Method and device for determining queuing waiting duration of user

Abstract

The invention discloses a method and a device for determining queuing waiting time of a user, and relates to the field of financial science and technology. The method comprises the following steps: under the condition of user authorization, obtaining to-be-handled service types and age information of N queued users; according to the to-be-handled service type of the user, detecting whether the user has handled a historical service corresponding to the to-be-handled service type; when the business has been handled, determining a predicted business handling duration according to the duration of handling the historical business; when the to-be-handled business is not handled, determining a predicted business handling time length according to a preset handling time length and age information corresponding to the to-be-handled business type; and determining the queuing waiting time of the jth user according to the predicted business handling time of all queuing users before the jth user. According to the method and the device, the technical problems of influence on user service experience and low user service processing efficiency caused by excessive queuing personnel due to difficulty in judging queuing time length by a user because only a queuing serial number is provided during financial institution network service in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and more specifically, to a method and device for determining the waiting time of a user in a queue. Background Art

[0002] In the current operating model of financial institution branches, customers communicate their needs with the lobby manager after arriving at the store, and are then directed to the self-service machine area or cash counter area according to the type of business. When customers need to go to the cash counter to handle business, they usually need to use their bank card or ID card to get a number in order to obtain a position identifier in the queue sequence. However, the existing queuing system has significant limitations, that is, the receipt provided after taking the number only contains the customer's queue number and the number of customers ahead of him in the queue, but lacks an estimate and display of the expected waiting time. This lack of information makes it impossible for customers to accurately judge how long they need to wait during the waiting process, especially when the number of people in the queue is small but the business processing is complex. The short wait may be extended to an unbearable length of time, which in turn causes customer complaints and dissatisfaction.

[0003] This situation highlights the inadequacy of existing technologies in which financial institutions only provide queue numbers when providing services. It is difficult for users to judge the actual waiting time based on simple queue numbers, resulting in too many people queuing at the outlets, affecting the customer service experience. At the same time, the lack of accurate prediction of waiting time has caused low efficiency in user service processing.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The present application provides a method and device for determining the waiting time of a user in a queue, so as to at least solve the technical problem in the prior art that financial institutions only provide queue numbers when providing services at branches, which makes it difficult for users to judge the waiting time in the queue, resulting in too many people in the queue, thus affecting the user service experience and causing low user business processing efficiency.

[0006] According to one aspect of the present application, a method for determining the waiting time of a user in a queue is provided, comprising: with user authorization, obtaining the pending business type and age information of each user among N users waiting to handle business in a target window, wherein N is an integer greater than 1; detecting, based on the pending business type of each user, whether each user has handled a historical business corresponding to the pending business type; when it is detected that the i-th user has handled the corresponding historical business, determining the estimated business handling time of the i-th user in the target window based on the time the i-th user handled the historical business, wherein the i-th user is any one of the N users; when it is detected that the i-th user has not handled the corresponding historical business, determining the estimated business handling time of the i-th user in the target window based on the preset handling time corresponding to the pending business type of the i-th user and the age information of the i-th user; determining the queue waiting time of the j-th user based on the estimated business handling time of all queued users before the j-th user, wherein j is an integer less than or equal to N and greater than 1.

[0007] Optionally, when it is detected that the ith user has handled the corresponding historical business, the estimated business handling time of the ith user in the target window is determined based on the time the ith user took to handle the historical business, including: when it is detected that the ith user has handled historical business H times, obtaining the time the ith user took to handle each historical business, where H is an integer greater than or equal to 1; calculating the average time the ith user took to handle historical business based on the time the ith user took to handle each historical business; and determining the average time as the estimated business handling time of the ith user in the target window.

[0008] Optionally, when it is detected that the i-th user has not handled the corresponding historical business, the estimated business handling time of the i-th user in the target window is determined according to the preset handling time corresponding to the pending business type of the i-th user and the age information of the i-th user, including: when it is detected that the i-th user has not handled the corresponding historical business, the preset handling time corresponding to the pending business type of the i-th user is determined from the first mapping table, wherein the first mapping table is used to record the preset handling time corresponding to each business type handled by the target window, and the pending business type of the i-th user belongs to a business type handled by the target window; determining the target weight value according to the age information of the i-th user, wherein the target weight value is used to adjust the preset handling time corresponding to the pending business type of the i-th user; calculating the product of the preset handling time corresponding to the pending business type of the i-th user and the target weight value to obtain the estimated business handling time of the i-th user.

[0009] Optionally, determining the target weight value based on the age information of the i-th user includes: using the age interval corresponding to the age information of the i-th user in the second mapping table as the target age interval, wherein the second mapping table is used to record multiple age intervals and the weight values ​​corresponding to each age interval; and using the weight value set for the target age interval as the target weight value.

[0010] Optionally, before determining the queue waiting time of the jth user based on the estimated service processing time of all queued users before the jth user, the method for determining the user queue waiting time also includes: obtaining an initial waiting time of the target window, wherein the initial waiting time represents the time the target window needs to wait before starting to process the service of the first user among N users; and using the initial waiting time as the queue waiting time of the first user among N users.

[0011] Optionally, the queue waiting time of the jth user is determined based on the estimated service processing time of all queued users before the jth user, including: calculating the sum of the estimated service processing time of all queued users before the jth user to obtain a first waiting time; summing the first waiting time and the initial waiting time to obtain the queue waiting time of the jth user.

