CN114118496B - Method and system for automatically scheduling queuing reservation based on big data analysis - Google Patents
Method and system for automatically scheduling queuing reservation based on big data analysis Download PDFInfo
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Abstract
The invention discloses a method and a system for automatically scheduling queuing reservation based on big data analysis, and relates to the technical field of scheduling management. The method comprises the following steps: acquiring historical service data and queuing waiting condition information of staff; the historical service data and queuing waiting number information of the office staff are imported into a preset data analysis model, and office recommendation information is generated and sent; acquiring real-time transaction reservation information; and acquiring and importing the transaction parameters and the real-time transaction reservation information into a preset scheduling model to generate queuing scheduling information. The invention can reasonably schedule the service and improve the efficiency of reservation work.
Description
Technical Field
The invention relates to the technical field of scheduling management, in particular to a method and a system for automatically scheduling queuing reservation based on big data analysis.
Background
The existing reservation system basically uses the conditions that the reservation number in a certain period is manually configured, the service transacted by different service windows is different, the transacted time of each service is different, the transacted time of different persons, masses and the like is different due to the fact that the same service, different persons, masses and the like are different in required transacted time, and therefore the conditions that the staff reservation is caused, the on-site queuing number taking is uneven, the queuing team and window transacted efficiency is different, the transacted staff waits for a long time and the like occur.
Disclosure of Invention
In order to overcome the problems or at least partially solve the problems, the embodiment of the invention provides a method and a system for automatically scheduling queuing and reserving based on big data analysis, which can perform reasonable service scheduling and improve the efficiency of reserving and working.
Embodiments of the present invention are implemented as follows:
in a first aspect, an embodiment of the present invention provides a method for automatically scheduling queuing reservations based on big data analysis, including the steps of:
Acquiring historical service data and queuing waiting condition information of staff;
the historical service data and queuing waiting number information of the office staff are imported into a preset data analysis model, and office recommendation information is generated and sent;
acquiring real-time transaction reservation information;
And acquiring and importing the transaction parameters and the real-time transaction reservation information into a preset scheduling model to generate queuing scheduling information.
In order to solve the technical problems of low working efficiency caused by uneven busy and idle conditions, different working efficiency of a long queue and a window working efficiency and long waiting time of working staff in the prior art, the invention analyzes the conditions of working staff business capability, service business types, emergency and the like, automatically carries out reservation configuration on different businesses and time periods through longitudinal and transverse comparison and analysis of historical data, analyzes the data of real-time working time, waiting number and the like of a service site, reasonably dispatches, automatically dispatches reasonable queuing reservation and automatically shunts the queuing number. The office staff can acquire the optimal office time and the optimal office address according to the historical data analysis model and data guidance, and the office staff can improve the office efficiency and the performance assessment according to scheduling; the window service condition is reasonably scheduled, uneven window arrangement busy and idle is avoided, and the window service rate is improved; automatic regulation, no need of manual intervention, reduced labor cost investment, and easy and efficient management.
Based on the first aspect, in some embodiments of the present invention, the method for importing historical service data and queuing equal sign information of a office into a preset data analysis model to generate office recommendation information includes the following steps:
The historical service data and queuing waiting number condition information of the office staff are imported into a preset data analysis model;
longitudinally analyzing the historical business data based on hot business transaction data and queuing waiting number condition information of the transaction staff through a data analysis model to generate first recommendation information;
transversely analyzing the historical business data based on the business time data and queuing number waiting condition information of the office staff through a data analysis model to generate second recommendation information;
and generating office recommendation information according to the first recommendation information and the second recommendation information.
Based on the first aspect, in some embodiments of the present invention, the method for generating the first transaction recommendation information by performing longitudinal analysis based on hot transaction data and queuing equal sign information of a transaction person in the historical transaction data through the data analysis model includes the following steps:
Dimension division is carried out on hot business transaction data in the historical business data so as to obtain hot business transaction data with multiple dimensions;
Calculating weight scores of hot business transaction data of each dimension through a data analysis model, determining overall transaction average score, and determining optimal transaction time and place according to the overall transaction average score analysis;
And generating first transaction recommendation information based on the optimal transaction time and place and transaction service data in queuing etc. condition information of the transaction staff through a data analysis model.
