Financial institution offline business handling resource scheduling method and device and electronic equipment

Abstract

The invention discloses a financial institution offline business handling resource scheduling method and device and electronic equipment. The method comprises the following steps: determining a target customer set of a target website waiting for business handling, and determining N windows of the target website for business handling; determining a waiting penalty coefficient based on the customer number of the target customer set, determining the initial priority of the customer based on the customer type of each customer in the target customer set, and determining the waiting duration of each customer; calculating a target priority of each customer based on the initial priority, the waiting duration and the waiting penalty coefficient of each customer, and sorting the customers in the target customer set according to a descending order of the target priorities to obtain a target sequence; and extracting the first N clients from the target sequence, and distributing the first N clients to N windows for business handling based on a preset distribution algorithm. According to the invention, the problem of long waiting time of business handling by a client in the related technology is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of financial technology, in particular to a financial institution offline business handling resource scheduling method and device and electronic equipment. BACKGROUND

[0002] With the improvement of economic conditions and the continuous emergence of financial product innovation, the financial institution network is facing increasing customer flow and increasingly complex business needs. The queuing system in the related art generally follows the FCFS (First Come First Served) principle, which ensures the fairness of the order of customers handling business to a certain extent, and also gives priority to service to specific customer groups such as privileged groups, VIP customers and appointment customers. Whenever the service window is idle, the system will automatically dispatch the next customer in the queue for service.

[0003] However, the queuing system in the related art tends to ignore the needs of ordinary customers when dealing with priority customers, especially during periods of high priority customer flow, ordinary customers may be in a waiting state for a long time and cannot handle business in time, resulting in a "hunger" phenomenon. The "hunger" phenomenon is because there are always high-priority groups to handle business, so even if many ordinary customers come early, they will also have to wait for a long time and cannot be served. This phenomenon not only affects the business handling experience of ordinary customers, but also may lead to a decline in customer satisfaction.

[0004] In view of the problem of long waiting time for customers to handle business in the related art, no effective solution has been proposed so far. SUMMARY

[0005] The main purpose of the present application is to provide a financial institution offline business handling resource scheduling method and device and electronic equipment to solve the problem of long waiting time for customers to handle business in the related art.

[0006] To achieve the above object, according to one aspect of the present application, a financial institution offline business handling resource scheduling method is provided. The method comprises: determining a target customer set waiting to handle business at a target site, and determining N windows for handling business at the target site, wherein N is a positive integer; determining a waiting penalty coefficient based on the number of customers in the target customer set, determining an initial priority of each customer in the target customer set based on the customer type of each customer, and determining the waiting time of each customer; calculating the target priority of each customer based on the initial priority, the waiting time and the waiting penalty coefficient of each customer, sorting the customers in the target customer set in descending order of the target priority to obtain a target sequence; extracting the first N customers from the target sequence, and allocating the first N customers to the N windows to handle business based on a preset allocation algorithm, wherein each window handles business for one customer at a time.

[0007] Optionally, calculating the target priority of each customer based on the initial priority, the waiting time and the waiting penalty coefficient of each customer comprises: for each customer, inputting the waiting time of the customer into a preset logarithmic function to obtain a target logarithmic value; calculating the product of the target logarithmic value and the waiting penalty coefficient to obtain a dynamic priority; and calculating the sum of the initial priority and the dynamic priority to obtain the target priority of the customer.

[0008] Optionally, allocating the first N customers to the N windows to handle business based on the preset allocation algorithm comprises: for each window, determining the average handling time of each type of business handled by the window to obtain a set of average times; determining the target business type of the business that each customer in the first N customers needs to handle to obtain N target business types; determining the first time saved by the window for handling the same type of business compared to handling different types of business, and determining the second time saved by the window for handling business for the same customer compared to handling business for different customers within a preset period; calculating the expected time for the window to handle the business of each customer based on the set of average times, the N target business types, the first time and the second time respectively to obtain N expected times; constructing a handling time matrix based on the N expected times of the N windows, processing the handling time matrix through a bipartite graph matching algorithm to obtain a target allocation scheme, and allocating the first N customers to the N windows to handle business based on the target allocation scheme, wherein the target allocation scheme is the allocation scheme with the minimum total expected time for the N windows to handle business.

[0009] Optionally, calculating the expected time for the window to handle the business of each customer based on the set of average times, the N target business types, the first time and the second time comprises: for each customer, calculating the sum of the first time and the second time corresponding to the customer to obtain a total saved time; and calculating the difference between the average time corresponding to the target business type of the business that the customer needs to handle and the total saved time to obtain the expected time for the customer to handle business at the window.

[0010] Optionally, determining the initial priority of a customer based on the customer type of each customer in the target customer set includes: for each customer, if the customer type is type 1, the initial priority of the customer is determined to be a first value, where type 1 is the type of customer for which the target branch needs to prioritize processing business; if the customer type is type 2, the initial priority of the customer is determined to be a second value, where the second value is less than the first value, where type 2 is the type of customer for which the target branch has marked a preset customer tag; if the customer type is an appointment customer, the initial priority of the customer is determined to be a third value, where the third value is less than the second value; and if the customer type is an unappointed customer, the initial priority of the customer is determined to be a fourth value, where the fourth value is less than the third value.

[0011] Optionally, determining the waiting penalty coefficient based on the number of customers in the target customer set includes: determining whether the number of customers is greater than or equal to a first quantity threshold; if the number of customers is greater than or equal to the first quantity threshold, determining the waiting penalty coefficient as the first coefficient; if the number of customers is less than the first quantity threshold, determining whether the number of customers is greater than or equal to a second quantity threshold, wherein the second quantity threshold is less than the first quantity threshold; if the number of customers is greater than or equal to the second quantity threshold, determining the waiting penalty coefficient as the second coefficient, wherein the second coefficient is less than the first coefficient; if the number of customers is less than the second quantity threshold, determining the waiting penalty coefficient as a third coefficient, wherein the third coefficient is less than the second coefficient.

[0012] Optionally, determining the initial priority of customers based on the customer type of each customer in the target customer set further includes: for customers of the same customer type in the target customer set, determining the queue number of each customer; and determining the initial priority of each customer in ascending order of the queue number, wherein the initial priority of the customer with the smaller queue number is greater than the initial priority of the customer with the larger queue number.

