Processing method and system for relieving queuing of customers in business hall

Abstract

The invention discloses a processing method for relieving customer queuing in a business hall, which comprises the following steps of: acquiring customer identity information, business intention and mobile terminal associated data through a multi-mode interaction terminal, analyzing customer demand types by utilizing a natural language processing algorithm, and dividing the customer demand types into simple businesses or complex businesses; triggering a self-service business processing flow for the simple business or guiding the simple business to a simple business rapid processing window, and generating an application form for the complex business and distributing the application form to a corresponding professional business area; monitoring a service load condition in real time, and dynamically adjusting a foreground and background human resource allocation strategy based on real-time service load data and a historical traffic prediction model; the queuing state information is synchronously pushed through the split-screen LED display terminal and the mobile terminal application, and client feedback data is collected to optimize the business process. The invention further discloses a corresponding system. According to the invention, the queuing condition of customers in the business hall can be optimized, and the business handling efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of business hall service management, and particularly to a processing method and system for alleviating customer queuing in a business hall. Background Art

[0002] In the daily operation of the power grid service business hall, due to centralized meter reading, centralized fee calculation, and centralized bill printing, a large number of customers will pour into the business hall to handle invoice printing and payment during certain periods, resulting in serious queuing problems. Before handling business, customers often need to spend a long time on tasks such as business query and data preparation. This not only increases the time cost of customers, but also makes the allocation of human resources in the business hall unreasonable, with excessive pressure on front-line staff and sometimes idle back-office staff. Currently, there is a lack of an effective comprehensive device to solve these problems, so as to achieve accurate decomposition of customer needs, reasonable allocation of human resources, and optimization of the business handling process. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a processing method and system for alleviating customer queuing in a business hall, which can optimize the customer queuing situation in the business hall and improve the business handling efficiency.

[0004] To solve the above technical problem, as one aspect of the present invention, a processing method for alleviating customer queuing in a business hall is provided, which includes the following steps:

[0005] Step S1, collecting customer identity information, business intentions, and mobile terminal associated data through a multi-modal interaction terminal, and using natural language processing algorithms to analyze the types of customer needs, which are classified into simple services or complex services;

[0006] Step S2, according to the results of the customer need type, triggering a self-service business processing process for simple services or guiding to a quick processing window for simple services, generating an application form for complex services and allocating it to the corresponding professional business area;

[0007] Step S3, real-time monitoring of the business load situation, and dynamically adjusting the front and back office human resource allocation strategy based on real-time business load data and historical traffic prediction models;

[0008] Step S4, synchronously pushing the queuing status information through a split-screen LED display terminal and a mobile application, and collecting customer feedback data to optimize the business process.

[0009] Preferably, the step S1 further includes:

[0010] Receive touch input and voice commands through the interactive interface in the multimodal interactive terminal; extract the structured data of the ID card / business license through the OCR recognition unit integrated with the document scanning module in the multimodal interactive terminal; realize binding with the customer's mobile terminal and synchronization of business history records through the Bluetooth / NFC near-field communication module in the multimodal interactive terminal;

[0011] Perform semantic analysis based on deep learning natural language processing algorithms to determine the type of customer needs.

[0012] Preferably, the step S2 further comprises:

[0013] Used to instruct users to handle simple businesses according to customer demand types, or automatically generate business application forms for complex businesses for customers to review and supplement;

[0014] Customers are assigned a queue sequence based on the business pre-processing results, and are guided to the corresponding processing window or area through electronic signs and voice navigation.

[0015] Among them, generate an application form, including:

[0016] Recommended business solution options based on automatic matching of customer profile database;

[0017] Use RPA robots to retrieve existing data from related systems to fill in form fields;

[0018] Generate electronic documents ready for signing that include timestamp and digital watermark.

[0019] Preferably, the step S3 further comprises:

[0020] Real-time statistics of business data;

[0021] Intelligent personnel dispatching unit, which is used to automatically issue early warnings to back-end management personnel based on business load monitoring data and prediction models, and dispatch appropriate staff to provide support;

[0022] Among them, when the number of people queuing at the business window exceeds the preset threshold, the skill matrix matching algorithm is triggered, and employees who meet the skill labels are screened from the background personnel database for reinforcement; during business low periods, employee capability assessment tests are automatically started, and customized training courses are pushed to idle personnel terminals.

