Business handling optimization method and system based on big data

Abstract

The invention provides a business handling optimization method and system based on big data, and relates to the technical field of business handling, and the method comprises the steps: obtaining a business handling application initiated by an applicant; obtaining business handling historical data corresponding to the applicant from a big data platform based on the name and the identity label; inputting the age, the educational background, the number of times of business handling and the total number of times of business handling as variables into a pre-constructed handling duration prediction model, and outputting predicted handling duration; according to the business type, the age and the educational background, obtaining the average handling duration of the corresponding local historical handling business information; based on the predicted handling duration and the average handling duration, determining the predicted handling duration corresponding to the applicant; calculating to obtain waiting duration corresponding to each handling window; according to the waiting duration of each handling window, generating a queuing number of the handling window with the shortest waiting duration for the applicant, and printing the queuing number; the waiting time of the applicant for handling business can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of business processing technology, and in particular to a business processing optimization method and system based on big data. Background Art

[0002] The government service hall refers to the service hall set up by government agencies or administrative service centers, which provides various government affairs processing and consulting services. When handling business in the government service hall, applicants often need to queue up to get a number first, then get the corresponding application form according to the specific business to be handled, fill in the application form, and then wait for the number to be called. When it is your turn, you need to submit the materials and go through the formalities.

[0003] There are usually multiple service windows for the same type of business. When an applicant queues up to get a number through the queuing system, the queuing system will assign the applicant a queue number of a service window with fewer people in the queue, which may cause the applicant to wait longer and affect the applicant's experience in handling business. Summary of the invention

[0004] In response to the above problems, the present invention provides a business processing optimization method and system based on big data, which solves the technical problem in the prior art that when an applicant queues up to get a number through the queuing system, the queuing system assigns the applicant a queuing number of a processing window with a small number of people in the queue, which may cause the applicant to wait for a longer time. The method and system can generate a queuing number for the processing window with the shortest waiting time for the applicant based on the applicant's own reasons and the staff's ability reasons, thereby reducing the waiting time for the applicant to handle business.

[0005] The embodiment of the present invention provides a business processing optimization method based on big data, including: Obtaining a business application initiated by an applicant, wherein the business application includes business type, name, identity, age and education level; Based on the name and identity identifier, obtain the business handling history data corresponding to the applicant from the big data platform; Classify and count the business handling history data to determine the number of times the applicant has handled the business and the total number of times the applicant has handled the business; Input age, education level, number of times the business is handled and total number of times the business is handled as variables into the pre-built processing time prediction model, and output the predicted processing time; According to the business type, age and education level, obtain the average processing time of the corresponding local historical business information; Determine the estimated processing time corresponding to the applicant based on the predicted processing time and the average processing time; Get the capability values ​​of the staff at each processing window; Based on the estimated processing time of applicants queuing at the processing window and the ability value of the staff, the waiting time corresponding to each processing window is calculated; According to the waiting time of each processing window, a queue number of the processing window with the shortest waiting time is generated for the applicant and printed.

[0006] In some embodiments, the calculation formula of the processing time prediction model includes: G(A)+ F(E)+ + ; In the formula, To predict the processing time, A is the applicant's age information, E is the applicant's education level, The number of times the applicant has handled this business. The total number of business operations handled by the applicant. To handle the bias term of the duration prediction model, , , , is the regression coefficient of the corresponding variable, G(A) is the unified conversion function for age, F(E) is the unified conversion function for education level, In order to unify the conversion function for the number of times this business is handled, In order to unify the conversion function for the total number of business transactions, To predict the processing time.

[0007] In some embodiments, the obtaining of the average processing time of the corresponding local historical processing business information according to the business type, age and education level includes: Obtain the number of business transactions and the processing time of each business transaction in the local historical business transaction information; Calculate the average processing time based on the number of business transactions and the processing time: ; In the formula, The average processing time is The number of business transactions, For the The business processing time of each business.

[0008] In some embodiments, determining the estimated processing time corresponding to the applicant based on the predicted processing time and the average processing time includes: The weighted average algorithm is used to determine the estimated processing time corresponding to the applicant: ; In the formula, To estimate the processing time, It is the weight coefficient used to balance the predicted processing time and the average processing time.