[0012] Optionally, obtaining the initial waiting time of the target window includes: obtaining processing progress information of the target business being processed by the target window; inputting the processing progress information into the prediction model, determining the business nodes of the processing progress information in the processing of the target business and the subsequent unfinished business nodes through the prediction model, and determining the remaining time for completing the processing of the target business based on the subsequent unfinished business nodes, wherein the training data of the prediction model includes: multiple business progress data of the same type as the target business, and a generated time series corresponding to the business progress data; and using the remaining time for completing the processing of the target business as the initial waiting time of the target window.

[0013] Optionally, after determining the queue waiting time of the jth user based on the estimated service processing time of all queued users before the jth user, the method for determining the queue waiting time of the user also includes: generating a queue credential for the jth user based on the queue waiting time of the jth user, wherein the queue credential includes at least the queue number and queue waiting time of the jth user.

[0014] Optionally, the method for determining the user's waiting time in a queue also includes: collecting the actual processing time of the i-th user for a service of any service type, and detecting the difference between the actual processing time and the preset processing time corresponding to the service type recorded in the first mapping table; when the difference is greater than a preset threshold, updating the preset processing time corresponding to the service type recorded in the first mapping table according to the actual processing time.

[0015] According to another aspect of an embodiment of the present application, a device for determining the waiting time of a user in a queue is also provided, comprising: a first acquisition unit, which, with user authorization, acquires the pending business type and age information of each user among N users waiting to handle business in a target window, wherein N is an integer greater than 1; a first detection unit, which detects, based on the pending business type of each user, whether each user has handled a historical business corresponding to the pending business type; a first determination unit, which, when detecting that the i-th user has handled the corresponding historical business, determines the estimated business handling time of the i-th user in the target window based on the time for the i-th user to handle the historical business, wherein the i-th user is any one of the N users; a second determination unit, which, when detecting that the i-th user has not handled the corresponding historical business, determines the estimated business handling time of the i-th user in the target window based on the preset handling time corresponding to the pending business type of the i-th user and the age information of the i-th user; a third determination unit, which determines the waiting time of the j-th user in the queue according to the estimated business handling time of all queuing users before the j-th user, wherein j is an integer less than or equal to N and greater than 1.

[0016] According to another aspect of the present application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned method for determining the waiting time of a user in a queue.

[0017] According to another aspect of the present application, an electronic device is provided, including: a memory storing an executable program; and a processor for running the program, wherein the method for determining the waiting time of a user in a queue is executed when the program is running.

[0018] According to another aspect of the present application, a computer program product is also provided, including computer instructions, which, when executed by a processor, implement the steps of the above-mentioned method for determining the waiting time of a user in a queue.

[0019] In the present application, with user authorization, the pending business type and age information of each user among the N users waiting to handle business in the target window are obtained, wherein N is an integer greater than 1. Then, according to the pending business type of each user, it is detected whether each user has handled the historical business corresponding to the pending business type. When it is detected that the ith user has handled the corresponding historical business, the estimated business handling time of the ith user in the target window is determined based on the time it took the ith user to handle the historical business. Among them, the ith user is any one of the N users. When it is detected that the ith user has not handled the corresponding historical business, the preset handling time corresponding to the ith user's pending business type and the ith user's The age information of the i-th user is used to determine the estimated business processing time of the i-th user in the target window, and then the queuing waiting time of the j-th user is determined according to the estimated business processing time of all the queuing users before the j-th user, where j is an integer less than or equal to N and greater than 1. That is, through the business processing time prediction and the dynamic calculation of the queuing waiting time, the purpose of estimating the personalized waiting time and providing a reference for the user is achieved, thereby achieving the technical effect of improving user satisfaction and user business processing efficiency, and further solving the technical problem that in the prior art, financial institutions only provide queuing numbers when providing services, and it is difficult for users to judge the queuing time, resulting in too many people in the queue, thereby affecting the user service experience and causing low user business processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0021] Figure 1 This is a flow chart of an optional method for determining the waiting time of a user in a queue according to an embodiment of the present application. Figure 1 ;

[0022] Figure 2 This is a flow chart of an optional method for determining the waiting time of a user in a queue according to an embodiment of the present application. Figure 2 ;

[0023] Figure 3 It is a schematic diagram of an optional device for determining the waiting time of a user in a queue according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] It should be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with relevant laws, regulations and standards, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making; if the user chooses to refuse, the expert decision-making process will be entered.

[0027] It should be noted that an intelligent analysis system can be used as the execution subject of the method for determining the waiting time of a user in a queue in the embodiment of the present application. It is understandable that the method for determining the waiting time of a user in a queue provided in the embodiment of the present application can also be executed by other systems or devices, and the embodiment of the present application does not specifically limit this.

[0028] According to an embodiment of the present application, a method embodiment of a method for determining the waiting time of a user in a queue is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] Figure 1 This is a flow chart of an optional method for determining the waiting time of a user in a queue according to an embodiment of the present application. Figure 1 ,like Figure 1 As shown, the method comprises the following steps:

[0030] Step S101, with user authorization, obtaining the pending business type and age information of each user among N users waiting to handle business in the target window.

[0031] In step S101, N is an integer greater than 1.

[0032] Optionally, the intelligent analysis system first needs to obtain the user's consent, that is, user authorization. With the user's authorization, the system collects the pending business type and age information of each user among the N users waiting to handle business at the target window (a specific counter or window providing services in the bank, such as a cash counter).

[0033] Step S102: According to the pending service type of each user, it is detected whether each user has handled a historical service corresponding to the pending service type.

