CN115700627A - Scheduling method and device for personnel at bank outlets - Google Patents
Scheduling method and device for personnel at bank outlets Download PDFInfo
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Abstract
The invention provides a method and a device for scheduling personnel at a bank outlet, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring first characteristic data of the bank outlets based on the number of the bank outlets in the set area, the basic environment information of each bank outlet and the predicted number of people for handling each business of each bank outlet; acquiring second characteristic data of the banking outlets based on the distance between the banking outlets in the set area; predicting the number of required people of each business of each bank outlet based on the first characteristic data and the second characteristic data of the bank outlet and a outlet business personnel prediction model; and acquiring the number of the equipped persons of each business of each bank outlet according to the number of the required persons of each business of each bank outlet and the total number of the persons of each business in the set area. The device is used for executing the method. The scheduling method and device of the personnel at the bank outlets provided by the embodiment of the invention improve the reasonability of personnel scheduling.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a scheduling method and device for personnel at bank outlets.
Background
The bank outlets are places where banks work outside, including branches, branch processing and the like. It is necessary to arrange the staff of the bank outlets reasonably to provide high-quality service for the customers.
Traditional commercial banks have a large number of offline network points, and with the popularization of more convenient and faster service handling channels such as intelligent counters, mobile phone banks and the like, customers who handle services on site at bank network points are gradually reduced. Internet banks represented by the micro-public banks and the internet business banks are gradually emerging, and as physical network points are not set up in the internet banks and entity cards are not issued, all business operations can be completed on the mobile terminal, which brings challenges to traditional commercial banks. Therefore, how to reasonably schedule the staff of the banking outlets to save the labor cost becomes an important issue to be urgently solved in the field.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a method and a device for scheduling staff at a bank outlet, which can at least partially solve the problems in the prior art.
In a first aspect, the present invention provides a method for scheduling staff at a bank outlet, including:
acquiring first characteristic data of the bank outlets based on the number of the bank outlets in the set area, the basic environment information of each bank outlet and the predicted handling number of each business of each bank outlet; the predicted number of transacted people of each business of each bank outlet is obtained in advance;
acquiring second characteristic data of the banking outlets based on the distance between the banking outlets in the set area;
predicting the number of required people of each business of each bank branch based on the first characteristic data and the second characteristic data of the bank branches and a branch business personnel prediction model; the website business personnel prediction model is obtained based on historical business personnel sample data and corresponding first label training;
and acquiring the number of the equipped persons of each business of each bank outlet according to the number of the required persons of each business of each bank outlet and the total number of the persons of each business in the set area.
In a second aspect, the present invention provides a scheduling apparatus for personnel at a bank outlet, including:
the system comprises a first obtaining module, a second obtaining module and a third obtaining module, wherein the first obtaining module is used for obtaining first characteristic data of the bank outlets based on the number of the bank outlets in a set area, the basic environment information of each bank outlet and the predicted handling number of each business of each bank outlet; the predicted transacted number of each business of each bank outlet is obtained in advance;
the second obtaining module is used for obtaining second characteristic data of the banking outlets based on the distance between the banking outlets in the set area;
the prediction module is used for predicting the number of required people of each business of each bank outlet based on the first characteristic data and the second characteristic data of the bank outlet and a outlet business personnel prediction model; the website business personnel prediction model is obtained based on historical business personnel sample data and corresponding first label training;
and the third obtaining module is used for obtaining the number of the equipped persons of each business of each bank outlet according to the number of the required persons of each business of each bank outlet and the total number of the persons of each business in the set area.
In a third aspect, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the method for scheduling people at a banking site according to any one of the above embodiments.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for scheduling people at banking outlets according to any one of the above embodiments.
In a fifth aspect, the present invention provides a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the method for scheduling people at a banking site according to any one of the above embodiments.
The scheduling method and device for the bank outlets provided by the embodiment of the invention can be used for obtaining the first characteristic data of the bank outlets based on the number of the bank outlets in the set area, the environment basic information of each bank outlet and the predicted handling number of each business of each bank outlet, obtaining the second characteristic data of the bank outlets based on the distance between each bank outlet in the set area, predicting the required number of people of each business of each bank outlet based on the first characteristic data and the second characteristic data of the bank outlets and a outlet business personnel prediction model, obtaining the allocated number of each business of each bank outlet according to the required number of each business of each bank outlet and the total number of people of each business in the set area, scheduling the staff of the whole bank outlets in the set area, and improving the reasonability of staff scheduling.
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. In the drawings:
fig. 1 is a schematic flow chart of a scheduling method for staff at a banking outlet according to a first embodiment of the present invention.
Fig. 2 is a schematic flowchart of a scheduling method for people at banking outlets according to a second embodiment of the present invention.
Fig. 3 is a schematic flowchart of a scheduling method for people at banking outlets according to a third embodiment of the present invention.
Fig. 4 is a schematic flowchart of a scheduling method for people at banking outlets according to a fourth embodiment of the present invention.
Fig. 5 is a flowchart illustrating a scheduling method for staff at a banking outlet according to a fifth embodiment of the present invention.
Fig. 6 is a flowchart illustrating a scheduling method for staff at a banking outlet according to a sixth embodiment of the present invention.
Fig. 7 is a schematic structural diagram of a scheduling apparatus for persons at banking outlets according to a seventh embodiment of the present invention.
