Data processing method and device, storage medium and electronic equipment

Abstract

The invention discloses a data processing method and device, a storage medium and electronic equipment. The method comprises the following steps: predicting the passenger flow volume of a target date based on target data; based on the passenger flow volume and the business demand distribution data of the target date, determining business resources for providing business services on the target date, the business demand distribution data including the predicted proportion of the service demand of each business type in the passenger flow volume; on the target date, taking minimization of the user waiting time as a target, and based on a preset constraint condition set, the passenger flow volume and the total number of the service resources, carrying out solving operation to obtain the service resources corresponding to each service type; and scheduling business resources to provide business services based on the business resources corresponding to each business type. According to the invention, the problem of low business resource scheduling accuracy caused by the fact that business resources are allocated to provide business services based on a fixed mode in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence, and more specifically, to a data processing method, apparatus, storage medium, and electronic device. Background Technology

[0002] In traditional financial institutions' business services, the rational allocation of business resources (such as teller resources) is crucial to ensuring the efficient operation of branches. For a long time, most financial institutions have adopted resource allocation strategies based on fixed patterns, such as tellinger scheduling based on fixed schedules. While this strategy can meet business needs to a certain extent under normal circumstances, it cannot flexibly adjust according to fluctuations in customer flow, easily leading to congestion at the counter during certain periods, excessively long customer waiting times, and consequently, decreased customer satisfaction and excessive service pressure. Therefore, related technologies suffer from low accuracy in business resource scheduling.

[0003] There is currently no effective solution to the aforementioned problems in the relevant technologies. Summary of the Invention

[0004] The main objective of this application is to provide a data processing method, apparatus, storage medium, and electronic device to solve the problem of low accuracy in business resource scheduling caused by the fixed-pattern allocation of business resources in related technologies.

[0005] To achieve the above objectives, according to one aspect of this application, a data processing method is provided. The method includes: predicting passenger flow on a target date based on target data, wherein the target data includes at least historical passenger flow data and weather forecast data for the target date; determining business resources for providing business services on the target date based on passenger flow and business demand distribution data for the target date, wherein the business demand distribution data includes the predicted proportion of service demand for each business type in the passenger flow; on the target date, with the objective of minimizing user waiting time, performing calculations based on a preset set of constraints, the number of people awaiting business processing, and the total number of business resources to obtain business resources corresponding to each business type; and scheduling business resources to provide business services based on the business resources corresponding to each business type.

[0006] Optionally, the data processing method further includes: determining passenger flow and historical weather data for at least one historical date from historical passenger flow data; and processing the passenger flow, historical weather data, and weather forecast data for at least one historical date using a target prediction model to obtain the passenger flow for the target date.

[0007] Optionally, the data processing method further includes: obtaining a training sample set, wherein the training samples in the training sample set include weather data for the target date and historical passenger flow data for the sample, and the true label of the training samples is the actual passenger flow for the target date; and training an initial prediction model through the training sample set to obtain a target prediction model.

[0008] Optionally, the data processing method further includes: constructing business service constraints based on business skills of business resources; constructing shift scheduling constraints based on working time thresholds of business resources; constructing business load constraints based on business load thresholds of business resources; and constructing a set of constraints based on at least one of the business service constraints, shift scheduling constraints, and business load constraints.

[0009] Optionally, the data processing method further includes: determining historical distribution data of business demand for at least one historical date from historical passenger flow data; determining business demand distribution data for a target date based on the historical distribution data of business demand for at least one historical date; or, processing weather forecast data and passenger flow, historical weather data and historical distribution data for at least one historical date through a target prediction model to obtain passenger flow and business demand distribution data for the target date.

[0010] Optionally, the data processing method further includes: after scheduling business resources to provide business services based on the business resources corresponding to each business type, on the target date, upon receiving a user's request to obtain a number, determining the target business type to be obtained from the request; obtaining the queuing waiting time and idle rate of each business resource corresponding to the target business type; and determining the target queuing number based on the queuing waiting time and idle rate of each business resource, wherein the target queuing number is used to indicate the business resource that provides business services to the user.

[0011] Optionally, the data processing method further includes: after determining the target service type to be obtained from the number retrieval request, determining whether the target service type is the first service type, wherein the first service type refers to the service type that provides service through the target device; if the target service type is the first service type, generating target information, wherein the target information is used to prompt the user to interact with the target device.

[0012] To achieve the above objectives, according to another aspect of this application, a data processing apparatus is provided. The apparatus includes: a prediction module for predicting passenger flow on a target date based on target data, wherein the target data includes at least historical passenger flow data and weather forecast data for the target date; a first determination module for determining business resources to provide business services on the target date based on passenger flow and business demand distribution data for the target date, wherein the business demand distribution data includes the predicted proportion of service demand for each business type in the passenger flow; a first processing module for performing calculations on the target date, with the objective of minimizing user waiting time, based on a preset set of constraints, the number of people awaiting business processing, and the total number of business resources, to obtain business resources corresponding to each business type; and a second processing module for scheduling business resources to provide business services based on the business resources corresponding to each business type.

