CN111401711A - Intelligent scheduling method, device, equipment and storage medium - Google Patents
Intelligent scheduling method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN111401711A CN111401711A CN202010158244.8A CN202010158244A CN111401711A CN 111401711 A CN111401711 A CN 111401711A CN 202010158244 A CN202010158244 A CN 202010158244A CN 111401711 A CN111401711 A CN 111401711A
- Authority
- CN
- China
- Prior art keywords
- service
- data
- transaction
- client
- point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000004590 computer program Methods 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 description 19
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 2
- 238000013480 data collection Methods 0.000 description 2
- 230000037361 pathway Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
Abstract
The invention relates to an intelligent scheduling method, an intelligent scheduling device, intelligent scheduling equipment and a storage medium, wherein the intelligent scheduling method comprises the following steps: acquiring data for target scheduling; the target scheduling data comprises client transaction requirement data and service state data of each service transaction point; inputting the client transaction demand data and the service state data of each service transaction point into a preset service transaction point matching model to obtain an identifier of a target service transaction point; the preset service transaction point matching model is used for matching the optimal service transaction point which is suitable for the client transaction requirement data according to the service state data of each service transaction point; and sending the identification of the target service transaction point to a client side terminal so that the client goes to the target service transaction point according to the identification of the target service transaction point. The method and the system can shorten the time required by a client to handle the business, improve the business handling efficiency of a financial website and enable the client to experience better.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent scheduling method, an intelligent scheduling device, intelligent scheduling equipment and a storage medium.
Background
With the development of the internet financial industry, it is more common to go to banks to handle business. At present, the general flow of handling business by going to bank is as follows: first, the client performs a number fetch operation. The system then randomly matches the business transaction windows to the customers. And finally, each window sequentially transacts services for each client according to the number sequence of the clients. In addition, the customer may also choose to self-service the transaction at a terminal device (e.g., an ATM) at the bank. The duration required by the client to handle the service comprises the duration for waiting to handle the service and the duration required by the service handling point to handle the service.
However, in the current business transaction process of the bank, a business transaction window exists, which is matched with a client and the time length of waiting for transaction is not the shortest. Secondly, when the customer selects to handle the service on the terminal device by self, the customer may also arrange the service to the queue corresponding to the terminal device with the service waiting duration not shortest. Therefore, the client needs to wait for a long time to turn to the service processing point to process the own service, and the client experience is poor. In addition, the existing banking business handling system cannot match the business handling point with the shortest time required for handling the customer business according to the transaction requirement type of the customer (for example, when the customer is handling small-amount money, the money can be drawn at the fastest when the customer is handling the money on an ATM, and the ATM is the business handling point with the shortest time required for handling the customer business), so that the time required for handling the business by the customer is longer, and the banking business handling efficiency is lower.
Disclosure of Invention
In view of the above, in order to solve the above problem, the present application provides an intelligent scheduling method, apparatus, device and storage medium.
The invention adopts the following technical scheme:
in a first aspect, the present invention provides an intelligent scheduling method, including: acquiring data for target scheduling; the target scheduling data comprises client transaction requirement data and service state data of each service transaction point; inputting the client transaction demand data and the service state data of each service transaction point into a preset service transaction point matching model to obtain an identifier of a target service transaction point; the preset service transaction point matching model is used for matching the optimal service transaction point which is suitable for the client transaction requirement data according to the service state data of each service transaction point; and sending the identification of the target service transaction point to a client side terminal so that the client goes to the target service transaction point according to the identification of the target service transaction point.
In a second aspect, the present invention provides an intelligent scheduling apparatus, configured to implement an intelligent scheduling method of the present application, including: the data acquisition module is used for acquiring data for target scheduling; the target scheduling data comprises client transaction requirement data and service state data of each service transaction point; the data matching module is used for inputting the client transaction demand data and the service state data of each service transaction point into a preset service transaction point matching model to obtain an identifier of a target service transaction point; the preset service transaction point matching model is used for matching the optimal service transaction network points which are suitable for the client transaction requirement data according to the service state data of each service transaction point; and the notification module is used for sending the identifier of the target service transaction point to a client side terminal so that a client can go to the target service transaction point according to the identifier of the target service transaction point.
In a third aspect, the present invention provides an apparatus comprising: a processor, and a memory coupled to the processor; the memory is used for storing a computer program, and the computer program is at least used for executing the intelligent scheduling method; the processor is used for calling and executing the computer program in the memory.
In a fourth aspect, the present invention provides a storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the intelligent scheduling method.