[0009] In some embodiments, obtaining the capability values ​​corresponding to the staff at each processing window includes: Get the staff's education background , Years of working experience and number of people served ; The staff's competence value is calculated using the following formula based on their education, years of service and number of service personnel: F( )+ G( )+ ; In the formula, is the ability value, F( ) is a conversion function to unify the educational background of the staff, G( ) is a conversion function for unifying the working years of staff members, To unify the number of staff members served, , , The weights corresponding to the staff’s education level, years of work experience, and number of people served.

[0010] In some embodiments, the waiting time corresponding to each processing window is calculated based on the estimated processing time of the applicants queuing at the processing window and the ability value of the staff, including: Determine the correction factor based on the staff's capability value, including: when , the correction factor is ; when , the correction factor is ; when , the correction factor is ,in, is the corresponding capability value of the staff member, is the standard capability value, ; When the correction factor is determined, the waiting time is calculated using the following formula: ; In the formula, For the waiting time, To handle the window Estimated processing time for each applicant: is the number of applicants in the queue.

[0011] In some embodiments, including: When there are multiple processing windows with the shortest waiting time, obtain the capability value of the staff member corresponding to the processing window with the shortest waiting time; Generate and print the queue number of the processing window with the highest staff ability value for the applicant.

[0012] The embodiment of the present invention provides a business processing optimization system based on big data, including: A first acquisition module is used to acquire a business application initiated by an applicant, wherein the business application includes a business type, name, identity, age and education level; A second acquisition module is used to obtain the business handling history data corresponding to the applicant from the big data platform based on the name and identity identifier; A classification statistics module is used to classify and count the business handling history data to determine the number of times the applicant has handled the business and the total number of times the applicant has handled the business; The processing time prediction module is used to input age, education level, number of times the business is processed and the total number of times the business is processed as variables into a pre-built processing time prediction model and output the predicted processing time; The third acquisition module is used to obtain the average processing time of the corresponding local historical business information according to the business type, age and education level; A determination module, used to determine the estimated processing time corresponding to the applicant based on the predicted processing time and the average processing time; The fourth acquisition module is used to obtain the capability values ​​corresponding to the staff at each processing window; A calculation module, for calculating the waiting time corresponding to each processing window based on the estimated processing time of the applicants queuing at the processing window and the ability value of the staff; The first generating module is used to generate and print the queue number of the processing window with the shortest waiting time for the applicant according to the waiting time of each processing window.

[0013] Compared with the prior art, the present invention has the following beneficial effects: By pre-building a processing time prediction model, various characteristics of the applicants are analyzed and predicted to obtain the applicant's predicted processing time. By extracting and analyzing local historical business information, the average processing time is obtained. By comprehensively considering the predicted processing time and the average processing time, the applicant's estimated processing time is obtained. The waiting time at the processing window is predicted by the ability value of the staff at the processing window and the estimated processing time of the applicants in line. The applicant can be accurately assigned to the processing window with the shortest waiting time, which can reduce the waiting time for the applicant to handle business. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The embodiments of the present invention are further described below with reference to the accompanying drawings: Figure 1 A schematic diagram of the implementation process of the business processing optimization method based on big data provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a business processing optimization system based on big data provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0016] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0017] If similar descriptions of "first\second\third" appear in the application documents, the following instructions are added. In the following description, the terms "first\second\third" involved are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0019] Based on the problems existing in the related art, an embodiment of the present invention provides a business processing optimization method based on big data, and the execution subject of the optimization method can be an electronic device. The electronic device can be various types of terminals such as a laptop, a tablet computer, a desktop computer, a set-top box, a mobile device (for example, a mobile phone, a portable music player, a personal digital assistant, a dedicated messaging device, a portable gaming device), and can also be implemented as a server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0020] In some embodiments, the functions implemented by the optimization method provided by the embodiments of the present invention may be implemented by a processor of an electronic device calling a program code, wherein the program code may be stored in a computer storage medium.