[0013] To achieve the above objectives, according to another aspect of this application, a resource scheduling device for offline business processing in financial institutions is provided. The device includes: a first determining unit, configured to determine a target customer set waiting for business processing at a target branch, and to determine N windows at the target branch for processing business, where N is a positive integer; a second determining unit, configured to determine a waiting penalty coefficient based on the number of customers in the target customer set, determine the initial priority of each customer based on the customer type in the target customer set, and determine the waiting time for each customer; a calculation unit, configured to calculate the target priority of each customer based on the initial priority, waiting time, and waiting penalty coefficient, and sort the customers in the target customer set in descending order of target priority to obtain a target sequence; and an allocation unit, configured to extract the first N customers from the target sequence and allocate the first N customers to the N windows for business processing based on a preset allocation algorithm, wherein each window processes one customer at a time.

[0014] In this embodiment, a target customer set waiting for business at a target branch is determined, and N windows at the target branch are selected for business processing, where N is a positive integer. A waiting penalty coefficient is determined based on the number of customers in the target customer set, and the initial priority of each customer is determined based on their customer type. The waiting time for each customer is also determined. A target priority for each customer is calculated based on their initial priority, waiting time, and waiting penalty coefficient. Customers in the target customer set are then sorted in descending order of target priority to obtain a target sequence. The top N customers are extracted from the target sequence and assigned to N windows for business processing based on a preset allocation algorithm. Each window processes one customer at a time. By dynamically calculating the target priority and sorting customers in descending order of target priority, this approach reflects the requirement for high-priority service for priority customers while avoiding the "starvation" phenomenon where ordinary customers cannot receive service due to long waiting times. By using a preset allocation algorithm to allocate windows, the utilization efficiency of window resources can be maximized, thereby improving business processing speed and customer satisfaction. This achieves the technical effect of reducing the waiting time for customers to handle business, and thus solves the technical problem of long waiting time for customers to handle business. Attached Figure Description

[0015] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing a resource scheduling method for offline business processing in financial institutions is shown.

[0017] Figure 2 This is a flowchart of a resource scheduling method for offline business processing in financial institutions, provided in the embodiments of this application.

[0018] Figure 3 This is a schematic diagram of a resource scheduling system for offline business processing of financial institutions provided in the embodiments of this application;

[0019] Figure 4 This is a schematic diagram of a resource scheduling device for offline business processing of financial institutions provided in the embodiments of this application;

[0020] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand 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. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0024] Example 1

[0025] According to the embodiments of this application, a method embodiment for resource scheduling of offline business processing of financial institutions is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing a resource scheduling method for offline business processing in financial institutions is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, processing devices such as MCU (Microcontroller Unit) or FPGA (Field-Programmable Gate Array), memory 104 for storing data, and transmission device 106 for communication functions. In addition, it may also include: a display, input / output interfaces (I / O interfaces), a USB (Universal Serial Bus) port (which may be included as one of the ports of a BUS (Business Bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0027] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0028] 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 resource scheduling method for offline business processing of financial institutions in this embodiment of the application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned resource scheduling method for offline business processing of financial institutions. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0029] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0030] The display may be, for example, a touchscreen LCD display that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0031] Under the aforementioned operating environment, this application provides a method for resource scheduling in offline business processing for financial institutions. Figure 2 This is a flowchart of a resource scheduling method for offline business processing in financial institutions provided in an embodiment of this application, such as... Figure 2 As shown, the method includes:

[0032] Step S201: Determine the set of target customers waiting to process business at the target outlet, and determine the N windows at the target outlet for processing business, where N is a positive integer.

[0033] In step S201, the target branch can be a branch of a financial institution, and the target customer set refers to the set of all customers who need to wait at the financial institution branch to conduct business at a certain point in time. This is achieved through real-time monitoring of ticket dispensers, online banking appointment systems, mobile banking applications, and other customer information sources. Information on all customers who have already obtained a ticket or made an appointment is collected from these sources and integrated into the target customer set. The number of service windows is limited by the branch size, staffing, current staff status (e.g., mealtime, rest), and window opening schedule. By accessing the branch's staffing system and window management module, it is possible to obtain in real-time information on which windows are currently available and which tellers are on standby to serve customers.

[0034] Step S202: Determine the waiting penalty coefficient based on the number of customers in the target customer set, determine the initial priority of each customer based on the customer type of each customer in the target customer set, and determine the waiting time for each customer.

[0035] In step S202, the waiting penalty coefficient is a parameter used to adjust customer priority, reflecting the degree to which waiting time affects customer priority. As the number of customers increases, the waiting penalty coefficient also increases accordingly to prevent "starvation," ensuring that customers who have waited a long time eventually receive service. The waiting penalty coefficient is dynamically adjusted based on the number of customers in the current target customer set. For example, if the number of customers is small, the waiting penalty coefficient can be set to a lower value; when the number of customers increases, especially when the number of priority customers (such as bank preferential groups, VIP customers, etc.) increases significantly, the waiting penalty coefficient should be increased to balance the impact of waiting time on the priority of ordinary customers and prevent "starvation." The waiting penalty coefficient can be set based on statistical analysis of the number of customers or based on the frequency of "starvation" in historical data. For example, if historical data shows that the waiting time for ordinary customers increases significantly when the number of customers exceeds 100, then when the number of customers approaches or exceeds 100, the system can automatically adjust the waiting penalty coefficient to a higher value, such as 0.3 or higher.

[0036] A customer's initial priority is directly determined by their customer type. For example, customers in the bank's preferential treatment programs, VIP customers, and those with appointments will have a higher initial priority than regular customers. Each customer's waiting time is the difference between their arrival time at the branch (or the time they begin waiting online) and the current system time.

[0037] Step S203: Calculate the target priority of each customer based on the initial priority, waiting time and waiting penalty coefficient of each customer, and sort the customers in the target customer set in descending order of target priority to obtain the target sequence.

[0038] In step S203, the waiting time is multiplied by the waiting penalty coefficient to calculate the waiting priority. The waiting priority is statistically calculated by adding or multiplying the initial priority to obtain the target priority. For example, after calculation, Wang Er's target priority is 4.11, Li Si's target priority is about 2.96, and Zhang San's target priority is 1.22. The calculation results are sorted in descending order of target priority to generate a target sequence. This sorting ensures that high-priority customers (such as those receiving preferential treatment at the bank) enjoy priority services while ordinary customers who have been waiting for a long time can also receive a reasonable priority upgrade, preventing the occurrence of "hunger".