[0013] Optionally, the prediction module further includes: a first determining submodule, used to determine passenger flow and historical weather data for at least one historical date from historical passenger flow data; and a first processing submodule, used to process the passenger flow, historical weather data, and weather forecast data for at least one historical date through a target prediction model to obtain the passenger flow for the target date.

[0014] Optionally, the data processing device further includes: a first acquisition module for acquiring a training sample set, wherein the training samples in the training sample set include weather data for the target date and historical passenger flow data for the sample, and the true label of the training samples is the actual passenger flow for the target date; and a training module for training an initial prediction model using the training sample set to obtain a target prediction model.

[0015] Optionally, the data processing device further includes: a first construction module for constructing business service constraints based on business skills of business resources; a second construction module for constructing shift scheduling constraints based on working duration thresholds of business resources; a third construction module for constructing business load constraints based on business load thresholds of business resources; and a fourth construction module for constructing a set of constraints based on at least one of the business service constraints, shift scheduling constraints, and business load constraints.

[0016] Optionally, the data processing apparatus further includes: a second determining module, configured to determine historical distribution data of business demand for at least one historical date from historical passenger flow data; and to determine business demand distribution data for a target date based on the historical distribution data of business demand for at least one historical date; or, a third processing module, configured to process weather forecast data and passenger flow, historical weather data and historical distribution data for at least one historical date through a target prediction model to obtain passenger flow and business demand distribution data for the target date.

[0017] Optionally, the data processing device further includes: a third determining module, used to determine the target service type to be obtained from the user's number-obtaining request on the target date; a second obtaining module, used to obtain the queuing waiting time and idle rate of each service resource corresponding to the target service type; and a fourth determining module, used to determine the target queue number based on the queuing waiting time and idle rate of each service resource, wherein the target queue number is used to indicate the service resource that provides service to the user.

[0018] Optionally, the data processing device further includes: a judgment module for judging whether the target business type is a first business type, wherein the first business type refers to the business type that provides business services through the target device; and a generation module for generating target information when the target business type is the first business type, wherein the target information is used to prompt the user to interact with the target device.

[0019] To achieve the above objectives, according to another aspect of this application, a computer-readable storage medium is provided, which includes a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the above-described data processing method.

[0020] To achieve the above objectives, according to another aspect of this application, an electronic device is provided, the electronic device including a memory storing an executable program; and a processor for running the program, wherein the program executes the above-described data processing method during runtime.

[0021] To achieve the above objectives, according to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of the data processing method described above.

[0022] In this embodiment, the business resources for providing business services on the target date are determined by the predicted passenger flow and business demand distribution data on the target date. This enables the dynamic determination of the total number of business resources on the target date based on the predicted passenger flow information. On the target date, with the goal of minimizing user waiting time, the business resources corresponding to each business type are obtained by solving a set of preset constraints, the number of people waiting to handle business, and the total number of business resources. This allows for the dynamic adjustment of the allocation of business resources among business types based on real-time business demand on the target date. As a result, the scheduling accuracy of business resources can be effectively improved when scheduling business resources to provide business services based on the business resources corresponding to each business type.

[0023] Therefore, the method provided in this application achieves the goal of dynamically scheduling business resources based on passenger flow information and business information on the target date, realizes the technical effect of improving the scheduling accuracy of business resources, and solves the technical problem of low scheduling accuracy of business resources caused by related technologies allocating business resources based on a fixed pattern to provide business services. Attached Figure Description

[0024] 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:

[0025] Figure 1 This is a hardware structure block diagram of a computer terminal provided according to an embodiment of this application;

[0026] Figure 2 This is a flowchart of a data processing method provided according to an embodiment of this application;

[0027] Figure 3 This is a schematic diagram of a data processing apparatus provided according to an embodiment of this application;

[0028] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0029] 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.

[0030] 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.

[0031] 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 operation entry 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 operation entry points for them to choose to agree to or refuse automated decision results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0032] Example 1

[0033] According to an embodiment of this application, an embodiment of a data processing method 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. Furthermore, 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.

[0034] 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 data processing method 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, a microprocessor (MCU) or a field-programmable gate array (FPGA), etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output (I / O) interface, a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS 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.

[0035] 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).

[0036] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the data processing method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the aforementioned data processing method. 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.

[0037] 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.

[0038] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0039] Under the aforementioned operating environment, this application provides the following: Figure 2 The data processing method shown. Figure 2 This is a flowchart of the data processing method according to Embodiment 1 of this application.

[0040] Step S201: Predict passenger flow for a target date based on target data, wherein the target data includes at least historical passenger flow data and weather forecast data for the target date.

[0041] Optionally, electronic devices, application systems, servers, or other similar devices can be used as the execution subject of this application. In this embodiment, the target processing system is used as the execution subject to execute the above-described data processing method.

