CN109189818B - Tobacco data granularity division method in value-added service environment - Google Patents
Tobacco data granularity division method in value-added service environment Download PDFInfo
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Abstract
The invention discloses a method for dividing tobacco data granularity under a value added service environment, which divides data into a first class of data granularity, a second class of data granularity and a third class of data granularity, and correspondingly arranges the data granularity in sequence, and marks a class number label on each class of data; and then stored in a database. When data is searched, the class number label is automatically added in front of the search request data to form the search request data with the class number label; and when the database calls the data, calling the data according to the class number label. According to the invention, the region data is automatically added into the class number label according to the authority; when the database calls data, the data are called according to the class number labels, so that the calculation amount of the computer database can be reduced. The granularity of each datum in the invention is more refined, which is convenient for data classification and retrieval. Therefore, managers at different levels can manage the business conveniently, and the effect that the previous-level manager has the right to check the data of the whole managed area and the next-level manager cannot check the data in the previous-level management is realized in the simplest mode.
Description
Technical Field
The invention relates to a computer data storage and processing technology, in particular to a data granularity division method suitable for the tobacco logistics industry.
Background
As the number of smokers in China is large, the tobacco quantity scale is large, and the tobacco is required to pass through a complex and standard logistics system from a cigarette factory to the smokers. In order to ensure the well-known and orderly tobacco logistics process with large quantity and large scale, an efficient logistics management computing system, namely a business service system, must be established.
Taking the metropolis tobacco company as an example, 42 existing main business systems of the metropolis tobacco company are served. Each application system is designed and developed according to the requirements of each business function management department in the tobacco industry, the system construction time is long, the span is large, and the business requirements are changed a lot. Firstly, the phenomenon of information isolated island and application isolated island exists among all application systems; secondly, a mass data processing mechanism is difficult to meet the requirements; the continuous uplink of new services such as a mobile office platform and the like, the relevance of each application is more complex, and the running response speed of the system is slow; fourthly, an integrated environment for the whole reutilization is not available, more valuable information is difficult to be mined by utilizing data resources, and the decision of each level of leader layers cannot be effectively supported; fifthly, provincial logistics systems are developed by taking cities as units, and logistics information systems of Sichuan provinces are not unified.
2.5 ten thousand households of tobacco retailers in the metropolis, the delivery amount is 56 ten thousand households per year, a new logistics center built by a metropolis logistics company in 2013 adopts a delivery mode of automatic three-dimensional storage, full-automatic sorting equipment and fixed line delivery, the delivery range is that except all urban areas, with the development of the internet of things, managers, retailers and consumers put forward more requirements on the running condition state of tobacco logistics, and the existing logistics control system cannot meet the development requirements of intelligent logistics, so that a comprehensive information control platform of the tobacco logistics is planned to be completed.
In the tobacco logistics platform, because the amount of calculation data involved is huge, and meanwhile, the data query of each management level unit according to respective data authority needs to be met, a more comprehensive data retention scheme, the data query authority of each level and a faster data query scheme when data is queried at each level need to be established.
Disclosure of Invention
In order to solve the above problems, an object of the present invention is to overcome the disadvantages of the prior art, and provide a more comprehensive data retention scheme suitable for the tobacco logistics industry, and data query permissions of each level in the tobacco logistics industry, and a faster data query scheme when data is queried at each level.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for dividing tobacco data granularity under a value added service environment is disclosed, wherein the granularity data is divided into a first class data granularity, a second class data granularity and a third class data granularity, the first class data granularity, the second class data granularity and the third class data granularity are correspondingly arranged in sequence, and each class of data is marked with a class number label; and then stored in a database.
When data is searched, the class number label is automatically added in front of the search request data to form the search request data with the class number label; and when the database calls the data, calling the data according to the class number label.
The method for classifying the tobacco data granularity under the value-added service environment as described above is further described as that the first type of data granularity is a region data granularity, the region data granularity is divided into more than one hierarchy, the first region hierarchy is used as a highest hierarchy, and at least a second region hierarchy, a third region hierarchy and a fourth region hierarchy are generated sequentially downwards.