[0039] Step S204: Extract the first N customers from the target sequence, and assign the first N customers to N windows for business processing based on a preset allocation algorithm, wherein each window processes business for one customer at a time.

[0040] In step S204, the "first N customers" refers to the top N customers in the sequence after sorting by target priority from highest to lowest. The value of N represents the number of service windows currently available, ensuring that every window has customers waiting to be served during each system scheduling, while also avoiding resource idleness or over-allocation. Starting from the first customer in the target sequence, N customers are continuously extracted to form a list of customers to be assigned. These customers have the highest priority, and their waiting time has been reasonably compensated; they are the customer group that should be prioritized for service. These customers are then assigned to open windows. Considering factors such as teller processing capabilities, business proficiency, and customer service history, the system uses a preset allocation algorithm, such as the Hungarian algorithm, to achieve efficient and fair window allocation.

[0041] The Hungarian algorithm is a classic algorithm for solving the bipartite graph minimum cost allocation problem. Here, it is used to find the lowest-cost (most efficient) match between each teller window and a customer. A cost matching matrix is ​​constructed based on each teller's processing capabilities and permissions, their proficiency, and the customer's historical service records. Rows in the matrix represent customers, columns represent windows, and each element represents the expected cost or time for a specific customer to complete their transaction at a specific window. By executing the Hungarian algorithm, a minimum-cost customer and service window matching scheme is found.

[0042] Each teller window will focus on serving only one customer until the transaction is completed. By matching customers with the tellers most skilled in handling their transactions, service efficiency can be significantly improved, customer wait times can be reduced, and tellers' own transaction processing capabilities can be better utilized.

[0043] The resource scheduling method for offline business processing of financial institutions provided in this application involves determining a target customer set waiting for business processing at a target branch, and determining N windows at the target branch for business processing, where N is a positive integer; determining a waiting penalty coefficient based on the number of customers in the target customer set, determining the initial priority of each customer based on the customer type in the target customer set, and determining the waiting time for each customer; calculating the target priority of each customer based on the initial priority, waiting time, and waiting penalty coefficient, and sorting the customers in the target customer set in descending order of target priority to obtain a target sequence; extracting the top N customers from the target sequence, and allocating the top N customers to N windows for business processing based on a preset allocation algorithm, where each window processes one customer at a time. By dynamically calculating the target priority and sorting the customers for business processing in descending order of target priority, this method reflects the requirement for high-priority service for priority customers and avoids the "starvation" phenomenon where ordinary customers cannot receive service due to long waiting times. By using a preset allocation algorithm to allocate windows, the utilization efficiency of window resources can be maximized, thereby improving business processing speed and customer satisfaction. This achieves the technical effect of reducing the waiting time for customers to handle business, and thus solves the technical problem of long waiting time for customers to handle business.

[0044] To balance priority customers with those experiencing excessively long wait times, a target priority is calculated based on the initial priority, wait duration, and wait penalty coefficient. Optionally, in the resource scheduling method for offline business processing of financial institutions provided in this application embodiment, calculating the target priority for each customer based on the initial priority, wait duration, and wait penalty coefficient includes: for each customer, inputting the customer's wait duration into a preset logarithmic function to obtain a target logarithmic value; calculating the product of the target logarithmic value and the wait penalty coefficient to obtain a dynamic priority; and calculating the sum of the initial priority and the dynamic priority to obtain the customer's target priority.

[0045] In some embodiments, priority customers are determined using a multi-level priority queue with dynamic priority values: for all waiting customers, their current priority value is calculated according to the following formula, and the customers with the highest priority in the current batch are selected based on their priority values ​​(e.g., the first n customers in the waiting queue). The formula for calculating the target priority is as follows:

[0046] P n = p + α·ln(1 + w);

[0047] Where p is the customer's initial priority value, a is the customer's arrival time, t is the current time, w is the current customer waiting time, w = t – a; α is the waiting penalty coefficient, used to control the degree of influence of waiting time on priority, P n This is the customer's target priority value; the higher the value, the higher the priority. Once the customer reaches the target location, their arrival time 'a' is determined. As time passes, the current time 't' increases, and the customer's waiting time 'w = ta' increases. The initial priority value 'p' is also a constant; therefore, when the penalty coefficient takes different values, P... n The change will differ from the change in waiting time w. When the waiting penalty coefficient α is 0, it becomes a static multi-level priority queue, with preferential customers, VIP customers, and appointment customers having higher priority values. If these types of users arrive continuously, ordinary users will be unable to receive service, resulting in a "starvation" phenomenon.

[0048] When the waiting penalty coefficient α is greater than 0, as the customer's waiting time w increases, the customer's priority value increases, resulting in a higher priority. This prevents the customer from waiting for an extended period without receiving service, thus avoiding a "starvation" phenomenon. The larger the value of α, the greater the impact of the waiting time w on priority. The key term in the formula uses a logarithmic function with a natural number base, which smooths the rate of increase and prevents an explosive increase in priority as the waiting time grows, thus avoiding disruption to scheduling.

[0049] This embodiment uses a target priority calculation method that assigns initial priority, waiting time, and waiting penalty coefficient to each customer. This enables branches to achieve fairer and more efficient service allocation, thereby improving customer experience and branch business processing efficiency.

[0050] After selecting N customers, a pre-defined allocation algorithm is used to assign windows for business processing to each customer. Optionally, in the offline business processing resource scheduling method for financial institutions provided in this application embodiment, allocating the first N customers to N windows based on the pre-defined allocation algorithm includes: for each window, determining the average processing time for each type of business, obtaining a set of average times; determining the target business type for each of the first N customers, obtaining N target business types; and determining the first time saved by processing the same type of business at a window compared to processing different types of business. The second time saved by a window in processing business for the same customer within a preset period compared to processing business for different customers is determined. Based on a set of average time, N target business types, the first time and the second time, the expected time for processing business for each customer at the window is calculated, resulting in N expected time. A processing time matrix is ​​constructed based on the N expected time of the N windows. The processing time matrix is ​​processed by a bipartite graph matching algorithm to obtain a target allocation scheme. Based on the target allocation scheme, the top N customers are allocated to N windows for processing business. The target allocation scheme is the allocation scheme with the minimum total expected time for processing business at N windows.

[0051] In some embodiments, the preset allocation algorithm can be the Hungarian algorithm. After a batch of priority service objects (let's say n) are determined through a multi-level priority queue equipped with dynamic priority values, a matching cost matrix is ​​constructed for this batch of customers to be allocated.

