CN111210053A - Resource information configuration method and device and server - Google Patents
Resource information configuration method and device and server Download PDFInfo
- Publication number
- CN111210053A CN111210053A CN201911329659.0A CN201911329659A CN111210053A CN 111210053 A CN111210053 A CN 111210053A CN 201911329659 A CN201911329659 A CN 201911329659A CN 111210053 A CN111210053 A CN 111210053A
- Authority
- CN
- China
- Prior art keywords
- value
- resource information
- configuration
- resource
- characteristic value
- 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
- 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"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Educational Administration (AREA)
- Technology Law (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the application discloses a method for configuring resource information, which comprises the following steps: acquiring resource configuration data within preset time, and converting the format of the resource configuration data into a standard format; analyzing the resource configuration data in the standard format through a Time Series Analysis (TSA) module to obtain a predicted value; increasing the predicted value by a first preset percentage to obtain an upper bound characteristic value of a predicted interval; reducing the predicted value by a second preset percentage to obtain a lower bound characteristic value of a prediction interval; analyzing the resource configuration data in the standard format to extract a relevant characteristic value; analyzing the upper bound characteristic value of the prediction interval, the lower bound characteristic value of the prediction interval and the related characteristic value through a Support Vector Machine (SVM) module to obtain a configuration value of resource information; and the resource information is configured by utilizing the configuration value, so that the accuracy of the configuration value is improved. The application also discloses a resource information configuration device and a server.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a method, a system, and a server for configuring resource information.
Background
In the presence of a large amount of data, the large amount of data is manually processed to obtain a processing result, which is not only laborious and time-consuming, but also has the influence of human subjective factors, and causes the problem of inaccurate processing result.
For example, when the vault resource information of a bank is configured, configuration results are often inaccurate due to human subjective factors, and therefore the following problems occur: redundant resources are configured to be in an idle state, and value cannot be generated; or, the resource allocation is insufficient, and the customer requirements cannot be met. The income of the bank is reduced, and the experience of the customer is reduced.
Disclosure of Invention
In order to solve the technical problem, the application provides a resource information configuration method, a resource information configuration device and a server. The income of the bank and the experience of the customer are improved.
The embodiment of the application discloses the following technical scheme:
in a first aspect, an embodiment of the present application provides a method for configuring resource information, where the method includes:
acquiring resource configuration data within preset time, and converting the format of the resource configuration data into a standard format;
analyzing the resource configuration data in the standard format through a Time Series Analysis (TSA) module to obtain a predicted value; increasing the predicted value by a first preset percentage to obtain an upper bound characteristic value of a predicted interval; reducing the predicted value by a second preset percentage to obtain a lower bound characteristic value of a prediction interval; analyzing the resource configuration data in the standard format to extract a relevant characteristic value;
analyzing the upper bound characteristic value of the prediction interval, the lower bound characteristic value of the prediction interval and the related characteristic value through a Support Vector Machine (SVM) module to obtain a configuration value of resource information;
and configuring the resource information by using the configuration value.
Optionally, the relevant feature value is one or more of a combination of a region feature value, a scale feature value and a time feature value.
Optionally, the method further includes:
acquiring a difference value relation between the configuration value of the resource information and the actual use value of the resource information;
and correcting the configuration value of the subsequently acquired resource information by using the difference relation.
Optionally, the difference relationship is a percentage of a difference between the configuration value and the actual usage value, and the method further includes:
and monitoring the difference percentage, and if the difference percentage exceeds a threshold value, increasing the preset time.
Optionally, the method further includes:
and training the TSA module and the SVM module by using the resource configuration data in the standard format so as to improve the accuracy of the TSA module and the SVM module.
In a second aspect, an embodiment of the present application provides a device for configuring resource information, where the system includes: a memory, a processor, and a display;
the memory is used for storing programs;
the processor is configured to process the program to perform the following operations:
acquiring resource configuration data within preset time, and converting the format of the resource configuration data into a standard format; analyzing the resource configuration data in the standard format through a Time Series Analysis (TSA) module to obtain a predicted value; increasing the predicted value by a first preset percentage to obtain an upper bound characteristic value of a predicted interval; reducing the predicted value by a second preset percentage to obtain a lower bound characteristic value of a prediction interval; analyzing the resource configuration data in the standard format to extract a relevant characteristic value; analyzing the upper bound characteristic value of the prediction interval, the lower bound characteristic value of the prediction interval and the related characteristic value through a Support Vector Machine (SVM) module to obtain a configuration value of resource information; configuring the resource information by using the configuration value;
the display is used for displaying the configuration values.
Optionally, the relevant feature value is one or more of a combination of a region feature value, a scale feature value and a time feature value.
Optionally, the processor is further configured to obtain a difference relationship between the configuration value of the resource information and the actual usage value of the resource information; and correcting the configuration value of the subsequently acquired resource information by using the difference relation.