Based on the first aspect, in some embodiments of the present invention, the method for performing dimension division on the hot business transaction data in the historical business data to obtain hot business transaction data with multiple dimensions includes the following steps:
And carrying out dimension division on the hot business transaction data in the historical business data, wherein the dimension division is divided into a hot business transaction dimension, a Zhou Remen transaction time dimension, a daily hot transaction time period dimension and a hot transaction point dimension so as to obtain hot business transaction data with multiple dimensions.
Based on the first aspect, in some embodiments of the present invention, the method for generating the second transaction recommendation information by performing the lateral analysis based on the service time data in the historical service data and queuing information of the transaction staff through the data analysis model includes the following steps:
extracting and analyzing all business event handling heat data in a certain time period in the historic year, month, week and day of business time data in the historic business data through a data analysis model, and generating optimal time business handling information;
Generating and sending second office recommendation information to corresponding offices according to the optimal time business handling information and queuing waiting information of the offices.
Based on the first aspect, in some embodiments of the present invention, the method for acquiring and importing the transaction parameters and the real-time transaction reservation information into a preset scheduling model to generate queuing scheduling information includes the following steps:
Acquiring and importing the office parameters into a preset scheduling model, and generating office window efficiency information according to the personnel office efficiency data and the office window data in the office parameters through the scheduling model;
and generating queuing scheduling information according to the efficiency information of the transaction window and the real-time transaction reservation information through the scheduling model.
Based on the first aspect, in some embodiments of the invention, the queuing equal-number case information includes network reservation case information and on-site number taking case information.
In a second aspect, an embodiment of the present invention provides a system for automatically scheduling queuing reservations based on big data analysis, including a situation acquisition module, an analysis recommendation module, a real-time reservation module, and a scheduling module, where:
the situation acquisition module is used for acquiring historical service data and queuing waiting situation information of the office staff;
the analysis recommendation module is used for importing historical service data and queuing waiting condition information of the office staff into a preset data analysis model, generating and sending office recommendation information;
The real-time reservation module is used for acquiring real-time transaction reservation information;
the scheduling module is used for acquiring and importing the transaction parameters and the real-time transaction reservation information into a preset scheduling model to generate queuing scheduling information.
In order to solve the technical problems of low working efficiency caused by uneven busy and idle conditions, different working efficiency of a long queue and a window working efficiency and long waiting time of working staff in the prior art, the system analyzes the conditions of working staff business capacity, service business types, emergency and the like, automatically carries out reservation configuration on different businesses and time periods through longitudinal and transverse comparison and analysis of historical data, analyzes data of real-time working time, waiting number and the like on a service site, reasonably dispatches, automatically dispatches reasonable queuing reservation and automatically shunts the queuing number. The office staff can acquire the optimal office time and the optimal office address according to the historical data analysis model and data guidance, and the office staff can improve the office efficiency and the performance assessment according to scheduling; the window service condition is reasonably scheduled, uneven window arrangement busy and idle is avoided, and the window service rate is improved; automatic regulation, no need of manual intervention, reduced labor cost investment, and easy and efficient management.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory for storing one or more programs; a processor. The method of any of the first aspects described above is implemented when one or more programs are executed by a processor.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as in any of the first aspects described above.
The embodiment of the invention has at least the following advantages or beneficial effects:
The embodiment of the invention provides a method and a system for automatically scheduling queuing reservation based on big data analysis, which solve the technical problems of uneven busy hours, different queuing and window handling efficiency and low handling efficiency caused by long-time waiting of staff in staff reservation or on-site queuing and number taking in the prior art. The office staff can acquire the optimal office time and the optimal office address according to the historical data analysis model and data guidance, and the office staff can improve the office efficiency and the performance assessment according to scheduling; the window service condition is reasonably scheduled, uneven window arrangement busy and idle is avoided, and the window service rate is improved; automatic regulation, no need of manual intervention, reduced labor cost investment, and easy and efficient management.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for automatically scheduling queuing reservations based on big data analysis in accordance with an embodiment of the present invention;
FIG. 2 is a specific schematic diagram of a method for automatically scheduling queuing reservations based on big data analysis according to an embodiment of the present invention;
FIG. 3 is a schematic block diagram of a system for automatically scheduling queuing reservations based on big data analysis in accordance with an embodiment of the present invention;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.
