CN116993179A - Mineral resource intelligent management system based on big data analysis - Google Patents
Mineral resource intelligent management system based on big data analysis Download PDFInfo
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- 229910052500 inorganic mineral Inorganic materials 0.000 title claims abstract description 57
- 239000011707 mineral Substances 0.000 title claims abstract description 57
- 238000007405 data analysis Methods 0.000 title claims abstract description 15
- 238000005065 mining Methods 0.000 claims abstract description 547
- 238000004458 analytical method Methods 0.000 claims abstract description 103
- 238000009826 distribution Methods 0.000 claims abstract description 34
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000007667 floating Methods 0.000 claims description 26
- 238000011156 evaluation Methods 0.000 claims description 14
- 238000000605 extraction Methods 0.000 claims description 12
- 230000001360 synchronised effect Effects 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 230000006641 stabilisation Effects 0.000 claims description 3
- 238000011105 stabilization Methods 0.000 claims description 3
- 238000011946 reduction process Methods 0.000 claims description 2
- 238000010276 construction Methods 0.000 description 3
- 101150059057 BET1 gene Proteins 0.000 description 2
- 101150054552 bet3 gene Proteins 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000000087 stabilizing effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000011038 discontinuous diafiltration by volume reduction Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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- 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
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- G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
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- G—PHYSICS
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Abstract
The invention discloses an intelligent management system for mineral resources based on big data analysis, which relates to the technical field of mineral information acquisition and management, and solves the technical problems that in the prior art, mineral deposit distribution analysis cannot be performed on a mineral area in the mining process, so that mining point setting cannot be performed aiming at a spatial distribution type, mineral deposit distribution analysis is performed on the mineral area, the spatial distribution of mineral deposits in the current mineral area is judged through data acquisition and analysis, thereby carrying out targeted acquisition according to the spatial distribution type, ensuring the feasibility of mineral acquisition in the mineral area, preventing the mineral acquisition efficiency in the mineral area from not meeting the current requirement, and further causing unqualified mining efficiency of the current mineral area so as to increase unnecessary cost; and the sub-area is subjected to real-time exploitation trend analysis, so that the exploitation points of the sub-area are conveniently controlled in real time, the exploitation points in the sub-area are prevented from being timely changed, exploitation efficiency is reduced, and the exploitation cost of the corresponding sub-area is easily increased.
Description
Technical Field
The invention relates to the technical field of mineral information acquisition and management, in particular to an intelligent mineral resource management system based on big data analysis.
Background
Mineral resource potential evaluation is the task of evaluating a larger area and evaluating its near, medium, and long-term supply assurance levels. Mineral resource potential evaluation is based on geological, geophysical, geochemical, remote sensing geological and mineral data of the region being evaluated. There are two evaluation methods of the sum type and the non-sum type. The prediction of the total amount of mineral resources belongs to a total formula, while the non-total formula estimates the number, the position, the quality and the quantity of the mineral resources within a range of a certain probability (0.05-0.95). The evaluation of mineral resource potential plays an important role in the actual mineral exploitation process.
However, in the prior art, the mining area cannot perform deposit distribution analysis in the mining process, so that mining point setting cannot be performed for the spatial distribution type, and meanwhile, real-time mining trend analysis cannot be performed after the mining point setting is completed, so that mining efficiency is reduced.
In view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to solve the problems, and provides an intelligent mineral resource management system based on big data analysis, which is used for carrying out mining area potential analysis on each subarea and judging whether the real-time potential of each subarea is qualified or not so as to carry out balanced distribution of mining resources of the mining area and prevent the reduction of the overall mining efficiency of the mining area caused by unreasonable distribution of mining equipment; the real-time mineral exploitation quantity in the mining area is predicted and evaluated, so that the mining area is accurately predicted, construction progress control of the exploitation building site is facilitated, the exploitation quantity in the mining area is controlled in real time, and the corresponding cost of exploitation equipment is prevented from being timely controlled when the exploitation quantity is reduced.