[0021] The embodiment of the present invention provides a business processing optimization method based on big data. Figure 1 A schematic diagram of the implementation flow of the business processing optimization method based on big data provided by an embodiment of the present invention, such as Figure 1 As shown, including: Step S1: Obtaining a business application initiated by an applicant, wherein the business application includes business type, name, identity, age and education level; In the embodiment of the present invention, the government hall can set up an information collection terminal or make an online appointment to allow the applicant to initiate a business application. The business application requires the applicant to fill in the business type, name, identity, age and education. Then the business application initiated by the applicant is obtained through the system background.

[0022] Step S2: Based on the name and identity identifier, obtain the business handling history data corresponding to the applicant from the big data platform; In the embodiment of the present invention, the background system can determine whether the identity information of the applicant is correct, that is, whether the name and the identity identifier correspond to each other, according to the name and the identity identifier, wherein the identity identifier can be the ID card number. When the identity information of the applicant is determined to be correct, the business handling history data corresponding to the applicant can be obtained from the big data platform, and the business handling history data here refers to the business involved in the government affairs hall.

[0023] Step S3: classify and count the business handling history data to determine the number of times the applicant has handled the business and the total number of times the applicant has handled the business; In an embodiment of the present invention, when obtaining the business handling history data corresponding to the applicant, the business handling history data can be classified and counted according to the business type to obtain the number of times the business is handled, and the total number of times the business is handled in the business handling history data can be counted.

[0024] Step S4: input age, education level, number of times the business is handled and total number of times the business is handled as variables into a pre-built processing time prediction model, and output the predicted processing time; In some embodiments, the calculation formula of the processing time prediction model includes: G(A)+ F(E)+ + ; In the formula, To predict the processing time, A is the applicant's age information, E is the applicant's education level, The number of times the applicant has handled this business. The total number of business operations handled by the applicant. To handle the bias term of the duration prediction model, , , , is the regression coefficient of the corresponding variable, G(A) is the unified conversion function for age, F(E) is the unified conversion function for education level, In order to unify the conversion function for the number of times this business is handled, In order to unify the conversion function for the total number of business transactions, To predict the processing time.

[0025] In an embodiment of the present invention, a linear regression model is used to construct a processing time prediction model. Linear regression is a simple and commonly used machine learning algorithm that works better when processing continuous output variables. The linear regression model can be used to take various characteristics of the applicant as input variables and the predicted processing time as the output variable, and the predicted processing time of the applicant for this business can be predicted by fitting a linear relationship. In specific processing, it is necessary to convert age information, education level, number of times this business is handled, and the total number of times the business is handled into a unified standard through a corresponding conversion function. For example, the conversion can be performed by normalization method, unique hot encoding, label encoding, etc.

[0026] Step S5: According to the business type, age and education level, obtain the average processing time of the corresponding local historical business information; In some embodiments, step S5 includes: Step S51: Obtain the number of business transactions and the business transaction duration of each business in the local historical business transaction information; Step S52: Calculate the average processing time based on the number of business transactions and the business processing time: ; In the formula, The average processing time is The number of business transactions, For the The business processing time of each business.

[0027] In the embodiment of the present invention, the local historical business information of people with the same age, education level and business type as the applicant is obtained according to the business type, age and education level. This facilitates the subsequent extraction of effective features by analyzing the local historical business information of similar people. The number of business transactions and the business transaction time of each business are extracted from the local historical business information, and the business transaction time of each business is summed to obtain the total business transaction time. Then, the total business transaction time is divided by the number of business transactions to calculate the average transaction time.

[0028] Step S6: Determine the estimated processing time corresponding to the applicant based on the predicted processing time and the average processing time; In some embodiments, step S6 includes: Step S61: Using a weighted average algorithm, determine the estimated processing time corresponding to the applicant: ; In the formula, To estimate the processing time, It is the weight coefficient used to balance the predicted processing time and the average processing time.

[0029] In an embodiment of the present invention, a weighted average is taken of the predicted processing time and the average processing time to determine the estimated processing time corresponding to the applicant, thereby correcting the predicted processing time by the average processing time to improve the accuracy of the estimated processing time.