By adopting the technical scheme, the invention grasps the service handling condition of each service handling point in real time by acquiring the data for target scheduling. This application can be based on customer transaction demand data with the business state data matching of each business transaction point goes out the best business transaction point that adapts to customer transaction demand data, if demand in the customer transaction demand data contains when handling the demand fast, alright in order to match the business transaction point that the required length of time of handling customer's business is the shortest, and then shortened the required length of time of customer's business transaction, improved the business transaction efficiency of financial website simultaneously. In addition, the service transaction point with the shortest service waiting time of the client can be matched according to the client transaction demand data and the service state data of each service transaction point, so that the service waiting time of the client is shortened, and the client experience is better.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of an intelligent scheduling method according to an embodiment of the present invention.
Fig. 2 is a schematic flowchart of another intelligent scheduling method according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an intelligent scheduling apparatus according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
Fig. 1 is a schematic flowchart of an intelligent scheduling method according to an embodiment of the present invention. As shown in fig. 1, the method includes:
s101, acquiring data for target scheduling; the target scheduling data comprises client transaction requirement data and service state data of each service transaction point.
Specifically, the step of obtaining the target scheduling data includes backing up the target scheduling data to ensure that the intelligent scheduling process corresponding to the intelligent scheduling method of the present application can be completed under any condition. Currently, service transaction points capable of handling client services are roughly divided into two types, including terminal equipment service transaction points and manual window service transaction points. The terminal equipment type service handling points can be service handling points such as ATM machines and automatic card vending machines which have the function of supporting clients to handle services by themselves, and the manual window type service handling points are manual counter service handling points in the prior art. The client transaction requirement data comprises the services which the client needs to transact, and the service condition data of each service transaction point comprises the services which each service transaction point is transacting respectively, the time consumed for transacting the services, the services which each service transaction point needs to transact respectively and the residual quantity of resources. The time length required by each service transaction point for completing the current service can be calculated according to the service being transacted by each service transaction point, the consumed time length for transacting the service, the services to be transacted respectively and the preset theoretical transaction time length corresponding to each service; the resource remaining amount is a resource remaining amount corresponding to the service, for example, when the service is a withdrawal service, the corresponding resource remaining amount is a money remaining amount at a service transaction point.
In a specific application process, the target scheduling data may be acquired through an online acquisition pathway and/or an offline acquisition pathway. The specific process of acquiring the target scheduling data through the on-line acquisition approach may be: and networking the terminal equipment corresponding to each business handling point and the terminal equipment for acquiring the customer transaction requirements through the Internet of things technology, so that the data stored in each terminal equipment can be collected, and in order to improve the accuracy of subsequent data matching, the collected data of each terminal equipment can be screened and the like to obtain the target data for scheduling. The specific process of acquiring the target scheduling data through the offline acquisition way includes setting a manual input end on a data collection device for collecting the target scheduling data, so that a user can input data to the data collection device through the manual input end.
S102, inputting the client transaction demand data and the service state data of each service transaction point into a preset service transaction point matching model to obtain an identifier of a target service transaction point; and the preset service transaction point matching model is used for matching the optimal service transaction point which is suitable for the client transaction requirement data according to the service state data of each service transaction point.
In detail, an optimal service transaction point corresponding to each service is defined in the preset service transaction point matching model, so that each service can be transacted most quickly. The preset service transaction point matching model also defines a matching rule for matching the optimal service transaction point adapted to the customer transaction requirement data, and the optimal service transaction point can be the optimal service transaction point in various aspects, for example, the optimal service transaction point can be the service transaction point which can shorten the time required by the customer to transact the service.
In a specific application process, after the customer transaction requirement data and the service state data of each service transaction point are input into a preset service transaction point matching model, all optimal service transaction points corresponding to customer services are determined according to the customer transaction requirement data. And then determining the optimal service transaction point which is suitable for the client transaction requirement data from all the optimal service transaction points, defining the optimal service transaction point as a target service transaction point, and acquiring the identifier of the target service transaction point. The mark is a mark containing information such as the mark number of the target service handling point, so that a client can find the corresponding service handling point according to the mark.