Optionally, the difference relationship is a difference percentage between the configuration value and the actual usage value, and the processor is further configured to monitor the difference percentage, and increase the preset time if the difference percentage exceeds a threshold. .
In a third aspect, an embodiment of the present application provides a server, which includes the apparatus in the second aspect.
According to the technical scheme, the embodiment of the application has the following advantages:
the resource configuration information is predicted by combining the historical data information of the bank vault resources, objective data is combined, the problem of subjectivity of prediction is avoided, various characteristic factors are considered, the prediction accuracy is further improved, and the bank income and the customer experience are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a diagram illustrating an exemplary method for configuring resource information;
FIG. 2 is a diagram illustrating another example of a method for configuring resource information;
fig. 3 is a diagram illustrating an example of a device for configuring resource information.
Detailed Description
The applicant finds, through research, that in the prior art, the problem that due to the fact that the configuration information of the vault is predicted subjectively by people, objectivity is lacked, and the configuration information is not predicted in combination with objective data, the prediction result is often inaccurate is caused, and accordingly, the resource information configuration method, the resource information configuration device and the resource information configuration server are provided. The accuracy of the prediction result is improved, and the income of the bank and the experience of the customer are improved.
Embodiments of the present application are described below with reference to the accompanying drawings.
The first embodiment is as follows:
referring to fig. 1, fig. 1 is an exemplary diagram of a resource information configuration method, and an embodiment of the present application discloses a resource information configuration method, including:
step 101: acquiring resource configuration data within preset time, and converting the format of the resource configuration data into a standard format.
As an embodiment, the resource allocation data within one year may be obtained and converted into a standard format, and the conversion process may be to perform data coding on the resource allocation data to convert the resource allocation data into numerical data, and quantify the qualitative resource allocation data.
Step 102: analyzing the resource configuration data in the standard format through a Time Series Analysis (TSA) module to obtain a predicted value; increasing the predicted value by a first preset percentage to obtain an upper bound characteristic value of a predicted interval; reducing the predicted value by a second preset percentage to obtain a lower bound characteristic value of a prediction interval; and analyzing the resource configuration data in the standard format to extract a relevant characteristic value.
As an implementation manner, analyzing the resource configuration data in the standard format through a time series analysis TSA module to obtain a predicted value; increasing the predicted value by 5%, and taking the increased value as an upper bound characteristic value of a prediction interval; reducing the predicted value by 2.5%, and taking the reduced value as a lower bound characteristic value of a prediction interval; and analyzing the resource configuration data in the standard format to extract a relevant characteristic value.
It should be noted that the first preset percentage and the second preset percentage may be in a multiple relationship, and the related characteristic value may be one or a combination of more of a region characteristic value, a scale characteristic value, or a time characteristic value.
Step 103: and analyzing the upper bound characteristic value of the prediction interval, the lower bound characteristic value of the prediction interval and the related characteristic value through a Support Vector Machine (SVM) module to obtain a configuration value of the resource information.
As an implementation manner, a Support Vector Machine (SVM) module analyzes the upper bound feature value of the prediction interval, the lower bound feature value of the prediction interval, and the feature value of the region to obtain a configuration value of resource information related to the region.
It should be noted that the correlation characteristic value may also be a scale characteristic value.
Example two:
referring to fig. 2, fig. 2 is a diagram illustrating another example of a configuration method of resource information, and a second embodiment of the present application is introduced on the basis of the first embodiment of the present application, and parts identical or similar to the first embodiment of the present application are not repeated. A second method for configuring resource information provided in the embodiment of the present application includes:
step 201-step 203: see step 101-step 103 of the embodiments of the present application.
Step 204: and correcting the configuration value.
As an embodiment, obtaining a difference relationship between a configuration value of the resource information and an actual usage value of the resource information; and correcting the configuration value of the subsequently acquired resource information by using the difference relation.
It should be noted that the difference relationship is a percentage difference between the configuration value and the actual usage value.
As an embodiment, the difference percentage is monitored, and if the difference percentage exceeds a threshold, the preset time is increased.
It should be noted that, increasing the preset time can increase the original objective data, and the more the original objective time is, the more accurate the prediction result is.
Step 205: see step 104 of embodiments of the present application.
Step 206: and training the TSA module and the SVM module by utilizing the resource configuration data in the standard format.
As an embodiment, the TSA module and the SVM module may be repeatedly trained using a self-learning ADABOOST algorithm to improve prediction accuracy.
In the process of configuring the bank vault resource information, the resource configuration information is predicted by combining the historical data information of the bank vault resources, objective data is combined, the problem of subjectivity of prediction is avoided, multi-aspect characteristic factors are considered, the accuracy of prediction is further improved, and the bank income and the customer experience are improved.