Icon: 100. a situation acquisition module; 200. an analysis recommendation module; 300. a real-time reservation module; 400. a scheduling module; 101. a memory; 102. a processor; 103. a communication interface.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the description of the embodiments of the present invention, "plurality" means at least 2.
Examples
As shown in fig. 1-2, in a first aspect, an embodiment of the present invention provides a method for automatically scheduling queuing reservations based on big data analysis, including the steps of:
s1, acquiring historical service data and queuing waiting condition information of a clerk; the queuing equal number condition information comprises network reservation condition information and site number taking condition information.
In some embodiments of the invention, office staff queuing may be performed by way of network reservations and on-site queuing. The people to be handled and the backlog can be statistically determined by acquiring the arrangement condition of the office staff, and comprehensive data reference is provided for subsequent data analysis.
S2, importing historical service data and queuing waiting condition information of the office staff into a preset data analysis model, generating and transmitting office recommendation information;
Further, the historical service data and queuing waiting condition information of the office staff are imported into a preset data analysis model; longitudinally analyzing the historical business data based on hot business transaction data and queuing waiting number condition information of the transaction staff through a data analysis model to generate first recommendation information; transversely analyzing the historical business data based on the business time data and queuing number waiting condition information of the office staff through a data analysis model to generate second recommendation information; and generating office recommendation information according to the first recommendation information and the second recommendation information.
Further, dimension division is carried out on the hot business transaction data in the historical business data so as to obtain hot business transaction data with multiple dimensions; calculating weight scores of hot business transaction data of each dimension through a data analysis model, determining overall transaction average score, and determining optimal transaction time and place according to the overall transaction average score analysis; and generating first transaction recommendation information based on the optimal transaction time and place and transaction service data in queuing etc. condition information of the transaction staff through a data analysis model.
Further, dimension division is performed on the hot business transaction data in the historical business data, and the dimension division is divided into a hot business transaction dimension, a Zhou Remen transaction time dimension, a daily hot transaction time period dimension and a hot transaction point dimension, so that hot business transaction data with multiple dimensions is obtained.
Further, extracting and analyzing all business event handling heat data in a certain time period in the historic year, month, week and daily history in business time data in the historical business data through a data analysis model to generate optimal time business handling information; generating and sending second office recommendation information to corresponding offices according to the optimal time business handling information and queuing waiting information of the offices.
In some embodiments of the invention, the system automatically recommends office locations through a data analysis model based on a plurality of conditions such as geographic location, appointment time, live real-time queuing, office time length, etc. at the time of appointment. If the office place is unique, the system recommends conditions such as office reservation time, time period and the like through a data analysis model and through historical data analysis.
The above longitudinal analysis refers to: weight scores are calculated from dimensions such as hot business matters, zhou Remen business time, daily hot business time period, hot business point and the like respectively, optimal business time and place are calculated through overall average division, and optimal suggestions are recommended through the business matters selected in the reservation process. The above lateral analysis refers to: and extracting the work-in-process heat of all business matters in a certain time period in the histories of each year, each month and each week through historical data, and informing the masses of what business is handled optimally through recommendation, wherein the more the collected historical data are, the more accurate the calculated result is.
Analyzing by combining historical data and real-time office data through a data analysis model, analyzing office efficiency through the data analysis model, extracting the office completion time of each office window worker every day, analyzing different business office average time, analyzing the time required by different workers for handling the same business, comprehensively calculating the office quantity of different businesses every day, and automatically setting the number limit of people reserved for ranking by the system to achieve the optimal office quantity every day. The method is characterized in that the method also carries out on-site real-time analysis through a data analysis model, and because the real-time queuing number of the hall cannot be reserved in real time, the window setting of the hall needs flexible configuration, and the high-efficiency transaction is realized through multiparty data calculation, such as the reservation number of a certain business in the day, the queuing number of the current hall, the average transaction time of the business, the efficiency of the current window transaction personnel and the like, according to the weight calculation of each item of data, if the maximum number of hall transaction is reached, if the maximum number exceeds the urgent need, the window allocation and personnel scheduling are carried out.