The second type of data granularity is time data granularity, the time data granularity is divided into more than one hierarchy, the first time hierarchy is taken as the highest hierarchy, and at least a second time hierarchy, a third time hierarchy and a fourth time hierarchy are sequentially generated downwards.
The third kind of data granularity is the standard data granularity.
As described above, the method for dividing the tobacco data granularity in the value-added service environment is further described as that the region data granularity is a geographic region data granularity, and the first type of data granularity is divided into a first region level to a sixth region level, which are respectively: the first regional level is an administrative region, the second regional level is an economic circle region, the third regional level is a branch company region, the fourth regional level is a parcel region, the fifth regional level is a customer manager region, and the sixth regional level is a customer region; and setting managers for each regional level, which are respectively as follows: administrative district administrator, economic district administrator, branch regional administrator, sub-district administrator, customer manager district administrator, customer district administrator.
The second type of data granularity is time data granularity, the first time level is annual time, the second time level is semiannual time, the third time quarter time, the fourth time level is month time, the fifth time level is ten-day time, and the sixth time level is day time.
The third kind of data granularity is a standard data granularity and further comprises: type of place of origin, manufacturing enterprise, brand, tobacco specification.
The method for dividing the tobacco data granularity under the value-added service environment as described above further includes setting a first label code for the administrative area, a second label code for the economic circle area, a third label code for the branch area, a fourth label code for the parcel area, a fifth label code for the customer manager area, and a sixth label code for the customer area; the first tag code comprises a second tag code, the second tag code comprises a third tag code, the third tag code comprises a fourth tag code, the fourth tag code comprises a fifth tag code, and the fifth tag code comprises a sixth tag code.
The annual time comprises a half-year time, the half-year time comprises a quarterly time, the quarterly time comprises a month time, the month time comprises a ten-day time, and the ten-day time comprises a day time.
The method for tobacco data granularity division in a value-added service environment as described above may further include that the administrative regional administrator has a calling database that includes all data set with the first tag code, the economic circle regional administrator has a calling database that includes all data set with the second tag code, the division regional administrator has a calling database that includes all data set with the third tag code, the segment regional administrator has a calling database that includes all data set with the fourth tag code, the client administrative regional administrator has a calling database that includes all data set with the fifth tag code, and the client regional administrator has a calling database that includes all data set with the sixth tag code.
The method for classifying the tobacco data granularity under the value-added service environment further includes that when data is searched, the class number label is automatically added before the search request data to form the search request data with the class number label; when the database calls the data, calling the data according to the class number label; specifically, when the administrative district administrator searches data, the first label code data is automatically added to the generated search request data, when the economic district administrator searches data, the second label code data is automatically added to the generated search request data, when the division district administrator searches data, the third label code data is automatically added to the generated search request data, when the segment district administrator searches data, the fourth label code data is automatically added to the generated search request data, when the client manager searches data, the fifth label code data is automatically added to the generated search request data, and when the client district administrator searches data, the sixth label code data is automatically added to the generated search request data.
The invention has the beneficial effects that:
a flexible analysis model is established, and the model can independently define the analysis report required by the model, thereby effectively reducing the working strength of basic-level analysis and statistics personnel and improving the working efficiency.
According to the method, the regional data is automatically added into the class number label according to the authority; when the database calls data, the data are called according to the class number labels, so that the calculation amount of the computer database can be reduced.
The service analyst can use the data from a brand-new, three-dimensional and all-around view angle, so that the activity and creativity of the service analyst are stimulated, and a greater value in the data resource is excavated.
The integration and utilization of mass data are completed through cloud computing, and a strong service system is provided; by integrating basic operation and management monitoring, multi-dimensional logistics information is sensible, can be monitored, can be early-warned and can be compared, and key information support is provided for accurate and efficient enterprise operation analysis and management decision making.
Aiming at the data characteristics of the tobacco industry, the invention can carry out standardization to establish system data so as to achieve the data sharing condition.
The data processing method is beneficial to integrating various resources such as calculation, network and storage in the tobacco industry, a powerful service system is formed, support is provided for customizing information service in the business field, the maintenance workload of the information system is greatly reduced, and the rapid development of various businesses is met.