[0042] The target processing system can predict customer traffic on a specific date (target date) by collecting and analyzing target data. The aforementioned target data includes at least historical customer traffic data and weather forecast data for the target date. Historical customer traffic data can include customer traffic data for at least one historical date and historical weather data. For example, historical customer traffic data includes customer traffic data and historical weather data for multiple historical dates, where multiple historical periods refer to dates within the month preceding the target date. Historical customer traffic data is used to capture long-term trends and cyclical changes. Weather forecast data takes into account the impact of external environmental factors on customer behavior; for example, severe weather may cause customers to visit financial institutions earlier or later, thus indirectly affecting customer traffic.

[0043] Optionally, the target processing system can predict passenger flow for a target date based on target data using a neural network model, a multiple regression model, or other models.

[0044] Step S202: Determine the business resources to provide business services on the target date based on passenger flow and business demand distribution data for the target date. The business demand distribution data includes the predicted proportion of service demand for each business type in the passenger flow.

[0045] In an optional embodiment, prior to the target date, based on passenger flow forecasts and real-time monitoring data, the system determines which business resources need to provide services on the target date. The target processing system can acquire business demand distribution data for the target date to match business resources with the corresponding skills for the business type before the target date.

[0046] Optionally, the aforementioned business resources are resources within a financial institution capable of providing services. For example, business resources can refer to tellers (also known as salespersons) within a financial institution, or business service equipment within a financial institution. In this embodiment, tellers are used as an example of business resources to illustrate the data processing method. It should be emphasized that the example content presented in this embodiment also applies when the business resource is business service equipment.

[0047] For example, based on predicted customer traffic and business demand distribution data for a target date, the system determines the quantity and type of business resources (such as tellers) needed to provide services on the target date (i.e., which teller will provide the service). Business demand distribution data reflects the expected proportion of various services (e.g., cash withdrawals, money transfers, credit card repayments, inquiries, etc.) within customer traffic. This data helps the target processing system understand the relative importance and urgency of different service types on a specific date. By comprehensively considering customer traffic forecasts and business demand distribution, the target processing system can reasonably determine the tellers who will provide services on the target date.

[0048] In an optional embodiment, the business demand distribution data is predicted data. The business demand distribution data can be determined by averaging or weighted summing based on historical data. Alternatively, the business demand distribution data can be obtained by processing historical data using a relevant neural network model.

[0049] Step S203: On the target date, with the goal of minimizing user waiting time, the solution is performed based on a preset set of constraints, the number of people with pending business, and the total number of business resources to obtain the business resources corresponding to each business type.

[0050] When the target date arrives, if a counter experiences queuing delays or a sudden surge in demand for a single service on that date, the target processing system can immediately trigger a scheduling mechanism to temporarily reassign some tellers. For example, suppose the teller pool for a certain period is... The set of business requirements is The scheduling problem can be abstracted into the following mathematical model with the goal of minimizing user waiting time:

[0051]

[0052] in, Indicates business The waiting time The target waiting threshold is set for the system. The preset set of constraints may include teller skill matching constraints, shift scheduling time limits, and maximum load constraints. Under the target represented by the above mathematical model, the target processing system can use a heuristic algorithm to perform calculations based on the preset set of constraints, the number of customers awaiting processing, and the total number of business resources to find a near-optimal allocation scheme. The number of customers awaiting processing refers to the number of customers at the financial institution's branches awaiting processing on the target date when the calculation is triggered, and this includes the types of business each user expects to process.

[0053] In an optional embodiment, on the target date, at a preset time interval, with the goal of minimizing user waiting time, calculations are performed based on a preset set of constraints, the number of people with pending business, and the total number of business resources to obtain the business resources corresponding to each business type.

[0054] In an optional embodiment, on the target date, the number of queuing users under each business type and the number of queuing users for each teller are monitored in real time. In the case of a business type where the number of queuing users exceeds a first threshold, or a teller where the number of queuing users exceeds a second threshold, the business resources corresponding to each business type are obtained by solving a calculation based on a preset set of constraints, the number of pending transactions, and the total number of business resources, with the goal of minimizing user waiting time.

[0055] In an optional embodiment, the target processing system can provide branch managers with queuing pressure maps and scheduling suggestions through a visual interface to improve the timeliness and accuracy of business resource scheduling.

[0056] Step S204: Based on the business resources corresponding to each business type, schedule business resources to provide business services.

[0057] In an optional embodiment, after determining the business resources corresponding to each business type, real-time scheduling is performed based on the optimal business resource configuration for each business type obtained from the above solution. That is, on the target date, financial institutions can flexibly adjust the allocation of tellers according to actual business needs, prioritizing high-demand or high-priority business types while ensuring that low-demand businesses also receive appropriate service.

[0058] Optionally, a single business resource may possess business skills corresponding to multiple business types. Therefore, a single business resource can be allocated to provide different business services according to actual needs.

[0059] In this embodiment, the business resources for providing business services on the target date are determined by the predicted passenger flow and business demand distribution data on the target date. This enables the dynamic determination of the total number of business resources on the target date based on the predicted passenger flow information. On the target date, with the goal of minimizing user waiting time, the business resources corresponding to each business type are obtained by solving a set of preset constraints, the number of people waiting to handle business, and the total number of business resources. This allows for the dynamic adjustment of the allocation of business resources among business types based on real-time business demand on the target date. As a result, the scheduling accuracy of business resources can be effectively improved when scheduling business resources to provide business services based on the business resources corresponding to each business type.