[0052] C = [c ij ];

[0053] c ij =T ij -λ·T s -μ·T r ;

[0054] Among them, c ij This is the expected time required for bank teller j to assist customer i with this transaction, which can range from 1 to 60 minutes, depending on the values ​​of other variables in the formula; T ij This is the average time required for a bank teller (j) to assist a customer (i) with their transaction. It can range from 1 to 60 minutes. If the teller lacks the skill or authority, it is set to 9999. s This refers to the time saved when the teller's previous task was of the same type as this task, i.e., the first time limit, which can be 0-30 minutes; μ·T rThis refers to the time saved by the teller in processing related transactions for customers recently, also known as the second time limit, for example, 0-30 minutes. Processing Time Matrix C: This matrix consists of the processing times for all possible combinations of teller j and customer i. For example, there are 3 tellers, designated E1, E2, and E3, and 3 customers, designated Q1, Q2, and Q3. An example of a processing time matrix is ​​shown in Table 1.

[0055] Table 1

[0056] Customer / Teller E1 E2 E3 Q1 8 10 9999 Q2 9999 14 12 Q3 8 7 9999

[0057] The values ​​in the table represent the time required for a teller to process a transaction for a customer. A value of 9999 indicates that the teller does not yet possess the skill or authority to process that transaction. The table shows that the overall transaction processing time is minimized when customer Q1 is processed by teller E1, customer Q2 by teller E3, and customer Q3 by teller E2, thus achieving the optimal overall customer service experience. The optimal matching relationship can be mathematically represented as: (Q1, E1), (Q2, E3), (Q3, E1).

[0058] The Hungarian algorithm achieves this optimal matching solution as follows: First, subtract the minimum value of each row, and then subtract the minimum value of each column to create a cost matrix containing zeros. Next, cover all zero elements with the fewest possible horizontal and vertical lines. If the number of lines used equals the matrix dimension, then the optimal allocation has been found. Otherwise, find the minimum value among the uncovered elements and adjust the matrix: subtract the value from uncovered elements and add the value to double-covered elements to further optimize the distribution of zero elements. Repeat the covering and adjustment process until only n lines are needed to cover all zeros. Then, select non-conflicting one-to-one matches among these zeros to finally obtain the optimal allocation scheme with the minimum total cost.

[0059] This embodiment uses a preset allocation algorithm to assign windows for customers to handle their business. It takes into account factors such as whether the teller has the corresponding business handling ability and authority, differences in proficiency, whether adjacent business is the same type, and whether the teller has recently handled business for the customer, so as to optimize the overall service efficiency for customers and improve business handling efficiency.

[0060] Optionally, in the resource scheduling method for offline business processing of financial institutions provided in the embodiments of this application, the calculation of the expected time for each customer to process business at the window based on a set of average time, N target business types, a first time and a second time includes: for each customer, calculating the sum of the first time and the second time corresponding to the customer to obtain the total time saved; calculating the difference between the average time corresponding to the target business type that the customer needs to process and the total time saved to obtain the expected time for the customer to process business at the window.

[0061] In some embodiments, for each window, the system needs to first determine the average time for handling each type of business, which can be based on historical data statistics. For example, the average time for window 1 to handle "deposit" business is 10 minutes, the average time for handling "loan consultation" business is 25 minutes, and so on, forming a set of average times. The target business type for each of the first N customers is determined, resulting in N target business types. This is achieved by the business type selected by the customer when taking a number or by the business type automatically identified by the customer identification module. The system also considers the time saved when handling the same business type compared to handling different business types. For example, if window 1 handles two "deposit" businesses consecutively, 2 minutes of preparation time can be saved because the teller is familiar with the process, i.e., the first time saving is 2 minutes. Finally, the system considers the time saved when handling business for the same customer within a preset period compared to handling business for different customers. For example, if window 1 handles "deposit" and "withdrawal" businesses for customer A consecutively within an hour, 3 minutes can be saved because customer A does not need to re-verify their identity, i.e., the second time saving is 3 minutes.

[0062] For each customer, calculate the sum of their first and second time periods to obtain the total time saved. Then, calculate the difference between the average time for the customer's target business type and the total time saved; this is the expected time for the customer to complete the transaction at the window. For example, for customer X: target business type: "deposit," the average time for window 1 to process "deposit" transactions is 10 minutes. First time saving: 2 minutes (assuming the previous customer at window 1 also processed a "deposit" transaction). Second time saving: 3 minutes (assuming window 1 processed the same customer continuously in the previous cycle). Expected time for customer X to complete the transaction at window 1: (10 - (2 + 3) = 5) minutes.

[0063] This embodiment uses a set of methods for calculating the expected duration of average processing time, target business type, first duration, and second duration to determine the allocation strategy between the window with the highest processing efficiency and the customer. This allows bank branches to significantly improve operational efficiency and provide customers with more efficient, personalized, and fair services.

[0064] The initial priority of a customer is determined by the customer type. Optionally, in the resource scheduling method for offline business processing of financial institutions provided in this application embodiment, determining the initial priority of a customer based on the customer type of each customer in the target customer set includes: for each customer, if the customer type is a first type, the initial priority of the customer is determined to be a first value, wherein the first type is the type of customer for which the target branch needs to prioritize business processing; if the customer type is a second type, the initial priority of the customer is determined to be a second value, wherein the second value is less than the first value, wherein the second type is the type of customer for which the target branch has marked a preset customer tag; if the customer type is an appointment customer, the initial priority of the customer is determined to be a third value, wherein the third value is less than the second value; if the customer type is an unappointed customer, the initial priority of the customer is determined to be a fourth value, wherein the fourth value is less than the third value.

[0065] In some embodiments, the first type of customer can be a priority customer, a special group requiring priority service at the target branch, such as bank privileged groups (military personnel, disabled persons, senior citizens, etc.). Their business processing has the highest priority, with an initial priority value of the first value. The second type of customer can be a marked customer, such as a VIP customer, with an initial priority value of the second value, falling between the first and third values. Appointment customers have an initial priority value of the third value, falling between the second and fourth values. Appointment customers have booked their service time in advance, therefore they should also enjoy a higher priority than non-appointment customers. Non-appointment customers have an initial priority value of the fourth value, the lowest initial priority. These customers have not made an appointment in advance, therefore they have the lowest service priority.

