CN112288562B - Bank teller intelligent scheduling method and system based on big data - Google Patents
Bank teller intelligent scheduling method and system based on big data Download PDFInfo
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Abstract
The invention discloses a bank teller intelligent scheduling method and system based on big data, wherein the method comprises the following steps: predicting the transaction amount and the transaction peak value of the target date of the network site to be scheduled based on a pre-constructed prediction model; based on the transaction amount and the transaction peak value of the target date of the network site to be scheduled and the preset classified teller types, the teller number and the target teller types of the target date of the network site to be scheduled are configured; and dynamically generating a teller scheduling table of the target date of the mesh point to be scheduled based on the number of teller and the target teller type of the target mesh point target date and the commuting time of the target teller type on duty teller to the mesh point to be scheduled. The invention can realize the dynamic adjustment of the teller in different sites and different dates in the same city, so as to improve the utilization efficiency of human resources, improve the site benefit and improve the customer experience.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent scheduling method and system for bank teller based on big data.
Background
The counter channel of the commercial bank is still an important channel for initiating business under the bank, the business under each commercial bank has a plurality of counter points, the business volume of each counter point is different, the business volume of different days of the same counter point is also different, the counter number of the counter points under each commercial bank is fixed, the counter number in the counter point is also stable, and the counter points conduct business handling through a mode of alternate shift. Therefore, the current business bank scheduling mode has a certain problem, the problem that part of network point traffic is insufficient, customers are queued and waiting, in addition, part of network point traffic is small, the network point traffic is free and is not transacted, the same network point is insufficient in part of date traffic, and in the condition that part of date traffic is small, the network point traffic is free, uneven distribution of human resources occurs, human waste is caused, and customer experience is affected.
Therefore, how to realize dynamic adjustment of teller in different sites and on different dates in the same city so as to improve the utilization efficiency of human resources, improve the benefits of the sites and improve the customer experience is a problem to be solved urgently.
Disclosure of Invention
In view of the above, the invention provides a bank teller intelligent scheduling method based on big data, which can realize dynamic adjustment of teller in different cities and different dates, so as to improve the utilization efficiency of human resources, improve the benefit of the website and improve the customer experience.
The invention provides a bank teller intelligent scheduling method based on big data, which comprises the following steps:
predicting the transaction amount and the transaction peak value of the target date of the network site to be scheduled based on a pre-constructed prediction model;
based on the transaction amount and the transaction peak value of the target date of the to-be-scheduled network point and the preset classified teller types, configuring the teller number and the target teller type of the target date of the to-be-scheduled network point;
and dynamically generating a teller scheduling table of the target date of the to-be-scheduled website based on the number of teller and the target teller type of the target teller and the commuting time from the on-duty teller to the to-be-scheduled website.
Preferably, pre-constructing the prediction model includes:
acquiring historical transaction data of each website;
and analyzing the historical transaction data of each website by utilizing a big data analysis technology, and constructing a prediction model of the transaction amount and the transaction peak value of each website.
Preferably, the acquiring historical transaction data of each website includes:
automatically collecting daily transaction data of each website;
storing the daily transaction data of each website automatically collected into a database to form historical transaction data of each website;
and acquiring historical transaction data of each website from the database.
Preferably, the pre-classifying the teller type includes:
acquiring historical business processing data of each teller;
analyzing the historical business processing data of each teller by utilizing a big data analysis technology, and calculating the efficiency value of each teller;
and classifying the teller based on the efficiency value of each teller and a preset threshold value to obtain the teller type.
Preferably, the acquiring historical business processing data of each teller includes:
automatically collecting daily business processing data of each teller;
storing the automatically collected daily business processing data of each teller into a database to form historical business processing data of each teller;
and acquiring historical business processing data of each teller from the database.
A big data based intelligent bank teller scheduling system comprising:
the prediction module is used for predicting the transaction amount and the transaction peak value of the target date of the to-be-scheduled network point based on a pre-constructed prediction model;
the configuration module is used for configuring the number of teller and the target teller type of the target date of the to-be-scheduled network point based on the transaction amount and the transaction peak value of the target date of the to-be-scheduled network point and the pre-classified teller type;
and the intelligent scheduling module is used for dynamically generating a teller scheduling table of the target date of the to-be-scheduled network point based on the number of teller and the target teller type of the target date of the to-be-scheduled network point and the commuting time from the on-duty teller of the target teller type to the to-be-scheduled network point.
Preferably, the system further comprises:
the first acquisition module is used for acquiring historical transaction data of each website;
the construction module is used for analyzing the historical transaction data of each website by utilizing a big data analysis technology and constructing a prediction model of the transaction amount and the transaction peak value of each website.
Preferably, the first obtaining module is specifically configured to:
automatically collecting daily transaction data of each website;
storing the daily transaction data of each website automatically collected into a database to form historical transaction data of each website;
and acquiring historical transaction data of each website from the database.
Preferably, the system further comprises:
the second acquisition module is used for acquiring historical business processing data of each teller;
the calculation module is used for analyzing the historical business processing data of each teller by utilizing a big data analysis technology and calculating the efficiency value of each teller;
and the classification module is used for classifying the teller based on the efficiency value of each teller and a preset threshold value to obtain the teller type.
Preferably, the second obtaining module is specifically configured to:
automatically collecting daily business processing data of each teller;
storing the automatically collected daily business processing data of each teller into a database to form historical business processing data of each teller;
and acquiring historical business processing data of each teller from the database.
In summary, the invention discloses a bank teller intelligent scheduling method based on big data, when a bank website needs to be scheduled, firstly, predicting the transaction amount and the transaction peak value of the target date of the website to be scheduled based on a pre-constructed prediction model, and then, configuring the number of teller and the target teller type of the target date of the website to be scheduled based on the transaction amount and the transaction peak value of the target date of the website to be scheduled and the pre-classified teller type; and dynamically generating a teller scheduling table of the target date of the mesh point to be scheduled based on the number of teller and the target teller type of the target mesh point target date and the commuting time of the target teller type on duty teller to the mesh point to be scheduled. The invention can realize the dynamic adjustment of the teller in different sites and different dates in the same city, so as to improve the utilization efficiency of human resources, improve the site benefit and improve the customer experience.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment 1 of a bank teller intelligent scheduling method based on big data;
FIG. 2 is a flow chart of an embodiment 2 of a bank teller intelligent scheduling method based on big data;
fig. 3 is a schematic structural diagram of an embodiment 1 of a bank teller intelligent scheduling system based on big data disclosed by the invention;
fig. 4 is a schematic structural diagram of an embodiment 2 of a bank teller intelligent scheduling system based on big data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a method flowchart of an embodiment 1 of a bank teller intelligent scheduling method based on big data disclosed in the present invention may include the following steps:
s101, predicting the transaction amount and the transaction peak value of a target date of a to-be-scheduled website based on a pre-constructed prediction model;
when intelligent scheduling is required for a teller of a bank website, for example, when the bank teller of the bank website A, 8/21 in 2020 needs to be scheduled, the name of the bank website A and the target date (8/21 in 2020) are input into the prediction model according to a pre-constructed prediction model, and the transaction amount and the transaction peak value of the target date of the bank website are predicted.
S102, configuring the number of teller and the type of target teller on the basis of the transaction amount and the transaction peak value of the target date of the network point to be scheduled and the type of teller classified in advance;
after the transaction amount and the transaction peak value of the target date of the shift to be shifted are predicted, the optimal number of the teller and the target teller type are configured for the target date of the shift to be shifted by comprehensively considering the on-duty condition of the teller of each type at present based on the transaction amount and the transaction peak value of the target date of the shift to be shifted and the pre-classified teller type.
S103, dynamically generating a teller scheduling table of the target date of the to-be-scheduled website based on the number of teller and the target teller type of the target teller and the commuting time from the on-duty teller to the to-be-scheduled website.
After the number of the teller and the type of the target teller on the target date of the mesh point to be scheduled are configured, dynamically generating a teller scheduling table on the target date of the mesh point to be scheduled according to the number of the teller on the target date of the mesh point to be scheduled, the type of the target teller and the commuting time (priority of the teller with short commuting time) of the target teller on the on-duty teller to be scheduled, automatically transmitting the generated teller scheduling table to the mesh point to be scheduled, and automatically notifying the corresponding teller of on-duty work after confirmation after the teller is confirmed after the teller is received by the teller scheduling table.
In summary, in the above embodiment, when a shift is required to be performed on a banking website, firstly, based on a pre-constructed prediction model, predicting the transaction amount and the transaction peak value of the target date of the website to be shifted, and then, based on the transaction amount and the transaction peak value of the target date of the website to be shifted, and the pre-classified teller types, configuring the number of teller and the target teller types of the target date of the website to be shifted; and dynamically generating a teller scheduling table of the target date of the mesh point to be scheduled based on the number of teller and the target teller type of the target mesh point target date and the commuting time of the target teller type on duty teller to the mesh point to be scheduled. Dynamic adjustment of teller in different sites and on different dates in the same city can be achieved, so that the utilization efficiency of human resources is improved, site benefits are improved, and customer experience is improved.
As shown in fig. 2, a flowchart of a method of embodiment 2 of a bank teller intelligent scheduling method based on big data disclosed in the present invention may include the following steps:
s201, acquiring historical transaction data of each website;
when intelligent scheduling is required for teller in banking outlets, a prediction model is built first. When the prediction model is constructed, historical transaction data of each website are firstly obtained.
Specifically, one implementation manner of obtaining historical transaction data of each website may be: when the salesmen in the same city normally check out business, the daily transaction data of each website are automatically collected, wherein the daily transaction data comprise: the transaction amount, the transaction type and the transaction peak value are stored in a database, and then daily transaction data of each website are automatically collected to form historical transaction data of each website; and when the historical transaction data of each website is required to be acquired, acquiring the historical transaction data of each website from a database.
S202, analyzing historical transaction data of each website by utilizing a big data analysis technology, and constructing a prediction model of transaction amount and transaction peak value of each website;
after historical transaction data of each website are obtained, the historical transaction data of each website are analyzed by utilizing a big data analysis technology, and a prediction model of transaction amount and transaction peak value of each website is constructed.
S203, acquiring historical business processing data of each teller;
when intelligent scheduling is required for the teller at the banking website, the teller types are classified at the same time. When the types of the teller are classified, the historical business processing data of each teller are firstly obtained.
Specifically, in one implementation manner of obtaining the historical service processing data of each teller, when the teller in the same city performs normal visitor handling, the daily service processing data of each teller is automatically collected, where the daily service processing data of the teller includes: the service type, service processing time and daily service processing number are stored in a database, and then the automatically collected daily service processing data of each teller are stored in the database to form historical service processing data of each teller; and when the historical service processing data of each teller are required to be acquired, acquiring the historical service processing data of each teller from the database.
S204, analyzing the historical business processing data of each teller by utilizing a big data analysis technology, and calculating the efficiency value of each teller;
after the historical business processing data of each teller are obtained, the historical business processing data of each teller are analyzed by utilizing a big data analysis technology, and the efficiency value (how many A transactions are completed every day) of each teller is calculated.
S205, classifying the teller based on the efficiency value of each teller and a preset threshold value to obtain the teller type.
And classifying the teller according to a preset threshold value to obtain the teller type. For example, the teller is classified into three categories A, B, C.
S206, predicting the transaction amount and the transaction peak value of the target date of the to-be-scheduled website based on the prediction model;
when intelligent scheduling is required for a teller of a bank website, for example, when the bank teller of the bank website A, 8/21 in 2020 needs to be scheduled, the name of the bank website A and the target date (8/21 in 2020) are input into the prediction model according to a pre-constructed prediction model, and the transaction amount and the transaction peak value of the target date of the bank website are predicted.
S207, configuring the number of teller and the type of target teller on the basis of the transaction amount and the transaction peak value of the target date of the to-be-scheduled website and the type of teller;
after the transaction amount and the transaction peak value of the target date of the network site to be scheduled are predicted, the optimal number of the teller and the target teller type are configured for the target date of the network site to be scheduled by comprehensively considering the on-duty condition of the teller of each type at present based on the transaction amount and the transaction peak value of the target date of the network site to be scheduled and the teller type.
S208, dynamically generating a teller scheduling table of the target date of the to-be-scheduled website based on the number of teller and the target teller type of the target teller and the commuting time from the on-duty teller to the to-be-scheduled website.
After the number of the teller and the type of the target teller on the target date of the mesh point to be scheduled are configured, dynamically generating a teller scheduling table on the target date of the mesh point to be scheduled according to the number of the teller on the target date of the mesh point to be scheduled, the type of the target teller and the commuting time (priority of the teller with short commuting time) of the target teller on the on-duty teller to be scheduled, automatically transmitting the generated teller scheduling table to the mesh point to be scheduled, and automatically notifying the corresponding teller of on-duty work after confirmation after the teller is confirmed after the teller is received by the teller scheduling table.
In summary, the intelligent scheduling of each network point in the same city can be realized by utilizing the big data analysis technology, all the network points in the same city are dynamically adjusted according to daily transaction amount and transaction peak conditions of the network points, the situations that network point traffic is insufficient for the client waiting for the client and network point traffic is little for the client waiting for too many network points are avoided, the dynamic adjustment and full utilization of resources are realized, the utilization efficiency of the network points is improved, the network point benefit is improved, the client waiting is greatly reduced, and the client experience is improved.
As shown in fig. 3, a schematic structural diagram of an embodiment 1 of a bank teller intelligent scheduling system based on big data according to the present invention may include:
the prediction module 301 is configured to predict a transaction amount and a transaction peak value of a target date of a to-be-scheduled shift network point based on a pre-constructed prediction model;
when intelligent scheduling is required for a teller of a bank website, for example, when the bank teller of the bank website A, 8/21 in 2020 needs to be scheduled, the name of the bank website A and the target date (8/21 in 2020) are input into the prediction model according to a pre-constructed prediction model, and the transaction amount and the transaction peak value of the target date of the bank website are predicted.
The configuration module 302 is configured to configure the number of teller and the type of target teller on the basis of the transaction amount and the transaction peak value of the target date of the to-be-scheduled website and the type of teller classified in advance;
after the transaction amount and the transaction peak value of the target date of the shift to be shifted are predicted, the optimal number of the teller and the target teller type are configured for the target date of the shift to be shifted by comprehensively considering the on-duty condition of the teller of each type at present based on the transaction amount and the transaction peak value of the target date of the shift to be shifted and the pre-classified teller type.
The intelligent scheduling module 303 is configured to dynamically generate a teller scheduling table of the target date of the to-be-scheduled website based on the number of teller and the target teller type of the target website and the commuting time from the on-duty teller to the to-be-scheduled website.
After the number of the teller and the type of the target teller on the target date of the mesh point to be scheduled are configured, dynamically generating a teller scheduling table on the target date of the mesh point to be scheduled according to the number of the teller on the target date of the mesh point to be scheduled, the type of the target teller and the commuting time (priority of the teller with short commuting time) of the target teller on the on-duty teller to be scheduled, automatically transmitting the generated teller scheduling table to the mesh point to be scheduled, and automatically notifying the corresponding teller of on-duty work after confirmation after the teller is confirmed after the teller is received by the teller scheduling table.
In summary, in the above embodiment, when a shift is required to be performed on a banking website, firstly, based on a pre-constructed prediction model, predicting the transaction amount and the transaction peak value of the target date of the website to be shifted, and then, based on the transaction amount and the transaction peak value of the target date of the website to be shifted, and the pre-classified teller types, configuring the number of teller and the target teller types of the target date of the website to be shifted; and dynamically generating a teller scheduling table of the target date of the mesh point to be scheduled based on the number of teller and the target teller type of the target mesh point target date and the commuting time of the target teller type on duty teller to the mesh point to be scheduled. Dynamic adjustment of teller in different sites and on different dates in the same city can be achieved, so that the utilization efficiency of human resources is improved, site benefits are improved, and customer experience is improved.
As shown in fig. 4, a schematic structural diagram of an embodiment 2 of a bank teller intelligent scheduling system based on big data according to the present invention may include:
a first obtaining module 401, configured to obtain historical transaction data of each website;
when intelligent scheduling is required for teller in banking outlets, a prediction model is built first. When the prediction model is constructed, historical transaction data of each website are firstly obtained.
Specifically, one implementation manner of obtaining historical transaction data of each website may be: when the salesmen in the same city normally check out business, the daily transaction data of each website are automatically collected, wherein the daily transaction data comprise: the transaction amount, the transaction type and the transaction peak value are stored in a database, and then daily transaction data of each website are automatically collected to form historical transaction data of each website; and when the historical transaction data of each website is required to be acquired, acquiring the historical transaction data of each website from a database.
The construction module 402 is configured to analyze historical transaction data of each website by using a big data analysis technology, and construct a prediction model of transaction amount and transaction peak value of each website;
after historical transaction data of each website are obtained, the historical transaction data of each website are analyzed by utilizing a big data analysis technology, and a prediction model of transaction amount and transaction peak value of each website is constructed.
A second obtaining module 403, configured to obtain historical service processing data of each teller;
when intelligent scheduling is required for the teller at the banking website, the teller types are classified at the same time. When the types of the teller are classified, the historical business processing data of each teller are firstly obtained.
Specifically, in one implementation manner of obtaining the historical service processing data of each teller, when the teller in the same city performs normal visitor handling, the daily service processing data of each teller is automatically collected, where the daily service processing data of the teller includes: the service type, service processing time and daily service processing number are stored in a database, and then the automatically collected daily service processing data of each teller are stored in the database to form historical service processing data of each teller; and when the historical service processing data of each teller are required to be acquired, acquiring the historical service processing data of each teller from the database.
The calculation module 404 is configured to analyze the historical service processing data of each teller by using a big data analysis technology, and calculate an efficiency value of each teller;
after the historical business processing data of each teller are obtained, the historical business processing data of each teller are analyzed by utilizing a big data analysis technology, and the efficiency value (how many A transactions are completed every day) of each teller is calculated.
And the classification module 405 is configured to classify the teller based on the efficiency value of each teller and a preset threshold value, so as to obtain the teller type.
And classifying the teller according to a preset threshold value to obtain the teller type. For example, the teller is classified into three categories A, B, C.
A prediction module 406, configured to predict a transaction amount and a transaction peak value of a target date of a to-be-scheduled website based on a prediction model;
when intelligent scheduling is required for a teller of a bank website, for example, when the bank teller of the bank website A, 8/21 in 2020 needs to be scheduled, the name of the bank website A and the target date (8/21 in 2020) are input into the prediction model according to a pre-constructed prediction model, and the transaction amount and the transaction peak value of the target date of the bank website are predicted.
The configuration module 407 is configured to configure the number of teller and the type of target teller on the basis of the transaction amount and the transaction peak value of the target date of the to-be-scheduled website and the type of teller;
after the transaction amount and the transaction peak value of the target date of the network site to be scheduled are predicted, the optimal number of the teller and the target teller type are configured for the target date of the network site to be scheduled by comprehensively considering the on-duty condition of the teller of each type at present based on the transaction amount and the transaction peak value of the target date of the network site to be scheduled and the teller type.
The intelligent scheduling module 408 is configured to dynamically generate a teller scheduling table of the target date of the to-be-scheduled website based on the number of teller and the target teller type of the target website and the commuting time from the on-duty teller to the to-be-scheduled website.
After the number of the teller and the type of the target teller on the target date of the mesh point to be scheduled are configured, dynamically generating a teller scheduling table on the target date of the mesh point to be scheduled according to the number of the teller on the target date of the mesh point to be scheduled, the type of the target teller and the commuting time (priority of the teller with short commuting time) of the target teller on the on-duty teller to be scheduled, automatically transmitting the generated teller scheduling table to the mesh point to be scheduled, and automatically notifying the corresponding teller of on-duty work after confirmation after the teller is confirmed after the teller is received by the teller scheduling table.
In summary, the intelligent scheduling of each network point in the same city can be realized by utilizing the big data analysis technology, all the network points in the same city are dynamically adjusted according to daily transaction amount and transaction peak conditions of the network points, the situations that network point traffic is insufficient for the client waiting for the client and network point traffic is little for the client waiting for too many network points are avoided, the dynamic adjustment and full utilization of resources are realized, the utilization efficiency of the network points is improved, the network point benefit is improved, the client waiting is greatly reduced, and the client experience is improved.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (2)
1. The intelligent scheduling method for the bank teller based on the big data is characterized by comprising the following steps of:
predicting the transaction amount and the transaction peak value of the target date of the network site to be scheduled based on a pre-constructed prediction model;
based on the transaction amount and the transaction peak value of the target date of the to-be-scheduled network point and the preset classified teller types, configuring the teller number and the target teller type of the target date of the to-be-scheduled network point;
dynamically generating a teller scheduling table of the target date of the to-be-scheduled website based on the number of teller and the target teller type of the target teller and the commuting time from the on-duty teller to the to-be-scheduled website;
pre-building a predictive model, comprising:
acquiring historical transaction data of each website;
analyzing the historical transaction data of each website by utilizing a big data analysis technology, and constructing a prediction model of transaction amount and transaction peak value of each website;
the step of obtaining historical transaction data of each website comprises the following steps:
automatically collecting daily transaction data of each website;
storing the daily transaction data of each website automatically collected into a database to form historical transaction data of each website;
acquiring historical transaction data of each website from the database;
pre-classifying teller types, including:
acquiring historical business processing data of each teller;
analyzing the historical business processing data of each teller by utilizing a big data analysis technology, and calculating the efficiency value of each teller;
classifying the teller based on the efficiency value of each teller and a preset threshold value to obtain teller type;
the obtaining historical business processing data of each teller comprises the following steps:
automatically collecting daily business processing data of each teller;
storing the automatically collected daily business processing data of each teller into a database to form historical business processing data of each teller;
and acquiring historical business processing data of each teller from the database.
2. Big data-based intelligent bank teller scheduling system is characterized by comprising:
the prediction module is used for predicting the transaction amount and the transaction peak value of the target date of the to-be-scheduled network point based on a pre-constructed prediction model;
the configuration module is used for configuring the number of teller and the target teller type of the target date of the to-be-scheduled network point based on the transaction amount and the transaction peak value of the target date of the to-be-scheduled network point and the pre-classified teller type;
the intelligent scheduling module is used for dynamically generating a teller scheduling table of the target date of the to-be-scheduled network point based on the number of teller and the target teller type of the target date of the to-be-scheduled network point and the commuting time from the on-duty teller of the target teller type to the to-be-scheduled network point;
further comprises:
the first acquisition module is used for acquiring historical transaction data of each website;
the construction module is used for analyzing the historical transaction data of each website by utilizing a big data analysis technology and constructing a prediction model of the transaction amount and the transaction peak value of each website;
the first obtaining module is specifically configured to:
automatically collecting daily transaction data of each website;
storing the daily transaction data of each website automatically collected into a database to form historical transaction data of each website;
acquiring historical transaction data of each website from the database;
further comprises:
the second acquisition module is used for acquiring historical business processing data of each teller;
the calculation module is used for analyzing the historical business processing data of each teller by utilizing a big data analysis technology and calculating the efficiency value of each teller;
the classification module is used for classifying the teller based on the efficiency value of each teller and a preset threshold value to obtain the teller type;
the second obtaining module is specifically configured to:
automatically collecting daily business processing data of each teller;
storing the automatically collected daily business processing data of each teller into a database to form historical business processing data of each teller;
and acquiring historical business processing data of each teller from the database.
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