Business auditing information dispatching method and device

Abstract

The application provides a business audit information distribution method and device, which can be used in the technical field of machine learning. The method comprises the following steps: receiving business audit reminding information sent by a target intelligent self-service terminal in a financial institution outlet, wherein the financial institution outlet is provided with a plurality of intelligent self-service terminals and mobile terminal devices, the target intelligent self-service terminal is at least one of the plurality of intelligent self-service terminals, the mobile terminal device is used for business audit, and the intelligent self-service terminal is used for customer business processing; obtaining real-time coordinate information of each mobile terminal device; determining a mobile terminal device closest to the target intelligent self-service terminal according to a preset classification model, pre-acquired coordinate information of each intelligent self-service terminal, and real-time coordinate information of each mobile terminal device, and distributing the business audit reminding information to the mobile terminal device. The application can improve the accuracy and efficiency of the distribution of the business audit reminding information, thereby improving the customer business processing experience.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method and apparatus for distributing business review information. Background Technology

[0002] The rapid development of internet and computer technology has provided strong support for bank branches to implement intelligent services. Today, most banking operations are moving towards electronic and intelligent models, with key applications including intelligent self-service terminals, self-service receipt printers, and mobile devices for customer service managers, such as mobile verification assistant tablets. Among these, intelligent self-service terminals are the main equipment for diverting branch business, enabling various transactions including password changes, mobile phone number changes, and tuition payments. Since their implementation, they have not only improved the efficiency of handling frequently used services and brought convenience to users, but also freed up some counter staff.

[0003] However, when customers conduct transactions such as card activation, password or mobile number changes, and large transfers on smart self-service devices, authorization from a customer service manager on-site is often required. The smart self-service terminal system's method of sending transaction verification reminders to customer service managers' mobile devices is random, which may result in the message not being sent to the mobile device of the customer service manager closest to the self-service terminal. This dispatching method has two shortcomings:

[0004] 1. The customer service manager in charge needs to ask other customer service managers to forward the business review reminder information received on the mobile terminal device to the mobile terminal device they are logged into before the review can be carried out;

[0005] 2. The forwarding of authorization business review reminders on different mobile terminal devices affects the work efficiency of hall staff, resulting in longer customer waiting time and a poor user experience. Summary of the Invention

[0006] To address at least one problem in the prior art, this application proposes a method and apparatus for distributing business review information, which can improve the accuracy and efficiency of distributing business review reminder information, thereby enhancing the customer's business processing experience.

[0007] To address the aforementioned technical problems, this application provides the following technical solution:

[0008] Firstly, this application provides a method for distributing business review information, including:

[0009] The system receives a business review reminder message from a target smart self-service terminal in a financial institution branch. The financial institution branch is equipped with multiple smart self-service terminals and mobile terminal devices. The target smart self-service terminal is at least one of the multiple smart self-service terminals. The mobile terminal device is used for business review, and the smart self-service terminal is used for customers to conduct business.

[0010] Obtain the real-time coordinate information of each mobile terminal device;

[0011] Based on the preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, the mobile terminal device closest to the target smart self-service terminal is determined, and the business review reminder information is dispatched to that mobile terminal device.

[0012] Further, the step of determining the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, pre-acquired coordinate information of each smart self-service terminal, and real-time coordinate information of each mobile terminal device, and dispatching the business review reminder information to that mobile terminal device, includes:

[0013] Based on the preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, all mobile terminal devices are divided into multiple categories, each category corresponds to a unique smart self-service terminal, and the smart self-service terminals corresponding to each category are different.

[0014] If the number of mobile terminal devices to be tested is unique, then the mobile terminal device to be tested is determined to be the mobile terminal device closest to the target smart self-service terminal, and the business review reminder information is sent to that mobile terminal device.

[0015] The mobile terminal device to be detected is a mobile terminal device in the class corresponding to the target smart self-service terminal.

[0016] Furthermore, after determining whether the number of mobile terminal devices to be detected is unique, the method further includes:

[0017] If there are multiple mobile terminal devices to be detected, the mobile terminal device closest to the target smart self-service terminal is determined based on the real-time coordinate information of each mobile terminal device to be detected and the pre-acquired coordinate information of the target smart self-service terminal, and the business review reminder information is dispatched to that mobile terminal device.