The data processing method of the invention sets the tobacco data standard and specification, establishes a data quality, safety and data life cycle management method, and is beneficial to forming a big data center basic architecture in the tobacco industry.
The data processing method can meet the requirements of different office workers in different units, and is convenient for the expansion and redevelopment of other systems in the future; the logistics management and control platform realizes the coverage of provincial and urban two-level application main bodies, runs through three-level information systems of logistics operation, management and decision, and realizes the integration of whole-area logistics information.
The data processing of the invention establishes a data model of the Chengdu tobacco, and the dimensionality of the data is as follows: 3 dimensions of mechanism, time and cigarettes, wherein the granularity of each dimension is finer and is the minimum granularity. And the data classification and retrieval are convenient.
The information interface application and flexible analysis are different according to the data granularity adopted by the characteristics of the functions of the information interface application and flexible analysis. The granularity of the organization dimension is refined from the original fragment area to a client manager and a client, so that managers at different levels can manage the business conveniently; the granularity of the cigarette is refined from the brand to the specification, so that more value-added services can be provided and a reasonable and effective marketing strategy can be formulated. And different data retrieval tag codes are automatically generated aiming at different administrators, the administrators are used for obtaining corresponding data types, and the upper-level administrators contain the tag codes of the lower-level administrators, so that the effect that the upper-level administrators are authorized to check the data of the managed whole area and the lower-level administrators cannot check the data in the upper-level administration is realized in the simplest mode.
In the aspect of analysis and statistics, data statistics requirements which are difficult to meet by a service system are met, such as circle data statistics, direct-operated store affiliation branch statistics, daily real-time order month accumulation, historical trend query and the like, and meanwhile, the analysis and statistics have better performance compared with the service system. The method comprises customized application for meeting the personalized analysis requirement of the tobacco industry and self-service application for expanding application requirements. The customized application comprises theme analysis and real-time monitoring, and is characterized in that the style and content of the customized application are close to the actual work of a user, so that the aim of reducing the working intensity of business analysts and improving the working efficiency is fulfilled.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention.
Example 1:
the granularity data is divided into a first class data granularity, a second class data granularity and a third class data granularity, the first class data granularity, the second class data granularity and the third class data granularity are correspondingly arranged in sequence, and each class data is marked with a class number label; and then stored in a database.
The first-class data granularity is a region data granularity, the region data granularity is divided into more than one hierarchy, the first region hierarchy is taken as the highest hierarchy, and at least a second region hierarchy, a third region hierarchy and a fourth region hierarchy are sequentially generated downwards, in this embodiment, 6 hierarchies are set, and respectively: the first area level is an administrative region, the second area level is an economic circle region, the third area level is a branch region, the fourth area level is a parcel region, the fifth area level is a customer manager region, and the sixth area level is a customer region; and setting managers for each regional level, which are respectively as follows: administrative district administrator, economic district administrator, branch regional administrator, sub-district administrator, customer manager district administrator, customer district administrator. The number of administrators per regional level is not limited, and there are multiple administrators.
The administrative region sets a first label code, the economic circle region sets a second label code, the branch region sets a third label code, the district region sets a fourth label code, the customer manager region sets a fifth label code, and the customer region sets a sixth label code.
For example: first tag code N, second tag code N1, third tag code N17, fourth tag code N17s, fifth tag code N17s5, and sixth tag code N17s 56. The first tag code comprises a second tag code, the second tag code comprises a third tag code, the third tag code comprises a fourth tag code, the fourth tag code comprises a fifth tag code, and the fifth tag code comprises a sixth tag code.
The administrative regional administrator has the function of calling the data with all the first label codes set in the database, the economic zone regional administrator has the function of calling the data with all the second label codes set in the database, the branch regional administrator has the function of calling the data with all the third label codes set in the database, the segment regional administrator has the function of calling the data with all the fourth label codes set in the database, the client regional administrator has the function of calling the data with all the fifth label codes set in the database, and the client regional administrator has the function of calling the data with all the sixth label codes set in the database.