S3, acquiring real-time transaction reservation information;
s4, acquiring and importing the transaction parameters and the real-time transaction reservation information into a preset scheduling model to generate queuing scheduling information.
Further, acquiring and importing the office parameters into a preset scheduling model, and generating office window efficiency information according to personnel office efficiency data and office window data in the office parameters through the scheduling model; and generating queuing scheduling information according to the efficiency information of the transaction window and the real-time transaction reservation information through the scheduling model.
In some embodiments of the invention, the management end automatically shunts queuing people by acquiring conditions such as the reservation people on the same day, the real-time number taking people on site, the working efficiency of the working personnel, the hot handling matters and the like, setting a window through a scheduling model and according to the working efficiency of the working personnel.
In order to solve the technical problems of low working efficiency caused by uneven busy and idle conditions, different working efficiency of a long queue and a window working efficiency and long waiting time of working staff in the prior art, the invention analyzes the conditions of working staff business capability, service business types, emergency and the like, automatically carries out reservation configuration on different businesses and time periods through longitudinal and transverse comparison and analysis of historical data, analyzes the data of real-time working time, waiting number and the like of a service site, reasonably dispatches, automatically dispatches reasonable queuing reservation and automatically shunts the queuing number. The office staff can acquire the optimal office time and the optimal office address according to the historical data analysis model and data guidance, and the office staff can improve the office efficiency and the performance assessment according to scheduling; the window service condition is reasonably scheduled, uneven window arrangement busy and idle is avoided, and the window service rate is improved; automatic regulation, no need of manual intervention, reduced labor cost investment, and easy and efficient management.
The invention realizes high-efficiency automatic scheduling queuing reservation through the mutual cooperation of a plurality of terminal devices, the office hall and the management scheduling background, and a user can reserve and take numbers in a network reservation mode, can take numbers on site, and then carry out real-time data acquisition by combining the number taking conditions of the number taking terminals and sends the data to the management background; simultaneously acquiring and transmitting data such as working efficiency, the number of working windows, working time and the like of the working staff in the working hall to a management background; and analyzing the data through the management background to obtain proper queuing scheduling information and pushing the proper queuing scheduling information to the user mobile terminal. According to the invention, the data of the previous day of network reservation is combined, the next day of the background is subjected to optimization analysis according to the historical data and the data of the previous day, so that the reservation number is maximized, and the working efficiency is maximized; and the background optimizes in real time according to real-time data of the office hall, reduces the current queuing number, the office efficiency, the busy window and other conditions.
As shown in fig. 3, in a second aspect, an embodiment of the present invention provides a system for automatically scheduling queuing reservations based on big data analysis, including a situation acquisition module 100, an analysis recommendation module 200, a real-time reservation module 300, and a scheduling module 400, where:
A situation acquisition module 100, configured to acquire historical service data and queuing waiting situation information of a clerk;
the analysis recommendation module 200 is used for importing historical service data and queuing number information of the office staff into a preset data analysis model, generating and sending office recommendation information;
The real-time reservation module 300 is used for acquiring real-time transaction reservation information;
the scheduling module 400 is configured to obtain and import the transaction parameters and the real-time transaction reservation information into a preset scheduling model, and generate queuing scheduling information.
In order to solve the technical problems of low working efficiency caused by uneven busy and idle conditions, different working efficiency of a long queue and a window working efficiency and long waiting time of working staff in the prior art, the system analyzes the conditions of working staff business capacity, service business types, emergency and the like, automatically carries out reservation configuration on different businesses and time periods through longitudinal and transverse comparison and analysis of historical data, analyzes data of real-time working time, waiting number and the like on a service site, reasonably dispatches, automatically dispatches reasonable queuing reservation and automatically shunts the queuing number. The office staff can acquire the optimal office time and the optimal office address according to the historical data analysis model and data guidance, and the office staff can improve the office efficiency and the performance assessment according to scheduling; the window service condition is reasonably scheduled, uneven window arrangement busy and idle is avoided, and the window service rate is improved; automatic regulation, no need of manual intervention, reduced labor cost investment, and easy and efficient management.