[0018] Furthermore, before determining the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, pre-acquired coordinate information of each smart self-service terminal, and real-time coordinate information of each mobile terminal device, the method further includes:

[0019] Obtain a sample training set, which includes: batch training samples and their corresponding actual classification results. Each training sample includes: historical coordinate information of each smart self-service terminal and mobile terminal device.

[0020] The k-nearest neighbor algorithm is trained using the sample training set to obtain the classification model.

[0021] Furthermore, obtaining the real-time coordinate information of each mobile terminal device includes:

[0022] Receive wireless access point signals sent by various mobile terminal devices;

[0023] Based on the wireless access point signal, the real-time coordinate information of each mobile terminal device is determined.

[0024] Furthermore, the step of distributing the business review reminder information to the mobile terminal device includes:

[0025] The business review reminder information is sent to the mobile terminal device and displayed.

[0026] The business review reminder information includes: the coordinates of the target smart self-service terminal and the type of business to be reviewed.

[0027] Furthermore, the preset classification model is obtained by pre-training using the k-nearest neighbor algorithm.

[0028] Secondly, this application provides a business review information distribution device, comprising:

[0029] The receiving module is used to receive business review reminder information sent by a target smart self-service terminal in a financial institution branch. The financial institution branch is equipped with multiple smart self-service terminals and mobile terminal devices. The target smart self-service terminal is at least one of the multiple smart self-service terminals. The mobile terminal device is used for business review, and the smart self-service terminal is used for customers to conduct business.

[0030] The acquisition module is used to acquire real-time coordinate information of each mobile terminal device;

[0031] The dispatch module is used to determine the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, and to dispatch the business review reminder information to that mobile terminal device.

[0032] Furthermore, the distribution module includes:

[0033] The classification unit is used to divide all mobile terminal devices into multiple categories based on the preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device. Each category corresponds to a unique smart self-service terminal, and the smart self-service terminals corresponding to each category are different.

[0034] The detection unit is used to detect whether the number of mobile terminal devices to be detected is unique. If so, the mobile terminal device to be detected is determined to be the mobile terminal device closest to the target smart self-service terminal, and the business review reminder information is dispatched to the mobile terminal device. The mobile terminal device to be detected is a mobile terminal device in the class corresponding to the target smart self-service terminal.

[0035] Furthermore, the business review information distribution device also includes:

[0036] The detection module is used to determine the mobile terminal device closest to the target smart self-service terminal among the mobile terminal devices to be detected, based on the real-time coordinate information of each mobile terminal device to be detected and the pre-acquired coordinate information of the target smart self-service terminal, and to dispatch the business review reminder information to that mobile terminal device if there are multiple mobile terminal devices to be detected.

[0037] Furthermore, the business review information distribution device also includes:

[0038] The historical data acquisition module is used to acquire a sample training set, which includes: batch training samples and their corresponding actual classification results. Each training sample includes: historical coordinate information of each smart self-service terminal and mobile terminal device.

[0039] The training module is used to train the k-nearest neighbor algorithm using the sample training set to obtain the classification model.

[0040] Furthermore, the acquisition module includes:

[0041] The receiving unit is used to receive wireless access point signals sent by various mobile terminal devices;

[0042] The determining unit is used to determine the real-time coordinate information of each mobile terminal device based on the wireless access point signal.

[0043] Furthermore, the detection module is used for:

[0044] The business review reminder information is sent to the mobile terminal device and displayed.

[0045] The business review reminder information includes: the coordinates of the target smart self-service terminal and the type of business to be reviewed.

[0046] Furthermore, the preset classification model is obtained by pre-training using the k-nearest neighbor algorithm.

[0047] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the business review information distribution method.

[0048] Fourthly, this application provides a computer-readable storage medium storing computer instructions thereon, which, when executed, implement the aforementioned business review information distribution method.