[0023] Preferably, the step S4 further comprises:

[0024] Several large LED queuing information display screens are set up at designated locations in the business hall, and personalized queuing information is pushed through the customer's mobile phone application;

[0025] Collect customer feedback information through self-service interactive terminals, mobile applications, or by scanning QR codes;

[0026] Optimize business processes, including: using LSTM neural networks to perform sentiment polarity analysis on customer evaluation texts; identifying frequently occurring problem keywords to generate a service improvement heat map; and injecting optimization strategies back into the weight parameters of the business diversion decision tree.

[0027] Correspondingly, as another aspect of the present invention, there is also provided a processing system for alleviating customer queuing in business halls, which includes:

[0028] A customer demand analysis module, configured to collect customer identity information, business intentions, and mobile device-related data through a multi-modal interactive terminal, and use natural language processing algorithms to analyze the types of customer demands, classifying them into simple services or complex services;

[0029] A business diversion processing module, configured to trigger a self-service business processing flow for simple services or guide them to a quick processing window for simple services according to the results of customer demand types, and generate a pre-filled form for complex services and allocate them to corresponding professional business areas;

[0030] A dynamic human resource allocation module, configured to monitor the business load situation in real time, and dynamically adjust the front and back office human resource allocation strategies based on real-time business load data and historical traffic prediction models;

[0031] A customer queuing optimization and feedback module, configured to synchronously push queuing status information through a split-screen LED display terminal and a mobile application, and collect customer feedback data to optimize business processes.

[0032] Preferably, the customer demand analysis module further includes:

[0033] An interaction information collection unit, equipped with a high-definition touch screen, a near-field communication module, and a document scanning device. Among them, the high-definition touch screen is used to receive touch inputs and voice commands; the document scanning device is used to extract structured data of ID cards / business licenses; the near-field communication module is used to bind to the customer's mobile terminal and synchronize business history records;

[0034] A demand in-depth analysis unit, configured to perform semantic analysis based on deep learning natural language processing algorithms to determine the types of customer demands.

[0035] Preferably, the business diversion processing module includes:

[0036] A business quick preprocessing unit, which is connected to the business database and payment platform of the business hall, and is configured to determine the types of customer demands, where it instructs users to process simple services or automatically generate business application forms for complex services for customers to review and supplement;

[0037] The intelligent diversion and guidance unit is used to assign queue sequences to customers based on business pre-processing results, and guide customers to the corresponding processing windows or areas through electronic signs and voice navigation.

[0038] Preferably, the dynamic human resource deployment module further includes:

[0039] The business load monitoring unit is connected to each business processing system and terminal equipment of the business hall to collect business data in real time;

[0040] The intelligent personnel scheduling unit is used to automatically issue early warnings to back-end managers based on business load monitoring data and prediction models, and deploy appropriate staff to provide support.

[0041] Preferably, the customer queuing optimization and feedback module further comprises:

[0042] The precise queue information management unit is used to set up multiple large LED queue information display screens at predetermined locations in the business hall and push personalized queue information through the customer's mobile phone application;

[0043] The customer feedback collection and processing unit is used to collect customer feedback information through self-service interactive terminals, mobile applications or by scanning QR codes.

[0044] The implementation of the embodiments of the present invention has the following beneficial effects:

[0045] The present invention provides a processing method and system to alleviate the queue of customers in the business hall. Through intelligent customer demand analysis and efficient business pre-processing and diversion, a large number of simple business customers can quickly complete business processing at the self-service terminal, and complex business customers can also complete part of the preparation work in advance and be accurately diverted, reducing the ineffective waiting time in the queuing process. At the same time, dynamic human resource allocation ensures the efficient operation of the business window, further reducing the overall waiting time of customers.