As shown in fig. 4, in a third aspect, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; a processor 102. The method of any of the first aspects described above is implemented when one or more programs are executed by the processor 102.
And a communication interface 103, where the memory 101, the processor 102 and the communication interface 103 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules that are stored within the memory 101 for execution by the processor 102 to perform various functional applications and data processing. The communication interface 103 may be used for communication of signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a random access Memory 101 (Random Access Memory, RAM), a Read Only Memory 101 (ROM), a programmable Read Only Memory 101 (Programmable Read-Only Memory, PROM), an erasable Read Only Memory 101 (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory 101 (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 102 may be an integrated circuit chip with signal processing capabilities. The processor 102 may be a general purpose processor 102, including a central processor 102 (Central Processing Unit, CPU), a network processor 102 (Network Processor, NP), etc.; but may also be a digital signal processor 102 (DIGITAL SIGNAL Processing, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components.
In the embodiments provided in the present application, it should be understood that the disclosed method, system and method may be implemented in other manners. The above-described method and system embodiments are merely illustrative, for example, flow charts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by the processor 102, implements a method as in any of the first aspects described above. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory 101 (ROM), a random access Memory 101 (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. The method for automatically scheduling queuing reservation based on big data analysis is characterized by comprising the following steps:
Acquiring historical service data and queuing waiting condition information of staff;
the historical service data and queuing waiting number information of the office staff are imported into a preset data analysis model, and office recommendation information is generated and sent;
acquiring real-time transaction reservation information;
Acquiring and importing the transaction parameters and the real-time transaction reservation information into a preset scheduling model to generate queuing scheduling information, wherein the queuing scheduling information comprises the following steps: acquiring and importing the office parameters into a preset scheduling model, and generating office window efficiency information according to the personnel office efficiency data and the office window data in the office parameters through the scheduling model; generating queuing scheduling information according to the efficiency information of the transaction window and the real-time transaction reservation information through a scheduling model;
the method for importing the historical service data and queuing waiting information of the office staff into a preset data analysis model and generating the office recommendation information comprises the following steps:
The historical service data and queuing waiting number condition information of the office staff are imported into a preset data analysis model; longitudinally analyzing the historical business data based on hot business transaction data and queuing waiting number condition information of the transaction staff through a data analysis model to generate first recommendation information; transversely analyzing the historical business data based on the business time data and queuing number waiting condition information of the office staff through a data analysis model to generate second recommendation information; generating office recommendation information according to the first recommendation information and the second recommendation information;
The method for generating the first transaction recommendation information by longitudinally analyzing the historical service data based on the hot service transaction data and queuing equal number condition information of the transaction staff through the data analysis model comprises the following steps:
Dimension division is carried out on hot business transaction data in the historical business data so as to obtain hot business transaction data with multiple dimensions; calculating weight scores of hot business transaction data of each dimension through a data analysis model, determining overall transaction average score, and determining optimal transaction time and place according to the overall transaction average score analysis; generating first transaction recommendation information based on the optimal transaction time and place and transaction service data in queuing equal number condition information of the transaction staff through a data analysis model;
The method for generating the second transaction recommendation information by transversely analyzing the service time data in the historical service data and queuing waiting number condition information of the transaction staff through the data analysis model comprises the following steps:
Extracting and analyzing all business event handling heat data in a certain time period in the historic year, month, week and day of business time data in the historic business data through a data analysis model, and generating optimal time business handling information; generating and sending second office recommendation information to corresponding offices according to the optimal time business handling information and queuing waiting information of the offices.
2. The method for automatically scheduling queuing reservations based on big data analysis according to claim 1, wherein the method for dimension-dividing hot business transaction data in historical business data to obtain hot business transaction data with multiple dimensions comprises the following steps:
And carrying out dimension division on the hot business transaction data in the historical business data, wherein the dimension division is divided into a hot business transaction dimension, a Zhou Remen transaction time dimension, a daily hot transaction time period dimension and a hot transaction point dimension so as to obtain hot business transaction data with multiple dimensions.