[0049] As can be seen from the above technical solution, this application provides a method and apparatus for distributing business review information. The method includes: receiving business review reminder information sent by a target smart self-service terminal in a financial institution branch, wherein the financial institution branch has multiple smart self-service terminals and mobile terminal devices, the target smart self-service terminal is at least one of the multiple smart self-service terminals, the mobile terminal device is used for business review, and the smart self-service terminal is used for customer business processing; acquiring real-time coordinate information of each mobile terminal device; determining the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, and distributing the business review reminder information to that mobile terminal device. This improves the accuracy and efficiency of business review reminder information distribution, thereby enhancing the customer's business processing experience. Specifically, by embedding a KNN classification algorithm into the smart self-service terminal, the classification of mobile terminal devices and the nearest smart self-service terminal can be dynamically realized, thereby binding and linking mobile terminal devices grouped into the same category, enabling the sending of business review reminder information to the nearest mobile terminal device, reducing the forwarding of business review reminder information across different mobile terminal devices, and helping to improve the work efficiency of bank branch staff. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of the first process of the business review information distribution method in the embodiments of this application;

[0052] Figure 2 This is a schematic diagram of the second process of the business review information distribution method in the embodiments of this application;

[0053] Figure 3 This is a schematic diagram of the third process of the business review information distribution method in the embodiments of this application;

[0054] Figure 4 This is a schematic diagram of the fourth process of the business review information distribution method in the embodiments of this application;

[0055] Figure 5 This is a schematic diagram of the business review information distribution device in the embodiments of this application;

[0056] Figure 6 This is a schematic diagram illustrating the positional relationship between an intelligent self-service terminal and a PAD, as exemplified in this application.

[0057] Figure 7 This is a schematic block diagram illustrating the system configuration of an electronic device according to an embodiment of this application. Detailed Implementation

[0058] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0059] To address the problem of random assignment of review information by existing smart terminals, this application provides a method and apparatus for distributing business review information. Based on the K-Nearest Neighbor Classification (KNN) algorithm, it aims to enable business review information from smart terminals to be sent to the nearest mobile review assistant PAD, thereby optimizing the traditional random assignment mechanism for review information and improving the accuracy of matching smart terminal approval PADs.

[0060] To improve the accuracy and efficiency of business review reminder information distribution, thereby enhancing the customer's business processing experience, this application provides a business review information distribution device. This device can be a server or a client device. The client device can include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, and smart wearable devices, etc. The smart wearable devices can include smart glasses, smartwatches, and smart bracelets, etc.

[0061] In practical applications, the part that distributes business review information can be executed on the server side as described above, or all operations can be completed on the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed on the client device, the client device may further include a processor.

[0062] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0063] The server and the client device can communicate using any suitable network protocol, including network protocols not yet developed as of the date of this application. Such network protocols may include, for example, TCP / IP, UDP / IP, HTTP, HTTPS, etc. Of course, the network protocols may also include, for example, RPC (Remote Procedure Call Protocol) and REST (Representational State Transfer) protocols used on top of the above protocols.

[0064] It should be noted that the business review information distribution method and device disclosed in this application can be used in the field of financial technology, or in any field other than financial technology. The application field of the business review information distribution method and device disclosed in this application is not limited.

[0065] The following examples illustrate this in detail.

[0066] To improve the accuracy and efficiency of business review reminder information distribution, thereby enhancing the customer's business processing experience, this embodiment provides a business review information distribution method in which the execution entity is a business review information distribution device. This business review information distribution device includes, but is not limited to, a server, such as... Figure 1 As shown, this method specifically includes the following:

[0067] Step 101: Receive a business review reminder message sent by a target smart self-service terminal in a financial institution branch. The financial institution branch is equipped with multiple smart self-service terminals and mobile terminal devices. The target smart self-service terminal is at least one of the multiple smart self-service terminals. The mobile terminal device is used for business review, and the smart self-service terminal is used for customers to conduct business.

[0068] Specifically, the financial institution branch can be a bank branch, the smart self-service terminal can be a bank self-service terminal, and the mobile terminal device can be a tablet computer (portable Android device, abbreviated as PAD) carried by the account manager in the financial institution branch. The business review information distribution device can be an independent server or one of the multiple smart self-service terminals.

[0069] Step 102: Obtain the real-time coordinate information of each mobile terminal device.

[0070] Specifically, in order to assist customers with business transactions, the location of the account manager in the branch may change, and therefore the location of the mobile terminal device may also change; it can receive wireless access point signals sent by each mobile terminal device; based on the wireless access point signals, it can determine the real-time coordinate information of each mobile terminal device to improve the accuracy of determining the real-time coordinate information of each mobile terminal device.