[0066] This embodiment optimizes branch operations and improves overall service quality by quickly and accurately assigning initial priorities to each customer. It utilizes a multi-priority queue data structure to ensure a first-come, first-served principle, guaranteeing fairness to all customers, while also assigning different priorities to bank priority groups, VIP customers, and appointment customers to ensure a positive experience for them.

[0067] To avoid excessively long customer waiting times, the waiting penalty coefficient is dynamically adjusted based on the number of customers. Optionally, in the resource scheduling method for offline business processing of financial institutions provided in this application embodiment, determining the waiting penalty coefficient based on the number of customers in the target customer set includes: determining whether the number of customers is greater than or equal to a first quantity threshold; if the number of customers is greater than or equal to the first quantity threshold, determining the waiting penalty coefficient as a first coefficient; if the number of customers is less than the first quantity threshold, determining whether the number of customers is greater than or equal to a second quantity threshold, wherein the second quantity threshold is less than the first quantity threshold; if the number of customers is greater than or equal to the second quantity threshold, determining the waiting penalty coefficient as a second coefficient, wherein the second coefficient is less than the first coefficient; if the number of customers is less than the second quantity threshold, determining the waiting penalty coefficient as a third coefficient, wherein the third coefficient is less than the second coefficient.

[0068] In some embodiments, the values ​​of the core term αlin(1+w) are shown in Table 2 when the waiting penalty coefficient α takes different values:

[0069] Table 2

[0070] w (minutes) α＝0.1 α＝0.2 α＝0.3 α＝0.4 α＝0.5 α＝0.6 0 0 0 0 0 0 0 5 0.179 0.358 0.538 0.717 0.896 1.075 10 0.240 0.480 0.719 0.959 1.199 1.439 30 0.343 0.687 1.030 1.374 1.717 2.060 60 0.411 0.822 1.233 1.644 2.055 2.467 90 0.451 0.902 1.353 1.804 2.255 2.707 120 0.480 0.959 1.439 1.918 2.398 2.877 180 0.520 1.040 1.560 2.079 2.599 3.119

[0071] The target branch can adjust the value of coefficient α based on customer traffic to ensure a priority experience for key customers while optimizing the overall customer experience. First, two quantity thresholds are set: a first threshold (higher) and a second threshold (lower), where the second threshold is lower than the first. When the number of customers reaches or exceeds the first threshold, the system detects significant customer flow pressure. To ensure that ordinary customers do not experience long wait times, the waiting penalty coefficient is set to a higher value (the first coefficient). Even if ordinary customers have not yet reached the high-priority level, their priority will be increased more quickly due to the increased waiting time.

[0072] When the number of customers falls between the first and second thresholds, the system determines the customer flow pressure to be moderate. In this case, the waiting penalty coefficient is set to a middle value (the second coefficient), which is lower than the first coefficient but still moderately increases the priority of ordinary customers to prevent "starvation," without excessively increasing it and causing a decline in service quality for high-priority customers. When the number of customers falls below the second threshold, the customer flow pressure is lower, and the waiting penalty coefficient is set to a lower value (the third coefficient). Ordinary customers need to wait a longer time before their priority is significantly increased, thus prioritizing service for high-priority customers and ensuring service quality.

[0073] This embodiment, by flexibly adjusting the waiting penalty coefficient, can balance the service experience of high-priority customers and ordinary customers under different customer traffic scenarios, preventing the "starvation" phenomenon while maintaining high overall service efficiency. In high customer traffic scenarios, quickly elevating the priority of ordinary customers can alleviate queuing pressure, while in low customer traffic scenarios, maintaining the service quality of high-priority customers is more important.

[0074] The initial priority for customers of the same type is set based on the order of queuing. Optionally, in the resource scheduling method for offline business processing of financial institutions provided in this application embodiment, determining the initial priority of customers based on the customer type of each customer in the target customer set further includes: determining the queue number of each customer for customers of the same type in the target customer set; determining the initial priority of each customer in ascending order of queue number, wherein the initial priority of the customer with the smaller queue number is greater than the initial priority of the customer with the larger queue number.

[0075] In some embodiments, upon customer arrival and registration, a queue number is assigned to each customer based on their customer type (e.g., preferential banking customers, VIP customers, appointment customers, regular customers). This queue number should be unique and used for subsequent service allocation and priority calculation. After queue numbers are assigned, initial priority is calculated for customers of the same customer type based on their queue numbers. Customers with smaller queue numbers have higher initial priority, and customers with larger queue numbers have lower initial priority. During the customer's waiting period, customer priority can be dynamically adjusted based on factors such as waiting time and business needs to further optimize service allocation. For example, customers with longer waiting times may receive higher priority due to an increased "waiting penalty coefficient," thereby accelerating the service process. When a service window becomes available, the customer with the highest priority is selected for service based on the adjusted priority. In the case of equal priority, customers with smaller queue numbers are given priority.

[0076] This embodiment sets an initial priority for customers of the same type by determining the order in which they take their numbers, ensuring that customers who arrive first within the same customer type receive priority service, thus reflecting fairness.

[0077] According to another embodiment of this application, a resource scheduling system for offline business processing of financial institutions is also provided. Figure 3 This is a schematic diagram of a resource scheduling system for offline business processing of financial institutions provided in the embodiments of this application, such as... Figure 3 As shown, the system includes: a number dispensing module, an identity recognition module, a manual review module, a core scheduling module, a scheduling status visualization module, a window and personnel configuration module, a business configuration module, and a number calling reminder module.

[0078] The queuing number module is used to obtain a queue number. This module can be configured on online banking, mobile clients, WeChat mini-programs, and offline queuing machines. Customers select the service they wish to conduct through this module and obtain a queue number. For appointment-based services, an appointment number can also be obtained, giving the queuing number a higher priority and specifying the service time slot.

[0079] Identity verification module: Used to verify customer identity and ensure real-name registration for queuing. For bank VIP customers, their identity can be automatically verified using their ID card information, and their queuing number can be assigned a higher priority. For preferential treatment groups, if they have corresponding electronic documents that can be verified through external system interfaces, their identity can also be automatically verified, and their queuing number can be assigned a higher priority.

[0080] Manual verification module: Used for manual verification when online identity recognition fails. For priority groups, if only physical identification is available, verification can also be conducted offline at a bank branch. Once verified, the customer's queue number will be assigned a higher priority.