[0034] Optionally, the intelligent analysis system checks the pending business type of each user to see whether each user has a record of handling the same type of business at the bank branch in the past.

[0035] Step S103, when it is detected that the ith user has handled the corresponding historical service, the estimated service handling time of the ith user in the target window is determined according to the time when the ith user handled the historical service.

[0036] In step S103, the i-th user is any one of the N users.

[0037] Optionally, for users who have handled related services before (the i-th user), the intelligent analysis system will estimate their current service handling time in the target window (estimated service handling time) based on the average handling time of their historical services (duration of historical services).

[0038] Step S104, when it is detected that the ith user has not handled the corresponding historical business, the estimated business handling time of the ith user in the target window is determined according to the preset handling time corresponding to the type of business to be handled by the ith user and the age information of the ith user.

[0039] Optionally, if the user (i-th user) has no relevant historical business records, the intelligent analysis system will use the preset average business processing time (preset processing time) and combine the user's age information (age time coefficient a) to estimate their current business processing time in the target window (estimated business processing time). For example, young people may process business faster than the elderly.

[0040] Step S105: determining the queuing waiting time of the j-th user according to the estimated service processing time of all queuing users before the j-th user, where j is an integer less than or equal to N and greater than 1.

[0041] Optionally, for each user in the queue (the jth user), the intelligent analysis system will calculate the waiting time (queue waiting time) of the user based on the sum of the estimated service processing time of all the users in the queue before the user (the 1st to the j-1th users). For example, if the first two users are expected to take 10 minutes and 15 minutes respectively, then the waiting time of the jth user (assuming j=3) is at least 25 minutes.

[0042] It can be seen from the contents of steps S101 to S105 that in this application, with user authorization, the pending business type and age information of each user among the N users waiting to handle business in the target window are obtained, wherein N is an integer greater than 1, and then, according to the pending business type of each user, it is detected whether each user has handled the historical business corresponding to the pending business type. When it is detected that the i-th user has handled the corresponding historical business, the estimated business handling time of the i-th user in the target window is determined according to the time duration of the i-th user handling the historical business, wherein the i-th user is any one of the N users. When it is detected that the i-th user has not handled the corresponding historical business, the corresponding to the i-th user's pending business type is determined. The preset processing time and the age information of the ith user are used to determine the estimated business processing time of the ith user in the target window, and then the queuing waiting time of the jth user is determined according to the estimated business processing time of all queuing users before the jth user, where j is an integer less than or equal to N and greater than 1. That is, through the business processing time prediction and the dynamic calculation of the queuing waiting time, the purpose of estimating the personalized waiting time and providing a reference for the user is achieved, thereby achieving the technical effect of improving user satisfaction and user business processing efficiency, and further solving the technical problem that in the prior art, financial institutions only provide queuing numbers when providing services, and it is difficult for users to judge the queuing time, resulting in too many queuing personnel, thereby affecting the user service experience and causing low user business processing efficiency.

[0043] In an optional embodiment, when the intelligent analysis system detects that the ith user has handled historical services H times, it obtains the duration of each historical service handled by the ith user, where H is an integer greater than or equal to 1, and then calculates the average duration of the ith user's historical services based on the duration of each historical service handled by the ith user. The average duration is then determined as the estimated service handling duration of the ith user in the target window.

[0044] Optionally, the intelligent analysis system first checks whether the i-th user in the queue list has handled H historical transactions at the target window (such as a cash counter). This means that the system will query the database to confirm whether the user is a frequent customer and the number of times they have handled the same type of business at the branch before. Once the system detects that the i-th user has indeed handled H historical transactions, it will extract the specific processing time of each transaction from the database. Here, the duration of each transaction recorded by the system refers to the entire service cycle from the customer starting to handle the business to the completion of the business. The system will then add up the duration of the H historical transactions collected and divide it by H to obtain the average duration of the i-th user handling historical transactions. The average duration can more accurately reflect the usual time spent by the user in handling specific transactions, thereby providing a more personalized service estimate. Based on the average duration calculated above, the intelligent analysis system uses it as the estimated business processing time of the i-th user at the target window (such as a cash counter).

[0045] Optionally, when the i-th user has handled three historical services, the estimated service handling time t of the i-th user is calculated as shown in formula (1):

[0046]

[0047] Among them, c1 is the time for the first transaction, c2 is the time for the second transaction, and c3 is the time for the third transaction.

[0048] From the above content, we can see that for frequent customers (i.e. the i-th user who has handled H historical services), the intelligent analysis system uses the average historical service time to make an estimate. This method takes into account the user's specific service processing speed, so the estimated time is closer to reality, which helps to reduce customer dissatisfaction caused by long waiting time. For banks, this method helps to more effectively manage customer flow, reasonably allocate human resources, and improve service efficiency. At the same time, it can also enhance customers' trust and satisfaction with the bank.

[0049] In an optional embodiment, when the intelligent analysis system detects that the ith user has not handled the corresponding historical business, the intelligent analysis system determines the preset processing time corresponding to the pending business type of the ith user from the first mapping table, wherein the first mapping table is used to record the preset processing time corresponding to each business type handled by the target window, and the pending business type of the ith user belongs to a business type handled by the target window. Then, the target weight value is determined according to the age information of the ith user, wherein the target weight value is used to adjust the preset processing time corresponding to the pending business type of the ith user, and then the product of the preset processing time corresponding to the pending business type of the ith user and the target weight value is calculated to obtain the estimated business processing time of the ith user.