In a specific example, the transaction demand of the customer is a small amount of money withdrawal, the place where the customer transacts the business is a certain website of the bank, and the website needs to ensure that the business of each customer can be transacted most quickly in order to improve the satisfaction degree of the customer, that is, in this embodiment, the best business transaction point is the business transaction point which can shorten the time required by the customer to transact the business most. In the specific matching process, firstly, all the optimal service handling points corresponding to the transaction demand are determined to be all the ATM machines according to the small-amount withdrawal transaction demand of the customer. And then determining all the ATM machines capable of handling the small amount withdrawal transaction requirements according to the residual amount of the coins of all the ATM machines. And finally, calculating the service waiting time duration of each ATM capable of handling the small-amount withdrawal transaction requirement according to the service being handled respectively of each ATM capable of handling the small-amount withdrawal transaction requirement, the consumed time duration for handling the service, the service to be handled respectively and the theoretical handling time duration corresponding to each service, namely how long each ATM capable of handling the small-amount withdrawal transaction requirement needs to be capable of handling the small-amount withdrawal transaction requirement respectively, and determining the service waiting time duration with the shortest time duration from all the service waiting time durations of the clients, wherein the ATM corresponding to the service waiting time duration with the shortest time duration is the service handling point capable of shortening the service handling time duration of the clients most.
S103, the identification of the target service transaction point is sent to a client side terminal, so that a client can go to the target service transaction point according to the identification of the target service transaction point.
Specifically, the client-side terminal may be a registration machine in the prior art, and the registration machine may be configured to support a client to input a transaction requirement and display an identifier of a service transaction point for transacting the client transaction requirement. After the client obtains the identification of the target service transaction point through the registration machine, the client can go to the target service transaction point according to the identification of the target service transaction point.
By adopting the technical scheme, the invention grasps the service handling condition of each service handling point in real time by acquiring the data for target scheduling. This application can be based on customer transaction demand data with the business state data matching of each business transaction point goes out the best business transaction point that adapts to customer transaction demand data, if demand in the customer transaction demand data contains when handling the demand fast, alright in order to match the business transaction point that the required length of time of handling customer's business is the shortest, and then shortened the required length of time of customer's business transaction, improved the business transaction efficiency of financial website simultaneously. In addition, the service transaction point with the shortest service waiting time of the client can be matched according to the client transaction demand data and the service state data of each service transaction point, so that the service waiting time of the client is shortened, and the client experience is better.
Further, the data for target scheduling also comprises client deposit data and client historical transaction data; the intelligent scheduling method further comprises the following steps: firstly, matching financial products for clients according to the client deposit data and the client historical transaction data; and then sending the product information of the financial product to the business transaction point so that the customer can know the product information through the business transaction point.
In detail, when the transaction point transacts the transaction for the client, in order to effectively promote the financial products of the bank, the financial products suitable for the client can be recommended to each user according to the deposit of each user in the bank and the historical transaction of the client.
In a specific application process, when a user self-transacts business at a terminal equipment business transaction point, the deposit and the historical business transaction of the user at the bank can be determined according to the obtained customer identity information. Then, the financial products which are interested by the client are determined according to the historical transaction of the client, and then the target financial products which are suitable for the client are determined according to the deposit of the client in the bank and the financial products which are interested by the client. And finally, the product information of the target product is sent to the terminal equipment corresponding to the terminal equipment type business handling point, and the terminal equipment corresponding to the terminal equipment type business handling point displays the product information of the target product, so that a client can know the product information through the product information displayed by the terminal equipment corresponding to the terminal equipment type business handling point, and finally, the financial product of the bank is effectively popularized.
When a user transacts business at a manual window business transaction point, the same as when the user transacts business at a terminal equipment business transaction point by self, the deposit and the historical transaction business of the user at the bank can be determined according to the obtained client identity information. Then, the financial products which are interested by the client are determined according to the historical transaction of the client, and then the target financial products which are suitable for the client are determined according to the deposit of the client in the bank and the financial products which are interested by the client. Different from the self-service transaction of the user at the terminal equipment service transaction point, the manual window service transaction point is used for the bank staff to operate the corresponding terminal equipment to transact the service for the client, therefore, the product information of the target product is sent to the terminal equipment corresponding to the manual window business handling point, after the terminal device corresponding to the manual window business handling point displays the product information of the target product, the customer cannot know the product information through the product information displayed by the terminal equipment corresponding to the manual window-type service handling point, in this case, the product information may be known by the bank staff through the product information displayed by the terminal device corresponding to the manual window-type business handling point, and the product information may be notified to the customer, so that the customer may know the product information.
Further, the inputting the customer transaction demand data and the service state data of each service transaction point into a preset service transaction point matching model includes: firstly, selecting a target preset service transaction point matching model from all preset service transaction point matching models according to a preset selection rule; the preset service transaction point matching model comprises a priority client time saving matching model and a priority service high-end client matching model. And then, inputting the client transaction demand data and the service state data of each service transaction point into the target preset service transaction point matching model.