Fig. 8 is a schematic structural diagram of a scheduling apparatus for persons at a banking outlet according to an eighth embodiment of the present invention.
Fig. 9 is a schematic structural diagram of a scheduling apparatus for persons at banking outlets according to a ninth embodiment of the present invention.
Fig. 10 is a schematic structural diagram of a scheduling apparatus for persons at a banking outlet according to a tenth embodiment of the present invention.
Fig. 11 is a schematic structural diagram of a scheduling apparatus for persons at banking outlets according to an eleventh embodiment of the present invention.
Fig. 12 is a schematic structural diagram of a scheduling apparatus for persons at banking outlets according to a twelfth embodiment of the present invention.
Fig. 13 is a schematic physical structure diagram of an electronic device according to a thirteenth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
In order to facilitate understanding of the technical solutions provided in the present application, the following first describes relevant contents of the technical solutions in the present application.
The scheduling method of the bank outlets personnel, provided by the embodiment of the invention, can predict the number of transacted personnel of different services of each bank outlet in a set area, predict the required number of the personnel of each service of each bank outlet on the basis of the predicted number of transacted personnel of each service of each bank outlet, and perform personnel scheduling on each bank outlet on the basis of the total number of the personnel of each service in the set area, thereby realizing dynamic scheduling of the personnel of each bank outlet in the set area, improving the reasonability of personnel allocation of each bank outlet, reducing the waste of human resources of each outlet, relieving the working pressure of more outlets of the service, shortening the waiting time of users and improving the satisfaction degree of the users.
The following describes a specific implementation process of the scheduling method for personnel at a bank outlet according to the embodiment of the present invention, taking a server as an execution subject.
Fig. 1 is a schematic flow chart of a scheduling method for people at a banking outlet according to a first embodiment of the present invention, and as shown in fig. 1, the scheduling method for people at a banking outlet according to the embodiment of the present invention includes:
s101, acquiring first characteristic data of the banking outlets based on the number of the banking outlets in a set area, basic environment information of each banking outlet and predicted number of people handling each business of each banking outlet; the predicted number of transacted people of each business of each bank outlet is obtained in advance;
specifically, the server may obtain the number of the banking outlets in the set area, the basic environment information of each banking outlet, and the predicted number of people handling each service of each banking outlet, and then perform feature processing on the number of the banking outlets in the set area, the basic environment information of each banking outlet, and the predicted number of people handling each service of each banking outlet to obtain first feature data of the banking outlets. The setting area is set according to actual needs, and the embodiment of the invention is not limited. The basic environment information of the bank outlets is set according to actual needs, and the embodiment of the invention is not limited. The characteristic processing includes, but is not limited to, digitizing non-numerical data, standardizing the data, and the like, and is set according to actual needs, which is not limited in the embodiments of the present invention.
For example, the basic information of the environment of the banking outlet includes, but is not limited to, the location of the banking outlet, the number of workers, the number of devices of the self-service teller machine and the intelligent terminal of the banking outlet, the operating area, the number of people in a preset range around the banking outlet, the number of floating people, the information of a peer outlet, and the like. The predicted number of people handled for each business of the banking site includes the predicted number of people handled for each business of the banking site.
For example, the set area is a city or a city jurisdiction of a city.
S102, acquiring second characteristic data of the banking outlets based on the distance between the banking outlets in the set area;
specifically, the server may calculate and obtain a distance between any two banking outlets according to the position coordinates of the banking outlets, and then construct an adjacency matrix according to the distance between the banking outlets in the set area to obtain second feature data of the banking outlets.
S103, predicting the number of required people of each business of each bank outlet based on the first characteristic data and the second characteristic data of the bank outlets and a outlet business personnel prediction model; the website service personnel prediction model is obtained based on historical service personnel sample data and corresponding first label training;
specifically, the server inputs the first characteristic data of the bank outlets and the second characteristic data of the bank outlets into a outlet business personnel prediction model, and predicts the number of people required by each business of each bank outlet to obtain the number of people required by each business of each bank outlet. The website service personnel prediction model is obtained based on historical service personnel sample data and corresponding first label training, and the first label is the actual teller number of different services of each bank website.
And S104, acquiring the number of the equipped persons of each business of each bank outlet according to the number of the required persons of each business of each bank outlet and the total number of the persons of each business in the set area.
Specifically, for each business, the server counts the total number of the business demands of each bank outlet, calculates the demand proportion of each bank outlet according to the business demand personnel of each bank outlet and the total number of the business demands of each bank outlet, and calculates the number of the business persons allocated to each bank outlet according to the total number of the business persons in the set area and the demand proportion of each bank outlet. The above process is repeated, and the number of the equipped persons of each business of each bank outlet can be obtained. Wherein, the total number of people of each service in the set area is preset.
The scheduling method of the bank outlets provided by the embodiment of the invention obtains the first characteristic data of the bank outlets based on the number of the bank outlets in the set area, the environment basic information of each bank outlet and the predicted handling number of each business of each bank outlet, obtains the second characteristic data of the bank outlets based on the distance between each bank outlet in the set area, predicts the required number of the businesses of each bank outlet based on the first characteristic data and the second characteristic data of the bank outlets and a outlet business personnel prediction model, obtains the allocated number of each business of each bank outlet according to the required number of each business of each bank outlet and the total number of the businesses in the set area, and schedules the workers of each bank outlet in the set area. In addition, the bank outlets with sufficient staff reduce the waste of human resources of the bank outlets, and for the bank outlets with insufficient manpower, the working pressure of the bank outlets with more services is relieved, the waiting time of customers is shortened, and the satisfaction degree of the customers is improved.