[0050] Optionally, when it is detected that the i-th user has not handled the corresponding historical business at the target window (such as a cash counter) before, the intelligent analysis system will search the first mapping table for the preset processing time corresponding to the pending business type of the user, where the first mapping table is a data structure for storing each business type that the target window can handle and its average estimated processing time. For example, the preset processing time for a deposit business may be 5 minutes, for a withdrawal business may be 5 minutes, and for a small inheritance business may be 30 minutes. Next, the system determines a target weight value based on the age information of the i-th user. The age-time coefficient (target weight value) is based on the age group. The settings are as follows, such as 1.5 for 1-16 years old, 0.8 for 16-24 years old, and so on, until 1.5 again for those over 65 years old. These values ​​reflect the average speed differences in handling business for users of different age groups. Finally, the system calculates the product of the preset handling time corresponding to the pending business type of the ith user and the target weight value, so as to obtain the estimated business handling time of the ith user in the target window. For example, if the preset handling time for the deposit business is 5 minutes, and the age weight of the user is 1.1 (for example, the user is between 46 and 55 years old), then the estimated business handling time for the user is 5*1.1=5.5 minutes.

[0051] Optionally, when the i-th user has not handled the corresponding historical service, the estimated service handling time t of the i-th user is calculated as shown in formula (2):

[0052] t=a×b (2)

[0053] Among them, a is the age time coefficient, and b is the time usually required for the business.

[0054] Optionally, Table 1 is an example of an optional age-time coefficient according to an embodiment of the present application. As shown in Table 1, when the age group is 1-16 years old, the age-time coefficient is 1.5, when the age group is 16-24 years old, the age-time coefficient is 0.8, when the age group is 25-35 years old, the age-time coefficient is 0.9, when the age group is 36-45 years old, the age-time coefficient is 1, when the age group is 46-55 years old, the age-time coefficient is 1.1, when the age group is 56-65 years old, the age-time coefficient is 1.3, and when the age group is over 65 years old, the age-time coefficient is 1.5.

[0055] Table 1

[0056] Age Group Age time coefficient a 1-16 1.5 16-24 0.8 25-35 0.9 36-45 1 46-55 1.1 56-65 1.3 65 and above 1.5

[0057] Optionally, Table 2 is an example of the processing time corresponding to different optional services according to an embodiment of the present application. As shown in Table 2, for small inheritance services, the time usually required is 30 minutes, for account opening services for children under 16 years old, the time usually required is 30 minutes, for coin / small change exchange services (a large number of banknotes needs to be counted), the time usually required is 30 minutes, for card making services, the time usually required is 25 minutes, for card collection services (card collection after changing the card without changing the number, card collection after swallowing the card), the time usually required is 25 minutes, for deposit services, the time usually required is 5 minutes, and for withdrawal services, the time usually required is 5 minutes.

[0058] Table 2

[0059] business Usually the time required Small inheritance 30min Account opening for those under 16 years old 30min Coin / small change exchange (large amount needs to be counted) 30min Social Security Card 25min Get the card (get the card after changing the card but not the number, get the card after the card is swallowed) 10min deposit 5min Withdrawal 5min

[0060] From the above content, it can be seen that the intelligent analysis system can provide an estimate of the user's waiting time in the target window even without the user's historical business records. This mechanism combines the preset duration of the business type and the business processing speed adjustment based on age. It not only covers all users, but also makes personalized estimates based on the user's specific attributes, greatly improving customer satisfaction. Users can arrange their time reasonably based on more accurate waiting time estimates, reducing anxiety and dissatisfaction during the waiting process. At the same time, for banks, this estimation mechanism helps to better manage customer traffic, optimize service resource allocation, improve service efficiency, and reduce customer complaints, thereby enhancing the bank's overall operational efficiency and customer trust.

[0061] In an optional embodiment, the intelligent analysis system uses the age interval corresponding to the age information of the i-th user in the second mapping table as the target age interval, wherein the second mapping table is used to record multiple age intervals and the weight values ​​corresponding to each age interval, and then uses the weight value set for the target age interval as the target weight value.

[0062] Optionally, the intelligent analysis system will first compare the age information of the i-th user with the second mapping table to determine which specific age range (target age range) the user's age belongs to. The second mapping table (such as Table 1) is a pre-set data table, which lists multiple age ranges and sets a weight value for each range. After determining the age range of the i-th user, the system will read the weight value corresponding to the age range from the second mapping table, that is, the weight value set for the target age range. For example, if the user's age falls between 46 and 55 years old, the corresponding weight value is 1.1.

[0063] From the above content, it can be seen that the intelligent analysis system can call the corresponding weight value according to the user's age group by mapping age information to specific age ranges. This step is the key to realizing personalized business processing time estimation, and the acquisition of weight values ​​ensures that the adjustment factors based on age ranges can be applied in subsequent steps, making the estimated business processing time closer to the actual processing speed of users in this age group.

[0064] In an optional embodiment, the intelligent analysis system first obtains the initial waiting time of the target window, where the initial waiting time represents the time the target window needs to wait before starting to handle the services of the first user among N users, and then uses the initial waiting time as the queue waiting time of the first user among N users.