Specifically, bank outlets of different levels are positioned differently, for example, a larger bank outlet such as a first-level branch bank outlet has more high-end customers to receive, so that the bank outlet needs to handle business for the high-end customers first and then handle business for common customers, so as to improve the satisfaction of the high-end customers and create greater benefits for the bank outlet. The bank outlets which are smaller like the branch outlets receive the services of common users, so the bank outlets need to maximally shorten the time required by each client for handling the services, so that the satisfaction of the clients is improved, and the service handling efficiency of the bank outlets is improved.
Therefore, aiming at different situations of bank outlets with different levels, in a specific application process, firstly, a target preset service transaction point matching model is selected from all preset service transaction point matching models according to a preset selection rule; the preset selection rule is used for selecting a preset business handling point matching model which is suitable for positioning of the banking outlets of different levels, for example, when the banking outlets are banking outlets which receive high-end customers, the preset business handling point matching model selected according to the preset selection rule is a high-end customer priority service matching model; and when the banking outlets only receive common customers, the preset business handling point matching model selected according to the preset selection rule is a matching model for preferentially saving the time of the customers. And then, inputting the client transaction demand data and the service state data of each service transaction point into the target preset service transaction point matching model. Therefore, the purposes of improving the satisfaction degree of high-end customers and common customers and improving the business handling efficiency of bank outlets are achieved.
Further, when the target preset service transaction point matching model is a matching model that preferentially saves client time, the client transaction demand data and the service state data of each service transaction point are input into the preset service transaction point matching model to obtain an identifier of the target service transaction point, which specifically includes: firstly, determining all service transaction points capable of handling the client transaction requirements according to the client transaction requirement data and the service state data of each service transaction point; then, determining the service transaction point with the shortest expected transaction duration from all the service transaction points; the expected transaction duration is the duration which is calculated according to the client transaction demand data and the service state data of each service transaction point and is required from the acquisition of the client transaction demand data to the completion of the transaction of the client transaction demand; and finally, determining the service transaction point with the shortest expected transaction duration as a target service transaction point, and acquiring the identifier of the target service transaction point.
In detail, when the target preset service transaction point matching model is a time-saving client time-first matching model, it is necessary to maximally shorten the service transaction time of each client, and there is no client requiring priority processing. In a specific application process, firstly, all optimal service transaction points corresponding to the client service are determined according to the transaction requirements of the client and a preset matching sub-model for matching all optimal service transaction points corresponding to the client service, and all service transaction points capable of handling the transaction requirements of the client are determined in all the optimal service transaction points according to service state data of all the optimal service transaction points, for example, the service transaction points handling the service can handle the transaction requirements of the client. And then, calculating the target service waiting duration corresponding to all service handling points capable of handling the client transaction requirements according to the service handling of all service handling points capable of handling the client transaction requirements, the consumed duration for handling the service, the subsequent services to be handled and the theoretical handling duration corresponding to each service, and determining the target service waiting duration with the shortest duration from all the target service waiting durations for handling the service. And finally, determining the service transaction point corresponding to the service transaction time length of the target client with the shortest time length as the target service transaction point, and acquiring the identifier of the target service transaction point.
In a specific example, the customer transaction demand is a large amount of money to be withdrawn, and four manual window business handling points with the serial numbers of 1, 2, 3 and 4 are respectively arranged at the bank outlets. The manual window type service transaction points with the sequence numbers of 1, 2 and 3 are handling services, specifically, the manual window type service transaction point No. 1 is handling the service A, the time for handling the service A is 10 minutes, no service needs to be handled subsequently, the manual window type service transaction point No. 2 is handling the service B, the time for handling the service B is 5 minutes, the service A needs to be handled subsequently, the manual window type service transaction point No. 3 is handling the service A, the time for handling the service A is 15 minutes, and 2 services B need to be handled subsequently; the No. 4 manual window service transaction point suspends transaction; the theoretical transaction duration of the service A is 30 minutes, and the theoretical transaction duration of the service B is 20 minutes.