[0060] Therefore, the method provided in this application achieves the goal of dynamically scheduling business resources based on passenger flow information and business information on the target date, realizes the technical effect of improving the scheduling accuracy of business resources, and solves the technical problem of low scheduling accuracy of business resources caused by related technologies allocating business resources based on a fixed pattern to provide business services.

[0061] Optionally, in the data processing method provided in this application embodiment, predicting passenger flow on a target date based on target data includes: determining passenger flow and historical weather data for at least one historical date from historical passenger flow data; and processing the passenger flow, historical weather data, and weather forecast data for at least one historical date using a target prediction model to obtain the passenger flow on the target date.

[0062] Optionally, historical passenger flow data may include passenger flow data for multiple historical dates and historical weather data. Historical weather data may include, but is not limited to, weather type, temperature, humidity, and probability of rainfall for the corresponding historical dates.

[0063] In an alternative embodiment, at least one historical date can be a date in a different month but with the same date as the target date.

[0064] In another alternative embodiment, at least one historical date can be a date within a target historical time range, which can end with the target date. For example, the target historical time range is one month prior to the target date.

[0065] The target prediction model can be a neural network model. The target processing system can input passenger flow data, historical weather data, and weather forecast data for the target date into the target prediction model, and then use the target prediction model to predict passenger flow to obtain the passenger flow for the target date.

[0066] In an optional embodiment, in addition to inputting passenger flow data, historical weather data, and weather forecast data for the target date into the target prediction model for at least one historical date, the target processing system may also synchronously input historical distribution data of business demand for at least one historical date into the target prediction model to improve prediction accuracy.

[0067] In an optional embodiment, in addition to inputting passenger flow data, historical weather data, and weather forecast data for the target date into the target prediction model for at least one historical date, the target processing system can also simultaneously input historical distribution data of business demand for at least one historical date and the holiday type (e.g., non-holiday, A holiday, B holiday, etc.) for the target date into the target prediction model to improve prediction accuracy.

[0068] It should be noted that the above methods can effectively improve the accuracy of the predicted passenger flow for the target date.

[0069] Optionally, in the data processing method provided in this application embodiment, the target prediction model is obtained in the following way: obtaining a training sample set, wherein the training samples in the training sample set include weather data of the target date and historical passenger flow data of the sample, and the true label of the training sample is the actual passenger flow of the target date; training an initial prediction model through the training sample set to obtain the target prediction model.

[0070] In an optional embodiment, the training samples in the training sample set include weather data for the target date and historical passenger flow data. The true label of the training samples is the actual passenger flow on the target date. The historical passenger flow data corresponding to the target date has the same content format as the historical passenger flow data corresponding to the target date, so it will not be described again here.

[0071] In an optional embodiment, the sample historical passenger flow data includes passenger flow, historical weather data, and historical distribution data of business demand for at least one sample historical date. The true labels of the training samples are the actual passenger flow and actual business demand distribution data for the target date, so that the trained target prediction model can not only predict the passenger flow on the target date, but also predict the business demand distribution data on the target date.

[0072] For example, the target processing system can extract passenger flow data for each date within the past year from its internal information system, and simultaneously obtain weather conditions for the corresponding dates using publicly available weather forecast websites or meteorological agency data interfaces. Next, this data is cleaned, formatted, and standardized to ensure all variables are on the same scale for model training. Finally, the processed data is divided into "features" (weather data and historical passenger flow data) and "labels" (actual passenger flow, or, actual passenger flow and actual business demand distribution data), constructing a complete training sample set.

[0073] After obtaining the training sample set, an initial prediction model is trained using the training sample set to obtain the target prediction model. The initial prediction model can be a neural network model.

[0074] In an optional embodiment, the loss function value can be determined based on a preset loss function and the output of the initial prediction model. Then, the model parameters are iteratively updated based on the loss function value, and the target prediction model is obtained after multiple iterations.

[0075] It should be noted that the above method enables effective training of the initial prediction model, thereby improving the training effect of the target prediction model.

[0076] Optionally, in the data processing method provided in the embodiments of this application, the data processing method further includes: constructing business service constraints based on business skills of business resources; constructing shift time constraints based on working duration thresholds of business resources; constructing business load constraints based on business load thresholds of business resources; and constructing a set of constraints based on at least one of the business service constraints, shift time constraints, and business load constraints.

[0077] For example, business service constraints can be expressed as follows:

[0078]

[0079] Business service constraints indicate that they only apply when the teller... Business Only those with the required business skills can participate in the services provided by this business. Indicates business The set of requirements for teller skills. j represents the teller ID (there are a total of...). (Name of teller). 'i' represents the business type number (total number of tellers). (Class of business). Business service constraints are formulated based on the teller's business skills to ensure that tellers can be assigned to positions that match their skills during scheduling.

[0080] For example, scheduling constraints can be expressed as follows:

[0081]

[0082] in, Teller In time period Whether the employee is on duty (1 for on duty, 0 for otherwise); This indicates the maximum number of hours a teller can work per day. This constraint ensures that the teller's daily working hours comply with relevant regulations.