[0065] Optionally, the intelligent analysis system obtains an initial waiting time at the beginning of the day or before the target window (such as a cash counter) starts service. The initial waiting time represents the length of time the target window needs to wait before starting to process the business of the first user among the Nth users. For example, at the beginning of the bank opening, if there are no customers waiting outside the door, the initial waiting time can be set to 0, but if there are estimated customers who will come immediately, the system will preset a non-zero initial waiting time based on historical data, and then the system will directly use the obtained initial waiting time as the queue waiting time for the first user among the Nth users. This means that if there are no waiting customers when the bank opens, the queue waiting time for the first user is 0 minutes. If the system estimates that a customer will come immediately when the bank opens, the queue waiting time for the first user will be equal to the preset initial waiting time.

[0066] From the above, it can be seen that the intelligent analysis system can ensure that the estimate of the waiting time in the queue for the first user at the beginning of the operation is accurate. This mechanism first establishes a time benchmark at the beginning of the service by obtaining the initial waiting time of the target window. Then, this initial waiting time is directly used as the waiting time in the queue for the first user, ensuring the immediacy and accuracy of the estimate. The implementation of this dependent claim is a key starting point for the estimation effect of the entire queuing system. It not only reduces the anxiety of customers under uncertain waiting time, but also provides a solid foundation for the estimation of waiting time for subsequent users. By accurately estimating the waiting time of the first user, the system can continuously optimize the waiting time estimate for subsequent users, thereby improving customer satisfaction and banking service efficiency as a whole.

[0067] In an optional embodiment, the intelligent analysis system first calculates the sum of the estimated service processing times of all queuing users before the jth user to obtain a first waiting time, and then sums the first waiting time and the initial waiting time to obtain the queuing waiting time of the jth user.

[0068] Optionally, the intelligent analysis system first needs to sum up the estimated service processing time of all queuing users before the jth user to calculate the first waiting time. This calculation is based on the system's estimate of the service processing time for each user, which may come from user history records, service type mapping tables, or preset time adjusted by age. For example, if there are currently 4 users queuing before the jth user, namely the 1st, 2nd, 3rd, and 4th users, and their estimated service processing time is 6 minutes, 8 minutes, 10 minutes, and 7 minutes, respectively, then the first waiting time is 6+8+10+7=31 minutes. After obtaining the first waiting time, the intelligent analysis system will add this value to the initial waiting time to obtain the queuing waiting time of the jth user. Here, the initial waiting time refers to the time that the target window needs to wait before starting to process the service of the first user among the Nth users, which can be 0 or a non-zero value preset based on historical data. For example, if the initial waiting time is 5 minutes and the first waiting time is 31 minutes, then the queuing waiting time of the jth user is 36 minutes.

[0069] From the above content, it can be seen that the intelligent analysis system can provide each user in the queue (the jth user) with an accurate queue waiting time estimate based on the cumulative previous business waiting time and the estimated business processing time. This mechanism not only takes into account the business processing time of all users in the current queue sequence, but also combines the initial waiting time of the target window, thereby greatly enhancing the reliability and accuracy of the estimate. For customers, this means that they can more accurately understand the length of time they need to wait while waiting for service, so as to plan their time reasonably and reduce anxiety and dissatisfaction during the waiting process. For banks, providing accurate waiting time estimates helps to optimize customer flow management, reasonably allocate human resources, improve service efficiency, reduce customer complaints, and thus improve customer satisfaction and the overall service quality of the bank.

[0070] In an optional embodiment, the intelligent analysis system first obtains the processing progress information of the target business being processed in the target window, and then inputs the processing progress information into the prediction model, and determines the business nodes of the processing progress information in the processing of the target business and the subsequent unfinished business nodes through the prediction model, and determines the remaining time for completing the processing of the target business based on the subsequent unfinished business nodes, wherein the training data of the prediction model includes: multiple business progress data of the same type as the target business, and a generated time series corresponding to the business progress data, and then the remaining time for completing the processing of the target business is used as the initial waiting time of the target window.

[0071] Optionally, the intelligent analysis system first obtains the processing progress information of the target business currently being processed from the target window, which includes but is not limited to the current stage of the business processing, the completed steps, and the interaction information between the customer and the teller. Next, the system inputs the obtained processing progress information into a preset prediction model. The prediction model is obtained by training a large amount of business progress data and generating time series. It can predict the processing time of the business at subsequent nodes based on the processing progress of the current business. The prediction model will output the subsequent unfinished business nodes of the current business and the remaining processing time of each node. The system adds up these remaining times to obtain the remaining time for completing the target business. Finally, the system uses the calculated remaining time for completing the target business as the initial waiting time of the target window, which is used to estimate the queuing waiting time of subsequent users (users after the first user among the Nth users).

[0072] From the above content, we can see that the intelligent analysis system uses the real-time processing progress information of the target business, combined with the intelligent analysis of the prediction model. The system can not only predict the remaining time for the current business to be completed, but also dynamically adjust the initial waiting time of the target window accordingly. This mechanism plays an important role in the entire customer flow management, because it enables the estimated waiting time to more accurately reflect the actual operating conditions of the outlets, thereby effectively improving customer satisfaction and reducing customer dissatisfaction and churn caused by inaccurate waiting time estimates. At the same time, for banks, this dynamic adjustment mechanism also helps to optimize resource allocation, improve service efficiency, and ensure smooth and efficient operations.

[0073] In an optional embodiment, the intelligent analysis system generates a queue credential for the jth user according to the queue waiting time of the jth user, wherein the queue credential includes at least the queue sequence number and the queue waiting time of the jth user.