[0030] Step S7: Obtain the capability values ​​corresponding to the staff at each processing window; In some embodiments, the step S7 includes: Step S71: Obtain the staff's educational background , Years of working experience and number of people served ; Step S72: Calculate the staff member's competence value using the following formula based on the staff member's education level, years of service, and number of service personnel: F( )+ G( )+ ; In the formula, is the ability value, F( ) is a conversion function to unify the educational background of the staff, G( ) is a conversion function for unifying the working years of staff members, To unify the number of staff members served, , , The weights corresponding to the staff’s education level, years of work experience, and number of people served.

[0031] In the embodiment of the present invention, the higher the educational background of the staff, the longer the working years, the more people the staff serves, the greater the corresponding ability value. In the specific processing, the age information, educational background, the number of times the business is handled, and the total number of times the business is handled need to be converted into a unified standard through the corresponding conversion function. For example, the conversion can be performed by normalization method, unique hot encoding, label encoding, etc. The ability value of the staff is stored in the background system, and the corresponding ability value can be called according to the staff of the current window.

[0032] Step S8: Based on the estimated processing time of the applicants queuing at the processing window and the ability value of the staff, the waiting time corresponding to each processing window is calculated; In some embodiments, the step S8 includes: Step S81: Determine the correction coefficient according to the worker's capability value, including: when , the correction factor is ; when , the correction factor is ; when , the correction factor is ,in, is the corresponding capability value of the staff member, is the standard capability value, ; Step S82: When the correction coefficient is determined, the waiting time is calculated using the following formula: ; In the formula, For the waiting time, For window Estimated processing time for each applicant: is the number of applicants in the queue.

[0033] In an embodiment of the present invention, the capability value of the staff can be used to characterize the efficiency of handling business. The higher the efficiency, the more business is handled per unit time. Therefore, the estimated handling time is corrected according to the capability value of the staff at the handling window to obtain the waiting time corresponding to the handling window.

[0034] Step S9: According to the waiting time of each processing window, a queue number of the processing window with the shortest waiting time is generated for the applicant and printed.

[0035] To summarize, by pre-building a processing time prediction model, various characteristics of the applicant are analyzed and predicted to obtain the applicant's predicted processing time. By extracting and analyzing local historical business information, the average processing time is obtained. By comprehensively considering the predicted processing time and the average processing time, the applicant's estimated processing time is obtained. The waiting time at the processing window is predicted by the ability value of the staff at the processing window and the estimated processing time of the applicants in line. The applicant can be accurately assigned to the processing window with the shortest waiting time, which can reduce the waiting time for the applicant to handle business.

[0036] In some embodiments, including: Step S100: when there are multiple processing windows with the shortest waiting time, obtaining the capability value of the staff member corresponding to the processing window with the shortest waiting time; Step S200: Generate and print the queue number of the processing window with the highest staff capacity value for the applicant.

[0037] In the embodiment of the present invention, when there are multiple processing windows with the shortest waiting time, the applicant is assigned to the processing window with the highest staff capacity value, which can improve the applicant's processing experience.

[0038] Based on the foregoing embodiments, the embodiments of the present invention provide a business processing optimization system based on big data. The modules included in the system and the units included in each module can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0039] The embodiment of the present invention provides a business processing optimization system based on big data. Figure 2A schematic diagram of the structure of a business processing optimization system based on big data provided by an embodiment of the present invention, such as Figure 2 As shown, including: A first acquisition module is used to acquire a business application initiated by an applicant, wherein the business application includes a business type, name, identity, age and education level; A second acquisition module is used to obtain the business handling history data corresponding to the applicant from the big data platform based on the name and identity identifier; A classification statistics module is used to classify and count the business handling history data to determine the number of times the applicant has handled the business and the total number of times the applicant has handled the business; The processing time prediction module is used to input age, education level, number of times the business is processed and the total number of times the business is processed as variables into a pre-built processing time prediction model and output the predicted processing time; In some embodiments, the calculation formula of the processing time prediction model includes: G(A)+ F(E)+ + ; In the formula, To predict the processing time, A is the applicant's age information, E is the applicant's education level, The number of times the applicant has handled this business. The total number of business operations handled by the applicant. To handle the bias term of the duration prediction model, , , , is the regression coefficient of the corresponding variable, G(A) is the unified conversion function for age, F(E) is the unified conversion function for education level, In order to unify the conversion function for the number of times this business is handled, In order to unify the conversion function for the total number of business transactions, To predict the processing time.