[0046] The present invention is implemented to reduce the time cost of customers' preliminary inquiries by decomposing their needs, activate the front-end and back-end human resources and realize the variable frequency management of human resources, and finally achieve the effect of reducing customers' waiting time and improving business processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For a person skilled in the art, other drawings obtained based on these drawings still belong to the scope of the present invention without creative labor.

[0048] Figure 1 It is a schematic diagram of the main process of an embodiment of a method for alleviating customer queuing in a business hall provided by the present invention;

[0049] Figure 2 It is a schematic diagram of the structure of a system for alleviating customer queuing in a business hall provided by the present invention;

[0050] Figure 3 It is Figure 2 a schematic diagram of the structure of the customer demand analysis module in

[0051] Figure 4 It is Figure 2 a schematic diagram of the structure of the service diversion processing module in

[0052] Figure 5 It is Figure 2 a schematic diagram of the structure of the dynamic human resource allocation module in

[0053] Figure 6 It is Figure 2 a schematic diagram of the structure of the customer queuing optimization and feedback module in Detailed implementation manners

[0054] To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0055] As Figure 1 shown, it shows a schematic diagram of the main process of an embodiment of a method for alleviating customer queuing in a business hall provided by the present invention; in this embodiment, the method for alleviating customer queuing in the business hall includes the following steps:

[0056] Step S1, collecting customer identity information, service intentions and mobile terminal associated data through a multimodal interaction terminal, and using natural language processing algorithms to analyze the types of customer demands, and classifying them into simple services or complex services;

[0057] Specifically, the step S1 further includes:

[0058] Receiving touch inputs and voice commands through the interaction interface in the multimodal interaction terminal; extracting structured data of identity cards / business licenses through the OCR recognition unit integrated with a document scanning module in the multimodal interaction terminal; realizing the binding with the customer mobile terminal and synchronizing service history records through the near-field communication module (such as Bluetooth / NFC) in the multimodal interaction terminal;

[0059] Performing semantic analysis based on natural language processing algorithms of deep learning to determine the types of customer demands.

[0060] It is understandable that multiple self-service interactive terminals are set up in the business hall. The terminals are equipped with high-definition touch screens, near-field communication modules, and document scanning devices. After a customer arrives at the business hall, they can input their basic personal information and the general direction of the business they came to handle through touch operations or voice commands. At the same time, the document scanning device automatically reads the information of valid documents such as the customer's ID card and transmits it to the background system. In addition, the terminal also has the function of connecting to the customer's mobile phone via Bluetooth or scanning a code to obtain business-related information stored on the customer's mobile phone, such as electronic membership card information, recent business query records, etc.

[0061] After the background server receives the information transmitted by the interactive terminal, it uses natural language processing algorithms and data mining techniques based on deep learning to perform semantic analysis on the business direction information input by the customer, and combines the customer's identity information, historical business data, and relevant mobile phone information to accurately analyze the customer's actual business needs. For example, if a customer indicates that they want to "adjust the mobile phone package", the system will further analyze the customer's past package usage habits, consumption ability, and current market package options suitable for the customer to determine the specific package change plan that the customer may need and mark it as a complex business requirement; if the customer only queries the phone bill balance or pays the fee, the system classifies it as a simple business requirement.

[0062] Step S2 is used to trigger the self-service business processing process for simple businesses or guide them to the simple business quick processing window according to the customer demand type result, and generate an application form for complex businesses and allocate it to the corresponding professional business area;

[0063] Specifically, the step S2 further includes:

[0064] Used to indicate the user to process simple businesses according to the customer demand type, or automatically generate a business handling application form for complex businesses for the customer to review and supplement;

[0065] Allocate a queuing sequence for the customer according to the business preprocessing result, and guide the customer to the corresponding processing window or area through an electronic sign and voice navigation.

[0066] Among them, generating the application form includes:

[0067] Recommended business plan options automatically matched based on the customer portrait database;

[0068] Use an RPA robot to retrieve the stock data of the associated system to fill in the form fields;

[0069] Generate an electronic document to be signed that includes a timestamp and a digital watermark.