Fig. 2 is a schematic flow chart of a scheduling method for people at banking outlets according to a second embodiment of the present invention, and as shown in fig. 2, on the basis of the foregoing embodiments, the step of obtaining in advance the predicted number of people handled for each business of each banking outlet further includes:
s201, performing characteristic processing on the environment basic information and the website basic information of each bank website and the number of people handling each business in a preset time period to obtain client flow prediction characteristic data of each business of each bank website;
specifically, the server may obtain the basic environment information of each bank outlet, and the number of people handling each service of each bank outlet in a preset time period, and then perform feature processing on the basic environment information, and the number of people handling each service of each bank outlet to obtain the customer traffic prediction feature data of each service of each bank outlet. The characteristic processing includes, but is not limited to, digitizing non-numerical data, standardizing the data, and the like, and is set according to actual needs, which is not limited in the embodiments of the present invention. The preset time period is set according to actual needs, for example, 30 days, and the embodiment of the present invention is not limited. The basic information of the bank outlets is set according to actual needs, and the embodiment of the invention is not limited.
For example, the basic information of the banking site includes, but is not limited to, the number of customers per day, the amount of transactions per day, and the like. The number of people transacting each business of the bank outlets comprises the number of people transacting each business of the bank outlets, and the number of the people transacting the business is the total number of clients transacting the business every day of the bank outlets.
S202, obtaining the predicted transacted number of each business of each bank outlet based on the customer flow prediction characteristic data of each business of each bank outlet and a network outlet business customer flow prediction model; the network point service passenger flow prediction model is obtained based on service historical sample data and corresponding second label training.
Specifically, for each bank outlet, the server inputs the customer flow prediction characteristic data of each service of the bank outlet into the outlet service customer flow prediction model, so that the predicted number of transacted people of each service of the bank outlet can be predicted. The network service passenger flow prediction model is obtained based on service historical sample data and corresponding second label training, and the second label is the number of people handling each service of the bank network.
For example, the number of transacted persons for each business at a banking site on the next day of the past thirty days may be predicted based on environmental basic information, site basic information, and the number of transacted persons for each business at the banking site for the past thirty days.
Fig. 3 is a schematic flow chart of a scheduling method for staff at a banking outlet according to a third embodiment of the present invention, and as shown in fig. 3, on the basis of the foregoing embodiments, further training to obtain a prediction model of a business passenger flow volume of the banking outlet based on business history sample data and a corresponding second label includes:
s301, acquiring service history sample data and a corresponding second label; the service history sample data comprises basic environment information and basic site information of a bank site in a first history time period and actual number of people transacting each service;
specifically, the environmental basic information of the banking outlet, the basic information of the banking outlet and the actual number of people transacting each business in the first historical time period can be collected as business history sample data. The server may obtain traffic history sample data. The business history sample data can be divided into a plurality of groups of sample training data according to the training requirement of the actual model, and each group of sample training data corresponds to a second label. The first historical time period is set according to actual needs, and the embodiment of the invention is not limited.
For example, if the environmental basic information of the banking outlet, the outlet basic information and the actual number of people transacting the business a in 30 days are taken as a set of sample training data, the actual number of people transacting the business a in the next day of 30 days is taken as a second label corresponding to the set of sample training data.
S302, performing characteristic processing on the basic environment information and basic site information of the banking sites and the number of people handling each business in the first historical time period to obtain business training characteristic data;
specifically, the server performs characteristic processing on the environmental basic information of the banking outlet, the outlet basic information and the number of people handling each business in the first historical time period to obtain business training characteristic data. The business training feature data may include a plurality of sets of feature data, each set of feature data corresponds to each set of sample training data, and the second label corresponding to one set of sample training data corresponds to the feature data corresponding to the set of sample training data. The characteristic processing includes, but is not limited to, digitizing non-numerical data, standardizing the data, and the like, and is set according to actual needs, which is not limited in the embodiments of the present invention.
S303, based on the first original model, the service training characteristic data and the corresponding second label, training to obtain the network point service passenger flow prediction model.
Specifically, the server trains a first original model based on each group of feature data in the service training feature data and a second label corresponding to each group of service training feature data, so as to train and obtain the website service passenger flow volume prediction model. The first original model is selected according to actual experience, and the embodiment of the invention is not limited.
For example, the first original model is a GRU (Gate recovery Unit) model, which is a variant of a Long Short-Term Memory (LSTM) network model with a good effect, and is simpler in structure and better in effect than the LSTM network model.