[0083] For example, the load constraint can be expressed as follows:

[0084]

[0085] in, Teller Total service load Indicates business The product of the workload weight and the teller's processing time for this transaction. This indicates the maximum workload that a teller can handle. Indicate whether to include the business Assigning tasks to teller j results in a value of 1 if the task is assigned, and 0 otherwise. This constraint ensures a balanced distribution of tasks among tellers, prevents excessively long queues at local windows, and ensures that a teller's transaction volume (or transaction load) on a specific date does not exceed this threshold, thus preventing any teller from exceeding their reasonable and efficient processing capacity.

[0086] The difference between workload constraints and scheduling constraints lies in their focus: workload constraints do not aim to prevent overtime, but rather to prevent excessively long processing times for complex tasks that lead to overwork. For example, even if two tasks take the same amount of time (e.g., 10 minutes each), the workload (λi) of a complex form entry task might be twice that of the other. If an employee spends the entire day performing this task, their fatigue will negatively impact the reliability of the service.

[0087] After determining the business service constraints, scheduling time constraints, and business load constraints, construct a set of constraints based on at least one of the business service constraints, scheduling time constraints, and business load constraints.

[0088] It should be noted that the above method takes into account multiple factors when providing business services to business resources, thereby improving the accuracy of the determined set of constraints, and thus improving the accuracy of the business resources corresponding to each business type obtained from the solution.

[0089] Optionally, in the data processing method provided in the embodiments of this application, the data processing method further includes: determining historical distribution data of business demand for at least one historical date from historical passenger flow data; determining business demand distribution data for a target date based on the historical distribution data of business demand for at least one historical date; or, processing weather forecast data and passenger flow, historical weather data and historical distribution data for at least one historical date through a target prediction model to obtain passenger flow and business demand distribution data for the target date.

[0090] Optionally, historical passenger flow data includes historical distribution data of business demand over multiple historical dates.

[0091] In an optional embodiment, the historical distribution data of business demand for historical dates has the same content format as the business demand distribution data. The historical distribution data of business demand for historical dates refers to the proportional distribution of different types of business (such as cash withdrawals, money transfers, credit card repayments, and consulting services) in the total customer traffic on a specific past date. For example, an optional historical distribution data could be represented as "Cash withdrawals: 10%; Money transfers: 10%; Credit card repayments: 30%; Consulting services: 50%".

[0092] In an optional embodiment, after determining the historical distribution data of business demand for at least one historical date, the historical distribution data of business demand for at least one historical date can be averaged to determine the business demand distribution data for the target date based on the calculation result. For example, for each business type, the average proportion of the historical distribution data for at least one historical date under that business type is calculated to obtain the target proportion of that business type, and the business demand distribution data for the target date is determined based on the target proportions corresponding to all business types.

[0093] For example, a weighted average method can be used to predict the distribution of business demand on a target date, that is, to give higher weight to the most recent or historical dates most similar to the conditions of the target date, so as to more accurately reflect the current trends and conditions.

[0094] In another optional embodiment, the weather forecast data, along with passenger flow, historical weather data, and historical distribution data for at least one historical date, are processed by a target prediction model to obtain passenger flow and business demand distribution data for the target date. For example, a training sample set is obtained, wherein the training samples in the training sample set include weather data for the target date and historical passenger flow data. The historical passenger flow data includes passenger flow, historical weather data, and historical distribution data of business demand for at least one historical date. The true labels of the training samples are the actual passenger flow and actual business demand distribution data for the target date. An initial prediction model is trained using this training sample set to obtain the target prediction model. This allows the trained target prediction model to predict not only passenger flow on the target date but also the business demand distribution data for the target date.

[0095] It should be noted that whether analyzing historical business demand distribution independently or using predictive models, the aim is to enhance the predictive ability of business demand distribution data for a target date by utilizing historical data, thereby further improving the accuracy of business resource scheduling.

[0096] In another alternative embodiment, the business demand distribution data for passenger traffic on the target date can also be preset values ​​pre-set by the staff of the financial institution.

[0097] Optionally, in the data processing method provided in the embodiments of this application, after scheduling business resources to provide business services based on the business resources corresponding to each business type, the method further includes: on a target date, upon receiving a user's request to obtain a number, determining the target business type to be obtained from the request; obtaining the queuing waiting time and idle rate of each business resource corresponding to the target business type; and determining a target queuing number based on the queuing waiting time and idle rate of each business resource, wherein the target queuing number is used to indicate the business resource that provides business services to the user.

[0098] Optionally, a queue number request refers to a request initiated by a user at a financial institution branch through branch equipment or a mobile application, aiming to obtain a queue number for sequential business processing. The target business type refers to the specific type of business that the user explicitly specifies in the queue number request, which they wish to complete at the financial institution branch, such as cash withdrawal, money transfer, credit card repayment, or consultation services.