[0074] Optionally, the intelligent analysis system first needs to determine the position of the user in the queue, that is, the serial number of user j in the current waiting queue. This step ensures that each customer has a clear queue position, which is convenient for subsequent notification and management. Then, based on the above-mentioned estimated service processing time, the estimated service time of the previous user (or previous users), and the current number of open counters, the waiting time of user j in the queue is calculated. According to the queue serial number of user j and the calculated waiting time in the queue, the intelligent analysis system generates a queue voucher including at least these two pieces of information. The queue voucher is a voucher for customers while they are waiting for services. It provides customers with a tool for waiting confirmation and time planning by displaying the queue serial number and estimated waiting time.

[0075] Optionally, once the queue voucher is generated, it can be displayed to the user through the screen of the number-taking machine (which provides a customer service interface, the same as an intelligent teller machine), and can also be printed as a paper voucher. In addition, the system can also send the queue voucher information to the user through SMS or software notification, ensuring that the user can check his queue status at any time while on the move.

[0076] From the above content, we can see that the intelligent analysis system can provide each user (jth user) with a personalized queue voucher containing a queue number and estimated waiting time. This mechanism not only enhances the customer's understanding and sense of control over the queue process, but also reduces the anxiety of waiting through accurate estimated time, thereby improving customer satisfaction.

[0077] In an optional embodiment, the intelligent analysis system first collects the actual processing time of the i-th user for any business type, and detects the difference between the actual processing time and the preset processing time corresponding to the business type recorded in the first mapping table. When the difference is greater than a preset threshold, the preset processing time corresponding to the business type recorded in the first mapping table is updated according to the actual processing time.

[0078] Optionally, whenever a user (the i-th user) completes the processing of any business type, the intelligent analysis system will automatically collect the actual processing time of the business, that is, the entire time span from the start of the processing to the completion of the processing. The intelligent analysis system will then compare the collected actual processing time with the preset processing time corresponding to the business type recorded in the first mapping table, and calculate the difference between the two. For example, if the preset processing time is 15 minutes and the actual processing time is 20 minutes, then the difference is 5 minutes. The system then determines whether the detected difference exceeds a preset threshold value based on the detected difference value. The preset threshold value is a set value used to determine when the preset processing time in the first mapping table needs to be adjusted to reflect the actual changes in the business processing time. When the detected difference value exceeds the preset threshold value, the intelligent analysis system will update the preset processing time corresponding to the business type in the first mapping table based on the actual processing time of the business.

[0079] Optionally, Figure 2 This is a flow chart of an optional method for determining the waiting time of a user in a queue according to an embodiment of the present application. Figure 2 ,like Figure 2 As shown, when a customer arrives at the store, the lobby manager first asks the customer what business he needs to handle. If it is not necessary to handle it at the counter, the user is prompted to go to the self-service machine area to handle it. If it is necessary to go to the cash counter area to handle it, it is necessary to check whether the customer has brought all the relevant materials (certificates / cards) for the business he needs to handle. If the conditions are not met, it cannot be handled and materials need to be prepared. If the materials are complete, it can continue to be handled. The lobby manager assists the customer in selecting the corresponding business, and then the lobby manager uses the customer's card / ID card to queue for the customer (swipe the card or ID card). After the number is successfully taken, the background system can obtain the customer's age information. Then the background program queries the database to see if the customer is a frequent customer. A frequent customer is defined as someone who has visited the branch three times in the past three months and handled the same business. If the customer is a frequent customer, the business handling time is calculated according to the above formula (1). If the customer is not a frequent customer, the business handling time is calculated according to the above formula (2). At the same time, the lower right corner of the number-taking page of the queue machine prompts the current waiting time x for the queue. The calculation method is shown in formula (3):

[0080]

[0081] Where d is the initial waiting time of the window (the initial waiting time represents the time the window needs to wait before starting to handle the business of the first user among N users), n is the number of users waiting to handle business at the window, and t i The business processing time corresponding to each customer.

[0082] After the customer's queue receipt is printed out, the customer's current queue number, the number of people in front of him, and the estimated waiting time are displayed on the receipt. The lobby manager will ask the customer whether he is willing to wait for x minutes. If he accepts, the customer will go to the waiting area and wait for his number to be called. If he does not accept, the lobby manager will comfort the customer and provide the customer with the queue situation of other nearby outlets. The customer's queue number will be invalidated when the customer leaves the store.

[0083] From the above content, we can see that the intelligent analysis system has achieved continuous dynamic optimization of the estimated service processing time. This mechanism can not only detect the actual changes in service processing time in real time, but also automatically adjust the preset processing time according to these changes, making the estimate more accurate. For customers, this means that they will get a waiting time estimate that is closer to the actual situation, which enhances their trust and satisfaction with bank services. For banks, this mechanism helps to optimize resource allocation, improve service efficiency, and reduce customer complaints caused by inaccurate estimates, thereby improving the overall level of operational management.

[0084] The embodiment of the present application also provides a device for determining the waiting time of a user in a queue. It should be noted that the device for determining the waiting time of a user in a queue in the embodiment of the present application can be used to execute the method for determining the waiting time of a user in a queue provided in the embodiment of the present application. The device for determining the waiting time of a user in a queue provided in the embodiment of the present application is introduced below.

[0085] According to an embodiment of the present application, a device for determining the waiting time of a user in a queue is also provided. Figure 3 is a schematic diagram of an optional device for determining a user's waiting time in a queue according to an embodiment of the present application. Figure 3 As shown, the device includes: a first acquisition unit 301, a first detection unit 302, a first determination unit 303, a second determination unit 304, and a third determination unit 305.