In a specific application process, firstly, according to the transaction requirements of the client and a preset matching sub-model for matching all optimal service transaction points corresponding to the client services, all optimal service transaction points corresponding to the client services are determined to be all manual window service transaction points, namely four manual window service transaction points with the sequence numbers of 1, 2, 3 and 4 respectively, then, according to the manual window service transaction points with the sequence numbers of 1, 2 and 3, the services are being handled, and the service state that the 4 # manual window service transaction point suspends the services is determined to be that all the service transaction points capable of handling the client transaction requirements are the manual window service transaction points with the sequence numbers of 1, 2 and 3, the services are being handled. Then, respectively calculating the service waiting duration of the target client corresponding to the manual window service transaction points with the sequence numbers of 1, 2 and 3, wherein the calculation result is as follows: the waiting time of the target client corresponding to the No. 1 artificial window type service handling point is 20 minutes, the waiting time of the target client corresponding to the No. 2 artificial window type service handling point is 45 minutes, and the waiting time of the target client corresponding to the No. 3 artificial window type service handling point is 55 minutes, so that the waiting time of the target client with the shortest time for handling the service is determined to be 20 minutes. And finally, determining the No. 1 manual window class service transaction point corresponding to 20 minutes as a target service transaction point, and acquiring the identifier of the No. 1 manual window class service transaction point.
Fig. 2 is a flowchart illustrating another intelligent scheduling method according to another embodiment of the present invention. In this embodiment, the target preset service transaction point matching model is a priority service high-end client matching model. As shown in fig. 2, the method includes:
s201, acquiring data for target scheduling; the target scheduling data comprises client transaction requirement data and service state data of each service transaction point.
S202, determining all service transaction points capable of handling the customer transaction requirements according to the customer transaction requirement data and the service state data of each service transaction point.
S203, judging whether the client is a high-end client or not, if so, entering a step S204, otherwise, entering a step S205.
Specifically, the high-end customer is a high-end customer determined by a bank according to information such as a deposit of the customer, and the bank needs to preferentially serve the high-end customer.
S204, determining the service transaction point capable of serving the new client most quickly from all the service transaction points capable of handling the transaction requirements of the client, determining the determined service transaction point capable of serving the new client most quickly as a target service transaction point, and acquiring the identifier of the target service transaction point.
Specifically, when the client is a high-end client, firstly, how long time is required for each business handling point to handle the currently handled business respectively is determined according to the business state data of each business handling point, and the business handling point with the shortest time required for handling the currently handled business is determined as the business handling point capable of serving a new client most quickly. And then determining the determined service transaction point capable of serving the new client as the target service transaction point, and acquiring the identifier of the target service transaction point.
S205, determining the service transaction point with the shortest expected transaction duration from all the service transaction points, determining the determined service transaction point with the shortest expected transaction duration as a target service transaction point, and acquiring the identifier of the target service transaction point.
S206, the identification of the target service transaction point is sent to a client side terminal, so that a client can go to the target service transaction point according to the identification of the target service transaction point.
Further, the method also comprises the following steps: and determining a risk item with the resource surplus smaller than a preset resource surplus threshold according to the service state data of each service handling point, and sending alarm information to an operator side terminal according to the risk item.
In detail, the preset resource surplus threshold is a minimum resource surplus value corresponding to each service. The operator side terminal may be of various types, for example, may be a mobile phone of an operator or a computer that logs in an account of the operator. In a specific example, it is determined that the amount of money stored in an ATM is smaller than a preset money remaining amount threshold according to service state data of the ATM, and based on the situation, an alarm message is sent to an operator side terminal, so that an operator knows that the amount of money remaining in the ATM is smaller than the preset money remaining amount threshold, and the operator can replenish money to the ATM in time.
Further, the acquiring the target scheduling data includes: firstly, collecting data for scheduling; then, standardizing the data for scheduling to obtain standard data for scheduling; finally, judging whether the standard scheduling data meets a preset scheduling data standard or not; if so, determining the standard scheduling data as the target scheduling data; otherwise, updating the scheduling data according to the standard scheduling data and the preset scheduling data standard so that the scheduling data meet the preset scheduling data standard after being subjected to standardization processing.
Specifically, unnecessary data information may exist in the collected scheduling data, and the collected scheduling data is directly used in a subsequent model matching process, which may make the model matching process complicated and slow in matching speed. Next, in the case where the collected scheduling data may not be complete, the model matching process may fail by directly using the collected scheduling data in the subsequent model matching process. Therefore, in order to improve the matching speed and the matching success rate in the model matching process, after the scheduling data is collected, firstly, the scheduling data is subjected to standardization processing, specifically including screening unnecessary data in the scheduling data, deleting the unnecessary data, determining missing data in the scheduling data, automatically supplementing the missing data, and finally obtaining the standard scheduling data. Then, judging whether the standard scheduling data meets a preset scheduling data standard, and if so, determining the standard scheduling data as the target scheduling data; otherwise, determining which sub-standards in the preset scheduling data standard the standard scheduling data does not satisfy, and collecting corresponding data according to the sub-standards not satisfied by the quasi-scheduling data and the quasi-scheduling data to update the scheduling data, so that the scheduling data can satisfy the preset scheduling data standard after being subjected to standardization processing.