[0071] Specifically, the real-time coordinates of the PAD can be obtained through WiFi positioning. The specific steps are as follows: 1) When the mobile audit assistant PAD is connected to Wi-Fi, it scans and collects the signals of surrounding wireless access points (APs) and obtains the MAC addresses broadcast by the APs; 2) The PAD sends this data that identifies the APs to the smart self-service terminal. The smart self-service terminal retrieves the geographical location of each AP and, combined with the strength of each signal, calculates the geographical location of the PAD, thus achieving the positioning of the PAD.

[0072] Step 103: Based on the preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, determine the mobile terminal device closest to the target smart self-service terminal, and send the business review reminder information to that mobile terminal device.

[0073] Specifically, the coordinate information of each intelligent self-service terminal is fixed and can be pre-stored locally on the business review information distribution device; the business review reminder information can be distributed to the mobile terminal device closest to the target intelligent self-service terminal. The preset classification model can be pre-trained using the k-nearest neighbor algorithm. The KNN algorithm is a machine learning algorithm that can be used for data classification. The algorithm's idea is to find the K nearest neighbors of the predicted data in a fixed dataset space, and finally use majority voting to determine the category of the predicted data.

[0074] To further improve the accuracy of matching between intelligent self-service terminals and mobile terminal devices, such as Figure 2 As shown, in one embodiment of this application, step 103 includes:

[0075] Step 201: Based on the preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, divide all mobile terminal devices into multiple categories. Each category corresponds to a unique smart self-service terminal, and the smart self-service terminals corresponding to each category are different.

[0076] Specifically, each smart self-service terminal at the branch can be a separate category. A pre-defined classification model can be used to find the randomly moving verification PAD closest to the smart self-service terminal, thus classifying the mobile verification assistant PAD and the smart terminal to obtain the optimal PAD match. Here, the value of K in the KNN algorithm can be 1. The mobile terminal device in the category corresponding to a smart self-service terminal can be the mobile terminal device closest to that smart self-service terminal.

[0077] Step 202: Check if the number of mobile terminal devices to be tested is unique. If so, proceed to step 203.

[0078] Step 203: Determine the mobile terminal device to be detected as the mobile terminal device closest to the target smart self-service terminal, and send the business review reminder information to the mobile terminal device; the mobile terminal device to be detected is a mobile terminal device in the class corresponding to the target smart self-service terminal.

[0079] Specifically, if the mobile terminal device in the class corresponding to the target smart self-service terminal is unique, then the mobile terminal device to be detected is determined to be the mobile terminal device closest to the target smart self-service terminal.

[0080] like Figure 6As shown, for example, consider three smart self-service terminals A, B, and C placed in the lobby of a branch, representing three categories, and two mobile verification assistant PADs: PAD1 and PAD2. The positions of the smart self-service terminals A, B, and C are fixed, assuming they are: A(x A ,y A B(x) B ,y B ), C(x) C ,y C The positions of the mobile verification assistant PADs are dynamic, specifically PAD1(x1,y1) and PAD2(x2,y2). When bank users activate cards, change passwords or mobile phone numbers, or make large transfers at the smart self-service terminal, after completing the information and clicking "submit for review," the business verification information distribution device uses a preset classification model and the real-time location coordinates of PAD1 and PAD2 to calculate the distances between PAD1 / PAD2 and categories A, B, and C. Then, based on the principle of closest distance, PAD1 / PAD2 is classified. The KNN algorithm calculates D based on the coordinate information. C1 <D B1 <D A1 Therefore, PAD1 belongs to category C, D A2 <D B2 <D C2 Therefore, PAD2 belongs to category A. Then, the smart self-service terminal is temporarily bound to a mobile review assistant PAD belonging to the same category, sending business review information to the temporarily bound PAD. After the review is completed, the temporary binding is released, thus enabling the smart terminal's business review information to be sent to the nearest mobile review assistant PAD, achieving an optimization and upgrade of the traditional random assignment mechanism. Furthermore, it can also be done through D... C1 <D B1 <D A1 And D C1 <D C2 PAD1 is determined to belong to category C; D A2 <D B2 <D C2 And D A2 <D A1 PAD2 was determined to belong to category A.

