial service handling records of the target customer to obtain the attribute information, such as service type, handling time, handling object (such as manual window number, self-service device ID), type of handling object (such as manual window, self-service device), average speed of service processing, customer satisfaction evaluation of the handling object, etc.

[0106] For example, customer XX has handled 10 financial services at the XX financial institution network in the past year. Among them, 6 times they chose manual service window A, 2 times they chose self-service device B, and 2 times they chose service staff C. The system analysis found that customer XX's average satisfaction evaluation of service window A is 90%, the evaluation of self-service device B is 70%, and the evaluation of service staff C is 85%. At the same time, the average service processing speed of window A is 15 minutes, the average service processing speed of self-service device B is 10 minutes, and the average service processing speed of service staff C is 12 minutes.

[0107] Step 2: Determine the preference information of the first object based on the attribute information of the handling object.

[0108] Optionally, after obtaining the attribute information of the handling object, use data mining (such as clustering analysis, association rule learning), deep learning models, etc. to analyze the preference information. For example, use deep learning models such as long short-term memory networks for training. After the model training is completed, input the attribute information of the handling object into the trained model for prediction processing to obtain the target customer's service handling preference information. Among them, the above preference information can be the tendency information of the customer for the second object identified based on historical data and behavior analysis. For example, by analyzing the type of the second object previously selected by the customer, such as manual window, self-service device, etc., it may be found that the customer is more inclined to use the manual window when performing complex queries, while preferring the self-service device when performing regular transfer operations; it may also be able to identify the differences in service needs among different customer groups (such as young people, elderly people, corporate customers) and their specific preferences for the second object. For example, young customers may prefer digital services, while elderly customers are more dependent on manual windows.

[0109] Step 3: Screen multiple second objects based on the financial service to be handled and the preference information to obtain multiple candidate second objects.

[0110] Optionally, after clarifying the specific business requirements and preferences of the target customers, multiple second objects within the target financial institution are screened to obtain candidate second objects that can meet the customers' needs and conform to their preferences. For example, for credit card application services, service personnel or service windows with corresponding authorities and skills need to be matched, rather than any self-service devices.

[0111] For example, customer XX currently needs to apply for a credit card and prefers a human service window and values service efficiency. The candidate second objects obtained after screening are service windows X and Y. Among them, service window X is a human service window dedicated to credit card services, with an average processing time of 12 minutes and a customer satisfaction rate of 90%; service window Y is a comprehensive service window, with an average processing time of 15 minutes and a customer satisfaction rate of 85%. Since both service windows X and Y conform to XX's preference information, X and Y are listed as candidate second objects.

[0112] Through the above technical solution, the historical preferences and behavior patterns of customers are deeply understood, ensuring that when customers need to handle financial services, not only can second objects that meet the service requirements be provided, but also intelligent matching can be performed based on customers' preferences, thereby enhancing the customer experience and improving the service efficiency and resource utilization rate of financial institutions.

[0113] In an optional embodiment, after pushing the target path information to the first object to guide the first object to the target second object to handle the financial service to be handled, the method further includes:

[0114] First step, after the financial service to be handled is completed, feedback data fed back by the first object is obtained, where the feedback data is used to describe the satisfaction of the first object with the financial service handling process.

[0115] Optionally, after the service is completed, by collecting the feedback data of customers, the satisfaction of customers with the entire service process is determined to optimize the service strategy and resources subsequently. Among them, the above feedback data may include evaluations of business processing speed, service quality, environmental comfort, path planning satisfaction, second object matching degree, etc., and the above collection channels may be application programs, self-service terminals, voice input systems, etc.

[0116] For example, customer XX completed the credit card application service at the XX financial institution branch, and then submitted a satisfaction evaluation through the mobile financial institution APP. The feedback data includes: the business processing speed score is 85 points, the service quality score is 90 points, and the environmental comfort score is 80 points.

[0117] Second step, the facial expressions of the first object are collected to obtain facial expression data.

[0118] Optionally, by collecting the facial expression data of customers, an intuitive feedback on customer emotions is obtained, which supplements the non-verbal dimension of customer satisfaction assessment and provides a more comprehensive data reference for optimizing financial business handling strategies. Among them, devices such as high-definition surveillance cameras in the target financial institution, cameras built into self-service devices, and cameras within service windows can be used for collection to capture more complete customer facial expression data, which is not limited in this application.

[0119] For example, during the process of customer XX handling business, the intelligent camera in the financial institution branch captured the facial expression of customer XX. Through computer vision technology analysis, facial expression data was obtained: customer XX showed anxiety during the waiting process, but when communicating with the service staff, the facial expression captured by the camera at the service window changed to satisfaction.