[0086] Optionally, the first acquisition unit 301 is used to obtain the pending business type and age information of each user among the N users waiting to handle the business in the target window with the authorization of the user, wherein N is an integer greater than 1; the first detection unit 302 is used to detect whether each user has handled the historical business corresponding to the pending business type according to the pending business type of each user; the first determination unit 303 is used to determine the expected time of the i-th user in the target window according to the length of time the i-th user has handled the historical business when it is detected that the i-th user has handled the corresponding historical business. Service processing time, wherein the i-th user is any one of the N users; the second determining unit 304 is used to determine the estimated service processing time of the i-th user in the target window according to the preset processing time corresponding to the type of service to be processed by the i-th user and the age information of the i-th user when it is detected that the i-th user has not processed the corresponding historical service; the third determining unit 305 is used to determine the queuing waiting time of the j-th user according to the estimated service processing time of all queuing users before the j-th user, wherein j is an integer less than or equal to N and greater than 1.

[0087] Optionally, the first determination unit 303 includes: a first acquisition subunit, a first calculation subunit, and a first determination subunit. The first acquisition subunit is used to obtain the duration of each historical service handled by the i-th user when it is detected that the i-th user has handled H historical services, where H is an integer greater than or equal to 1; the first calculation subunit is used to calculate the average duration of the i-th user handling historical services based on the duration of each historical service handled by the i-th user; the first determination subunit is used to determine the average duration as the estimated service handling duration of the i-th user in the target window.

[0088] Optionally, the second determination unit 304 includes: a second determination subunit, a third determination subunit, and a second calculation subunit. The second determination subunit is used to determine the preset processing time corresponding to the pending business type of the i-th user from the first mapping table when it is detected that the i-th user has not handled the corresponding historical business, wherein the first mapping table is used to record the preset processing time corresponding to each business type handled by the target window, and the pending business type of the i-th user belongs to a business type handled by the target window; the third determination subunit is used to determine the target weight value according to the age information of the i-th user, wherein the target weight value is used to adjust the preset processing time corresponding to the pending business type of the i-th user; the second calculation subunit is used to calculate the product of the preset processing time corresponding to the pending business type of the i-th user and the target weight value to obtain the estimated business processing time of the i-th user.

[0089] Optionally, the third determination subunit includes: a first determination module and a second determination module. The first determination module is used to use the age interval corresponding to the age information of the i-th user in the second mapping table as the target age interval, wherein the second mapping table is used to record multiple age intervals and the weight value corresponding to each age interval; the second determination module is used to use the weight value set for the target age interval as the target weight value.

[0090] Optionally, the device for determining the waiting time of a user in a queue further includes: a second acquisition unit and a fourth determination unit. The second acquisition unit is used to acquire the initial waiting time of the target window, wherein the initial waiting time represents the waiting time required for the target window to start processing the service of the first user among the N users; and the fourth determination unit is used to use the initial waiting time as the waiting time of the first user in the N users in a queue.

[0091] Optionally, the third determination unit 305 includes: a third calculation subunit and a fourth calculation subunit. The third calculation subunit is used to calculate the sum of the estimated service processing time of all queuing users before the jth user to obtain a first waiting time; and the fourth calculation subunit is used to sum the first waiting time and the initial waiting time to obtain the queuing waiting time of the jth user.

[0092] Optionally, the second acquisition unit includes: a second acquisition subunit, a fourth determination subunit, and a fifth determination subunit. The second acquisition subunit is used to acquire the processing progress information of the target business being processed by the target window; the fourth determination subunit is used to input the processing progress information into the prediction model, determine the business nodes of the processing progress information in the processing of the target business and the subsequent unfinished business nodes through the prediction model, and determine the remaining time for the target business to be completed based on the subsequent unfinished business nodes, wherein the training data of the prediction model includes: a plurality of business progress data of the same type as the target business, and a generated time series corresponding to the business progress data; the fifth determination subunit is used to use the remaining time for the target business to be completed as the initial waiting time of the target window.

[0093] Optionally, the device for determining the waiting time of a user in a queue further includes: a first collection unit and a first updating unit. The first collection unit is used to collect the actual processing time of the service of any service type of the i-th user, and detect the difference between the actual processing time and the preset processing time corresponding to the service type recorded in the first mapping table; the first updating unit is used to update the preset processing time corresponding to the service type recorded in the first mapping table according to the actual processing time when the difference is greater than a preset threshold.

[0094] According to another aspect of the present application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned method for determining the waiting time of a user in a queue.

[0095] According to another aspect of the present application, an electronic device is provided, including: a memory storing an executable program; and a processor for running the program, wherein the method for determining the waiting time of a user in a queue is executed when the program is running.

[0096] According to another aspect of the present application, a computer program product is also provided, including computer instructions, which, when executed by a processor, implement the steps of the above-mentioned method for determining the waiting time of a user in a queue.

[0097] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0098] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0100] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0101] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0102] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present 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, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0103] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining the waiting time of a user in a queue, characterized in that: include: Under user authorization, obtain the pending business type and age information of each user among N users waiting to handle business in the target window, where N is an integer greater than 1; According to the pending business type of each user, detecting whether each user has handled a historical business corresponding to the pending business type; When it is detected that the i-th user has handled the corresponding historical service, the estimated service handling time of the i-th user in the target window is determined according to the time when the i-th user handled the historical service, wherein the i-th user is any one of the N users; When it is detected that the i-th user has not handled the corresponding historical service, the estimated service handling time of the i-th user in the target window is determined according to the preset handling time corresponding to the service type to be handled by the i-th user and the age information of the i-th user; The queuing waiting time of the j-th user is determined according to the estimated service processing time of all queuing users before the j-th user, where j is an integer less than or equal to N and greater than 1.