[0081] To further improve the efficiency of determining the mobile terminal device closest to the target smart self-service terminal, such as... Figure 3 As shown, in one embodiment of this application, after step 202, the method further includes:

[0082] Step 301: If there are multiple mobile terminal devices to be tested, then based on the real-time coordinate information of each mobile terminal device to be tested and the pre-acquired coordinate information of the target smart self-service terminal, determine the mobile terminal device closest to the target smart self-service terminal among the mobile terminal devices to be tested as the mobile terminal device closest to the target smart self-service terminal, and dispatch the business review reminder information to that mobile terminal device.

[0083] Specifically, if there are a large number of mobile terminal devices used for business verification in a financial institution's branches, the mobile terminal devices can be classified, and then the distance between each mobile terminal device to be tested and the target smart self-service terminal can be sorted from near to far based on the classification results. The mobile terminal device closest to the target smart self-service terminal among the mobile terminal devices to be tested is determined as the mobile terminal device closest to the target smart self-service terminal.

[0084] Furthermore, it can be determined whether the mobile terminal device closest to the target smart self-service terminal is currently busy. If so, the next device in the sorting is selected as the mobile terminal device closest to the target smart self-service terminal. The process continues until it is determined that the mobile terminal device closest to the target smart self-service terminal is idle, so that the business review reminder information can be sent to the idle mobile terminal device.

[0085] To obtain a reliable classification model, and then apply that model to improve the accuracy of business review information distribution, such as... Figure 4 As shown, in one embodiment of this application, the method further includes the following step before step 103:

[0086] Step 401: Obtain the sample training set, which includes: batch training samples and their corresponding actual classification results. Each training sample includes: historical coordinate information of each smart self-service terminal and mobile terminal device.

[0087] Specifically, each actual classification result may include: multiple types of smart self-service terminals, each type corresponding to a unique smart self-service terminal, and the smart self-service terminals corresponding to each type are different.

[0088] Step 402: Use the sample training set to train the k-nearest neighbor algorithm to obtain the classification model.

[0089] To facilitate timely receipt of business review reminders by account managers and prompt arrival at customer locations for business reviews, in one embodiment of this application, step 103, which involves distributing the business review reminder to the mobile terminal device, includes: distributing the business review reminder to the mobile terminal device and displaying it; the business review reminder includes: the coordinate information of the target smart self-service terminal and the type of business to be reviewed.

[0090] From a software perspective, in order to improve the accuracy and efficiency of business review reminder information distribution, thereby enhancing the customer's business processing experience, this application provides an embodiment of a business review information distribution device for implementing all or part of the aforementioned business review information distribution method. See [link to embodiment]. Figure 5 The business review information distribution device specifically includes the following components:

[0091] The receiving module 51 is used to receive a business review reminder message sent by a target smart self-service terminal in a financial institution branch. The financial institution branch is equipped with multiple smart self-service terminals and mobile terminal devices. The target smart self-service terminal is at least one of the multiple smart self-service terminals. The mobile terminal device is used for business review, and the smart self-service terminal is used for customers to handle business.

[0092] The acquisition module 52 is used to acquire the real-time coordinate information of each mobile terminal device;

[0093] The dispatch module 53 is used to determine the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, and to dispatch the business review reminder information to that mobile terminal device.

[0094] Specifically, the preset classification model can be pre-trained using the k-nearest neighbor algorithm.

[0095] In one embodiment of this application, the dispatch module includes:

[0096] The classification unit is used to divide all mobile terminal devices into multiple categories based on the preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device. Each category corresponds to a unique smart self-service terminal, and the smart self-service terminals corresponding to each category are different.

[0097] The detection unit is used to detect whether the number of mobile terminal devices to be detected is unique. If so, the mobile terminal device to be detected is determined to be the mobile terminal device closest to the target smart self-service terminal, and the business review reminder information is dispatched to the mobile terminal device. The mobile terminal device to be detected is a mobile terminal device in the class corresponding to the target smart self-service terminal.

[0098] In one embodiment of this application, the business review information distribution device further includes:

[0099] The detection module is used to determine the mobile terminal device closest to the target smart self-service terminal among the mobile terminal devices to be detected, based on the real-time coordinate information of each mobile terminal device to be detected and the pre-acquired coordinate information of the target smart self-service terminal, and to dispatch the business review reminder information to that mobile terminal device if there are multiple mobile terminal devices to be detected.