Fig. 3 is a schematic structural diagram of an intelligent scheduling apparatus according to an embodiment of the present invention. The intelligent scheduling device is used for realizing the intelligent scheduling method. As shown in fig. 3, the apparatus includes: a data acquisition module 31, a data matching module 32 and a notification module 33.
The data obtaining module 31 is configured to obtain data for target scheduling; the target scheduling data comprises client transaction requirement data and service state data of each service transaction point; the data matching module 32 is used for inputting the customer transaction demand data and the service state data of each service transaction point into a preset service transaction point matching model to obtain an identifier of a target service transaction point; the preset service transaction point matching model is used for matching the optimal service transaction network points which are suitable for the client transaction requirement data according to the service state data of each service transaction point; and the notification module 33 is configured to send the identifier of the target service transaction point to a client side terminal, so that the client goes to the target service transaction point according to the identifier of the target service transaction point.
The system further comprises a financial product recommending module, a financial product recommending module and a financial product recommending module, wherein the financial product recommending module is used for matching financial products for clients according to the client deposit data and the client historical transaction data; and sending the product information of the financial product to the business handling point so that the client can know the product information through the business handling point.
Further, the data matching module 32 is specifically configured to, first, select a target preset service transaction point matching model from all preset service transaction point matching models according to a preset selection rule; the preset service handling point matching model comprises a priority client time saving matching model and a priority service high-end client matching model; and then, inputting the client transaction demand data and the service state data of each service transaction point into the target preset service transaction point matching model.
Further, when the target preset service transaction point matching model is a matching model for preferentially saving the time of the client, the data matching module 32 is specifically configured to, first, determine all service transaction points capable of handling the transaction requirements of the client according to the transaction requirement data of the client and the service state data of each service transaction point; then, determining the service transaction point with the shortest expected transaction duration from all the service transaction points; the expected transaction duration is the duration which is calculated according to the client transaction demand data and the service state data of each service transaction point and is required from the acquisition of the client transaction demand data to the completion of the transaction of the client transaction demand; and finally, determining the service transaction point with the shortest expected transaction duration as a target service transaction point, and acquiring the identifier of the target service transaction point.
Further, when the target preset service transaction point matching model is a priority service high-end client matching model, the data matching module 32 is specifically configured to, first, determine all service transaction points capable of handling the client transaction requirements according to the client transaction requirement data and the service state data of each service transaction point; then, judging whether the client is a high-end client or not; when the client is a high-end client, determining a service transaction point capable of serving the new client most quickly from all the service transaction points capable of handling the transaction requirements of the client, determining the determined service transaction point capable of serving the new client most quickly as a target service transaction point, and acquiring the identifier of the target service transaction point; and when the client is not a high-end client, determining the service transaction point with the shortest expected transaction time from all the service transaction points, determining the determined service transaction point with the shortest expected transaction time as a target service transaction point, and acquiring the identifier of the target service transaction point.
And the risk reminding module is used for determining a risk item with the resource residual quantity smaller than a preset resource residual quantity threshold value according to the service state data of each service handling point and sending alarm information to the operator side terminal according to the risk item.
Further, the data obtaining module 31 is specifically configured to, first, collect data for scheduling; then, standardizing the data for scheduling to obtain standard data for scheduling; finally, judging whether the standard scheduling data meets a preset scheduling data standard or not; if the standard scheduling data meet a preset scheduling data standard, determining the standard scheduling data as the target scheduling data; and if the standard scheduling data does not meet the preset scheduling data standard, updating the scheduling data according to the standard scheduling data and the preset scheduling data standard so that the scheduling data meets the preset scheduling data standard after being subjected to standardization processing.
The intelligent scheduling device provided by the embodiment of the invention can execute the intelligent scheduling method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Fig. 4 is a schematic structural diagram of an apparatus according to an embodiment of the present invention. As shown in fig. 4, the apparatus includes:
a processor 410, and a memory 420 coupled to the processor 410; the memory 420 is used for storing a computer program, and the computer program is used for executing the intelligent scheduling method in the embodiment; the processor 410 is used to invoke and execute the computer programs in the memory 420.
The embodiment of the present invention may further include a storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the intelligent scheduling method according to the embodiment of the present invention can be implemented.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow diagrams or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present invention includes additional implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. An intelligent scheduling method, comprising:
acquiring data for target scheduling; the target scheduling data comprises client transaction requirement data and service state data of each service transaction point;
inputting the client transaction demand data and the service state data of each service transaction point into a preset service transaction point matching model to obtain an identifier of a target service transaction point; the preset service transaction point matching model is used for matching the optimal service transaction point which is suitable for the client transaction requirement data according to the service state data of each service transaction point;
and sending the identification of the target service transaction point to a client side terminal so that the client goes to the target service transaction point according to the identification of the target service transaction point.