Fig. 4 is a schematic flow chart of a scheduling method for staff at a banking outlet according to a fourth embodiment of the present invention, and as shown in fig. 4, on the basis of the foregoing embodiments, further obtaining a prediction model of staff at a banking outlet based on historical staff sample data and corresponding first label training includes:
s401, obtaining sample data of historical business personnel and a corresponding first label; the historical business personnel sample data comprises the number of the banking outlets in a preset area in a second historical time period, the distance between the banking outlets in the preset area, the environmental basic information of the banking outlets and the actual number of people handling the business of the banking outlets;
specifically, the number of banking outlets in a preset area, the distance between the banking outlets in the preset area, the environmental basic information of the banking outlets in the preset area, and the actual number of people handling each service of the banking outlets in the preset area in a second historical time period may be collected as historical service person sample data. The historical business person sample data can be divided into a plurality of groups of sample training data according to the training requirements of the actual model, and each group of business person sample training data corresponds to a first label. The second historical time period is set according to actual needs, and the embodiment of the invention is not limited.
For example, the number of banking outlets in a preset area, the distance between the banking outlets in the preset area, the environmental basic information of the banking outlets in the preset area, and the actual number of people handling a service at the banking outlets in the preset area are taken as a group of sample training data, and the first label corresponding to the group of sample training data is the actual number of staff handling the service at the banking outlets in the day. Because the banking outlets are not frequently moved and cancelled, the number of the banking outlets in the preset area and the distance between the banking outlets in the preset area in the sample training data of each group of business personnel are generally the same.
S402, obtaining first training characteristic data according to the number of the banking outlets in the preset area, the basic environment information of each banking outlet and the actual number of people handling each business of each banking outlet in the second historical time period;
specifically, the server performs feature processing on the number of banking outlets in the set area, the basic environment information of each banking outlet, and the actual number of people handling each business of each banking outlet in the second historical time period to obtain first training feature data.
For example, each bank branch in the preset area is taken as a node, for each bank branch, the basic environment information of the bank branch in the second historical time period and the actual number of transacted persons of each service are divided by day, the basic environment information of each bank branch and the actual number of transacted persons of each service are obtained and taken as the node characteristics of each day, the node characteristics of each day of N nodes in the preset area can form an N × M characteristic matrix, and M is the number of the node characteristics. If the second historical period of time is Q days, then the first training feature data includes Q N × M feature matrices.
S403, obtaining second training characteristic data according to the distance between the banking outlets in the preset area;
specifically, the server may calculate and obtain a distance between any two nodes according to the position coordinates of each banking node in the preset area, and then construct an adjacency matrix according to the distance between the banking nodes in the preset area to obtain second training feature data.
For example, N banking outlets are in the preset area, the N banking outlets are used as vertexes, the distance between the banking outlets is used as a side, the distance between the N banking outlets is stored through the two-dimensional array, an adjacent matrix is established, and the adjacent matrix is used as second training feature data.
S404, training to obtain the website service personnel prediction model based on a second original model, the first training characteristic data, the second training characteristic data and the corresponding first label.
Specifically, the server trains a second original model based on the first training feature data, the second training feature data and the corresponding first label, and may train to obtain the website service personnel prediction model. The second original model is selected according to actual experience, and the embodiment of the present invention is not limited.
For example, the second original model employs a graph convolutional neural network model. The graph convolutional neural network model comprises a first graph convolutional layer with 16 nodes, a relu active layer is connected after the first graph convolutional layer, the output of the relu active layer is connected with a dropout layer (a random inactivation layer for avoiding over-fitting), the output of the dropout layer is connected with a second graph convolutional layer with 2 nodes, and the output of the second graph convolutional layer is connected with a full connection layer.
Fig. 5 is a schematic flow chart of a scheduling method for staff at bank outlets according to a fifth embodiment of the present invention, and as shown in fig. 5, further, in addition to the above embodiments, the obtaining the number of staffing agents for each business at each bank outlet according to the number of demanded staff of each business at each bank outlet and the total number of staff of each business in the set area includes:
s501, acquiring the demand proportion of each business of each bank outlet according to the demand number of each business of each bank outlet;
specifically, the server counts the number of required people of each business of each bank outlet, obtains the total number of required people of each business, then calculates the ratio of the number of required people of each business of each bank outlet to the total number of required people of each business, and obtains the required proportion of each business of each bank outlet.
For example, for business A, the required number of business A at the ith bank outlet is
Total demand population of business A
And n is the total number of banking outlets. Demand proportion of service A of ith bank outlet
i is a positive integer and i is less than or equal to n.
S502, obtaining the number of the equipped persons of each service of each bank outlet according to the total number of persons of each service in the set area and the required proportion of each service of each bank outlet.
Specifically, for each bank outlet, the server calculates and rounds the result of the product of the total number of people of each service in the set area and the required proportion of each service of the bank outlet, and obtains the number of the equipped people of each service of the bank outlet. Wherein the total number of people of each service in the set area is preset.
For example, for service A, the total number of service A in the set area is Q A The demand ratio of the service A of the ith bank outlet is
The number of staffing persons of service a of the ith banking outlet is:
[]indicating rounding.
Fig. 6 is a schematic flow chart of a scheduling method for banking outlets according to a sixth embodiment of the present invention, and as shown in fig. 6, on the basis of the foregoing embodiments, further, the obtaining second feature data of the banking outlets based on distances between the banking outlets in the set area includes:
s601, obtaining distance parameters among the banking outlets according to the distance among the banking outlets in the set area and the truncation distance;
specifically, the server compares the distance between any two banking outlets in the set area with the cutoff distance, and obtains a distance parameter between any two banking outlets according to a comparison result. The truncation distance is set according to actual needs, and the embodiment of the invention is not limited.