[0100] In one embodiment of this application, the business review information distribution device further includes:

[0101] The historical data acquisition module is used to acquire a sample training set, which includes: batch training samples and their corresponding actual classification results. Each training sample includes: historical coordinate information of each smart self-service terminal and mobile terminal device.

[0102] The training module is used to train the k-nearest neighbor algorithm using the sample training set to obtain the classification model.

[0103] In one embodiment of this application, the acquisition module includes:

[0104] The receiving unit is used to receive wireless access point signals sent by various mobile terminal devices;

[0105] The determining unit is used to determine the real-time coordinate information of each mobile terminal device based on the wireless access point signal.

[0106] In one embodiment of this application, the detection module is used for:

[0107] The business review reminder information is sent to the mobile terminal device and displayed.

[0108] The business review reminder information includes: the coordinates of the target smart self-service terminal and the type of business to be reviewed.

[0109] The embodiments of the business review information distribution device provided in this specification can be used to execute the processing flow of the embodiments of the above-described business review information distribution method. Its functions will not be repeated here, but can be referred to the detailed description of the embodiments of the above-described business review information distribution method.

[0110] As described above, the business review information distribution method and apparatus provided in this application can improve the accuracy and efficiency of business review reminder information distribution, thereby enhancing the customer's business experience. Specifically, by embedding the KNN classification algorithm into the smart self-service terminal, the mobile terminal device and the nearest smart self-service terminal can be dynamically classified, thereby binding and linking mobile terminal devices grouped into the same category. This enables the sending of business review reminder information to the nearest mobile terminal device, reducing the forwarding of business review reminder information across different mobile terminal devices and helping to improve the work efficiency of bank branch staff.

[0111] Figure 7 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, as shown below. Figure 7 As shown, the electronic device may include: a processor 401, a communication interface 402, a memory 403, and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404. The processor 401 can call logical instructions in the memory 403 to execute the following method: receiving a business review reminder message sent by a target smart self-service terminal in a financial institution branch, wherein the financial institution branch is equipped with multiple smart self-service terminals and mobile terminal devices, the target smart self-service terminal is at least one of the multiple smart self-service terminals, the mobile terminal device is used for business review, and the smart self-service terminal is used for customer business processing; acquiring real-time coordinate information of each mobile terminal device; determining the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, and dispatching the business review reminder message to that mobile terminal device.

[0112] Furthermore, the logical instructions in the aforementioned memory 403 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the methods provided in the above-described method embodiments, such as: receiving a business review reminder message sent by a target smart self-service terminal in a financial institution branch, wherein the financial institution branch is equipped with multiple smart self-service terminals and mobile terminal devices, the target smart self-service terminal is at least one of the multiple smart self-service terminals, the mobile terminal device is used for business review, and the smart self-service terminal is used for customer business processing; acquiring real-time coordinate information of each mobile terminal device; determining the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, and dispatching the business review reminder message to that mobile terminal device.

[0114] This embodiment provides a computer-readable storage medium storing a computer program that causes a computer to execute the methods provided in the above-described method embodiments. For example, the method includes: receiving a business review reminder message sent by a target smart self-service terminal in a financial institution branch, wherein the financial institution branch has multiple smart self-service terminals and mobile terminal devices, the target smart self-service terminal is at least one of the multiple smart self-service terminals, the mobile terminal device is used for business review, and the smart self-service terminal is used for customer business processing; acquiring real-time coordinate information of each mobile terminal device; determining the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, and dispatching the business review reminder message to that mobile terminal device.