[0070] It is understandable that in this step, for customers with simple business requirements, such as payment and balance inquiry, the self-service interaction terminal directly connects to the business database and payment platform of the business hall. After the customer confirms the payment amount or query instruction on the terminal, the system quickly completes data query and payment processing, displays the processing result on the terminal screen, and at the same time provides the service of generating and pushing electronic invoices. For complex business requirements, such as package change and service activation, the system automatically generates a business handling application form according to the result of requirement analysis. Part of the customer information and the recommended business plan options are pre-filled in the form, and the customer only needs to review and supplement them on the terminal.

[0071] According to the business preprocessing result, the system assigns a queuing sequence to the customer. For customers with simple businesses, if the self-service terminal can complete the entire business handling process, the customer will be guided to complete the business in the self-service terminal area without entering the traditional queuing queue; if manual confirmation or special processing is still required, the system will assign them to a dedicated quick processing window for simple businesses and guide the customer there through electronic signs and voice navigation. For customers with complex businesses, the system assigns them to the corresponding professional business handling area according to the business type, estimates the waiting time, and notifies the customer through the customer's mobile phone and the display screen in the business hall.

[0072] Step S3: Monitor the business load situation in real time, and based on the real-time business load data and historical traffic prediction model, dynamically adjust the human resource allocation strategy for the front and back offices;

[0073] Specifically, step S3 further includes:

[0074] Statistically analyze business data in real time;

[0075] The personnel intelligent scheduling unit is used to automatically send an alarm to the back-office management personnel according to the business load monitoring data and prediction model, and allocate appropriate staff to go for support;

[0076] Among them, when the number of people queuing at the business window exceeds the preset threshold, trigger the skill matrix matching algorithm to screen employees with matching skill tags from the back-office personnel database for reinforcement; automatically start the employee ability assessment test during the business off-peak period and push customized training courses to the terminals of idle personnel.

[0077] It is understandable that in this step, by statistically analyzing data such as the number of business transactions, transaction duration, and the current number of queuing customers for each business window and each business type in real time. At the same time, analyze the business traffic patterns in different time periods (such as weekday mornings, afternoons, evenings, weekends, etc.) to establish a business traffic prediction model.

[0078] Based on the business load monitoring data and prediction model, when the customer traffic of a certain business area or business type exceeds the preset threshold, the system automatically issues an early warning to the back-end management personnel, and according to the staff scheduling and employee skill matrix, appropriate staff are deployed from the back-end to provide support. For example, during the peak period of package change business, back-end employees who are familiar with package business are deployed to the front desk; when there are more consulting business, employees with strong communication skills and comprehensive business knowledge are arranged to consulting positions. During the business trough period, some employees are reasonably arranged to receive training, rest, or engage in back-end data sorting and system maintenance work to achieve dynamic frequency conversion management of human resources.

[0079] Step S4, synchronously push queue status information through the split-screen LED display terminal and the mobile application, and collect customer feedback data to optimize business processes.

[0080] Specifically, the step S4 further includes:

[0081] Several large LED queuing information display screens are set up at designated locations in the business hall, and personalized queuing information is pushed through the customer's mobile phone application;

[0082] Collect customer feedback information through self-service interactive terminals, mobile applications or by scanning QR codes;

[0083] Optimize business processes, including: using LSTM neural network to perform sentiment polarity analysis on customer evaluation texts; identifying frequently occurring problem keywords to generate service improvement heat maps; and reversely injecting optimization strategies into the weight parameters of the business diversion decision tree.

[0084] It is understandable that in this step, multiple large LED queue information display screens are set up in prominent locations in the business hall, and the display screens are divided into different areas to display queue information of different business types, including the current number of queue members, estimated waiting time, and progress of customers who are processing business, etc. At the same time, personalized queue information is pushed to customers through the customer's mobile phone application, and customers can view their queue position changes and the business processing progress of the customers in front of them in real time on their mobile phones.