Specifically, for the granularity of the area data, for example, there are two client area administrators (a first client area administrator and a second client area administrator), and the first client area administrator sets the sixth tag code to be n.1.7.s.5.6, so that when the first client area administrator enters data, the entered data automatically generates a data code including n.1.7.s.5.6. The second client area administrator sets the sixth label code to be n.1.7.s.5.0, so that when the second client area administrator enters data, the entered data automatically generates a data code containing n.1.7.s.5.0.
The second type of data granularity is time data granularity, the time data granularity is divided into more than one hierarchy, the first time hierarchy is taken as the highest hierarchy, and at least a second time hierarchy, a third time hierarchy and a fourth time hierarchy are sequentially generated downwards; the second type of data granularity is time data granularity, the first time level is annual time, the second time level is semiannual time, the third time quarter time, the fourth time level is month time, the fifth time level is ten-day time, and the sixth time level is day time; the annual time comprises a half-year time, the half-year time comprises a quarterly time, the quarterly time comprises a month time, the month time comprises a ten-day time, and the ten-day time comprises a day time.
Specifically, for the granularity of time data, for example, 2016, 5, 24, the time code is set to 2016.1.2.5.3.25, and the data codes from left to right sequentially represent that: where 2016 is the time of year, the fifth digit 1 is the first half of the year, the sixth digit 2 is the second quarter of the year, the seventh digit 5 represents month 5, the eighth digit 3 represents last ten days of the month, and the last 25 represents day 25.
Wherein the third type of data granularity is a standard data granularity, and the third type of data granularity further comprises: type of place of production, manufacturing enterprise, brand, tobacco specification. Numbered here in the conventional manner and added to the overall data code, the numbering being free-numbered, such as scj25, which is understood to be the type of source: sichuan, manufacturing enterprises: chengdu cigarette factory, brand: certain brand, specification: and (5) packaging 20 pieces of the Chinese herbal medicines in a conventional manner.
And code rules of the first type data granularity, the second type data granularity and the third type data granularity are sequentially and correspondingly arranged and merged, a class number label is marked, and the final data is generated and then stored in a database. The results, combined with the examples already listed above, are: < N.1.7.s.5.0>, <2016.1.2.5.3.25>, < scj25 >.
When data is searched, the class number label is automatically added in front of the search request data to form the search request data with the class number label; and when the database calls the data, calling the data according to the class number label. Specifically, when the administrative district administrator searches data, the first label code data is automatically added to the generated search request data, when the economic district administrator searches data, the second label code data is automatically added to the generated search request data, when the division district administrator searches data, the third label code data is automatically added to the generated search request data, when the segment district administrator searches data, the fourth label code data is automatically added to the generated search request data, when the client manager searches data, the fifth label code data is automatically added to the generated search request data, and when the client district administrator searches data, the sixth label code data is automatically added to the generated search request data.
Specifically, for the granularity of the regional data, for example, there are two client region administrators (a first client region administrator and a second client region administrator), and the first client region administrator sets the sixth tag code to be n.1.7.s.5.6, so that when the first client region administrator retrieves the data, the retrieved data automatically generates the data code including n.1.7. s.5.6. The second client area administrator sets the sixth tag code to n.1.7.s.5.0, so that the second client area administrator automatically generates the search data including n.1.7.s.5.0 when searching the data. And the retrieval code generated when the client manager regional manager at the upper level retrieves the data is n.1.7.s.5, wherein the code n.1.7.s.5 comprises the retrieval data of n.1.7.s.5.6 and n.1.7.s.5.0. Therefore, when the data is called, the client manager area manager has limited right to call the data of the first client area manager and the second client area manager, and the first client area manager cannot call the data of the second client area manager or the client manager area manager. Therefore, the data processing method can give different authorities for different administrators, and meanwhile, the data retrieval mode has less calculation amount and occupies less calculation memory.
The technology of this example applies the following technical environments and operating schemes:
all application system data may be shared.
The static data is stored for 3 years, the dynamic data is updated to the same day, the storage space is 5T, data storage, analysis and mining can be achieved, and the cloud computing capability is achieved.