[0040] The third acquisition module is used to obtain the average processing time of the corresponding local historical business information according to the business type, age and education level; A determination module, used to determine the estimated processing time corresponding to the applicant based on the predicted processing time and the average processing time; The fourth acquisition module is used to obtain the capability values ​​corresponding to the staff at each processing window; A calculation module, for calculating the waiting time corresponding to each processing window based on the estimated processing time of the applicants queuing at the processing window and the ability value of the staff; The first generating module is used to generate and print the queue number of the processing window with the shortest waiting time for the applicant according to the waiting time of each processing window.

[0041] In some embodiments, the third acquisition module includes: The first acquisition unit is used to acquire the number of business transactions and the business transaction duration of each business in the local historical business transaction information; The first calculation unit is used to calculate the average processing time according to the number of business processing and the business processing time: ; In the formula, The average processing time is The number of business transactions, For the The business processing time of each business.

[0042] In some embodiments, the determining module includes: The second calculation unit is used to determine the estimated processing time corresponding to the applicant by using a weighted average algorithm: ; In the formula, To estimate the processing time, It is the weight coefficient used to balance the predicted processing time and the average processing time.

[0043] In some embodiments, the fourth acquisition module includes: The second acquisition unit is used to obtain the educational background of the staff member. , Years of working experience and number of people served ; The third calculation unit is used to calculate the staff's ability value using the following formula based on the staff's education level, years of service and number of service personnel: F( )+ G( )+ ; In the formula, is the ability value, F( ) is a conversion function to unify the educational background of the staff, G( ) is a conversion function for unifying the working years of staff members, To unify the number of staff members served, , , The weights corresponding to the staff’s education level, years of work experience, and number of people served.

[0044] In some embodiments, the computing module comprises: The first determination unit is used to determine the correction coefficient according to the capability value of the staff member, including: when , the correction factor is ; when , the correction factor is ; when , the correction factor is ,in, is the corresponding capability value of the staff member, is the standard capability value, ; The fourth calculation unit is used to calculate the waiting time by using the following formula when the correction coefficient is determined: ; In the formula, For the waiting time, For window Estimated processing time for each applicant: is the number of applicants in the queue.

[0045] In some embodiments, including: A fifth acquisition module is used to acquire the capability value of the staff member corresponding to the processing window with the shortest waiting time when there are multiple processing windows with the shortest waiting time; The second generating module is used to generate and print the queue number of the processing window with the highest staff ability value for the applicant.

[0046] It should be noted that in the embodiment of the present invention, if the above-mentioned optimization method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk. In this way, the embodiment of the present invention is not limited to any specific combination of hardware and software.

[0047] Accordingly, an embodiment of the present invention provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps in the optimization method provided in the above embodiment are implemented.

[0048] An embodiment of the present invention provides an electronic device; Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the electronic device 400 includes: a processor 401, at least one communication bus 402, a user interface 403, at least one external communication interface 404, and a memory 405. The communication bus 402 is configured to realize the connection and communication between these components. The user interface 403 may include a display screen, and the external communication interface 404 may include a standard wired interface and a wireless interface. The processor 401 is configured to execute the program of the optimization method stored in the memory to implement the steps in the optimization method provided in the above embodiment.

[0049] It should be noted that the description of the above storage medium and electronic device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of the present invention, please refer to the description of the method embodiments of the present invention for understanding.

[0050] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0051] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0052] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0053] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0054] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product, which is stored in a storage medium and includes several instructions for a controller to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0055] The above is only an embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A business processing optimization method based on big data, characterized in that: include: Obtaining a business application initiated by an applicant, wherein the business application includes business type, name, identity, age and education level; Based on the name and identity identifier, obtain the business handling history data corresponding to the applicant from the big data platform; Classify and count the business handling history data to determine the number of times the applicant has handled the business and the total number of times the applicant has handled the business; Input age, education level, number of times the business is handled and total number of times the business is handled as variables into the pre-built processing time prediction model, and output the predicted processing time; According to the business type, age and education level, obtain the average processing time of the corresponding local historical business information; Determine the estimated processing time corresponding to the applicant based on the predicted processing time and the average processing time; Get the capability values ​​of the staff at each processing window; Based on the estimated processing time of applicants queuing at the processing window and the ability value of the staff, the waiting time corresponding to each processing window is calculated; According to the waiting time of each processing window, a queue number of the processing window with the shortest waiting time is generated for the applicant and printed.