[0085] After the business is completed, customers can evaluate and provide feedback on their business experience through self-service interactive terminals, mobile applications, or by scanning QR codes. After the system collects customer feedback information, it uses text analysis technology to extract key information, such as customers' evaluations and suggestions on business processing speed, staff service attitude, and device convenience. Based on this feedback information, managers can promptly adjust business processes, personnel arrangements, and device function settings to continuously optimize the service quality of the business hall.

[0086] It can be understood that by implementing the method of the present invention, through the intelligent customer demand analysis module, the system can accurately analyze the actual business needs of customers, reducing business processing delays caused by unclear customer information, insufficient data preparation, etc. The efficient business preprocessing and diversion module enables simple businesses to be quickly processed at self-service terminals, while complex businesses can be quickly allocated to the corresponding professional business areas, simplifying the business processing process and improving the overall efficiency.

[0087] Through the dynamic human resource allocation module, the personnel arrangement is adjusted in real time according to the actual business needs, avoiding the idle and waste of human resources. During peak business periods, the system can automatically allocate appropriate staff to provide support, giving full play to the work efficiency of all employees; during off-peak periods, it can also reasonably arrange employees to carry out other valuable work, improving the overall utilization efficiency of human resources.

[0088] Customers can quickly input their personal information and business intentions through the self-service interactive terminal, reducing waiting time. The accurate queuing information management unit enables customers to view the changes in their queuing positions and the business processing progress of the customers in front of them in real time, reducing anxiety. The short waiting time and efficient business processing process have greatly improved the customer experience in the business hall.

[0089] The customer feedback collection and processing mechanism enables the business hall to timely understand customer needs and dissatisfaction, and adjust the business process, personnel arrangement, and function settings of the device according to the feedback information. By continuously optimizing the service, it enhances customer satisfaction and loyalty to the business hall service, which is conducive to the long-term stable operation of the business hall.

[0090] As Figure 2 shown, a schematic structural diagram of a processing system for alleviating customer queuing in a business hall provided by the present invention is shown; in combination with Figures 3 to 6 shown, in this embodiment, the processing system 1 for alleviating customer queuing in a business hall at least includes:

[0091] A customer demand analysis module 10, configured to collect customer identity information, business intentions, and mobile terminal associated data through a multimodal interaction terminal, and use natural language processing algorithms to analyze the types of customer demands, and classify them into simple businesses or complex businesses;

[0092] A business diversion processing module 11, configured to trigger a self-service business processing process for simple businesses or guide them to a simple business quick processing window according to the customer demand type result, and generate a pre-filled form for complex businesses and allocate them to the corresponding professional business areas;

[0093] A dynamic human resource allocation module 12, configured to monitor the business load situation in real time, and dynamically adjust the front and back office human resource allocation strategy based on the real-time business load data and the historical traffic prediction model;

[0094] The customer queuing optimization and feedback module 13 is used to synchronously push queuing status information through a split-screen LED display terminal and a mobile application, and collect customer feedback data to optimize the business process.

[0095] Specifically, as Figure 3 shown, the customer demand analysis module 10 further includes:

[0096] The interaction information collection unit 100 is configured with a high-definition touch screen, a near-field communication module, and a document scanning device. Among them, the high-definition touch screen is used to receive touch inputs and voice commands; the document scanning device is used to extract structured data of ID cards / business licenses; the near-field communication module is used to realize the binding with the customer's mobile terminal and the synchronization of business history records;

[0097] The demand in-depth analysis unit 101 is used to perform semantic analysis based on deep learning natural language processing algorithms to determine the type of customer demand.

[0098] Specifically, as Figure 4 shown, the business diversion processing module 11 includes:

[0099] The business quick preprocessing unit 110 is connected to the business database and the payment platform of the business hall, and is used to determine the type of customer demand, where it instructs the user to process simple services, or automatically generates a business handling application form for complex services for the customer to review and supplement;

[0100] The intelligent diversion guidance unit 111 is used to allocate a queuing sequence for the customer according to the business preprocessing result, and guide the customer to the corresponding processing window or area through an electronic sign and voice navigation.

