Minfo-India iron ore resources and mineral information prototype database

Minfo-India iron ore resources and mineral information prototype database

Minfo -- a protype mineral information database for iron ore resources of India

(Computers & Geosciences 27 (2001) 357-361)

Author: indranil Roy, B .C. Sarker, A. Chattopadhyay

Introduction

The increasing demand for mineral resources has brought about the same massive demand for resource-related information. Detection and related mining activities generate a large amount of data. In India, due to lack of communication and lack of a central data warehouse, the information remains in the original data state. This problem can be solved by establishing a computer mineral information database.
Dafeng iron ore resources in India are mainly distributed in five areas, namely Area A (South Bihar and North Orissa State) and Area B (rowghat region of Baster, rajhara, Madhya Pradesh ), zone C (Bellary, hospet region of Karnataka), Zone D (GOA), and Zone E (kudremukh region of bababudan and Karnataka) (figure 1 ). (Banerjee and Sharma, 1994 ). Currently, there are 259 operational iron mines and several hundred undeveloped mines (deposit) in India (India mining bureau, 1992 ). In this situation, in accordance with India's new national mineral affairs policy, we have attempted to develop India's iron ore resource mineral information database, minfo. So far, the prototype system includes information about 32 iron ore sites in Area. Information about these mines is collected from various published documents and mining reports. The form for processing information of each mine point is ready. In order to ensure reliability, the information is verified repeatedly at each site. In addition, create data files for each site and link them to the system.
This article describes minfo, a prototype database of mineral information developed for Indian mineral resources. It mainly highlights the structure and Information Management System of minfo databases, including the structure and user interface of data files.

System Requirements

Minfo databases are developed using Turbo Pascal (ver.6.0) and can run on dos3.0 or later. They are compatible with any ibm pc, preferably 486 or higher microprocessor and at least 2.46kb Ram. The core module requires a hard disk space of 2.46 MB. The storage space of the database depends on the size of the database, and the data of each mining site needs to be 1.84kb.

Variable organization of the database

According to Clark and Cook (1978), seven types of information must be considered to fully define a mine. Including (I) Cataloguing and overall organization information, (ii) Geographical location information of the site, (iii) lease information and other regulatory aspects, and (iv) Existing minerals, description of ore types and beneficiation, (v) reserves of various categories, (vi) geological information of ore sites, and (vii) Information of current mining activities. In the minfo mineral information database, these seven types of information are subdivided into 64 fields. Table 1 lists the category information and related fields.

Database structures and files

The minfo mineral information database is composed of three system files (config. MNF, fields. MNF and help. MNF) and two types of data files (that is, the master record file and information files of each mine point. In the system structure, both the system file and the master record file (master data file) are linked to the core program file. On the other hand, the data files of each mining point are linked to a hybrid structure of the master record file. The entire database structure 2 is shown.
The master record of the database as the mining point directory is a natural table. Each record in the table represents a mine point and links to a separate file containing information about the corresponding Mine Point. Every such file is very small (Kb), so that you can quickly access and get the best running effect in the limited storage space on the PC running dos. The records in the center of the master table include the miner's name, detailed record file name, miner's code, creation date, creator's name, last modification date, and modifier's name, and the mark that indicates whether or not the details of any category of a mining site exist. These tags increase the query speed. Adding or deleting any site is reflected in the master record. At the level, the structured information file of each mine point includes the title, Mine Point name, unique mine point code, and various category fields that describe the Mine Point Information, as shown in table 1.
The System File fields. MNF lists the data fields and their hierarchies. Used for structured User-Defined queries. In the query process, although the primary field is pre-defined, in fact, any combination of different fields can be used to create the final search variable. This makes the range of the constructed query more flexible. A user-defined query is used to search databases in two phases. First, the main record file is searched linearly by marking the mineral information category to create a list of mineral point names, that is, the requested mining point. Ore points marked with negative values are excluded from the list. Then, the master record file is used as a multi-link node (that is, the tag is used as the file pointer), and the query variable is used to access and test the data file of each mining point name in the list. Create a new list storage result.

User Interface

The minfo database user interface is a multi-level menu based on a few hotkeys (for special purposes, as shown in 3. Among the many options in the main menu (figure 3), the view option allows users to browse information stored in the database (figure 4 ). The Edit option is used to update and correct data item errors. Add new and add data are used to add a new mining record and information category to the database respectively. In this process, a new data file is created and the title information is inserted into the mining point of the main record file. On the other hand, the delete option is used to delete a record from the database, including physical deletion of data files and deletion of records in the master record file. The report option can print the stored information or export ASCII text files from the database.
The Query Process of minfo mineral information database is divided into two stages. You can use a series of expressions to create a menu structure. Each search variable is composed of a number or string specified by the user, and is connected to a specific data field by a logical operator. These search variables can be further connected using the and, or, and not operators. Continuous system reflection () helps users create queries that are very close to natural languages. After the parsed query is executed, the user can freely export the result to the screen (default), printer or file.

Conclusion

Considering India's extensive distribution of iron ore resources, the minfo database can provide rapid and effective methods for storing, searching, and extracting specific data, and minimize redundancy. Releasing well-organized and specific information to describe and quantify resources for future resource evaluation, planning and development, and planning and modifying policies will play a role.
The system is not complete in this province, and modules on environmental parameters and mining infrastructure are being integrated. For further development, the system will be fully open to other products in the future. With configuration options, the minfo mineral information database engine can be customized to suit any dataset, so it can be further developed into a wider mineral product information system.

Thanks

The first author would like to thank CSIR for its financial support for the Study Project Grant No. 9/85/(83)/96/EMR-1. Authors 2 and 3 thanked AICTE for its financial support for the Study Project Grant No. tmat 020/REC 387. I would also like to thank anonymous commentators who provided constructive comments.
 

References

(Omitted)