2. The intelligent scheduling method of claim 1 wherein the data for target scheduling further comprises customer deposit data and customer historical transaction data;
the intelligent scheduling method further comprises the following steps:
matching financial products for the client according to the client deposit data and the client historical transaction data;
and sending the product information of the financial product to the business handling point so that the client can know the product information through the business handling point.
3. The intelligent scheduling method of claim 1, wherein the inputting the customer transaction demand data and the service state data of each service transaction point into a preset service transaction point matching model comprises:
selecting a target preset service handling point matching model from all preset service handling point matching models according to a preset selection rule; the preset service handling point matching model comprises a priority client time saving matching model and a priority service high-end client matching model;
and inputting the client transaction demand data and the service state data of each service transaction point into the target preset service transaction point matching model.
4. The intelligent scheduling method according to claim 3, wherein when the target preset service transaction point matching model is a matching model that preferentially saves client time, the client transaction demand data and the service state data of each service transaction point are input into the preset service transaction point matching model to obtain an identifier of the target service transaction point, and specifically comprises:
determining all service transaction points capable of handling the client transaction requirements according to the client transaction requirement data and the service state data of each service transaction point;
determining the service handling point with the shortest expected handling time from all the service handling points; the expected transaction duration is the duration which is calculated according to the client transaction demand data and the service state data of each service transaction point and is required from the acquisition of the client transaction demand data to the completion of the transaction of the client transaction demand;
and determining the service transaction point with the shortest expected transaction duration as a target service transaction point, and acquiring the identifier of the target service transaction point.
5. The intelligent scheduling method according to claim 3, wherein when the target preset service transaction point matching model is a priority service high-end client matching model, the method of inputting the client transaction demand data and the service state data of each service transaction point into the preset service transaction point matching model to obtain the identifier of the target service transaction point specifically comprises:
determining all service transaction points capable of handling the client transaction requirements according to the client transaction requirement data and the service state data of each service transaction point;
judging whether the client is a high-end client or not;
if so, determining a service transaction point capable of serving the new client most quickly from all the service transaction points capable of handling the transaction requirements of the client, determining the determined service transaction point capable of serving the new client most quickly as a target service transaction point, and acquiring the identifier of the target service transaction point;
otherwise, the service transaction point with the shortest expected transaction time length is determined from all the service transaction points, the determined service transaction point with the shortest expected transaction time length is determined as a target service transaction point, and the identifier of the target service transaction point is obtained.
6. The intelligent scheduling method of claim 1, further comprising: and determining a risk item with the resource surplus smaller than a preset resource surplus threshold according to the service state data of each service handling point, and sending alarm information to an operator side terminal according to the risk item.
7. The intelligent scheduling method of claim 1, wherein the obtaining target scheduling data comprises:
collecting data for scheduling;
standardizing the data for scheduling to obtain standard data for scheduling;
judging whether the standard scheduling data meets a preset scheduling data standard or not;
if so, determining the standard scheduling data as the target scheduling data;
otherwise, updating the scheduling data according to the standard scheduling data and the preset scheduling data standard so that the scheduling data meet the preset scheduling data standard after being subjected to standardization processing.
8. An intelligent scheduling apparatus for implementing the intelligent scheduling method according to claim 1, comprising:
the data acquisition module is used for acquiring data for target scheduling; the target scheduling data comprises client transaction requirement data and service state data of each service transaction point;
the data matching module is used for inputting the client transaction demand data and the service state data of each service transaction point into a preset service transaction point matching model to obtain an identifier of a target service transaction point; the preset service transaction point matching model is used for matching the optimal service transaction network points which are suitable for the client transaction requirement data according to the service state data of each service transaction point;
and the notification module is used for sending the identifier of the target service transaction point to a client side terminal so that a client can go to the target service transaction point according to the identifier of the target service transaction point.
9. An apparatus, comprising:
a processor, and a memory coupled to the processor;
the memory is used for storing a computer program for executing the intelligent scheduling method according to any one of claims 1 to 7;
the processor is used for calling and executing the computer program in the memory.