[0081] Core Scheduling Module: This module implements the core scheduling functions of the system. Based on a multi-priority queue with an anti-starvation mechanism and the Hungarian algorithm, it ensures higher service priority for preferential customers, VIP clients, and those with appointments, while preventing starvation and guaranteeing a smooth transaction experience for ordinary customers. It also considers factors such as teller competence and permissions, proficiency levels, whether adjacent transactions are similar, and whether the customer has recently been served, thereby optimizing overall customer service efficiency.

[0082] The scheduling visualization module is used to view the current scheduling status. It includes the number of customers currently queuing for preferential treatment, VIP customers, customers with appointments, and regular customers, as well as the availability and waiting list of each service window. For customers who have urgent matters and choose not to wait, bank staff can remove their queue numbers from this module, allowing the system to calculate the optimal scheduling plan based on the latest queue information.

[0083] Window and Staff Configuration Module: This module is used in the bank's back-end system. Banks can configure the number of open windows, window numbers, staff providing services at each window, the staff's existing business processing skills and permissions, and the average processing time for each service.

[0084] Business Configuration Module: Used to configure the types of services that the bank can provide. Considering the different sizes and positioning of different bank branches, as well as personnel changes, the bank needs to dynamically adjust the types of services that can be handled.

[0085] Queue Number Reminder Module: This module is used to remind customers when their queue number is called. It consists of a display screen, a speaker, and corresponding system control software. The display screen shows the current queue status, including the total number of people waiting in line, the queue numbers of the first 10 customers to be served, and an announcement via the speaker when it's a customer's turn.

[0086] This embodiment optimizes the queuing mechanism at bank branches through the offline business processing resource scheduling system. It generally implements a first-come, first-served principle, ensuring fairness for all customers. Simultaneously, it assigns different priorities to preferential customers, VIP customers, and pre-booked customers, ensuring a positive experience for key clients. It takes into account factors such as teller's ability and authority to handle the relevant business, differences in proficiency, whether adjacent transactions are similar, and whether the customer has recently been served, thereby optimizing overall customer service efficiency and reducing the workload of frontline staff.

[0087] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0088] Example 2

[0089] This application also provides a resource scheduling device for offline business processing in financial institutions. It should be noted that this resource scheduling device can be used to execute the resource scheduling method for offline business processing in financial institutions provided in this application. The following describes the resource scheduling device for offline business processing in financial institutions provided in this application.

[0090] According to an embodiment of this application, an apparatus for implementing the above-described resource scheduling method for offline business processing in financial institutions is also provided. Figure 4 This is a schematic diagram of a resource scheduling device for offline business processing in financial institutions provided in the embodiments of this application, such as... Figure 4 As shown, the device includes:

[0091] The first determining unit 401 is used to determine the set of target customers waiting to handle business at the target outlet, and to determine the N windows at the target outlet for handling business, where N is a positive integer;

[0092] The second determining unit 402 is used to determine the waiting penalty coefficient based on the number of customers in the target customer set, determine the initial priority of the customer based on the customer type of each customer in the target customer set, and determine the waiting time of each customer.

[0093] The calculation unit 403 is used to calculate the target priority of each customer based on the initial priority, waiting time and waiting penalty coefficient of each customer, and sort the customers in the target customer set in descending order of target priority to obtain the target sequence;

[0094] The allocation unit 404 is used to extract the first N customers from the target sequence and allocate the first N customers to N windows for business processing based on a preset allocation algorithm, wherein each window processes business for one customer at a time.

[0095] The offline business processing resource scheduling device for financial institutions provided in this application embodiment, through a first determining unit 401, determines the target customer set waiting for business processing at the target branch and determines N windows at the target branch for business processing, where N is a positive integer; a second determining unit 402 determines a waiting penalty coefficient based on the number of customers in the target customer set, determines the initial priority of each customer based on the customer type in the target customer set, and determines the waiting time for each customer; a calculation unit 403 calculates the target priority of each customer based on the initial priority, waiting time, and waiting penalty coefficient, and sorts the customers in the target customer set in descending order of target priority to obtain a target sequence; an allocation unit 404 extracts the first N customers from the target sequence and allocates the first N customers to N windows for business processing based on a preset allocation algorithm, where each window processes one customer at a time. By dynamically calculating the target priority and sorting the customers for business processing in descending order of target priority, it reflects the requirement of high-priority service for priority customers and avoids the "starvation" phenomenon of ordinary customers not being able to receive service due to long waiting times. By using a preset allocation algorithm to allocate windows, the utilization efficiency of window resources can be maximized, thereby improving business processing speed and customer satisfaction. This achieves the technical effect of reducing the waiting time for customers to handle business, and thus solves the technical problem of long waiting time for customers to handle business.

[0096] Optionally, in the resource scheduling device for offline business processing of financial institutions provided in this application embodiment, the calculation unit 403 includes: an input module, used to input the customer's waiting time into a preset logarithmic function for each customer to obtain a target logarithmic value; a first calculation module, used to calculate the product of the target logarithmic value and the waiting penalty coefficient to obtain a dynamic priority; and a second calculation module, used to calculate the sum of the initial priority and the dynamic priority to obtain the customer's target priority.

[0097] Optionally, in the offline business processing resource scheduling device for financial institutions provided in this application embodiment, the allocation unit 404 includes: a first determining module, used to determine the average processing time for each type of business at each window, obtaining a set of average times; a second determining module, used to determine the target business type for each of the top N customers, obtaining N target business types; a third determining module, used to determine the first time saved by handling the same type of business at the window compared to handling different types of business, and to determine the second time saved by handling the same customer's business at the window within a preset period compared to handling different customers' business; a third calculation module, used to calculate the expected time for handling each customer's business at the window based on a set of average times, N target business types, the first time, and the second time, obtaining N expected times; and an allocation module, used to construct a processing time matrix based on the N expected times of the N windows, process the processing time matrix using a bipartite graph matching algorithm to obtain a target allocation scheme, and allocate the top N customers to N windows for business processing based on the target allocation scheme, wherein the target allocation scheme is the allocation scheme with the minimum total expected time for handling business at the N windows.

[0098] Optionally, in the offline business processing resource scheduling device for financial institutions provided in this application embodiment, the third calculation module includes: a first calculation submodule, used to calculate the sum of the first time and the second time corresponding to each customer to obtain the total saved time; and a second calculation submodule, used to calculate the difference between the average time corresponding to the target business type that the customer needs to process and the total saved time to obtain the expected time for the customer to process the business at the window.