2. The method for determining the waiting time of a user in a queue according to claim 1, characterized in that: In the case where it is detected that the i-th user has handled the corresponding historical service, determining the estimated service handling time of the i-th user in the target window according to the time when the i-th user handled the historical service, includes: When it is detected that the i-th user has handled the historical service H times, the duration of each time the i-th user handles the historical service is obtained, where H is an integer greater than or equal to 1; Calculate the average duration of the i-th user handling the historical services according to the duration of each historical service handled by the i-th user; The average duration is determined as the estimated service handling duration of the i-th user in the target window.

3. The method for determining the waiting time of a user in a queue according to claim 1, characterized in that: In the case where it is detected that the i-th user has not handled the corresponding historical service, determining the estimated service handling time of the i-th user in the target window according to the preset handling time corresponding to the service type to be handled by the i-th user and the age information of the i-th user, including: In the case where it is detected that the i-th user has not handled the corresponding historical business, determining the preset handling time corresponding to the type of business to be handled by the i-th user from the first mapping table, wherein the first mapping table is used to record the preset handling time corresponding to each type of business handled by the target window, and the type of business to be handled by the i-th user belongs to a type of business handled by the target window; Determining a target weight value according to the age information of the i-th user, wherein the target weight value is used to adjust a preset processing time corresponding to the type of service to be processed of the i-th user; The product of the preset processing time corresponding to the to-be-processed business type of the i-th user and the target weight value is calculated to obtain the estimated business processing time of the i-th user.

4. The method for determining the waiting time of a user in a queue according to claim 3, characterized in that: Determining a target weight value according to the age information of the i-th user includes: Using the age interval corresponding to the age information of the i-th user in the second mapping table as the target age interval, wherein the second mapping table is used to record multiple age intervals and the weight value corresponding to each age interval; The weight value set for the target age range is used as the target weight value.

5. The method for determining the waiting time of a user in a queue according to claim 1, characterized in that: Before determining the queuing waiting time of the j-th user according to the estimated service handling time of all queuing users before the j-th user, the method for determining the queuing waiting time of the user further includes: Acquire an initial waiting time of the target window, wherein the initial waiting time represents the time that the target window needs to wait before starting to process the service of the first user among the N users; The initial waiting time is used as the queue waiting time of the first user among the N users.

6. The method for determining the waiting time of a user in a queue according to claim 5, characterized in that: Determining the queuing waiting time of the j-th user according to the estimated service processing time of all queuing users before the j-th user includes: Calculate the sum of the estimated service processing time of all queuing users before the j-th user to obtain a first waiting time; The first waiting time and the initial waiting time are summed to obtain the queue waiting time of the j-th user.

7. The method for determining the waiting time of a user in a queue according to claim 5, characterized in that: Obtaining the initial waiting time of the target window includes: Obtaining the processing progress information of the target business being processed by the target window; The processing progress information is input into a prediction model, and the business nodes of the processing progress information in the processing of the target business and the subsequent unfinished business nodes are determined by the prediction model, and the remaining time for completing the processing of the target business is determined according to the subsequent unfinished business nodes, wherein the training data of the prediction model includes: a plurality of business progress data of the same type as the target business, and a generation time series corresponding to the business progress data; The remaining time for completing the target business is used as the initial waiting time of the target window.

8. The method for determining the waiting time of a user in a queue according to claim 1, characterized in that: After determining the queuing waiting time of the j-th user according to the estimated service handling time of all queuing users before the j-th user, the method for determining the queuing waiting time of the user further includes: A queuing credential of the j-th user is generated according to the queuing waiting time of the j-th user, wherein the queuing credential at least includes a queuing sequence number and a queuing waiting time of the j-th user.

9. The method for determining the waiting time of a user in a queue according to claim 3, characterized in that: The method for determining the waiting time of a user in a queue also includes: Collecting the actual processing time of the i-th user for a service of any service type, and detecting the difference between the actual processing time and the preset processing time corresponding to the service type recorded in the first mapping table; When the difference value is greater than a preset threshold, the preset processing time corresponding to the service type recorded in the first mapping table is updated according to the actual processing time.

10. A device for determining the waiting time of a user in a queue, characterized in that: include: The first acquisition unit, under the authorization of the user, acquires the pending business type and age information of each user among N users waiting to handle the business in the target window, where N is an integer greater than 1; A first detection unit, based on the pending service type of each user, detects whether each user has handled a historical service corresponding to the pending service type; A first determining unit, when detecting that the i-th user has handled a corresponding historical service, determines an estimated service handling time of the i-th user in the target window according to the time the i-th user handled the historical service, wherein the i-th user is any one of the N users; The second determining unit determines, when detecting that the i-th user has not handled the corresponding historical service, an estimated service handling time of the i-th user in the target window according to a preset handling time corresponding to the type of service to be handled by the i-th user and the age information of the i-th user; The third determining unit determines the queuing waiting time of the j-th user according to the estimated service processing time of all queuing users before the j-th user, where j is an integer less than or equal to N and greater than 1.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the method for determining the waiting time of a user in a queue as described in any one of claims 1 to 9.

12. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program, when running, executes the method for determining the waiting time of a user in a queue as described in any one of claims 1 to 9.

13. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the steps of the method for determining the waiting time of a user in a queue as described in any one of claims 1 to 9 are implemented.

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