[0120] In the third step, based on the facial expression data and feedback data, determine the optimization direction of the financial business handling strategy.

[0121] Optionally, analyze the customer feedback data and facial expression data to identify deficiencies and potential improvement points in the financial business handling process, providing data-driven guidance for financial institutions to formulate service optimization strategies. Among them, analyzing facial expression data can perform emotion recognition through computer vision technology and the like, which is not limited in this application.

[0122] For example, according to the feedback data submitted by XX (business processing speed 85 points, service quality 90 points, environmental comfort 80 points) and facial expression data (anxiety during waiting, satisfaction during communication), the system analyzes and obtains the optimization direction: shorten the customer waiting time (increase the number of manual windows during peak hours or improve the efficiency of self-service devices), improve the environmental comfort of the branch (such as adding rest seats, optimizing indoor air quality and temperature), etc.

[0123] Through the above technical solutions, deeply understand the real feelings of customers towards the financial business handling process. Based on these feedback data and facial expression data, timely adjust and optimize service strategies, improve customer satisfaction, and at the same time improve service efficiency.

[0124] The method for handling financial business provided by the embodiment of the present application obtains the first data information corresponding to the first object, where the first data information is used to represent the description information of the financial business to be handled by the first object; determines multiple second objects in the target financial institution and determines the second data information corresponding to each second object, where the second object is the object handling the financial business, and the second data information at least includes: the type information of the financial business that the second object can handle and the location information of the second object; determines the target second object and the target path information according to the first data information and the second data information; and pushes the target path information to the first object to guide the first object to the target second object to handle the to-be-handled financial business through the target path information, solving the problem in the related art that the waiting time of customers is too long when handling financial business in a financial institution, resulting in low efficiency of handling financial business. By configuring multiple second objects according to the predicted business volume and predicted business types of the target financial institution, determining the target second object and the target path information according to the target to-be-handled business and the financial business volume that the candidate second object currently needs to handle, and pushing the target path information to the target customer to guide the target customer to quickly reach the optimal position of the second object, the waiting time of the customer is significantly shortened, thus achieving the technical effect of improving the efficiency of handling financial business.

[0125] 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 the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0126] Embodiment 2

[0127] The embodiment of the present application also provides a device for handling financial business. It should be noted that the device for handling financial business in the embodiment of the present application can be used to execute the method for handling financial business provided by the embodiment of the present application. The following introduces the device for handling financial business provided by the embodiment of the present application.

[0128] According to the embodiment of the present application, there is also provided a device for implementing the above method for handling financial business, as Figure 6 shown, the device includes: a first acquisition unit 601, a first determination unit 602, a second determination unit 603, and a push unit 604.

[0129] Specifically, the first acquisition unit 601 is configured to acquire the first data information corresponding to the first object, where the first data information is used to represent the description information of the financial business to be handled by the first object;

[0130] The first determination unit 602 is configured to determine multiple second objects in the target financial institution and determine second data information corresponding to each second object, where the second object is an object handling financial business, and the second data information at least includes: the type information of the financial business that the second object can handle and the location information of the second object;

[0131] The second determination unit 603 is configured to determine a target second object and target path information according to the first data information and the second data information;

[0132] The push unit 604 is configured to push the target path information to the first object, so as to guide the first object to the target second object to handle the to-be-handled financial business through the target path information.

[0133] The financial business handling device provided by the embodiment of the present application, through the first acquisition unit 601, is configured to acquire first data information corresponding to a first object, where the first data information is used to represent the description information of the to-be-handled financial business of the first object; the first determination unit 602 is configured to determine multiple second objects in the target financial institution and determine second data information corresponding to each second object, where the second object is an object handling financial business, and the second data information at least includes: the type information of the financial business that the second object can handle and the location information of the second object; the second determination unit 603 is configured to determine a target second object and target path information according to the first data information and the second data information; the push unit 604 is configured to push the target path information to the first object, so as to guide the first object to the target second object to handle the to-be-handled financial business through the target path information, solves the problem in the related art that the waiting time is too long when a customer handles a financial business in a financial institution, resulting in low efficiency of handling financial business. Configure multiple second objects according to the predicted business volume and predicted business types of the target financial institution, determine the target second object and target path information according to the target to-be-handled business and the current financial business volume that the candidate second object needs to handle, and push the target path information to the target customer to guide the target customer to quickly reach the optimal second object location, significantly shortening the customer waiting time, thereby achieving the technical effect of improving the efficiency of handling financial business.

