Minfo-Indian iron ore resource mineral information prototype database
Minfo - a Protype Mineral Information Database for Iron Ore Resources of India
(COMPUTERS & Geosciences 27 (2001) 357-361)
Written: Indranil Roy, B.c. SARKER, A. Chattopadhyay
Introduction
Allowed mineral resources demand brings a lot of demand for information related to resources. The detection and related mining activities have produced a lot of data. In India, these information stay in the original data status due to the shortcomings of communication and without a central data warehouse. This problem can be solved by establishing a number of mineral information. India's large rich iron ore resources are mainly distributed in 5 districts, namely A district (South Bihar and Northern Osa), the BSTER, RAJHARA, MADHYA PRADESH ROWGHAT Regions (Bellary, Karnataka) HOSPET Regional), D Zone (GOA) and the E-E-E-En (Kudreme Kudremukh region of Bababudan, Karnataka) (Figure 1). (Banerjee and Sharma, 1994). Currently, 259 have been working in India and hundreds of unopened ore (Deposits) (Indian Mines, 1992). In this situation, in accordance with the new national mineral policy in India, I tried to develop Indian iron ore resource mineral information database, Minfo. So far, the prototype system includes information of 32 iron mine points in a region. The information of these mine points is collected from various published documents and each mining report. A form for each mine point information is ready. To ensure reliability, you have visited each mine point repeated verification information. In addition, data files are separately produced and linked to the system for each mine point. This article is a description of the mineral information prototype database-Minfo for the development of Indian mineral resources. Mainly highlight the construction and information management system of the Minfo database, including the structure of the data file and the user interface.
System Requirements
The Minfo database is developed with Turbo Pascal (Ver.6.0), which can be run in DOS3.0 or higher, which is preferably 486 or higher microprocessors and minimum 2.46kb RAM at any IBM PC compatible machine. The core module requires 2.46MB of the hard disk space. The storage space of the database portion is determined by the database, and each mine point requires 1.84KB.
Database variable organization
In accordance with CLARK and COOK (1978), 7 types of information must be considered in order to fully define a mine point. Including (i) information, (ii) geographic location information, (iii) lease information, (iii) lease information, (IV) Existing mineralogue, mineral and distribution, (V) Category reserves information, (VI) ore geological information, and (VII) Current mining activities. In the Minfo Mineral Information Database, this 7 type of information is divided into 64 domains. Each category information and related fields are shown in Table 1.
Database constructor and file
The Minfo Mineral Information Database consists of a core information management program file, 3 system files (config.mnf, fields.mnf and help.mp.mnf), and 2 types of data files (ie, the information files of the main record file, and various mine points). On the system structure, the system file and the main record file (main data file) are linked to the core program file. On the other hand, the data files of each mine point information are linked to a hybrid structure of the main record file. The entire database structure is shown in Figure 2. The main record portion of the database of the mine point directory is a natural table. Each record in the table represents a mine point and is linked to a separate file containing information about the corresponding mine. Each such file is small (184KB), so you can quickly access and achieve the best run effect in the finite storage space on the PC running DOS. The record of the primary table contains the name of the mine point, details record file name, mining point code, creation date, creator name, final modification date, modifier name, and marks that represent the existence of any category details of the ore point. These tags increase the speed of the query. Increase or delete any mine points is reflected on the main record. At the level, each mine structure structured information file includes title, mine name, and unique mine code, as well as various category fields describing the ore information, as shown in Table 1. The system file fields.mnf lists each data field and their hierarchical relationship. Used to structured user-defined queries. During the query process, although the primary field is predefined, any combination of different fields can be used to create the final search variable. This makes a lot of flexibility in the range of constructed queries. Practical User Custom Query, the system is divided into 2 phases to search the database. First, by testing the mark linear search of the mineral information category, the list of mine points name is created, that is, the mine points required by the query. The mine points with negative marked are excluded from the list. Then, the main record file is used as a multi-link node (i.e., the pointer to the file is used as a file), and the data file in the list is accessed and tested by querying the variable. Finally create a new list storage result. User Interface
The user interface of the Minfo database is a multi-level menu based on a few hot bonds (to do special purposes, as shown in Figure 3). In the many options in the main menu (Figure 3), the view (view) option allows the user to browse information stored in the database (Figure 4). Editing (edit) option is used to update and correct the error of the data item. New Records and Add Data (Add Data) are used to add new mine records and information categories to the database. In this process, new data files are created and the title information is inserted into the mine point in the main record file. On the other hand, the delete option is used to delete a record corresponding to a selected mine point from the database, including the physical deletion of the data file and the deletion recorded in the main record file. Practical Report Options, print stored information or export ASCII text files from the database. The Query process of the Minfo Mineral Information Database is divided into 2 phases. Users first use a series of related menu structures formulating a query. Each search variable is made by a number or string specified by the user, and is configured with a logical operator () to connect to a particular data field. These search variables can also be connected to the Boolean operator (AND, OR and NOT). Continuous system reflection () can help users create queries that are very close to natural language. After the resolved query is executed, the user can freely select the result to export the result to the screen (default), printer or file.
in conclusion
Considering a wide range of iron ore resources in India, the Minfo database can provide rapid and effective ways for storage, search, and extract specific data, and minimize redundancy. Publish good specific information, describe and quantify resources for future resource assessments, planning and development, while planning and revising policies, will play a role. The province is not complete, and the module on environmental parameters and mining infrastructure is being integrated. For further development, the system maintains a sufficient openness to future combined with other products. By utilizing configuration options, the Minfo Mineral Information database engine can be customized to fit any data set, so it can be further developed into a wider range of mineral product information systems. thank
The first authors grate on CSIR's financial support for research projects Grant NO. 9/85 / (83) / 96 / EMR-1. The second and third authors thank Aicte's financial support for research projects Grant No. TMAT 020 / REC 387. I would also like to thank an anonymous commentator for constructive advice.
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