[0101] Specifically, as Figure 5 shown, the dynamic human resource allocation module 12 further includes:

[0102] The business load monitoring unit 120 is connected to each business handling system and terminal device in the business hall, and is used to statistically analyze business data in real time;

[0103] The personnel intelligent scheduling unit 121 is used to automatically send an early warning to the background management personnel according to the business load monitoring data and the prediction model, and allocate appropriate staff to go for support.

[0104] Specifically, as Figure 6 shown, the customer queuing optimization and feedback module 13 further includes:

[0105] The precise queuing information management unit 130 is used to set up multiple large LED queuing information display screens at the reserved positions in the business hall, and push personalized queuing information through the customer mobile application.

[0106] The customer feedback collection and processing unit 131 is used to collect customer feedback information through self-service interaction terminals, mobile applications or scanning QR codes.

[0107] For more details, reference can be made to and combined with the foregoing description of Figure 1 which will not be elaborated here.

[0108] It can be understood that through the service diversion processing module of the present invention, the service handling process is made more simplified and orderly. The staff can focus on the core service handling, reducing service handling delays caused by unclear customer information, insufficient data preparation, etc. At the same time, the reasonable allocation of human resources ensures that there are sufficient and appropriate personnel participating in each service link, improving the overall efficiency of service handling. The dynamic human resource allocation module adjusts the personnel arrangement in real time according to the actual service needs, avoiding the idle and waste of human resources. During the peak service period, the work efficiency of all employees can be fully exerted, and during the off-peak period, employees can also be reasonably arranged to carry out other valuable work, improving the overall utilization efficiency of human resources. The short waiting time and efficient service handling process can greatly improve the customer experience in the business hall and reduce the customer's anxiety. The customer feedback collection and processing mechanism enables the business hall to timely understand customer needs and dissatisfaction, continuously optimize the service, thereby enhancing customer satisfaction and loyalty to the business hall service, which is beneficial to the long-term stable operation of the business hall.

[0109] Implementing the embodiments of the present invention has the following beneficial effects:

[0110] The present invention provides a method and system for alleviating customer queuing in a business hall. Through intelligent customer demand analysis and efficient service preprocessing and diversion, a large number of customers with simple services can quickly complete service handling at self-service terminals, and customers with complex services can also complete part of the preparatory work in advance and be precisely diverted, reducing the ineffective waiting time during the queuing process. At the same time, dynamic human resource allocation ensures the efficient operation of service windows, further reducing the overall waiting time of customers.

[0111] Implementing the present invention, by decomposing the needs of customers arriving at the hall, the time cost of pre-service query for customers is reduced, the human resources of the front and back offices are activated and the frequency conversion management of human resources is realized, ultimately achieving the effects of reducing customer waiting time and improving service handling efficiency.

[0112] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a unit, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0113] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a combination of multiple flows and / or blocks

[0114] The above-disclosed is only a preferred embodiment of the present invention, and of course, it cannot be used to limit the scope of the rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A processing method for alleviating customer queuing in business halls, characterized in that, The following steps are involved: Step S1, collecting customer identity information, business intentions and mobile terminal related data through a multimodal interactive terminal, and using a natural language processing algorithm to analyze customer demand types and classify them into simple business or complex business; Step S2 is used to trigger the self-service business processing flow or guide the customer to the simple business quick processing window according to the customer demand type result for simple business, and generate an application form for complex business and allocate it to the corresponding professional business area; Step S3, real-time monitoring of business load conditions, and dynamically adjusting front-end and back-end human resource allocation strategies based on real-time business load data and historical traffic prediction models; Step S4, synchronously push queue status information through the split-screen LED display terminal and the mobile application, and collect customer feedback data to optimize business processes.

2. The method according to claim 1, wherein The step S1 further comprises: Receive touch input and voice commands through the interactive interface in the multimodal interactive terminal; extract the structured data of the ID card / business license through the OCR recognition unit integrated with the document scanning module in the multimodal interactive terminal; realize binding with the customer's mobile terminal and synchronization of business history records through the Bluetooth / NFC near-field communication module in the multimodal interactive terminal; Through deep learning natural language processing algorithms, semantic analysis is performed to determine the type of customer needs.