S602, constructing an adjacency matrix as second characteristic data of the bank outlets according to the distance parameters among the bank outlets.
Specifically, the server stores the distance parameters between the banking outlets through a two-dimensional array by taking the banking outlets as vertexes and the distance parameters as sides, constructs an adjacency matrix, and takes the obtained adjacency matrix as second characteristic data of the banking outlets.
On the basis of the foregoing embodiments, further, the obtaining, according to the distance and the cutoff distance between the banking outlets in the set area, a distance parameter between the banking outlets includes:
if the distance between the two banking outlets is judged to be smaller than or equal to the truncation distance, the distance between the two banking outlets is used as a distance parameter between the two banking outlets;
and if the distance between the two banking outlets is judged to be larger than the truncation distance, the distance parameter between the two banking outlets is infinite.
Specifically, the server compares the distance between any two banking outlets with the truncation distance, and if the distance between two banking outlets is greater than the truncation distance, the two banking outlets are regarded as being connected without a side, and the distance parameter between the two banking outlets is infinite. And if the distance between the two banking outlets is smaller than or equal to the truncation distance, taking the distance between the two banking outlets as a distance parameter between the two banking outlets.
The following describes an implementation process of the scheduling method for people at banking outlets provided in the embodiment of the present invention with reference to scheduling objects of 10 banking outlets in a jurisdiction of city Y.
And acquiring the basic environment information and the basic site information of each bank site in 10 bank sites in the district of the city for 30 days continuously and the number of people handling each business. The basic environment information and the basic site information of each bank site in the past 30 days and the number of transacted people of each service are divided in a day unit, the data are sorted by taking the service as a dimension, the basic environment information and the basic site information of the kth day and the number of transacted people of the jth service of each bank site can be obtained, k is a positive integer, k is less than or equal to 30, j is less than or equal to P, and P is the total number of the services. The services include, but are not limited to, cash services, credit card services, financial services, and the like, and are set according to actual needs, which is not limited in the embodiments of the present invention.
And performing characteristic processing on the environment basic information and the website basic information of the kth day of each bank website and the number of transacted people of the jth business to obtain the customer flow prediction characteristic data of the jth business of the kth day of each bank website.
The customer flow prediction characteristic data of the jth service of the banking outlet in 30 consecutive days is input into the outlet service customer flow prediction model, so that the predicted number of people for handling the jth service in the next day in 30 consecutive days can be predicted. The process is repeated, and the predicted number of people to handle for each business of 10 bank outlets can be obtained.
And acquiring the number of the banking outlets in the district of the city, the environment basic information of 10 banking outlets and the predicted transacted number of the businesses of 10 banking outlets. And performing characteristic processing on the environment basic information of the 10 banking outlets and the predicted transacted number of the businesses of the 10 banking outlets to obtain characteristic data of each banking outlet, wherein the types of the characteristic data of each banking outlet are assumed to be M. Feature data of 10 nodes form a 10 × M feature matrix as first feature data of the banking nodes. Since the change of the basic information of the environment of the banking outlets every 1 day is negligible, the basic information of the environment of the banking outlets on the 30 th day of the consecutive 30 days can be used as the basic information of the environment of the banking outlets on the next day of the consecutive 30 days.
Obtaining the distance between 10 banking outlets in the district of the city, comparing the distance between any two banking outlets with the truncation distance, and if the distance between two banking outlets is greater than the truncation distance, the distance parameter between the two banking outlets is infinite; if the distance between any two banking outlets is smaller than or equal to the truncation distance, the distance between the two banking outlets is used as a distance parameter between the two banking outlets, and the distance parameter between the 10 banking outlets most takes the 10 banking outlets as an edge of a vertex to form an adjacent matrix which is used as second characteristic data of the banking outlets.
The first characteristic data and the second characteristic data of the bank outlets are input into the outlet business personnel prediction model, and the number of required people of each business of 10 bank outlets can be predicted.
And obtaining the total number of the demanded people of the jth service according to the demanded number of people of the jth service of 10 bank outlets, calculating the ratio of the demanded number of people of the jth service of each bank outlet to the total number of the demanded number of people of the jth service, and obtaining the demanded proportion of the jth service of each bank outlet. And respectively calculating the product of the total number of the jth business in the municipal administration area and the demand proportion of the jth business of each bank branch, and rounding up to obtain the number of the jth business of each bank branch. And repeating the process to finally obtain the number of the equipped persons of each business of each bank outlet.
Fig. 7 is a schematic structural diagram of a scheduling apparatus for staff at a banking outlet according to a seventh embodiment of the present invention, and as shown in fig. 7, the scheduling apparatus for staff at a banking outlet according to the embodiment of the present invention includes a first obtaining module 701, a second obtaining module 702, a predicting module 703, and a third obtaining module 704, where:
the first obtaining module 701 is configured to obtain first feature data of the banking outlets based on the number of the banking outlets in the set area, the basic environment information of each banking outlet, and the predicted number of people transacted by each service of each banking outlet; the predicted number of transacted people of each business of each bank outlet is obtained in advance; the second obtaining module 702 is configured to obtain second feature data of the banking outlets based on distances between the banking outlets in the set area; the prediction module 703 is configured to predict the number of required people for each service of each bank branch based on the first feature data and the second feature data of the bank branch and a branch service person prediction model; the website business personnel prediction model is obtained based on historical business personnel sample data and corresponding first label training; the third obtaining module 704 is configured to obtain the number of persons equipped for each service of each bank outlet according to the number of persons required for each service of each bank outlet and the total number of persons in each service of the setting area.