[0099] After determining the target business type, the queuing time and idle rate of each business resource corresponding to the target business type are obtained. For example, the queuing time is calculated using the formula "Queueing time = Current queue length × Average business processing time", and the idle rate is calculated using the formula "Idle rate = (Branch operating time on the target date - Total business processing time of business resources on the target date / Branch operating time on the target date) × 100%". Here, the branch operating time on the target date refers to the difference between the current time on the target date (i.e., the time when the idle rate calculation is triggered) and the time when the branch begins operating on the target date.

[0100] After determining the queuing time and idle rate of each service resource, the target processing system can calculate the queuing priority corresponding to each service resource:

[0101]

[0102] in, This indicates the priority of assigning user c to business resource k. For teller k, the current waiting time in the queue. Business resource utilization rate, business resource utilization rate = (1 - idle rate). These are the preset adjustment parameters.

[0103] After calculating the queuing priority of each business resource, the business resource with the highest queuing priority is determined as the business resource used to provide business services to the aforementioned users, thereby generating the target queuing number.

[0104] It should be noted that the above method enables the intelligent allocation of business resources for users to form queue numbers by combining the queuing time and idle rate of various business resources, thereby improving the accuracy of the generated target queue number and enhancing the user experience.

[0105] Optionally, in the data processing method provided in this application embodiment, after determining the target service type to be obtained from the number retrieval request, the method further includes: determining whether the target service type is a first service type, wherein the first service type refers to the service type that provides service through the target device; if the target service type is the first service type, generating target information, wherein the target information is used to prompt the user to interact with the target device.

[0106] In an optional embodiment, when a customer takes a number, the system can automatically identify whether the service is available through a self-service channel based on the type of service selected by the customer.

[0107] Optionally, after determining the target business type, the target processing system can determine whether the target business type is a first business type, which is the business type belonging to the self-service channel. For example, the first business type can be cash deposit and withdrawal, account inquiry, and transfer. In an optional embodiment, when the business resource is a business service device, the difference between the target device and the business service device can be that the business service device is more intelligent and can be transferred to manual processing, while the target device is relatively more basic and cannot be transferred to manual processing.

[0108] For example, a financial institution's internal database might contain a compatibility table between service types and target devices. This table details which service types can be completed using the target device. When the system receives a user's request to obtain a number and determines the target service type, it queries this table to see if the target service type is the primary service type.

[0109] Once the target service type is confirmed as the primary service type, the system can display or send target information, informing the user of the location of the nearest available target device, operation steps, and even providing a QR code or link to allow the user to directly access the target device's operation interface.

[0110] Optionally, if the target business type is not the first business type, the queuing time and idle rate of each business resource corresponding to the target business type are obtained, and the target queue number is determined based on the queuing time and idle rate of each business resource.

[0111] It should be noted that by identifying the primary business type that can be completed through the target device, these users can be effectively guided to self-service channels, thereby reducing the burden on business resources and improving overall service efficiency and user experience.

[0112] Therefore, the method provided in this application achieves the goal of dynamically scheduling business resources based on passenger flow information and business information on the target date, realizes the technical effect of improving the scheduling accuracy of business resources, and solves the technical problem of low scheduling accuracy of business resources caused by related technologies allocating business resources based on a fixed pattern to provide business services.

[0113] 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.

[0114] Example 2

[0115] This application also provides a data processing apparatus. It should be noted that the data processing apparatus of this application can be used to execute the data processing method provided in this application. The data processing apparatus provided in this application will be described below.

[0116] According to an embodiment of this application, an apparatus for implementing the above-described data processing method is also provided, such as... Figure 3 As shown, the device includes:

[0117] The prediction module 301 is used to predict passenger flow on a target date based on target data, wherein the target data includes at least historical passenger flow data and weather forecast data for the target date;

[0118] The first determining module 302 is used to determine the business resources that will provide business services on the target date based on passenger flow and business demand distribution data for the target date. The business demand distribution data includes the predicted proportion of service demand for each business type in the passenger flow.

[0119] The first processing module 303 is used to perform calculations on the target date, with the goal of minimizing user waiting time, based on a preset set of constraints, the number of people with pending business, and the total number of business resources, to obtain the business resources corresponding to each business type.

[0120] The second processing module 304 is used to schedule business resources to provide business services based on the business resources corresponding to each business type.

[0121] In this embodiment, the business resources for providing business services on the target date are determined by the predicted passenger flow and business demand distribution data on the target date. This enables the dynamic determination of the total number of business resources on the target date based on the predicted passenger flow information. On the target date, with the goal of minimizing user waiting time, the business resources corresponding to each business type are obtained by solving a set of preset constraints, the number of people waiting to handle business, and the total number of business resources. This allows for the dynamic adjustment of the allocation of business resources among business types based on real-time business demand on the target date. As a result, the scheduling accuracy of business resources can be effectively improved when scheduling business resources to provide business services based on the business resources corresponding to each business type.

[0122] Therefore, the method provided in this application achieves the goal of dynamically scheduling business resources based on passenger flow information and business information on the target date, realizes the technical effect of improving the scheduling accuracy of business resources, and solves the technical problem of low scheduling accuracy of business resources caused by related technologies allocating business resources based on a fixed pattern to provide business services.