2. The business processing optimization method based on big data according to claim 1 is characterized in that: The calculation formula of the processing time prediction model includes: G(A)+ F(E)+ + ; In the formula, To predict the processing time, A is the applicant's age information, E is the applicant's education level, The number of times the applicant has handled this business. The total number of business operations handled by the applicant. To handle the bias term of the duration prediction model, , , , is the regression coefficient of the corresponding variable, G(A) is the unified conversion function for age, F(E) is the unified conversion function for education level, In order to unify the conversion function for the number of times this business is handled, In order to unify the conversion function for the total number of business transactions, To predict the processing time.

3. The business processing optimization method based on big data according to claim 2 is characterized in that: The method of obtaining the average processing time of the corresponding local historical business information according to the business type, age and education level includes: Obtain the number of business transactions and the processing time of each business transaction in the local historical business transaction information; Calculate the average processing time based on the number of business transactions and the processing time: ； In the formula, The average processing time is The number of business transactions, For the The business processing time of each business.

4. The business processing optimization method based on big data according to claim 3 is characterized in that: Determining the estimated processing time corresponding to the applicant based on the predicted processing time and the average processing time includes: The weighted average algorithm is used to determine the estimated processing time corresponding to the applicant: ； In the formula, To estimate the processing time, It is the weight coefficient used to balance the predicted processing time and the average processing time.

5. The business processing optimization method based on big data according to claim 4 is characterized in that: The obtaining of the capability values ​​corresponding to the staff at each processing window includes: Get the staff's education background , Years of working experience and number of people served ; The staff's competence value is calculated using the following formula based on their education, years of service and number of service personnel: F( )+ G( )+ ; In the formula, is the ability value, F( ) is a conversion function to unify the educational background of the staff, G( ) is a conversion function for unifying the working years of staff members, To unify the number of staff members served, , , The weights corresponding to the staff’s education level, years of work experience, and number of people served.

6. The business processing optimization method based on big data according to claim 5 is characterized in that: The estimated processing time of the applicants queuing at the processing window and the ability value of the staff are used to calculate the waiting time corresponding to each processing window, including: Determine the correction factor based on the staff's capability value, including: when , the correction factor is ; when , the correction factor is ; when , the correction factor is ,in, is the corresponding capability value of the staff member, is the standard capability value, ; When the correction factor is determined, the waiting time is calculated using the following formula: ； In the formula, For the waiting time, To handle the window Estimated processing time for each applicant: is the number of applicants in the queue.

7. The business processing optimization method based on big data according to claim 1 is characterized in that: include: When there are multiple processing windows with the shortest waiting time, obtain the capability value of the staff member corresponding to the processing window with the shortest waiting time; Generate and print the queue number of the processing window with the highest staff ability value for the applicant.

8. The business processing optimization system based on big data is characterized by: include: A first acquisition module is used to acquire a business application initiated by an applicant, wherein the business application includes a business type, name, identity, age and education level; A second acquisition module is used to obtain the business handling history data corresponding to the applicant from the big data platform based on the name and identity identifier; A classification statistics module is used to classify and count the business handling history data to determine the number of times the applicant has handled the business and the total number of times the applicant has handled the business; The processing time prediction module is used to input age, education level, number of times the business is processed and the total number of times the business is processed as variables into a pre-built processing time prediction model and output the predicted processing time; The third acquisition module is used to obtain the average processing time of the corresponding local historical business information according to the business type, age and education level; A determination module, used to determine the estimated processing time corresponding to the applicant based on the predicted processing time and the average processing time; The fourth acquisition module is used to obtain the capability values ​​corresponding to the staff at each processing window; A calculation module, for calculating the waiting time corresponding to each processing window based on the estimated processing time of the applicants queuing at the processing window and the ability value of the staff; The first generating module is used to generate and print the queue number of the processing window with the shortest waiting time for the applicant according to the waiting time of each processing window.

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