[0115] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0120] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for distributing business review information, characterized in that, include: The system receives a business review reminder message from a target smart self-service terminal in a financial institution branch. The financial institution branch is equipped with multiple smart self-service terminals and mobile terminal devices. The target smart self-service terminal is at least one of the multiple smart self-service terminals. The mobile terminal device is used for business review, and the smart self-service terminal is used for customers to conduct business. Obtain the real-time coordinate information of each mobile terminal device; Based on the preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, the mobile terminal device closest to the target smart self-service terminal is determined, and the business review reminder information is sent to that mobile terminal device. A temporary binding relationship is established between the target smart self-service terminal and the mobile terminal device, and the temporary binding relationship is released after the mobile terminal device completes the business review. The step of determining the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, pre-acquired coordinate information of each smart self-service terminal, and real-time coordinate information of each mobile terminal device, and then dispatching the business review reminder information to that mobile terminal device, includes: Based on the preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, all mobile terminal devices are divided into multiple categories, each category corresponds to a unique smart self-service terminal, and the smart self-service terminals corresponding to each category are different. If the number of mobile terminal devices to be tested is unique, then the mobile terminal device to be tested is determined to be the mobile terminal device closest to the target smart self-service terminal, and the business review reminder information is sent to that mobile terminal device. The mobile terminal device to be detected is a mobile terminal device in the class corresponding to the target smart self-service terminal; After determining whether the number of mobile terminal devices to be detected is unique, the process further includes: If there are multiple mobile terminal devices to be detected, the mobile terminal device closest to the target smart self-service terminal is determined based on the real-time coordinate information of each mobile terminal device to be detected and the pre-acquired coordinate information of the target smart self-service terminal, and the business review reminder information is dispatched to that mobile terminal device. The preset classification model is pre-trained using the k-nearest neighbor algorithm.

2. The business review information distribution method according to claim 1, characterized in that, Before determining the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, pre-acquired coordinate information of each smart self-service terminal, and real-time coordinate information of each mobile terminal device, the method further includes: Obtain a sample training set, which includes: batch training samples and their corresponding actual classification results. Each training sample includes: historical coordinate information of each smart self-service terminal and mobile terminal device. The k-nearest neighbor algorithm is trained using the sample training set to obtain the classification model.

3. The business review information distribution method according to claim 1, characterized in that, The process of obtaining the real-time coordinate information of each mobile terminal device includes: Receive wireless access point signals sent by various mobile terminal devices; Based on the wireless access point signal, the real-time coordinate information of each mobile terminal device is determined.

4. The business review information distribution method according to claim 1, characterized in that, The step of sending the business review reminder information to the mobile terminal device includes: The business review reminder information is sent to the mobile terminal device and displayed. The business review reminder information includes: the coordinates of the target smart self-service terminal and the type of business to be reviewed.

5. A business review information distribution device, characterized in that, include: The receiving module is used to receive business review reminder information sent by a target smart self-service terminal in a financial institution branch. The financial institution branch is equipped with multiple smart self-service terminals and mobile terminal devices. The target smart self-service terminal is at least one of the multiple smart self-service terminals. The mobile terminal device is used for business review, and the smart self-service terminal is used for customers to conduct business. The acquisition module is used to acquire real-time coordinate information of each mobile terminal device; The dispatch module is used to determine the mobile terminal device closest to the target smart self-service terminal based on a preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device, and to dispatch the business review reminder information to that mobile terminal device. The relationship maintenance module is used to establish a temporary binding relationship between the target smart self-service terminal and the mobile terminal device, and the temporary binding relationship is released after the mobile terminal device completes the business review. The distribution module includes: The classification unit is used to divide all mobile terminal devices into multiple categories based on the preset classification model, the pre-acquired coordinate information of each smart self-service terminal, and the real-time coordinate information of each mobile terminal device. Each category corresponds to a unique smart self-service terminal, and the smart self-service terminals corresponding to each category are different. The detection unit is used to detect whether the number of mobile terminal devices to be detected is unique. If so, the mobile terminal device to be detected is determined to be the mobile terminal device closest to the target smart self-service terminal, and the business review reminder information is dispatched to the mobile terminal device. The mobile terminal device to be detected is a mobile terminal device in the class corresponding to the target smart self-service terminal. The device further includes: a detection module, used to determine, after detecting whether the number of mobile terminal devices to be detected is unique, if the number of mobile terminal devices to be detected is multiple, the mobile terminal device closest to the target smart self-service terminal among the mobile terminal devices to be detected is determined based on the real-time coordinate information of each mobile terminal device to be detected and the pre-acquired coordinate information of the target smart self-service terminal, and the business review reminder information is dispatched to that mobile terminal device; The preset classification model is pre-trained using the k-nearest neighbor algorithm.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the business review information distribution method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the instruction is executed, it implements the business review information distribution method according to any one of claims 1 to 4.

Images