[0123] Optionally, in the data processing apparatus provided in this application embodiment, the prediction module further includes: a first determining submodule, used to determine passenger flow and historical weather data for at least one historical date from historical passenger flow data; and a first processing submodule, used to process the passenger flow, historical weather data, and weather forecast data for at least one historical date through a target prediction model to obtain the passenger flow for a target date.

[0124] Optionally, in the data processing apparatus provided in the embodiments of this application, the data processing apparatus further includes: a first acquisition module, used to acquire a training sample set, wherein the training samples in the training sample set include weather data for the target date of the sample and historical passenger flow data of the sample, and the true label of the training sample is the actual passenger flow on the target date of the sample; and a training module, used to train an initial prediction model through the training sample set to obtain a target prediction model.

[0125] Optionally, in the data processing apparatus provided in the embodiments of this application, the data processing apparatus further includes: a first construction module, used to construct business service constraints based on business skills of business resources; a second construction module, used to construct scheduling time constraints based on working duration thresholds of business resources; a third construction module, used to construct business load constraints based on business load thresholds of business resources; and a fourth construction module, used to construct a set of constraints based on at least one of business service constraints, scheduling time constraints, and business load constraints.

[0126] Optionally, in the data processing apparatus provided in the embodiments of this application, the data processing apparatus further includes: a second determining module, configured to determine historical distribution data of business demand for at least one historical date from historical passenger flow data; and to determine business demand distribution data for a target date based on the historical distribution data of business demand for at least one historical date; or, a third processing module, configured to process weather forecast data and passenger flow, historical weather data and historical distribution data for at least one historical date through a target prediction model to obtain passenger flow and business demand distribution data for the target date.

[0127] Optionally, in the data processing apparatus provided in this application embodiment, the data processing apparatus further includes: a third determining module, used to determine the target service type to be obtained from the user's number-obtaining request on a target date; a second obtaining module, used to obtain the queuing waiting time and idle rate of each service resource corresponding to the target service type; and a fourth determining module, used to determine the target queuing number based on the queuing waiting time and idle rate of each service resource, wherein the target queuing number is used to indicate the service resource that provides service to the user.

[0128] Optionally, in the data processing apparatus provided in the embodiments of this application, the data processing apparatus further includes: a judgment module, used to judge whether the target business type is a first business type, wherein the first business type refers to the business type that provides business services through the target device; and a generation module, used to generate target information when the target business type is the first business type, wherein the target information is used to prompt the user to interact with the target device.

[0129] It should be noted that the prediction module 301, the first determination module 302, the first processing module 303, and the second processing module 304 mentioned above correspond to steps S201 to S204 in Embodiment 1. The four modules and their 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 can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0130] Example 3

[0131] Embodiments of this application may provide an electronic device. Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 (Only one is shown) processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0132] 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.

[0133] The processor can invoke information and applications stored in the memory via a transmission device to perform the following steps: predicting passenger flow on a target date based on target data, wherein the target data includes at least historical passenger flow data and weather forecast data for the target date; determining the business resources to provide business services on the target date based on passenger flow and business demand distribution data for the target date, wherein the business demand distribution data includes the predicted proportion of service demand for each business type in the passenger flow; on the target date, with the objective of minimizing user waiting time, performing calculations based on a preset set of constraints, the number of people waiting for business, and the total number of business resources to obtain the business resources corresponding to each business type; and scheduling business resources to provide business services based on the business resources corresponding to each business type.

[0134] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: determine the passenger flow and historical weather data for at least one historical date from the historical passenger flow data; process the passenger flow, historical weather data and weather forecast data for at least one historical date through the target prediction model to obtain the passenger flow for the target date.

[0135] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: obtain a training sample set, wherein the training samples in the training sample set include weather data and historical passenger flow data for the target date of the sample, and the true label of the training samples is the actual passenger flow on the target date of the sample; train an initial prediction model through the training sample set to obtain a target prediction model.

[0136] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: constructing business service constraints based on business skills of business resources; constructing scheduling time constraints based on working duration thresholds of business resources; constructing business load constraints based on business load thresholds of business resources; and constructing a set of constraints based on at least one of the business service constraints, scheduling time constraints, and business load constraints.

[0137] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: The data processing method further includes: determining historical distribution data of business demand for at least one historical date from historical passenger flow data; determining business demand distribution data for a target date based on the historical distribution data of business demand for at least one historical date; or, processing weather forecast data and passenger flow, historical weather data and historical distribution data for at least one historical date through a target prediction model to obtain passenger flow and business demand distribution data for the target date.

[0138] The processor can also invoke information and applications stored in the memory via the transmission device to perform the following steps: The data processing method further includes: after scheduling business resources to provide business services based on the business resources corresponding to each business type, on the target date, upon receiving a user's request to obtain a number, determining the target business type to be obtained from the request; obtaining the queuing waiting time and idle rate of each business resource corresponding to the target business type; determining the target queuing number based on the queuing waiting time and idle rate of each business resource, wherein the target queuing number is used to indicate the business resource that provides business services to the user.