Example three:
referring to fig. 3, fig. 3 is a diagram illustrating an exemplary apparatus for configuring resource information, the apparatus including: memory 301, processor 302, and display 303;
the memory 301 is used for storing programs;
the processor 302 is configured to process the program to perform the following operations:
acquiring resource configuration data within preset time, and converting the format of the resource configuration data into a standard format; analyzing the resource configuration data in the standard format through a Time Series Analysis (TSA) module to obtain a predicted value; increasing the predicted value by a first preset percentage to obtain an upper bound characteristic value of a predicted interval; reducing the predicted value by a second preset percentage to obtain a lower bound characteristic value of a prediction interval; analyzing the resource configuration data in the standard format to extract a relevant characteristic value; analyzing the upper bound characteristic value of the prediction interval, the lower bound characteristic value of the prediction interval and the related characteristic value through a Support Vector Machine (SVM) module to obtain a configuration value of resource information; configuring the resource information by using the configuration value;
the display 303 is configured to display the configuration value.
In the process of configuring the bank vault resource information, the resource configuration information is predicted by combining the historical data information of the bank vault resources, objective data is combined, the problem of subjectivity of prediction is avoided, multi-aspect characteristic factors are considered, the accuracy of prediction is further improved, and the bank income and the customer experience are improved.
Example four:
the fourth embodiment of the present application provides a server, and the server includes the apparatus in the third embodiment of the present application.
In the process of configuring the bank vault resource information, the resource configuration information is predicted by combining the historical data information of the bank vault resources, objective data is combined, the problem of subjectivity of prediction is avoided, multi-aspect characteristic factors are considered, the accuracy of prediction is further improved, and the bank income and the customer experience are improved.
The above description is only one specific embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for configuring resource information, comprising:
acquiring resource configuration data within preset time, and converting the format of the resource configuration data into a standard format;
analyzing the resource configuration data in the standard format through a Time Series Analysis (TSA) module to obtain a predicted value; increasing the predicted value by a first preset percentage to obtain an upper bound characteristic value of a predicted interval; reducing the predicted value by a second preset percentage to obtain a lower bound characteristic value of a prediction interval; analyzing the resource configuration data in the standard format to extract a relevant characteristic value;
analyzing the upper bound characteristic value of the prediction interval, the lower bound characteristic value of the prediction interval and the related characteristic value through a Support Vector Machine (SVM) module to obtain a configuration value of resource information;
and configuring the resource information by using the configuration value.
2. The method of claim 1, wherein the correlation eigenvalue is a combination of one or more of a regional eigenvalue, a scale eigenvalue, or a temporal eigenvalue.
3. The method of claim 2, further comprising:
acquiring a difference value relation between the configuration value of the resource information and the actual use value of the resource information;
and correcting the configuration value of the subsequently acquired resource information by using the difference relation.
4. The method of claim 3, wherein the difference relationship is a percentage difference between the configuration value and the actual usage value, the method further comprising:
and monitoring the difference percentage, and if the difference percentage exceeds a threshold value, increasing the preset time.
5. The method according to any one of claims 1-4, further comprising:
and training the TSA module and the SVM module by using the resource configuration data in the standard format so as to improve the accuracy of the TSA module and the SVM module.
6. An apparatus for configuring resource information, comprising: a memory, a processor, and a display;
the memory is used for storing programs;
the processor is configured to process the program to perform the following operations:
acquiring resource configuration data within preset time, and converting the format of the resource configuration data into a standard format; analyzing the resource configuration data in the standard format through a Time Series Analysis (TSA) module to obtain a predicted value; increasing the predicted value by a first preset percentage to obtain an upper bound characteristic value of a predicted interval; reducing the predicted value by a second preset percentage to obtain a lower bound characteristic value of a prediction interval; analyzing the resource configuration data in the standard format to extract a relevant characteristic value; analyzing the upper bound characteristic value of the prediction interval, the lower bound characteristic value of the prediction interval and the related characteristic value through a Support Vector Machine (SVM) module to obtain a configuration value of resource information; configuring the resource information by using the configuration value;
the display is used for displaying the configuration values.
7. The apparatus of claim 6, wherein the correlation eigenvalue is a combination of one or more of a regional eigenvalue, a scale eigenvalue, or a temporal eigenvalue.
8. The apparatus of claim 7, wherein the processor is further configured to obtain a difference relationship between a configuration value of the resource information and an actual usage value of the resource information; and correcting the configuration value of the subsequently acquired resource information by using the difference relation.
9. The apparatus of claim 8, wherein the difference relationship is a percentage difference between the configuration value and the actual usage value, and wherein the processor is further configured to monitor the percentage difference, and increase the predetermined time if the percentage difference exceeds a threshold.