The mobile data access mode through multiple channels such as a PC, a tablet computer, a smart phone and a data television can be supported.
Various resources such as calculation, network and storage of the local tobacco industry are integrated to form a powerful service system, support is provided for customized information service in the business field, and a server and data storage of the system are both located in a firewall of an application unit, so that safety is guaranteed.
The data standard and the standard of the local tobacco industry are formulated, a data quality, safety and data life cycle management method is established, a big data center basic framework of the local tobacco industry is formed, the big data center basic framework is a basic guarantee for the integration and data sharing of an information system of the local tobacco industry, and information isolated islands of all business systems are eliminated fundamentally.
The method comprises the steps of establishing an enterprise-level data model by combing and analyzing regional tobacco business services, establishing ODS, EDW and DM data models by combining acquired data analysis application requirements of all departments on the basis of the enterprise-level data model, considering model expandability and simultaneously considering model flexibility and data query performance, wherein a key technology of data modeling relates to data range and data granularity.
The data range involves two parts, internal and external data sources, the time span is increased, and the metadata amount is complex and much. The external data includes: plan target data, marketing personnel information data, benchmarking data, agreement data and the like; the internal system of data integration mainly is sales data, network construction data and the like of a marketing system, case, license, organization personnel data and the like of a monopoly system, sorting and distribution data and the like of a logistics system. Because of the limitation of the storage space of the database, the company-level data span of the core business data market is 2007-2015, the sub-company-level data span of the segment-2010-2015, and the client-client manager-level data span of the client-2015 at present. The metadata of the Chengdu tobacco industry is combined to be specific, the Chengdu tobacco comprehensive information management platform is used as a supplement for system data query and analysis, and the dimensionality of the data is as follows: 3 dimensionalities of mechanisms, time and cigarettes provide a query function for a management decision layer.
The information interface application and the flexible analysis are different in data granularity according to the characteristics of functions of the information interface application and the flexible analysis. The granularity of the organization dimension is refined from the original fragment area to a client manager and a client, so that managers at different levels can manage the business conveniently; the granularity of the cigarette is refined from brand to specification, so that more value-added services can be provided and a reasonable and effective marketing strategy can be formulated.
In the aspect of analysis and statistics, data statistics requirements which are difficult to meet by a service system are met, such as circle data statistics, direct-operated store affiliation branch statistics, daily real-time order month accumulation, historical trend query and the like, and meanwhile, the analysis and statistics have better performance compared with the service system. The method comprises a customized application for meeting the individual analysis requirement of the Chengdu tobacco and a self-service application for expanding the application requirement of the Chengdu tobacco. The customized application comprises theme analysis and real-time monitoring, and is characterized in that the style and the content of the customized application are close to the actual work of a user, so that the aim of reducing the working intensity of business analysts and improving the working efficiency is fulfilled.
The above embodiments are intended to illustrate the present invention, not to limit the present invention, and any modifications and changes made within the spirit of the present invention and the scope of the claims fall within the scope of the present invention.
Claims (6)
1. A method for dividing tobacco data granularity under a value added service environment is characterized in that the granularity data are divided into a first class data granularity, a second class data granularity and a third class data granularity which are correspondingly arranged in sequence, and each class of data is marked with a class number label; then storing in a database;
The first type of data granularity is regional data granularity, the second type of data granularity is time data granularity, and the third type of data granularity is standard data granularity;
the region data granularity is geographic region data granularity, and is divided into a first region level to a sixth region level, which are respectively: the first region level is an administrative region, and a first label code is set; the second area level is an economy circle area, and a second label code is set; the third area level is a branch area, and a third label code is set; the fourth area level is a parcel area, and a fourth label code is set; the fifth area level is a customer manager area, and a fifth label code is set; the sixth zone level is a customer zone; setting a sixth label code;
enabling the first tag code to contain a second tag code, the second tag code to contain a third tag code, the third tag code to contain a fourth tag code, the fourth tag code to contain a fifth tag code, and the fifth tag code to contain a sixth tag code;
and setting managers for each regional level, which are respectively as follows: administrative district administrator, economic circle district administrator, branch company district administrator, sub district administrator, customer manager district administrator, customer district administrator;
The administrative district administrator has the function of calling the data which comprise all the first label codes and are set in the database, the economic district administrator has the function of calling the data which comprise all the second label codes and are set in the database, the branch district administrator has the function of calling the data which comprise all the third label codes and are set in the database, the district administrator has the function of calling the data which comprise all the fourth label codes and are set in the database, the client administrative district administrator has the function of calling the data which comprise all the fifth label codes and are set in the database, and the client district administrator has the function of calling the data which comprise all the sixth label codes and are set in the database;
when data is searched, the class number label is automatically added in front of the search request data to form the search request data with the class number label; and when the database calls the data, calling the data according to the class number label.
2. The method for tobacco data granularity division in a value added service environment according to claim 1, wherein the region data granularity is divided into more than one hierarchy, a first region hierarchy is taken as a highest hierarchy, and at least a second region hierarchy, a third region hierarchy and a fourth region hierarchy are generated sequentially downwards.
3. The method for tobacco data granularity division in a value-added service environment according to claim 1, wherein the time data granularity is divided into more than one level, a first time level is taken as a highest level, and at least a second time level, a third time level and a fourth time level are generated downwards in sequence.
4. The method of claim 3, wherein the first time level is annual time, the second time level is half year time, third quarter time, the fourth time level is month time, the fifth time level is ten day time, and the sixth time level is day time;
the annual time comprises a half-year time, the half-year time comprises a quarterly time, the quarterly time comprises a month time, the month time comprises a ten-day time, and the ten-day time comprises a day time.
5. The method of claim 1, wherein the tobacco data granularity comprises: type of place of production, manufacturing enterprise, brand, tobacco specification.
6. The method for tobacco data granularity partition in a value added service environment of claim 1, wherein, when retrieving data, the class number label is automatically added before retrieving request data to form the retrieving request data with the class number label; when the database calls the data, calling the data according to the class number label; specifically, when the administrative district administrator searches data, the first label code data is automatically added to the generated search request data, when the economic district administrator searches data, the second label code data is automatically added to the generated search request data, when the division district administrator searches data, the third label code data is automatically added to the generated search request data, when the segment district administrator searches data, the fourth label code data is automatically added to the generated search request data, when the client manager searches data, the fifth label code data is automatically added to the generated search request data, and when the client district administrator searches data, the sixth label code data is automatically added to the generated search request data.
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106407445A (en) * | 2016-09-29 | 2017-02-15 | 重庆邮电大学 | Unstructured data resource identification and locating method based on URL (Uniform Resource Locator) |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1535433A (en) * | 2001-07-04 | 2004-10-06 | 库吉萨姆媒介公司 | An Extensible Interactive Document Retrieval System Based on Classification |
| WO2006089092A2 (en) * | 2005-02-16 | 2006-08-24 | Ziyad Dahbour | Hierarchal data management |
| CN101706926A (en) * | 2009-11-25 | 2010-05-12 | 河南省烟草公司鹤壁市公司 | Method for investigating and processing cigarette consumption information |
| CN101989301B (en) * | 2010-10-22 | 2012-05-23 | 复旦大学 | A Method of Index Maintenance Supporting Multiple Data Sources |
| CN104268254A (en) * | 2014-10-09 | 2015-01-07 | 浪潮电子信息产业股份有限公司 | A Statistical Method for Security Situation Analysis |
| CN105677486B (en) * | 2016-01-08 | 2019-03-22 | 上海交通大学 | Data parallel processing method and system |
| US10539936B2 (en) * | 2016-10-17 | 2020-01-21 | Fisher-Rosemount Systems, Inc. | Methods and apparatus for configuring remote access of process control data |
| CN106600103A (en) * | 2016-11-04 | 2017-04-26 | 国网江苏省电力公司 | Statistic data model building method facing programs, plans, and decisions |
-
2018
- 2018-07-05 CN CN201810732176.4A patent/CN109189818B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106407445A (en) * | 2016-09-29 | 2017-02-15 | 重庆邮电大学 | Unstructured data resource identification and locating method based on URL (Uniform Resource Locator) |
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