[0099] Optionally, in the offline business processing resource scheduling device for financial institutions provided in this application embodiment, the second determining unit 402 includes: a fourth determining module, used for determining the initial priority of each customer as a first value when the customer type is a first type, wherein the first type is the type of customer for which the target branch needs to prioritize business processing; a fifth determining module, used for determining the initial priority of the customer as a second value when the customer type is a second type, wherein the second value is less than the first value, and the second type is the type of customer for which the target branch has marked a preset customer tag; a sixth determining module, used for determining the initial priority of the customer as a third value when the customer type is an appointment customer, wherein the third value is less than the second value; and a seventh determining module, used for determining the initial priority of the customer as a fourth value when the customer type is an unappointed customer, wherein the fourth value is less than the third value.

[0100] Optionally, in the resource scheduling device for offline business processing of financial institutions provided in this application embodiment, the second determining unit 402 includes: a first determining module, used to determine whether the number of customers is greater than or equal to a first quantity threshold; an eighth determining module, used to determine the waiting penalty coefficient as a first coefficient when the number of customers is greater than or equal to the first quantity threshold; a second determining module, used to determine whether the number of customers is greater than or equal to a second quantity threshold when the number of customers is less than the first quantity threshold, wherein the second quantity threshold is less than the first quantity threshold; a ninth determining module, used to determine the waiting penalty coefficient as a second coefficient when the number of customers is greater than or equal to the second quantity threshold, wherein the second coefficient is less than the first coefficient; and a tenth determining module, used to determine the waiting penalty coefficient as a third coefficient when the number of customers is less than the second quantity threshold, wherein the third coefficient is less than the second coefficient.

[0101] Optionally, in the resource scheduling device for offline business processing of financial institutions provided in this application embodiment, the second determining unit 402 includes: an eleventh determining module, used to determine the queue number of each customer for customers of the same customer type in the target customer set; and a twelfth determining module, used to determine the initial priority of each customer in ascending order of queue number, wherein the initial priority of the customer with the smaller queue number is greater than the initial priority of the customer with the larger queue number.

[0102] It should be noted that the first determining unit 401, the second determining unit 402, the calculation unit 403, and the allocation unit 404 mentioned above correspond to steps S201 to S204 in Embodiment 1. The four units and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules or units can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0103] Example 3

[0104] Embodiments of this application may provide an electronic device. Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 Only one of the components is shown: processor 502, memory 504, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module, and display.

[0105] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0106] The processor can access information and applications stored in memory via a transmission device to perform the following steps: Determine the target customer set waiting for business at the target branch, and determine N windows at the target branch for business processing, where N is a positive integer; determine a waiting penalty coefficient based on the number of customers in the target customer set, determine the initial priority of each customer based on their customer type, and determine the waiting time for each customer; calculate the target priority for each customer based on their initial priority, waiting time, and waiting penalty coefficient, and sort the customers in the target customer set in descending order of target priority to obtain a target sequence; extract the first N customers from the target sequence, and allocate the first N customers to N windows for business processing based on a preset allocation algorithm, where each window processes one customer at a time.

[0107] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: For each customer, input the customer's waiting time into a preset logarithmic function to obtain the target logarithmic value; calculate the product of the target logarithmic value and the waiting penalty coefficient to obtain the dynamic priority; calculate the sum of the initial priority and the dynamic priority to obtain the customer's target priority.

[0108] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: For each window, determine the average processing time for each type of business, and obtain a set of average times; determine the target business type for each of the top N customers, and obtain N target business types; determine the first time saved by the window in processing the same type of business compared to processing different types of business, and determine the second time saved by the window in processing the same customer's business compared to processing different customers' business within a preset period; calculate the expected time for the window to process the business for each customer based on the set of average times, the N target business types, the first time, and the second time, and obtain N expected times; construct a processing time matrix based on the N expected times of the N windows, process the processing time matrix through a bipartite graph matching algorithm to obtain a target allocation scheme, and allocate the top N customers to N windows for business processing based on the target allocation scheme, wherein the target allocation scheme is the allocation scheme with the minimum total expected time for processing business at the N windows.

[0109] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: for each customer, calculate the sum of the first time and the second time corresponding to the customer to obtain the total time saved; calculate the difference between the average time corresponding to the target business type that the customer needs to handle and the total time saved to obtain the expected time for the customer to handle the business at the window.

[0110] The processor can also access information and applications stored in the memory via a transmission device to execute the following steps: For each customer, if the customer type is type 1, the initial priority of the customer is determined to be a first value, where type 1 is the type of customer for which the target branch needs to prioritize business; if the customer type is type 2, the initial priority of the customer is determined to be a second value, where the second value is less than the first value, where type 2 is the type of customer for which the target branch has marked a preset customer tag; if the customer type is an appointment customer, the initial priority of the customer is determined to be a third value, where the third value is less than the second value; if the customer type is an unappointed customer, the initial priority of the customer is determined to be a fourth value, where the fourth value is less than the third value.

[0111] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: determining whether the number of customers is greater than or equal to a first quantity threshold; if the number of customers is greater than or equal to the first quantity threshold, determining a waiting penalty coefficient as a first coefficient; if the number of customers is less than the first quantity threshold, determining whether the number of customers is greater than or equal to a second quantity threshold, wherein the second quantity threshold is less than the first quantity threshold; if the number of customers is greater than or equal to the second quantity threshold, determining a waiting penalty coefficient as a second coefficient, wherein the second coefficient is less than the first coefficient; if the number of customers is less than the second quantity threshold, determining a waiting penalty coefficient as a third coefficient, wherein the third coefficient is less than the second coefficient.

[0112] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: for customers of the same customer type in the target customer set, determine the queue number of each customer; determine the initial priority of each customer in ascending order of queue number, wherein the initial priority of the customer with the smaller queue number is greater than the initial priority of the customer with the larger queue number.