10. A storage medium storing a computer program which, when executed by a processor, performs the steps of the intelligent scheduling method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010158244.8A CN111401711A (en) | 2020-03-09 | 2020-03-09 | Intelligent scheduling method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010158244.8A CN111401711A (en) | 2020-03-09 | 2020-03-09 | Intelligent scheduling method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111401711A true CN111401711A (en) | 2020-07-10 |
Family
ID=71428597
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010158244.8A Pending CN111401711A (en) | 2020-03-09 | 2020-03-09 | Intelligent scheduling method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111401711A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112884839A (en) * | 2021-03-26 | 2021-06-01 | 中国工商银行股份有限公司 | Service resource allocation method applied to service robot and service robot |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104103113A (en) * | 2013-04-15 | 2014-10-15 | 株式会社日立制作所 | Queuing server and queuing method |
| CN104850937A (en) * | 2015-04-22 | 2015-08-19 | 中国建设银行股份有限公司 | Method and device of business handling shunting |
| CN107767080A (en) * | 2017-11-14 | 2018-03-06 | 中国银行股份有限公司 | A kind of bank outlets' Service Source dispatching method and device |
| CN109214676A (en) * | 2018-08-29 | 2019-01-15 | 中国建设银行股份有限公司 | A kind of service order processing method, device, server and storage medium |
| CN109740782A (en) * | 2019-02-02 | 2019-05-10 | 中国银行股份有限公司 | Reserving method and device, storage medium and electronic equipment |
| US20190176337A1 (en) * | 2017-06-06 | 2019-06-13 | Goertek Inc. | Method and device for dispatching service robots |
-
2020
- 2020-03-09 CN CN202010158244.8A patent/CN111401711A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104103113A (en) * | 2013-04-15 | 2014-10-15 | 株式会社日立制作所 | Queuing server and queuing method |
| CN104850937A (en) * | 2015-04-22 | 2015-08-19 | 中国建设银行股份有限公司 | Method and device of business handling shunting |
| US20190176337A1 (en) * | 2017-06-06 | 2019-06-13 | Goertek Inc. | Method and device for dispatching service robots |
| CN107767080A (en) * | 2017-11-14 | 2018-03-06 | 中国银行股份有限公司 | A kind of bank outlets' Service Source dispatching method and device |
| CN109214676A (en) * | 2018-08-29 | 2019-01-15 | 中国建设银行股份有限公司 | A kind of service order processing method, device, server and storage medium |
| CN109740782A (en) * | 2019-02-02 | 2019-05-10 | 中国银行股份有限公司 | Reserving method and device, storage medium and electronic equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112884839A (en) * | 2021-03-26 | 2021-06-01 | 中国工商银行股份有限公司 | Service resource allocation method applied to service robot and service robot |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109064268A (en) | Business recommended method, apparatus, server-side and storage medium | |
| CN108364085A (en) | A kind of take-away distribution time prediction technique and device | |
| CN111143673A (en) | Method and system for multi-channel customer information processing and automatic marketing | |
| CN111476460A (en) | Method, equipment and medium for intelligent operation scheduling of bank self-service equipment | |
| CN115964272A (en) | Transaction data automatic testing method, device, equipment and readable storage medium | |
| CN115438821A (en) | Intelligent queuing method and related device | |
| CN111401711A (en) | Intelligent scheduling method, device, equipment and storage medium | |
| CN105930245A (en) | Method and system for monitoring service state of transaction terminal | |
| US9503580B1 (en) | Computing device assistance for phone based customer service representative interaction | |
| CN107741885B (en) | Transaction and service association method and system based on CS framework | |
| CN106875268B (en) | Bank account information reminding method, server and terminal | |
| CN114170027A (en) | Processing method and device for public accumulation fund payment account, electronic equipment and storage medium | |
| CN113240188A (en) | Filling sheet processing method and device based on augmented reality AR | |
| CN114648410A (en) | Stock staring method, apparatus, system, device and medium | |
| CN111429092A (en) | Method, device and equipment for paying public accumulation fund and computer readable medium | |
| CN112036873B (en) | Cross-line payment mode determining method and device | |
| JP7000549B1 (en) | Information processing equipment, methods and programs | |
| US9996817B2 (en) | Managing requests for in-person transactions | |
| CN114443311B (en) | Third-party service configuration method and device and electronic equipment | |
| JP5625985B2 (en) | Information processing apparatus, information processing system, and program | |
| CN117745003A (en) | Business hall online connection management method, system, equipment and storage medium | |
| CN117611154A (en) | Service request processing method and device, electronic equipment and storage medium | |
| CN115471316A (en) | Repayment option default setting method and device | |
| CN116468524A (en) | Order processing method and device, electronic equipment and storage medium | |
| CN114297510A (en) | Product recommendation method and device, electronic equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |