CN110633863A - Bank note distribution prediction method and device based on GBDT algorithm - Google Patents
Bank note distribution prediction method and device based on GBDT algorithm Download PDFInfo
- Publication number
- CN110633863A CN110633863A CN201910891847.6A CN201910891847A CN110633863A CN 110633863 A CN110633863 A CN 110633863A CN 201910891847 A CN201910891847 A CN 201910891847A CN 110633863 A CN110633863 A CN 110633863A
- Authority
- CN
- China
- Prior art keywords
- cash
- data set
- distribution
- distribution network
- characteristic data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Medical Informatics (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention discloses a bank note distribution prediction method and a bank note distribution prediction device based on a GBDT algorithm, wherein the method comprises the following steps: acquiring data information influencing cash transaction of a cash distribution network; constructing a characteristic data set according to the data information; and training a money distribution quantity prediction model established based on a GBDT algorithm by using the characteristic data set, and predicting the money distribution quantity of the next day of the money distribution network according to the trained money distribution quantity prediction model. The invention does not need to distribute the bank notes according to the experience of the working personnel, has small deviation between the bank note distribution quantity and the actual demand quantity, and has lower labor intensity of the working personnel.
Description
Technical Field
The invention relates to the field of data processing, in particular to a bank note distribution prediction method and device based on a GBDT algorithm.
Background
At present, various banks have rapid development in the aspects of operation management standardization, informatization, centralization and the like, but the intelligent development of cash business is still in a lower level. For the network, the daily cash preparation amount must exceed and approach the daily cash demand amount as much as possible, and if the cash is insufficient, the user cannot take out the money; meanwhile, if a large amount of cash is stored in the network, the redundant cash cannot generate profit. Therefore, it is important to accurately allocate cash amounts to the outlets.
The prior art generally configures the amount of cash stored in a network every day according to the experience of workers. This can easily result in a large deviation between the dispensed amount and the actual demand, and can also increase the labor intensity of the worker.
Disclosure of Invention
The embodiment of the invention provides a bank note distribution prediction method based on a GBDT algorithm, which is used for reducing the deviation between the bank note distribution amount and the actual demand amount and the labor intensity of workers and comprises the following steps:
acquiring data information influencing cash transaction of a cash distribution network;
constructing a characteristic data set according to the data information;
and training a money distribution quantity prediction model established based on a GBDT algorithm by using the characteristic data set, and predicting the money distribution quantity of the next day of the money distribution network according to the trained money distribution quantity prediction model.
Optionally, the method further includes: and after the characteristic data set is obtained, optimizing the characteristic data in the characteristic data set.
Optionally, the data information affecting the cash transaction of the cash dispensing point includes: historical data of cash transactions of the cash distribution network, geographical position information of the cash distribution network, and customer group information of the cash distribution network.
Optionally, the feature data set includes: transaction amount, transaction number, deposit balance, withdrawal balance.
The embodiment of the invention also provides a banknote distribution prediction device based on the GBDT algorithm, which is used for reducing the deviation between the banknote distribution amount and the actual demand amount and the labor intensity of workers, and comprises the following components:
the information acquisition module is used for acquiring data information influencing cash transaction of a cash distribution network;
the characteristic construction module is used for constructing a characteristic data set according to the data information;
and the banknote distribution prediction module is used for training a banknote distribution quantity prediction model established based on the GBDT algorithm by utilizing the characteristic data set and predicting the banknote distribution quantity of the next day of a banknote distribution network point according to the trained banknote distribution quantity prediction model.
Optionally, the apparatus further comprises: and the optimization processing module is used for optimizing the characteristic data in the characteristic data set after the characteristic data set is obtained.
Optionally, the data information affecting the cash transaction of the cash dispensing point includes: historical data of cash transactions of the cash distribution network, geographical position information of the cash distribution network, and customer group information of the cash distribution network.
Optionally, the feature data set includes: transaction amount, transaction number, deposit balance, withdrawal balance.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program for executing the above method is stored.
In the embodiment of the invention, the data information influencing the cash transaction of the cash distribution network point to be distributed is obtained, the characteristic data set is constructed according to the data information, the characteristic data set is utilized to train the cash distribution quantity prediction model established based on the GBDT algorithm, the next-day cash distribution quantity of the cash distribution network point to be distributed can be predicted according to the trained cash distribution quantity prediction model, the cash distribution does not need to be carried out according to the experience of workers, the deviation between the cash distribution quantity and the actual demand is small, and the labor intensity of the workers is low.
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 banknote distribution prediction method based on a GBDT algorithm according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a banknote dispensing prediction device based on a GBDT algorithm according to an embodiment of the present invention;
fig. 3 is a diagram illustrating a banknote dispensing prediction apparatus based on GBDT algorithm according to an 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.
The embodiment of the invention provides a bank note distribution prediction method based on a GBDT algorithm, which comprises the following steps as shown in the attached figure 1:
And 102, constructing a characteristic data set according to the data information.
And 103, training a money distribution quantity prediction model established based on the GBDT algorithm by using the characteristic data set, and predicting the money distribution quantity of the next day of the money distribution network according to the trained money distribution quantity prediction model.
It should be noted that the GBDT algorithm is a machine learning algorithm based on a tree model, and the inside of the GBDT algorithm implements a feature selection function by pruning trees, so that learning can be performed well according to different feature weights, and meanwhile, the learning effect and the operation time of the algorithm, the size of data volume, the number of features, and the speed of the operation time can be flexibly adjusted by changing parameters of the algorithm.
When we use too many features that affect the cash transaction amount at a network site, depending too much on the historical data can degrade our prediction, i.e., overfit, due to some randomness of the historical data of these factors and unpredictability of the future cash usage amount. The GBDT algorithm can calculate the weight of the influence of various factors on the cash transaction of the website, and selects the factors which have larger influence on the cash transaction of the website for prediction through the weight, so that more factors can be considered in the prediction, and the negative effects brought by the factors, such as the interference of randomness of some characteristics on the prediction result, or the phenomenon of model overfitting caused by too little data set, and the like, are reduced through reasonable parameters.
Based on the above, the method for predicting the banknote distribution based on the GBDT algorithm provided by the embodiment of the present invention includes obtaining data information that affects the cash transaction of a to-be-distributed banknote network, constructing a feature data set according to the data information, training a banknote distribution quantity prediction model established based on the GBDT algorithm by using the feature data set, predicting the banknote distribution quantity of the to-be-distributed banknote network next day according to the trained banknote distribution quantity prediction model, dispensing according to the experience of a worker, and having a small deviation between the banknote distribution quantity and an actual demand and low labor intensity of the worker.
In step 101, the data information affecting the cash transaction of the cash dispensing point includes: historical data of cash transactions of the cash distribution network, geographical position information of the cash distribution network, and customer group information of the cash distribution network.
In specific implementation, a plurality of factors influencing cash transaction of the network point, such as geographical position of the network point, customer groups served by the network point, weather conditions of the same day, traffic conditions of the same day, holidays, historical rules in transaction flow and the like, are obtained through discussion with service personnel or network point cash transaction experts.
The technician then determines the data information to use based on the business importance of these factors, data availability, performance feasibility, relevance to the predicted objective, etc.
In step 102, the feature data set comprises: transaction amount, transaction number, deposit balance, withdrawal balance.
Specifically, the plurality of feature data in the feature data set are constructed by analyzing the data information in step 101, for example, the feature data may be constructed by constructing features such as total amount of daily transaction, total number of strokes, average amount of money per stroke, number of transaction customers, average amount of money per customer, and the like based on the cash transaction flow data.
In addition, the characteristic data of different data information affecting the cash transaction of the cash distribution point are different, for example, the characteristic data of the historical data of the cash transaction of the cash distribution point, such as: transaction amount, transaction number, deposit amount, deposit number, withdrawal amount, withdrawal number, and the like. Characteristic data of the geographical position information of the money distribution network points, such as: whether it is a town, distance from the center of a city, etc. Characteristic data of customer group information of the money distribution network service, such as: whether the primary customer is a student, a local resident, an elderly person, etc. Characteristic data of weather information, such as: whether it is raining, air temperature, sunshine index, etc. Characteristic data of holiday data, such as: whether it is a legal holiday, whether it is a weekend, whether it is a traditional holiday, etc.
Further, in order to ensure that the feature data in the feature data set meet the specification, the banknote distribution prediction method based on the GBDT algorithm further includes: and after the characteristic data set is obtained, optimizing the characteristic data in the characteristic data set.
Specifically, the optimization process includes: the feature data is normalized, unbiased, encoded, and the like.
In step 103, in order to ensure that the final prediction result is more accurate, in the process of training the banknote distribution amount prediction model established based on the GBDT algorithm by using the feature data set, variable hyperparameters are configured for parameter adjustment, and the storage and loading logic of the model after the algorithm training is realized. And then, the trained money distribution amount prediction model is used for predicting the money distribution amount of the next day of the money distribution network.
In one embodiment, for models of different versions, due to the fact that training data, machine learning algorithms and parameters are different, prediction effects of the models are different, and in order to select a model with a better prediction effect, the trained money dispensing amount prediction model can be evaluated, and then an optimal model is obtained.
In specific implementation, if a problem is found in the use process of the money distribution amount prediction model, the super parameters need to be adjusted again according to the actual effect, the model needs to be retrained after the super parameters are adjusted, and the use effect on the line is affected if the model training speed is slow.
The feature number used by the training model, the training duration of the model, the data volume of the training model and the like are continuously mined along with the feature data and the data volume is increased, the training duration of the model is also continuously increased, and at the moment, the corresponding parameters need to be adjusted to compromise the training process and the prediction effect of the model.
Based on the same inventive concept, the embodiment of the present invention further provides a banknote distribution prediction device based on GBDT algorithm, as described in the following embodiments. Because the principle of solving the problems of the GBDT algorithm-based banknote distribution prediction device is similar to the GBDT algorithm-based banknote distribution prediction method, the implementation of the GBDT algorithm-based banknote distribution prediction device can refer to the implementation of the GBDT algorithm-based banknote distribution prediction method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
The embodiment of the invention provides a bank note distribution prediction device based on a GBDT algorithm, and as shown in the attached figure 2, the device comprises:
the information acquisition module 201 is used for acquiring data information influencing cash transaction of a cash distribution network;
a feature construction module 202, configured to construct a feature data set according to the data information;
and the banknote distribution prediction module 203 is used for training a banknote distribution quantity prediction model established based on the GBDT algorithm by using the characteristic data set, and predicting the banknote distribution quantity of the next day of a banknote distribution network according to the trained banknote distribution quantity prediction model.
In an embodiment of the present invention, as shown in fig. 3, the device for predicting banknote distribution based on GBDT algorithm further includes: and the optimization processing module 204 is configured to perform optimization processing on the feature data in the feature data set after the feature data set is acquired.
In an embodiment of the present invention, the data information affecting the cash transaction of the to-be-dispensed cash network includes: historical data of cash transactions of the cash distribution network, geographical position information of the cash distribution network, and customer group information of the cash distribution network.
In an embodiment of the invention, the feature data set comprises: transaction amount, transaction number, deposit balance, withdrawal balance.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program for executing the above method is stored.
According to the method for predicting the cash distribution based on the GBDT algorithm, provided by the embodiment of the invention, the data information influencing the cash transaction of the cash distribution network point to be distributed is obtained, the characteristic data set is constructed according to the data information, the characteristic data set is utilized to train the cash distribution quantity prediction model established based on the GBDT algorithm, the next-day cash distribution quantity of the cash distribution network point to be distributed can be predicted according to the trained cash distribution quantity prediction model, the cash distribution is not required to be carried out according to the experience of workers, the deviation between the cash distribution quantity and the actual demand is small, and the labor intensity of the workers is low.
In the embodiment, the invention can reduce the cash stock of the network points, thereby preventing the occurrence of the cash case of the network points. Through more accurate cash allocation, the additional escort cost caused by insufficient cash allocation can be reduced; the cash reserve payment of the whole bank is reduced by reasonably reducing the pressure drop of the cash stock of each branch, so that the management cost of cash is reduced; by reducing cash back-up, the bank can use the vacant money on other financial products, thereby improving bank income; the influence on reputation caused by the fact that cash of a client cannot be processed due to insufficient cash allocation of a network is reduced through more accurate cash allocation. By using the GBDT algorithm, the time and hardware resource usage of model training can be adjusted, and the cost is saved.
In addition, compared with the existing linear regression algorithm and neural network algorithm, the GBDT algorithm has the following characteristics: when many factors need to be considered in the cash prediction of the network points, the GBDT algorithm can select the features which have larger influence on the prediction result through feature selection, so that the interference of useless features on the model can be avoided, and the calculation amount in the model training process can be reduced. Meanwhile, because the actual service is greatly influenced by the real environment, the artificial intelligent algorithm is used and manual adjustment is needed, and the common artificial intelligent algorithm such as a neural network cannot reasonably consider special conditions in the data and cannot feed back the internal characteristics of the model to a waiter. The GBDT model can check the change of each characteristic weight in the model training process, and when special conditions are met, a salesman can know the influence of each factor on cash demand of a network point through the characteristic weight output by the model, so that a reasonable cash distribution amount decision is made in combination with reality, and network point service is improved.
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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
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 (10)
1. A GBDT algorithm-based bank note dispensing prediction method is characterized by comprising the following steps:
acquiring data information influencing cash transaction of a cash distribution network;
constructing a characteristic data set according to the data information;
and training a money distribution quantity prediction model established based on a GBDT algorithm by using the characteristic data set, and predicting the money distribution quantity of the next day of the money distribution network according to the trained money distribution quantity prediction model.
2. The method of claim 1, further comprising: and after the characteristic data set is obtained, optimizing the characteristic data in the characteristic data set.
3. The method of claim 1, wherein the data information affecting the cash transaction at the cash dispensing point comprises: historical data of cash transactions of the cash distribution network, geographical position information of the cash distribution network, and customer group information of the cash distribution network.
4. The method of claim 1, wherein the feature data set comprises: transaction amount, transaction number, deposit balance, withdrawal balance.
5. A GBDT algorithm based banknote dispensing prediction apparatus, comprising:
the information acquisition module is used for acquiring data information influencing cash transaction of a cash distribution network;
the characteristic construction module is used for constructing a characteristic data set according to the data information;
and the banknote distribution prediction module is used for training a banknote distribution quantity prediction model established based on the GBDT algorithm by utilizing the characteristic data set and predicting the banknote distribution quantity of the next day of a banknote distribution network point according to the trained banknote distribution quantity prediction model.
6. The apparatus of claim 5, further comprising: and the optimization processing module is used for optimizing the characteristic data in the characteristic data set after the characteristic data set is obtained.
7. The apparatus of claim 5, wherein the data information affecting the cash transaction at the cash dispensing point comprises: historical data of cash transactions of the cash distribution network, geographical position information of the cash distribution network, and customer group information of the cash distribution network.
8. The apparatus of claim 5, wherein the feature data set comprises: transaction amount, transaction number, deposit balance, withdrawal balance.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910891847.6A CN110633863A (en) | 2019-09-20 | 2019-09-20 | Bank note distribution prediction method and device based on GBDT algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910891847.6A CN110633863A (en) | 2019-09-20 | 2019-09-20 | Bank note distribution prediction method and device based on GBDT algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110633863A true CN110633863A (en) | 2019-12-31 |
Family
ID=68971921
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910891847.6A Pending CN110633863A (en) | 2019-09-20 | 2019-09-20 | Bank note distribution prediction method and device based on GBDT algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110633863A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111612991A (en) * | 2020-06-05 | 2020-09-01 | 中国银行股份有限公司 | ATM maintenance method and device |
| CN112037047A (en) * | 2020-09-03 | 2020-12-04 | 中国银行股份有限公司 | Network point money distribution method and device and electronic equipment |
| CN113191880A (en) * | 2021-05-24 | 2021-07-30 | 中国工商银行股份有限公司 | Bank teller terminal cash adding suggestion determination method and device |
| CN114358448A (en) * | 2022-03-21 | 2022-04-15 | 中国工商银行股份有限公司 | Driving route planning method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014102618A (en) * | 2012-11-19 | 2014-06-05 | Hitachi Omron Terminal Solutions Corp | Automatic cash transaction device and method for managing reserve supply of money |
| CN109447354A (en) * | 2018-10-31 | 2019-03-08 | 中国银行股份有限公司 | A kind of intelligent bank note distribution method and device based on GA-BP neural network |
| CN109840637A (en) * | 2019-02-20 | 2019-06-04 | 中国银行股份有限公司 | Cash demand amount prediction technique, device and equipment |
| CN110222739A (en) * | 2019-05-23 | 2019-09-10 | 中国银行股份有限公司 | Model construction, site cash Demand Forecast method, apparatus and storage medium |
-
2019
- 2019-09-20 CN CN201910891847.6A patent/CN110633863A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014102618A (en) * | 2012-11-19 | 2014-06-05 | Hitachi Omron Terminal Solutions Corp | Automatic cash transaction device and method for managing reserve supply of money |
| CN109447354A (en) * | 2018-10-31 | 2019-03-08 | 中国银行股份有限公司 | A kind of intelligent bank note distribution method and device based on GA-BP neural network |
| CN109840637A (en) * | 2019-02-20 | 2019-06-04 | 中国银行股份有限公司 | Cash demand amount prediction technique, device and equipment |
| CN110222739A (en) * | 2019-05-23 | 2019-09-10 | 中国银行股份有限公司 | Model construction, site cash Demand Forecast method, apparatus and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 高永彬 等: "《Hadoop大数据分析》", 31 July 2019, 中国铁道出版社 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111612991A (en) * | 2020-06-05 | 2020-09-01 | 中国银行股份有限公司 | ATM maintenance method and device |
| CN112037047A (en) * | 2020-09-03 | 2020-12-04 | 中国银行股份有限公司 | Network point money distribution method and device and electronic equipment |
| CN112037047B (en) * | 2020-09-03 | 2023-09-26 | 中国银行股份有限公司 | Method and device for distributing cash at net points and electronic equipment |
| CN113191880A (en) * | 2021-05-24 | 2021-07-30 | 中国工商银行股份有限公司 | Bank teller terminal cash adding suggestion determination method and device |
| CN114358448A (en) * | 2022-03-21 | 2022-04-15 | 中国工商银行股份有限公司 | Driving route planning method and device |
| CN114358448B (en) * | 2022-03-21 | 2022-05-24 | 中国工商银行股份有限公司 | Driving route planning method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110633863A (en) | Bank note distribution prediction method and device based on GBDT algorithm | |
| CN110400022B (en) | Cash consumption prediction method and device for self-service teller machine | |
| CN102567391B (en) | Method and device for building classification forecasting mixed model | |
| CN106549772A (en) | Resource prediction method, system and capacity management device | |
| CN109978062A (en) | A kind of model on-line monitoring method and system | |
| CN110400021A (en) | Bank outlets' cash dosage prediction technique and device | |
| CN104424598A (en) | Cash demand quantity predicating device and method | |
| CN109859052A (en) | A kind of intelligent recommendation method, apparatus, storage medium and the server of investment tactics | |
| CN111985851B (en) | Bank outlet resource scheduling method and device | |
| CN111738505A (en) | Bank branch workload prediction method and device, electronic equipment and storage medium | |
| CN102496126A (en) | Custody asset transaction data monitoring equipment | |
| CN110458668A (en) | Determine the method and device of Products Show algorithm | |
| CN110097455A (en) | Bank client Optimal Configuration Method and device | |
| CN111695725A (en) | ATM (automatic teller machine) money adding method and device based on heuristic search | |
| CN111738507A (en) | Bank clearing position fund payment amount prediction method, device, equipment and medium | |
| CN111667307B (en) | Method and device for predicting financial product sales volume | |
| CN103903070B (en) | Application system resource requirement calculating system | |
| Kulakov et al. | Sustainable economic development at the meso level: factors and ratings | |
| CN111738506A (en) | Cash center cash stock usage amount prediction method and device, electronic device, and medium | |
| CN109657865A (en) | A kind of fund pool account facility extent prediction technique and device | |
| CN115186898A (en) | Bank account opening amount prediction method and device | |
| CN111783487B (en) | Fault early warning method and device for card reader equipment | |
| CN115238988A (en) | Commemorative coin distribution method, system and related equipment | |
| CN113850609A (en) | Customer management system, method, computer equipment and storage medium | |
| CN112862014A (en) | Client credit early warning method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191231 |