3. The method for automatically scheduling queuing reservations based on big data analysis of claim 1, wherein the queuing wait condition information comprises network reservation condition information and on-site number taking condition information.
4. The system for automatically scheduling queuing reservation based on big data analysis is characterized by comprising a situation acquisition module, an analysis recommendation module, a real-time reservation module and a scheduling module, wherein:
the situation acquisition module is used for acquiring historical service data and queuing waiting situation information of the office staff;
the analysis recommendation module is used for importing historical service data and queuing waiting condition information of the office staff into a preset data analysis model, generating and sending office recommendation information;
The real-time reservation module is used for acquiring real-time transaction reservation information;
the scheduling module is used for acquiring and importing the transaction parameters and the real-time transaction reservation information into a preset scheduling model to generate queuing scheduling information, and comprises the following steps: acquiring and importing the office parameters into a preset scheduling model, and generating office window efficiency information according to the personnel office efficiency data and the office window data in the office parameters through the scheduling model; generating queuing scheduling information according to the efficiency information of the transaction window and the real-time transaction reservation information through a scheduling model;
Wherein: the step of importing the historical service data and queuing waiting information of the office staff into a preset data analysis model, and the step of generating the office recommendation information comprises the following steps:
The historical service data and queuing waiting number condition information of the office staff are imported into a preset data analysis model; longitudinally analyzing the historical business data based on hot business transaction data and queuing waiting number condition information of the transaction staff through a data analysis model to generate first recommendation information; transversely analyzing the historical business data based on the business time data and queuing number waiting condition information of the office staff through a data analysis model to generate second recommendation information; generating office recommendation information according to the first recommendation information and the second recommendation information;
the step of longitudinally analyzing the historical business data based on the hot business transaction data and queuing equal number condition information of the transaction staff through the data analysis model, and the step of generating first transaction recommendation information comprises the following steps:
Dimension division is carried out on hot business transaction data in the historical business data so as to obtain hot business transaction data with multiple dimensions; calculating weight scores of hot business transaction data of each dimension through a data analysis model, determining overall transaction average score, and determining optimal transaction time and place according to the overall transaction average score analysis; generating first transaction recommendation information based on the optimal transaction time and place and transaction service data in queuing equal number condition information of the transaction staff through a data analysis model;
The step of performing transverse analysis based on the service time data in the historical service data and queuing number information of the office staff through the data analysis model, and the step of generating second office recommendation information comprises the following steps:
Extracting and analyzing all business event handling heat data in a certain time period in the historic year, month, week and day of business time data in the historic business data through a data analysis model, and generating optimal time business handling information; generating and sending second office recommendation information to corresponding offices according to the optimal time business handling information and queuing waiting information of the offices.
5. An electronic device, comprising:
A memory for storing one or more programs;
A processor;
the method of any of claims 1-3 being implemented when the one or more programs are executed by the processor.
6. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-3.
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| CN112650924B (en) * | 2020-12-23 | 2022-11-08 | 浪潮卓数大数据产业发展有限公司 | Specific event recommendation method |
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| CN115186853B (en) * | 2022-07-11 | 2023-08-29 | 重庆旅游云信息科技有限公司 | Scenic spot-based project play management method, scenic spot-based project play management device and storage medium |
| CN115545553B (en) * | 2022-11-07 | 2023-04-07 | 浪潮软件股份有限公司 | Number limiting and number placing method and system for business handling |
| CN115619356B (en) * | 2022-12-16 | 2023-03-21 | 广州明动软件股份有限公司 | Data automatic synchronization convergence and distribution method and system based on event public data |
| CN116308964B (en) * | 2023-05-23 | 2023-08-15 | 南京小微网络软件有限责任公司 | Internet civil service system platform based on big data |
| CN117576822B (en) * | 2023-11-20 | 2024-04-30 | 上海徽视科技集团有限公司 | Queuing and number calling guiding system based on Internet platform |
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