Specifically, the first obtaining module 701 may obtain the number of bank outlets in the set area, the basic environment information of each bank outlet, and the predicted number of transacted persons of each service of each bank outlet, and then perform feature processing on the number of bank outlets in the set area, the basic environment information of each bank outlet, and the predicted number of transacted persons of each service of each bank outlet, to obtain the first feature data of the bank outlets. The setting area is set according to actual needs, and the embodiment of the invention is not limited. The basic environment information of the bank outlets is set according to actual needs, and the embodiment of the invention is not limited. The characteristic processing includes, but is not limited to, digitizing non-numerical data, standardizing the data, and the like, and is set according to actual needs, which is not limited in the embodiments of the present invention.
The second obtaining module 702 may calculate and obtain a distance between any two banking outlets according to the position coordinates of the banking outlets, and then construct an adjacency matrix according to the distance between the banking outlets in the set area to obtain second feature data of the banking outlets.
The prediction module 703 inputs the first characteristic data of the bank outlets and the second characteristic data of the bank outlets into a outlet business person prediction model, and predicts the number of people required for each business of each bank outlet to obtain the number of people required for each business of each bank outlet. The network business personnel prediction model is obtained by training based on historical business personnel sample data and corresponding first labels, and the first labels are the actual teller numbers of different businesses of each bank network.
For each service, the third obtaining module 704 counts the total number of required services of each bank outlet, calculates the required proportion of each bank outlet according to the required personnel of the service of each bank outlet and the total number of required services of each bank outlet, and calculates the number of allocated services of each bank outlet according to the total number of required services in the set area and the required proportion of each bank outlet. Repeating the above process can obtain the number of the equipped persons of each business of each bank outlet. Wherein, the total number of people of each service in the set area is preset.
The scheduling device for the bank outlets personnel provided by the embodiment of the invention obtains the first characteristic data of the bank outlets based on the number of the bank outlets in the set area, the environment basic information of each bank outlet and the predicted handling number of each business of each bank outlet, obtains the second characteristic data of the bank outlets based on the distance between each bank outlet in the set area, predicts the required number of people of each business of each bank outlet based on the first characteristic data and the second characteristic data of the bank outlets and a outlet business personnel prediction model, obtains the allocated number of each business of each bank outlet according to the required number of each business of each bank outlet and the total number of people of each business in the set area, and schedules the workers of each bank outlet in the set area. In addition, the bank outlets with sufficient staff reduce the waste of human resources of the bank outlets, and for the bank outlets with insufficient manpower, the working pressure of the bank outlets with more services is relieved, the waiting time of customers is shortened, and the satisfaction degree of the customers is improved.
Fig. 8 is a schematic structural diagram of a scheduling apparatus for people at banking outlets according to an eighth embodiment of the present invention, as shown in fig. 8, on the basis of the foregoing embodiments, further, the scheduling apparatus for people at banking outlets according to the embodiment of the present invention further includes a first feature processing module 705 and a people number predicting module 706, where:
the first feature processing module 705 is configured to perform feature processing on the basic environment information and basic site information of each bank site and the number of people handling each service in a preset time period, and obtain customer traffic prediction feature data of each service of each bank site; the people number prediction module 706 is used for obtaining the predicted transacted people number of each business of each bank branch based on the customer flow prediction characteristic data of each business of each bank branch and the branch business customer flow prediction model; the network point service passenger flow volume prediction model is obtained based on service history sample data and corresponding second label training.
Fig. 9 is a schematic structural diagram of a scheduling apparatus for people at banking outlets according to a ninth embodiment of the present invention, as shown in fig. 9, on the basis of the foregoing embodiments, further, the scheduling apparatus for people at banking outlets according to the embodiment of the present invention further includes an obtaining module 707, a second feature processing module 708, and a first training module 709, where:
the first obtaining module 707 is configured to obtain service history sample data and a corresponding second tag; the service history sample data comprises basic environment information and basic site information of a bank site in a first history time period and actual number of people transacting each service; the second feature processing module 708 is configured to perform feature processing on the basic environment information of the banking outlet, the basic information of the banking outlet, and the number of people handling each service in the first historical time period, so as to obtain service training feature data; the first training module 709 is configured to train to obtain the branch service passenger flow volume prediction model based on the first original model, the service training feature data, and the corresponding second label.
Fig. 10 is a schematic structural diagram of a scheduling apparatus for persons at a banking outlet according to a tenth embodiment of the present invention, as shown in fig. 10, on the basis of the foregoing embodiments, further, the scheduling apparatus for persons at a banking outlet according to the embodiment of the present invention further includes a second obtaining module 710, a fourth obtaining module 711, a fifth obtaining module 712, and a second training module 713, where:
the second obtaining module 710 is configured to obtain sample data of a historical service person and a corresponding first tag; the historical business personnel sample data comprises the number of the banking outlets in a preset area in a second historical time period, the distance between the banking outlets in the preset area, the environmental basic information of the banking outlets and the actual number of people handling the business of the banking outlets; the third obtaining module 711 is configured to obtain first training feature data according to the number of banking outlets in the preset area, the basic environment information of each banking outlet, and the actual number of people transacting each business of each banking outlet in the second historical time period; a fourth obtaining module 712, configured to obtain second training feature data according to a distance between each banking outlet in the preset area; the second training module 713 is configured to train to obtain the website service staff prediction model based on a second original model, the first training feature data, the second training feature data, and the corresponding first label.