[0134] Optionally, in the financial service handling apparatus provided in the embodiments of the present application, the first determination unit 601 includes: a first acquisition subunit, configured to acquire environmental parameter information of a target financial institution within a preset time period; a second acquisition subunit, configured to acquire service parameter information of the financial services processed by the target financial institution within a historical time period; a processing subunit, configured to process the environmental parameter information and the service parameter information through a target prediction model to obtain a predicted financial service volume and predicted financial service types of the target financial institution within the preset time period; and a first determination subunit, configured to determine a plurality of second objects according to the predicted financial service volume and the predicted financial service types.

[0135] Optionally, in the financial service handling apparatus provided in the embodiments of the present application, the second determination unit 602 includes: a second determination subunit, configured to determine a financial service to be handled according to first data information; a screening subunit, configured to screen the plurality of second objects according to the financial service to be handled to obtain a plurality of candidate second objects; and a third determination subunit, configured to determine a target second object and target path information according to the currently required financial service volume corresponding to the plurality of candidate second objects and second data information.

[0136] Optionally, in the financial service handling apparatus provided in the embodiments of the present application, the third determination subunit includes: a generation module, configured to acquire the location information of a first object and generate a plurality of initial path information according to the location information of the first object and the location information of each candidate second object; a scoring module, configured to score each candidate second object according to the currently required financial service volume corresponding to the plurality of candidate second objects and the plurality of initial path information to obtain a score value corresponding to each candidate second object; and a first determination module, configured to determine a target second object from the plurality of candidate second objects according to the score value and determine the initial path information corresponding to the target second object among the plurality of initial path information as the target path information.

[0137] Optionally, in the financial service handling apparatus provided in the embodiments of the present application, the generation module includes: an acquisition sub-module, configured to acquire the spatial layout information of the target financial institution; a path planning sub-module, configured to perform path planning according to the location information of the first object, the location information of each candidate second object, and the spatial layout information to obtain a plurality of path information from the location information of the first object to the location information of each candidate second object; a screening sub-module, configured to screen the plurality of path information to obtain the initial path information corresponding to each candidate second object; and a determination sub-module, configured to obtain a plurality of initial path information according to the initial path information corresponding to each candidate second object.

[0138] Optionally, in the financial service handling device provided in the embodiments of the present application, the screening subunit includes: an obtaining module, configured to obtain the attribute information of the handling object corresponding to the historical financial services handled by the first object; a second determining module, configured to determine the preference information of the first object according to the attribute information of the handling object; and a screening module, configured to screen multiple second objects according to the financial service to be handled and the preference information to obtain multiple candidate second objects.

[0139] Optionally, in the financial service handling device provided in the embodiments of the present application, the device further includes: a second obtaining unit, configured to obtain the feedback data fed back by the first object after the financial service to be handled is completed, where the feedback data is used to describe the satisfaction of the first object with the financial service handling process; a collecting unit, configured to collect the facial expression of the first object to obtain facial expression data; and a third determining unit, configured to determine the optimization direction of the financial service handling strategy according to the facial expression data and the feedback data.

[0140] It should be noted here that the above-mentioned first obtaining unit 601, first determining unit 602, second determining unit 603, and pushing unit 604 correspond to steps S201 to S204 in Embodiment 1. The functions of the four units are the same as those of the corresponding steps in terms of the implemented examples and application scenarios, but are not limited to the content disclosed in the above-mentioned Embodiment 1. It should be noted that the above-mentioned modules or units may be hardware components or software components stored in a memory (for example, memory 104) and processed by one or more processors (for example, processors 102a, 102b,..., 102n). The above-mentioned modules may also be part of the device and can run on the computer terminal 10 provided in Embodiment 1.

[0141] Embodiment 3

[0142] Embodiments of the present application may provide an electronic device. Figure 7 It is a structural block diagram of an electronic device according to an embodiment of the present application. As Figure 7 shown, the electronic device may include: one or more ( Figure 7 only one is shown in the figure) processors 702, a memory 704, a storage controller, and a peripheral interface, where the peripheral interface is connected to a radio frequency module, an audio module, and a display.

[0143] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and devices in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, to implement the above-mentioned methods. 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 memories, or other non-volatile solid-state memories. In some instances, the memory may further include a memory remotely provided with respect to the processor, and these remote memories may be connected to the terminal through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranets, local area networks, mobile communication networks, and combinations thereof.

[0144] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: obtaining first data information corresponding to a first object, where the first data information is used to represent the description information of the financial business to be handled by the first object; determining a plurality of second objects in the target financial institution and determining second data information corresponding to each second object, where the second object is an object handling a financial business, and the second data information at least includes: the type information of the financial business that the second object can handle and the location information of the second object; determining a target second object and target path information based on the first data information and the second data information; and pushing the target path information to the first object to guide the first object to the target second object through the target path information to handle the financial business to be handled.

[0145] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: determining a plurality of second objects in the target financial institution includes: obtaining the environmental parameter information of the target financial institution within a preset time period; obtaining the business parameter information of the financial business processed by the target financial institution within a historical time period; processing the environmental parameter information and the business parameter information through the target prediction model to obtain the predicted financial business volume and predicted financial business types of the target financial institution within the preset time period; and determining a plurality of second objects based on the predicted financial business volume and the predicted financial business types.

[0146] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: determining a target second object and target path information based on the first data information and the second data information includes: determining the financial business to be handled based on the first data information; screening a plurality of candidate second objects from the plurality of second objects according to the financial business to be handled to obtain a plurality of candidate second objects; and determining the target second object and target path information based on the currently required financial business volume corresponding to the plurality of candidate second objects and the second data information.

[0147] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: determining the target second object and the target path information based on the current financial business volume to be processed corresponding to multiple candidate second objects and the second data information, including: obtaining the location information of the first object, and generating multiple initial path information based on the location information of the first object and the location information of each candidate second object; scoring each candidate second object according to the current financial business volume to be processed corresponding to the multiple candidate second objects and the multiple initial path information to obtain a score value corresponding to each candidate second object; determining the target second object from the multiple candidate second objects according to the score value, and determining the initial path information corresponding to the target second object among the multiple initial path information as the target path information.

[0148] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: generating multiple initial path information based on the location information of the first object and the location information of each candidate second object, including: obtaining the spatial layout information of the target financial institution; performing path planning based on the location information of the first object, the location information of each candidate second object, and the spatial layout information to obtain multiple path information from the location information of the first object to the location information of each candidate second object; screening the multiple path information to obtain the initial path information corresponding to each candidate second object; obtaining multiple initial path information based on the initial path information corresponding to each candidate second object.

[0149] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: screening multiple second objects according to the financial business to be processed to obtain multiple candidate second objects, including: obtaining the attribute information of the handling object corresponding to the historical financial business handled by the first object; determining the preference information of the first object according to the attribute information of the handling object; screening the multiple second objects according to the financial business to be processed and the preference information to obtain multiple candidate second objects.

[0150] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: after pushing the target path information to the first object to guide the first object to the target second object to handle the financial business to be processed through the target path information, the method further includes: after the financial business to be processed is completed, obtaining the feedback data fed back by the first object, where the feedback data is used to describe the satisfaction of the first object with the financial business handling process; collecting the facial expression of the first object to obtain facial expression data; determining the optimization direction of the financial business handling strategy according to the facial expression data and the feedback data.

[0151] An embodiment of the present application provides a solution for handling financial services. By obtaining first data information corresponding to a first object, where the first data information is used to represent descriptive information of the financial service to be handled by the first object; determining a plurality of second objects in a target financial institution and determining second data information corresponding to each second object, where the second object is an object handling a financial service, and the second data information at least includes: information on the types of financial services that the second object can handle and the location information of the second object; determining a target second object and target path information based on the first data information and the second data information; and pushing the target path information to the first object to guide the first object to the target second object to handle the financial service to be handled, which solves the problem in the related art that the waiting time is too long when a customer handles a financial service in a financial institution, resulting in low efficiency in handling financial services. By configuring a plurality of second objects according to the predicted business volume and predicted business types of the target financial institution, and determining the target second object and target path information according to the target service to be handled and the volume of financial services that the candidate second object currently needs to handle, and pushing the target path information to the target customer to guide the target customer to quickly reach the optimal location of the second object, the waiting time of the customer is significantly shortened, thus achieving the technical effect of improving the efficiency of handling financial services.

[0152] Those of ordinary skill in the art can understand that Figure 7 the structure shown is only schematic, and the electronic device can also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a handheld computer, and terminal devices such as Mobile Internet Devices (MID), PAD, etc. Figure 7 It does not limit the structure of the above electronic device. For example, the electronic device may further include more or fewer components (such as a network interface, a display device, etc.) than those shown Figure 7 in the figure, or have a different configuration from that shown Figure 7 in the figure.

[0153] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware of the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disc, etc.

[0154] Embodiment 4

[0155] An embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the above storage medium can be used to store the program code executed by the method for handling financial services provided in the first embodiment above.