10. A server, characterized in that it comprises the apparatus of any of claims 6-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911329659.0A CN111210053A (en) | 2019-12-20 | 2019-12-20 | Resource information configuration method and device and server |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911329659.0A CN111210053A (en) | 2019-12-20 | 2019-12-20 | Resource information configuration method and device and server |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111210053A true CN111210053A (en) | 2020-05-29 |
Family
ID=70789290
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911329659.0A Pending CN111210053A (en) | 2019-12-20 | 2019-12-20 | Resource information configuration method and device and server |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111210053A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113269558A (en) * | 2021-06-25 | 2021-08-17 | 中国银行股份有限公司 | Transaction processing method and device in distributed system |
| CN114971727A (en) * | 2022-05-31 | 2022-08-30 | 拉扎斯网络科技（上海）有限公司 | Electronic certificate distribution method and device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016126718A (en) * | 2015-01-08 | 2016-07-11 | 日本電信電話株式会社 | Time series data prediction device and time series data prediction method |
| CN106875027A (en) * | 2016-06-06 | 2017-06-20 | 阿里巴巴集团控股有限公司 | The Forecasting Methodology and device of resource request value, the Forecasting Methodology of trading volume |
| CN109800483A (en) * | 2018-12-29 | 2019-05-24 | 北京城市网邻信息技术有限公司 | A kind of prediction technique, device, electronic equipment and computer readable storage medium |
| CN109857550A (en) * | 2019-01-07 | 2019-06-07 | 平安科技（深圳）有限公司 | Resource allocation method, device, equipment and storage medium based on machine learning |
-
2019
- 2019-12-20 CN CN201911329659.0A patent/CN111210053A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016126718A (en) * | 2015-01-08 | 2016-07-11 | 日本電信電話株式会社 | Time series data prediction device and time series data prediction method |
| CN106875027A (en) * | 2016-06-06 | 2017-06-20 | 阿里巴巴集团控股有限公司 | The Forecasting Methodology and device of resource request value, the Forecasting Methodology of trading volume |
| CN109800483A (en) * | 2018-12-29 | 2019-05-24 | 北京城市网邻信息技术有限公司 | A kind of prediction technique, device, electronic equipment and computer readable storage medium |
| CN109857550A (en) * | 2019-01-07 | 2019-06-07 | 平安科技（深圳）有限公司 | Resource allocation method, device, equipment and storage medium based on machine learning |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113269558A (en) * | 2021-06-25 | 2021-08-17 | 中国银行股份有限公司 | Transaction processing method and device in distributed system |
| CN114971727A (en) * | 2022-05-31 | 2022-08-30 | 拉扎斯网络科技（上海）有限公司 | Electronic certificate distribution method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20160314064A1 (en) | Systems and methods to identify and classify performance bottlenecks in cloud based applications | |
| US9928831B2 (en) | Speech data recognition method, apparatus, and server for distinguishing regional accent | |
| JP6708219B2 (en) | Log analysis system, method and program | |
| CN111401314A (en) | Dressing information detection method, device, equipment and storage medium | |
| CN109375609B (en) | Abnormal attack detection method and device | |
| CN111669368B (en) | End-to-end network sensing abnormity detection and analysis method, system, device and medium | |
| CN113743607A (en) | Training method of anomaly detection model, anomaly detection method and device | |
| CN111210053A (en) | Resource information configuration method and device and server | |
| US20180267964A1 (en) | Causal analysis device, causal analysis method, and non-transitory computer readable storage medium | |
| CN114564345A (en) | Server abnormity detection method, device, equipment and storage medium | |
| WO2021047376A1 (en) | Data processing method, data processing apparatus and related devices | |
| CN109947728B (en) | Log file processing method and device | |
| CN115292146B (en) | System capacity estimation method, system, equipment and storage medium | |
| CN111047433A (en) | Method and device for analyzing reasons of user number abnormality, server and storage medium | |
| CN111081325A (en) | Medical data processing method and device | |
| CN110910905A (en) | Mute point detection method and device, storage medium and electronic equipment | |
| CN117252362A (en) | Scheduling method and device based on artificial intelligence, computer equipment and storage medium | |
| CN112232960B (en) | Transaction application system monitoring method and device | |
| CN114218574B (en) | Data detection method and device, electronic equipment and storage medium | |
| CN116129319A (en) | Weak supervision time sequence boundary positioning method and device, electronic equipment and storage medium | |
| Tunalı et al. | An empirical analysis on the relationship between Health Care Expenditures and Economic Growth in the European Union Countries | |
| CN107122480A (en) | A kind of cloud analysis method and system based on traffic violation data | |
| CN112131468A (en) | Data processing method and device in recommendation system | |
| JP7302439B2 (en) | System analysis method and system analysis program | |
| CN111274088B (en) | Real-time monitoring method, device, medium and electronic equipment for big data platform |
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: 20200529 |