Fig. 11 is a schematic structural diagram of a scheduling apparatus for persons at a banking outlet according to an eleventh embodiment of the present invention, and as shown in fig. 11, on the basis of the foregoing embodiments, further, the third obtaining module 704 includes a first obtaining unit 7041 and a second obtaining unit 7042, where:
the first obtaining unit 7041 is configured to obtain a demand proportion of each service of each banking outlet according to the number of people required by each service of each banking outlet; the second obtaining unit 7042 is configured to obtain the number of staffing agents for each service at each banking outlet according to the total number of staffing agents for each service in the setting area and the demand ratio of each service at each banking outlet.
Fig. 12 is a schematic structural diagram of a scheduling apparatus for persons at a banking outlet according to a twelfth embodiment of the present invention, and as shown in fig. 12, on the basis of the foregoing embodiments, further, the second obtaining module 702 includes a third obtaining unit 7021 and a building unit 7022, where:
a third obtaining unit 7021 is configured to obtain a distance parameter between each banking point according to a distance between each banking point in the set area and the cutoff distance; the constructing unit 7022 is configured to construct an adjacency matrix as second feature data of the banking outlets according to the distance parameter between the banking outlets.
On the basis of the foregoing embodiments, further, third obtaining unit 7021 is specifically configured to:
if the distance between the two banking outlets is judged to be smaller than or equal to the truncation distance, the distance between the two banking outlets is used as a distance parameter between the two banking outlets;
and if the distance between the two banking outlets is judged to be larger than the truncation distance, the distance parameter between the two banking outlets is infinite.
The embodiment of the apparatus provided in the embodiment of the present invention may be specifically configured to execute the processing flows of the foregoing method embodiments, and its functions are not described herein again, and reference may be made to the detailed description of the foregoing method embodiments.
It should be noted that the scheduling method and apparatus for people at banking outlets provided in the embodiments of the present invention may be used in the financial field, and may also be used in any technical field other than the financial field.
Fig. 13 is a schematic physical structure diagram of an electronic device according to a thirteenth embodiment of the present invention, and as shown in fig. 13, the electronic device may include: a processor (processor) 1301, a communication Interface (Communications Interface) 1302, a memory (memory) 1303, and a communication bus 1304, wherein the processor 1301, the communication Interface 1302, and the memory 1303 complete communication with each other via the communication bus 1304. Processor 1301 may call logic instructions in memory 1303 to perform the following method: acquiring first characteristic data of the bank outlets based on the number of the bank outlets in the set area, the basic environment information of each bank outlet and the predicted handling number of each business of each bank outlet; the predicted transacted number of each business of each bank outlet is obtained in advance; acquiring second characteristic data of the banking outlets based on the distance between the banking outlets in the set area; predicting the number of required people of each business of each bank branch based on the first characteristic data and the second characteristic data of the bank branches and a branch business personnel prediction model; the website business personnel prediction model is obtained based on historical business personnel sample data and corresponding first label training; and acquiring the number of the equipped persons of each business of each bank outlet according to the number of the required persons of each business of each bank outlet and the total number of the persons of each business in the set area.
In addition, the logic instructions in the memory 1303 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
The present embodiment discloses a computer program product comprising a computer program stored on a computer-readable storage medium, the computer program comprising program instructions, which when executed by a computer, enable the computer to perform the method provided by the above-mentioned method embodiments, for example, including: acquiring first characteristic data of the bank outlets based on the number of the bank outlets in the set area, the basic environment information of each bank outlet and the predicted handling number of each business of each bank outlet; the predicted number of transacted people of each business of each bank outlet is obtained in advance; acquiring second characteristic data of the banking outlets based on the distance between the banking outlets in the set area; predicting the number of required people of each business of each bank branch based on the first characteristic data and the second characteristic data of the bank branches and a branch business personnel prediction model; the website business personnel prediction model is obtained based on historical business personnel sample data and corresponding first label training; and acquiring the number of the equipped persons of each business of each bank outlet according to the number of the required persons of each business of each bank outlet and the total number of the persons of each business in the set area.
The present embodiment provides a computer-readable storage medium, which stores a computer program, where the computer program causes the computer to execute the method provided by the foregoing method embodiments, for example, the method includes: acquiring first characteristic data of the bank outlets based on the number of the bank outlets in the set area, the basic environment information of each bank outlet and the predicted number of people for handling each business of each bank outlet; the predicted number of transacted people of each business of each bank outlet is obtained in advance; acquiring second characteristic data of the banking outlets based on the distance between the banking outlets in the set area; predicting the number of required people of each business of each bank outlet based on the first characteristic data and the second characteristic data of the bank outlet and a outlet business personnel prediction model; the website business personnel prediction model is obtained based on historical business personnel sample data and corresponding first label training; and acquiring the number of the equipped persons of each business of each bank outlet according to the number of the required persons of each business of each bank outlet and the total number of the persons of each business in the set area.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In the description of the specification, reference to the description of "one embodiment," a specific embodiment, "" some embodiments, "" e.g., "an example," "a specific example," or "some examples," etc., means 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.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (11)
1. A method for scheduling personnel at a bank outlet is characterized by comprising the following steps:
acquiring first characteristic data of the bank outlets based on the number of the bank outlets in the set area, the basic environment information of each bank outlet and the predicted handling number of each business of each bank outlet; the predicted transacted number of each business of each bank outlet is obtained in advance;
acquiring second characteristic data of the banking outlets based on the distance between the banking outlets in the set area;
predicting the number of required people of each business of each bank branch based on the first characteristic data and the second characteristic data of the bank branches and a branch business personnel prediction model; the website business personnel prediction model is obtained based on historical business personnel sample data and corresponding first label training;
and acquiring the number of the equipped persons of each business of each bank outlet according to the number of the required persons of each business of each bank outlet and the total number of the persons of each business in the set area.
2. The method of claim 1, wherein the step of obtaining in advance a predicted number of transacted business for each banking outlet comprises:
performing characteristic processing on the environment basic information and the website basic information of each bank website and the number of people handling each business in a preset time period to obtain client flow prediction characteristic data of each business of each bank website;
obtaining the predicted transacted number of each business of each bank outlet based on the customer flow prediction characteristic data of each business of each bank outlet and a network outlet business customer flow prediction model; the network point service passenger flow prediction model is obtained based on service historical sample data and corresponding second label training.
3. The method of claim 2, wherein training to obtain a website traffic passenger flow prediction model based on traffic history sample data and a corresponding second label comprises:
acquiring service history sample data and a corresponding second label; the service history sample data comprises basic environment information and basic site information of the bank sites in a first history time period and actual number of people handling each service;
performing characteristic processing on the basic environment information and basic site information of the banking sites and the number of people handling each service in the first historical time period to obtain service training characteristic data;
and training to obtain the branch service passenger flow prediction model based on the first original model, the service training characteristic data and the corresponding second label.
4. The method of claim 1, wherein training to obtain a website business person prediction model based on historical business person sample data and a corresponding first label comprises:
acquiring sample data of historical business personnel and a corresponding first label; the historical business personnel sample data comprises the number of the banking outlets in a preset area in a second historical time period, the distance between the banking outlets in the preset area, the environmental basic information of the banking outlets and the actual number of people handling the business of the banking outlets;
acquiring first training characteristic data according to the number of the banking outlets in the preset area, the basic environment information of each banking outlet and the actual number of people handling each business of each banking outlet in the second historical time period;
acquiring second training characteristic data according to the distance between the banking outlets in the preset area;
and training to obtain the website business personnel prediction model based on a second original model, the first training characteristic data, the second training characteristic data and a corresponding first label.
5. The method of claim 1, wherein obtaining the number of the outfits of each business of each bank outlet according to the number of the required people of each business of each bank outlet and the total number of the businesses in the set area comprises:
acquiring the demand proportion of each business of each bank outlet according to the demand number of each business of each bank outlet;
and acquiring the number of the equipped persons of each service of each bank outlet according to the total number of persons of each service in the set area and the required proportion of each service of each bank outlet.
6. The method according to any one of claims 1 to 5, wherein the obtaining of the second feature data of the banking outlets based on the distances between the banking outlets in the set area comprises:
obtaining distance parameters among the banking outlets according to the distance among the banking outlets in the set area and the truncation distance;
and constructing an adjacency matrix as second characteristic data of the banking outlets according to the distance parameters among the banking outlets.
7. The method according to claim 6, wherein the obtaining the distance parameter between the banking outlets according to the distance between the banking outlets in the setting area and the truncation distance comprises:
if the distance between the two banking outlets is judged and known to be smaller than or equal to the truncation distance, the distance between the two banking outlets is used as a distance parameter between the two banking outlets;
and if the distance between the two banking outlets is judged to be larger than the truncation distance, the distance parameter between the two banking outlets is infinite.
8. A bank outlet personnel scheduling device, comprising:
the system comprises a first obtaining module, a second obtaining module and a third obtaining module, wherein the first obtaining module is used for obtaining first characteristic data of the bank outlets based on the number of the bank outlets in a set area, the basic environment information of each bank outlet and the predicted handling number of each business of each bank outlet; the predicted transacted number of each business of each bank outlet is obtained in advance;
the second obtaining module is used for obtaining second characteristic data of the banking outlets based on the distance between the banking outlets in the set area;
the prediction module is used for predicting the number of required people of each business of each bank outlet based on the first characteristic data and the second characteristic data of the bank outlet and a outlet business personnel prediction model; the website service personnel prediction model is obtained based on historical service personnel sample data and corresponding first label training;
and the third obtaining module is used for obtaining the number of the equipped persons of each business of each bank outlet according to the number of the required persons of each business of each bank outlet and the total number of the persons of each business in the set area.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the method of any one of claims 1 to 7.
11. A computer program product, characterized in that the computer program product comprises a computer program which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
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| CN116151950A (en) * | 2023-04-04 | 2023-05-23 | 四川博源科技有限责任公司 | Intelligent banking outlet scheduling management method, system and storage medium |
| CN116151950B (en) * | 2023-04-04 | 2023-09-01 | 四川博源科技有限责任公司 | Intelligent banking outlet scheduling management method, system and storage medium |
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