3. The method according to claim 2, characterized in that The step S2 further comprises: Used to instruct users to handle simple businesses according to customer demand types, or automatically generate business application forms for complex businesses for customers to review and supplement; Customers are assigned a queue sequence based on the business pre-processing results, and are guided to the corresponding processing window or area through electronic signs and voice navigation. Among them, generate an application form, including: Use RPA robots to retrieve existing data from related systems to fill in form fields; Recommended business solution options based on automatic matching of customer profile database; Generate electronic documents ready for signing that include timestamp and digital watermark.

4. The method according to claim 3, characterized in that, The step S3 further comprises: Real-time statistics of business data; Intelligent personnel dispatching unit, which is used to automatically issue early warnings to back-end management personnel based on business load monitoring data and prediction models, and is responsible for dispatching appropriate staff to provide support; Among them, when the number of people queuing at the business window exceeds the preset threshold, the skill matrix matching algorithm is triggered, and employees who meet the skill labels are screened from the background personnel database for reinforcement; during business low periods, employee capability assessment tests are automatically started, and customized training courses are pushed to idle personnel terminals.

5. The method according to claim 1, characterized in that, The step S4 further comprises: Several large LED queuing information display screens are set up at designated locations in the business hall, and personalized queuing information is pushed through the customer's mobile phone application; Collect customer feedback information through self-service interactive terminals, mobile applications or by scanning QR codes; Optimize business processes, including: using LSTM neural network to perform sentiment polarity analysis on customer evaluation texts; identifying high-frequency problem keywords to generate service improvement heat maps; and reversely injecting optimization strategies into the weight parameters of the business diversion decision tree.

6. A processing system for alleviating the queuing of customers in a business hall, characterized in that, include: The customer demand analysis module is used to collect customer identity information, business intentions and mobile terminal related data through multimodal interactive terminals, and use natural language processing algorithms to analyze customer demand types and classify them into simple or complex businesses; The business diversion processing module is used to trigger the self-service business processing flow or guide to the simple business quick processing window for simple business according to the type of customer demand, and generate pre-filled forms for complex business and allocate them to the corresponding professional business areas; Dynamic human resource allocation module, which is used to monitor business load in real time and dynamically adjust the front-end and back-end human resource allocation strategies based on real-time business load data and historical traffic prediction models; The customer queue optimization and feedback module is used to synchronously push queue status information through the split-screen LED display terminal and the mobile application, and collect customer feedback data to optimize business processes.

7. The system according to claim 6, wherein The customer demand analysis module further includes: The interactive information collection unit is equipped with a high-definition touch screen, a near-field communication module and a document scanning device, wherein the high-definition touch screen is used to receive touch input and voice commands; the document scanning device is used to extract the structured data of the ID card / business license; the near-field communication module is used to achieve binding with the customer's mobile terminal and synchronization of business history records; The demand in-depth analysis unit is used to perform semantic analysis based on deep learning natural language processing algorithms to determine the type of customer demand.

8. The system according to claim 7, wherein The business diversion processing module includes: The business quick pre-processing unit is connected to the business database and payment platform of the business hall and is used to determine the type of customer demand, instructing the user to process simple business, or automatically generating a business application form for complex business for the customer to review and supplement; The intelligent diversion and guidance unit is used to assign queue sequences to customers based on business pre-processing results, and guide customers to the corresponding processing windows or areas through electronic signs and voice navigation.

9. The system according to claim 8, wherein The dynamic human resource deployment module further includes: The business load monitoring unit is connected to each business processing system and terminal equipment of the business hall to collect business data in real time; The intelligent personnel scheduling unit is used to automatically issue early warnings to back-end managers based on business load monitoring data and prediction models, and deploy appropriate staff to provide support.

10. The system according to claim 9, wherein The customer queuing optimization and feedback module further includes: The precise queue information management unit is used to set up multiple large LED queue information display screens at predetermined locations in the business hall and push personalized queue information through the customer's mobile phone application; The customer feedback collection and processing unit is used to collect customer feedback information through self-service interactive terminals, mobile applications or by scanning QR codes.

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