[0139] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: The data processing method further includes: after determining the target service type to be obtained from the number retrieval request, determining whether the target service type is a first service type, wherein the first service type refers to the service type that provides service through the target device; if the target service type is the first service type, generating target information, wherein the target information is used to prompt the user to interact with the target device.

[0140] In this embodiment, the business resources for providing business services on the target date are determined by the predicted passenger flow and business demand distribution data on the target date. This enables the dynamic determination of the total number of business resources on the target date based on the predicted passenger flow information. On the target date, with the goal of minimizing user waiting time, the business resources corresponding to each business type are obtained by solving a set of preset constraints, the number of people waiting to handle business, and the total number of business resources. This allows for the dynamic adjustment of the allocation of business resources among business types based on real-time business demand on the target date. As a result, the scheduling accuracy of business resources can be effectively improved when scheduling business resources to provide business services based on the business resources corresponding to each business type.

[0141] Therefore, the method provided in this application achieves the goal of dynamically scheduling business resources based on passenger flow information and business information on the target date, realizes the technical effect of improving the scheduling accuracy of business resources, and solves the technical problem of low scheduling accuracy of business resources caused by related technologies allocating business resources based on a fixed pattern to provide business services.

[0142] Those skilled in the art will understand that Figure 4 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 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0143] 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.

[0144] Example 4

[0145] 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 data processing method provided in Embodiment 1.

[0146] 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.

[0147] This application also provides a computer program product, which, when executed on a data processing device, is a program adapted to perform data processing method steps.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 data processing method, characterized by, The method comprises: predicting the passenger flow of a target date based on target data, wherein the target data at least comprises historical passenger flow data and weather prediction data of the target date; determining business resources for providing business services on the target date based on the passenger flow and business demand distribution data of the target date, wherein the business demand distribution data comprises a predicted proportion of service demand of each business type in the passenger flow; solving and calculating based on a preset constraint condition set, the number of people to be served and the total number of business resources to obtain business resources corresponding to each business type on the target date, with the aim of minimizing user waiting time; scheduling business resources to provide business services based on the business resources corresponding to each business type.

2. The method of claim 1, wherein, The method comprises: determining the passenger flow, historical weather data of at least one historical date from the historical passenger flow data; processing the passenger flow, historical weather data of the at least one historical date, the weather prediction data through a target prediction model to obtain the passenger flow of the target date.

3. The method of claim 2, wherein, The target prediction model is obtained by: obtaining a training sample set, wherein the training sample in the training sample set comprises weather data of a sample target date and sample historical passenger flow data, and the real label of the training sample is the actual passenger flow of the sample target date; training an initial prediction model through the training sample set to obtain the target prediction model.

4. The method of claim 1, wherein, The method further comprises: constructing a business service constraint condition based on the business skills of the business resources; constructing a scheduling time constraint condition based on the work duration threshold of the business resources; constructing a business load constraint condition based on the business load threshold of the business resources; constructing the constraint condition set based on at least one of the business service constraint condition, the scheduling time constraint condition and the business load constraint condition.

5. The method of claim 1, wherein, The method further comprises: determining historical distribution data of business demand of at least one historical date from the historical passenger flow data; determining the business demand distribution data of the target date based on the historical distribution data of business demand of the at least one historical date; or processing the weather prediction data, the passenger flow, historical weather data and historical distribution data of the at least one historical date through a target prediction model to obtain the passenger flow of the target date and the business demand distribution data.

6. The method of claim 1, wherein, After scheduling business resources to provide business services based on the business resources corresponding to each business type, the method further comprises: on the target date, determining a target business type to be queued from a queuing request of a user in the case that the queuing request is received; obtaining the queuing waiting time and idle rate of each business resource corresponding to the target business type; determining a target queuing number based on the queuing waiting time and idle rate of each business resource, wherein the target queuing number is used to indicate a business resource for providing business services to the user.

7. The method of claim 6, wherein, After determining the target business type to be queued from the queuing request of the user, the method further comprises: determining whether the target service type is a first service type, wherein the first service type refers to a service type of providing a service by a target device; generating target information for prompting the user to interact with the target device, when the target service type is the first service type.

8. A data processing apparatus, characterized by, Comprise: a prediction module configured to predict a target date's passenger flow based on target data, wherein the target data comprises at least historical passenger flow data and weather prediction data of the target date; a first determination module configured to determine service resources for providing a service on the target date based on the passenger flow and service demand distribution data of the target date, wherein the service demand distribution data comprises a predicted proportion of each service type's service demand in the passenger flow; a first processing module configured to perform solving operation based on a preset constraint condition set, a number of people to be served and a total number of service resources to obtain service resources corresponding to each service type, with the aim of minimizing user waiting time on the target date; a second processing module configured to schedule the service resources to provide a service based on the service resources corresponding to each service type.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored executable program, wherein the executable program controls the device where the computer readable storage medium is located to execute the data processing method of any one of claims 1 to 7 when the executable program is running.

10. An electronic device, comprising: Comprise: a memory storing an executable program; a processor configured to run the program, wherein the program executes the data processing method of any one of claims 1 to 7 when the program is running.

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