[0156] Optionally, in this embodiment, the above storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0157] The present application also provides a computer program product, which is adapted to execute a program for the steps of a method for handling financial services when executed on a data processing device.

[0158] The serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments.

[0159] In the above embodiments of the present application, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0160] In several embodiments provided by the present 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 illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. 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 displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0161] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0162] In addition, the functional units in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0163] When 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 application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0164] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art in this technical field, 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. A method for handling financial business, characterized in that: include: Acquire first data information corresponding to a first object, wherein the first data information is used to represent description information of a financial business to be handled by the first object; Determine a plurality of second objects in a target financial institution and determine second data information corresponding to each second object, wherein the second object is an object for handling financial business, and the second data information at least includes: Information on the types of financial services that the second object can handle and location information of the second object; Determine a target second object and target path information according to the first data information and the second data information; The target path information is pushed to the first object, so as to guide the first object to the target second object to handle the financial business to be handled through the target path information.

2. The method according to claim 1, characterized in that Determining a plurality of second objects in the target financial institution includes: Obtaining environmental parameter information of the target financial institution within a preset time period; Acquiring business parameter information of financial businesses processed by the target financial institution within a historical time period; Processing the environmental parameter information and the business parameter information through a target prediction model to obtain a predicted financial business volume and a predicted financial business type of the target financial institution within the preset time period; The plurality of second objects are determined according to the predicted financial business volume and the predicted financial business type.

3. The method according to claim 1, characterized in that Determining the target second object and the target path information according to the first data information and the second data information includes: Determining the financial business to be processed according to the first data information; Screening the plurality of second objects according to the financial business to be processed to obtain a plurality of candidate second objects; The target second object and the target path information are determined according to the current financial business volume that needs to be handled corresponding to the multiple candidate second objects and the second data information.

4. The method according to claim 3, characterized in that Determining the target second object and the target path information according to the current amount of financial business to be handled corresponding to the multiple candidate second objects and the second data information includes: Acquire the position information of the first object, and generate a plurality of initial path information according to the position information of the first object and the position information of each candidate second object; Scoring each candidate second object according to the current amount of financial business that needs to be handled corresponding to the multiple candidate second objects and the multiple initial path information to obtain a score value corresponding to each candidate second object; The target second object is determined from the multiple candidate second objects according to the score value, and the initial path information corresponding to the target second object in the multiple initial path information is determined as the target path information.

5. The method according to claim 4, characterized in that Generating a plurality of initial path information according to the position information of the first object and the position information of each candidate second object includes: Obtain spatial layout information of target financial institutions; Performing path planning based on the position information of the first object, the position information of each candidate second object and the spatial layout information to obtain a plurality of path information from the position information of the first object to the position information of each candidate second object; The multiple path information are screened to obtain initial path information corresponding to each candidate second object; and the multiple initial path information are obtained based on the initial path information corresponding to each candidate second object.

6. The method according to claim 3, characterized in that The plurality of second objects are screened according to the financial business to be processed, and the plurality of candidate second objects obtained include: Acquire attribute information of a processing object corresponding to a historical financial business processed by the first object; Determining preference information of the first object according to the attribute information of the processing object; The plurality of second objects are screened according to the financial business to be processed and the preference information to obtain the plurality of candidate second objects.

7. The method according to claim 1, characterized in that After pushing the target path information to the first object so as to guide the first object to the target second object to handle the to-be-handled financial business through the target path information, the method further includes: After the pending financial service is completed, feedback data fed back by the first subject is obtained, wherein the feedback data is used to describe the first subject's satisfaction with the financial service handling process; collecting the facial expression of the first subject to obtain facial expression data; Based on the facial expression data and the feedback data, determine the optimization direction of the financial business handling strategy.

8. A financial business handling device, characterized in that: include: A first acquisition unit, configured to acquire first data information corresponding to a first object, wherein the first data information is used to represent description information of a financial business to be handled by the first object; A first determining unit is used to determine a plurality of second objects in a target financial institution and determine second data information corresponding to each second object, wherein the second object is an object for handling financial services, and the second data information at least includes: type information of financial services that the second object can handle and location information of the second object; A second determining unit, configured to determine a target second object and target path information according to the first data information and the second data information; A pushing unit is used to push the target path information to the first object, so as to guide the first object to the target second object to handle the financial business to be handled through the target path information.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the computer-readable storage medium is located is controlled to execute the method for handling financial business as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program, when running, executes the method for handling financial business described in any one of claims 1 to 7.

11. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the steps of the method for handling financial business described in any one of claims 1 to 7 are implemented.

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