[0113] This application provides a method for determining a target customer set waiting for business at a target branch, and identifying N windows at the target branch for handling business, where N is a positive integer. The method involves determining a waiting penalty coefficient based on the number of customers in the target customer set, determining the initial priority of each customer based on their customer type, and determining the waiting time for each customer. Based on each customer's initial priority, waiting time, and waiting penalty coefficient, the method calculates the target priority for each customer, and sorts the customers in the target customer set according to their target priority from highest to lowest to obtain a target sequence. The method then extracts the top N customers from the target sequence and assigns them to the N windows for business processing based on a preset allocation algorithm, where each window processes one customer at a time. By dynamically calculating the target priority and sorting customers according to their target priority from highest to lowest, this method reflects the requirement for high-priority service for high-priority customers while avoiding the "starvation" phenomenon where ordinary customers cannot receive service due to long waiting times. By using a preset allocation algorithm to allocate windows, the utilization efficiency of window resources can be maximized, thereby improving business processing speed and customer satisfaction. This achieves the technical effect of reducing the waiting time for customers to handle business, and thus solves the technical problem of long waiting time for customers to handle business.

[0114] Those skilled in the art will understand that Figure 5 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices.Figure 5 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 5 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 5 The different configurations shown.

[0115] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0116] Example 4

[0117] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the resource scheduling method for offline business processing of financial institutions provided in Embodiment 1.

[0118] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0119] This application also provides a computer program product that, when executed on a data processing device, is suitable for executing resource scheduling methods for offline business processing in financial institutions.

[0120] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0121] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0123] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If the integrated unit is implemented as 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 this 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. 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 this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0126] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for resource scheduling in offline business processing for financial institutions, characterized in that, include: Determine the set of target customers waiting to process business at the target outlet, and determine the N windows at the target outlet for processing business, where N is a positive integer; The waiting penalty coefficient is determined based on the number of customers in the target customer set, the initial priority of each customer is determined based on the customer type of each customer in the target customer set, and the waiting time for each customer is determined. Calculate the target priority of each customer based on the initial priority, the waiting time, and the waiting penalty coefficient. Sort the customers in the target customer set in descending order of the target priority to obtain the target sequence. The first N customers are extracted from the target sequence, and the first N customers are assigned to the N windows for business processing based on a preset allocation algorithm, wherein each window processes business for one customer at a time.

2. The method according to claim 1, characterized in that, Calculating the target priority for each customer based on their initial priority, waiting time, and waiting penalty coefficient includes: For each customer, the customer's waiting time is input into a preset logarithmic function to obtain the target logarithmic value; The dynamic priority is obtained by multiplying the target logarithm by the waiting penalty coefficient. The target priority of the customer is obtained by calculating the sum of the initial priority and the dynamic priority.

3. The method according to claim 1, characterized in that, The process of assigning the first N customers to the N service windows based on a preset allocation algorithm includes: For each window, determine the average processing time for each type of business at that window to obtain a set of average times; Determine the target business type for each of the first N customers, resulting in N target business types; The system determines the first time saved by the window in processing the same type of business compared to processing different types of business, and determines the second time saved by the window in processing the same customer's business within a preset period compared to processing different customers' business. Based on the set of average durations, the N target business types, the first duration, and the second duration, the expected duration for each customer's business at the window is calculated to obtain N expected durations; A processing time matrix is ​​constructed based on the N expected processing times of N windows. The processing time matrix is ​​then processed using a bipartite graph matching algorithm to obtain a target allocation scheme. Based on the target allocation scheme, the top N customers are allocated to the N windows for processing. The target allocation scheme is the allocation scheme that minimizes the total expected processing time of the N windows.

4. The method according to claim 3, characterized in that, Based on the aforementioned average duration, the N target service types, the first duration, and the second duration, the expected processing time for each customer's service at the window is calculated, including: For each customer, the sum of the first duration and the second duration corresponding to the customer is calculated to obtain the total saved time; The difference between the average time for the target business type that the customer needs to handle and the total time saved is calculated to obtain the expected time for the customer to handle the business at the window.

5. The method according to claim 1, characterized in that, Determining the initial priority of a customer based on the customer type of each customer in the target customer set includes: For each customer, if the customer type is the first type, the initial priority of the customer is determined to be a first value, wherein the first type is the customer type for which the target branch needs to prioritize handling business. If the customer type is the second type, the initial priority of the customer is determined to be the second value, wherein the second value is less than the first value, and the second type is the customer type of the target outlet that has been marked with a preset customer tag; If the customer type is an appointment customer, the initial priority of the customer is determined to be a third value, wherein the third value is less than the second value; If the customer type is a non-appointment customer, the initial priority of the customer is determined to be a fourth value, wherein the fourth value is less than the third value.

6. The method according to claim 1, characterized in that, The waiting penalty coefficient is determined based on the number of customers in the target customer set, including: Determine whether the number of customers is greater than or equal to a first quantity threshold; If the number of customers is greater than or equal to the first quantity threshold, the waiting penalty coefficient is determined as the first coefficient; If the number of customers is less than the first quantity threshold, determine whether the number of customers is greater than or equal to a second quantity threshold, wherein the second quantity threshold is less than the first quantity threshold; If the number of customers is greater than or equal to the second quantity threshold, the waiting penalty coefficient is determined as the second coefficient, wherein the second coefficient is less than the first coefficient; If the number of customers is less than the second quantity threshold, the waiting penalty coefficient is determined as a third coefficient, wherein the third coefficient is less than the second coefficient.

7. The method according to claim 1, characterized in that, Determining the initial priority of a customer based on the customer type of each customer in the target customer set also includes: For customers of the same type in the target customer set, determine the queue number for each customer; The initial priority of each customer is determined according to the queue number in ascending order, wherein the initial priority of the customer with the smaller queue number is greater than the initial priority of the customer with the larger queue number.

8. A resource scheduling device for offline business processing in financial institutions, characterized in that, include: The first determining unit is used to determine the set of target customers waiting to handle business at the target outlet, and to determine the N windows of the target outlet for handling business, where N is a positive integer; The second determining unit is used to determine the waiting penalty coefficient based on the number of customers in the target customer set, determine the initial priority of each customer based on the customer type of each customer in the target customer set, and determine the waiting time for each customer. The calculation unit is used to calculate the target priority of each customer based on the initial priority, the waiting time and the waiting penalty coefficient of each customer, and sort the customers in the target customer set in descending order of the target priority to obtain the target sequence; The allocation unit is used to extract the first N customers from the target sequence and allocate the first N customers to the N windows for business processing based on a preset allocation algorithm, wherein each window processes business for one customer at a time.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor is configured to run the program, wherein the program executes the resource scheduling method for offline business processing of financial institutions as described in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the resource scheduling method for offline business processing of financial institutions as described in any one of claims 1 to 7.

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