The aim of the invention can be achieved by the following technical scheme:
the utility model provides a mineral resources intelligent management system based on big data analysis, includes the acquisition platform, and acquisition platform communication is connected with:
the mining area distribution analysis unit is used for carrying out mining deposit distribution analysis on the mining area, judging the spatial distribution of the mining deposit in the current mining area through data acquisition analysis, dividing the mining area into i subareas, wherein i is a natural number greater than 1, presetting mining area mining points for each subarea in the mining area, analyzing the single subarea, and carrying out mining point setting and type division on each subarea;
the real-time exploitation trend analysis unit is used for carrying out real-time exploitation trend analysis on the sub-areas, setting the sub-areas of the corresponding types as exploitation trends through the real-time exploitation trend analysis, and dividing the exploitation trends into transverse exploitation and longitudinal exploitation;
the mining area potential analysis unit is used for carrying out mining area potential analysis on each subarea, marking the subarea mined in real time as a real-time execution area, acquiring mining area potential analysis coefficients of the real-time execution area, comparing and generating low mining potential signals or high mining potential signals according to the mining area potential analysis coefficients, and sending the low mining potential signals or the high mining potential signals to the acquisition platform;
and the mining area prediction evaluation unit is used for predicting and evaluating the real-time mining yield in the mining area, setting the prediction of the mining area as a decrement trend or a stabilization trend through analysis, and controlling the corresponding type trend.
As a preferred embodiment of the invention, collecting the peak value corresponding to the real-time exploitation quantity of each sub exploitation point in the subarea, setting the exploitation point corresponding to the peak value as a determined exploitation point, taking the determined exploitation point as a center, collecting the exploitation quantity excess value of the determined exploitation point and the exploitation point of the adjacent preset mining area, and withdrawing the exploitation point corresponding to the adjacent preset mining area if the exploitation quantity excess value does not exceed the excess value threshold; otherwise, if the extraction quantity excessive value exceeds the excessive value threshold, the extraction points of the corresponding adjacent preset mining areas and the determined extraction points are assembled into an extraction main group.
As a preferred embodiment of the present invention, the mining volume floating trend of the mining site of the preset mining area except the center with the mining main group as the center in the sub-area and the average floating span value of the corresponding mining volume are collected and analyzed:
if the mining amount floating trend of the mining area mining points except the center in the subarea is a continuous reducing trend or the average floating span value of the corresponding mining amount exceeds a floating span value threshold, the corresponding subarea is determined and set by taking the mining main group as the center and taking the distance between the center and the nearest preset mining area mining point as a set interval distance, the next interval distance after the mining area mining point is determined in the resetting process is set to be 1.5 times of the set interval distance, and meanwhile the corresponding subarea is marked as a mineral distributed area and the corresponding area number is sent to an acquisition platform;
if the mining amount floating trend of the mining areas except the center of the mining main group in the subarea is a reciprocating decreasing trend, or the average floating span value of the corresponding mining amounts does not exceed a floating span value threshold, acquiring the number of the mining areas from the center of the mining main group to the boundary of the corresponding subarea, taking one third of the corresponding number as the determined mining area mining point number, sorting the mining amounts of the mining areas corresponding to the preset mining area mining point number, taking the sorted front as the determined mining area mining point, and representing the sorted front as the determined mining area mining point number before sorting; and simultaneously marking the corresponding subarea as a mineral convergent area, and sending the corresponding area number to the acquisition platform.
As a preferred embodiment of the present invention, the operation of the real-time mining trend analysis unit is as follows:
analyzing the mining area in real time in the mining process of the mining area, collecting the mining amount deviation span of the mining areas adjacent to the determined mining areas in the mining area and the reduction of the mining amount corresponding to the determined mining areas, and comparing the mining area deviation span with the mining amount deviation span threshold and the mining amount reduction threshold respectively:
if the exploitation quantity deviation span of adjacent determined exploitation points in the mineral aggregate area exceeds the exploitation quantity deviation span threshold value, or the exploitation quantity reduction corresponding to the determined exploitation points in the mineral aggregate area exceeds the exploitation quantity reduction threshold value, carrying out transverse position deviation on the determined exploitation points of the corresponding exploitation points, namely setting the exploitation trend as transverse exploitation, stopping deviation when the transverse position deviation is a preset distance from the adjacent non-deviated determined exploitation points, and carrying out exploitation frequency reduction when the exploitation quantity reduction speed is reduced, and if the exploitation quantity is not available, carrying out exploitation point withdrawal;
if the exploitation quantity deviation span of the exploitation points of the adjacent determined mining areas in the mining convergence area does not exceed the exploitation quantity deviation span threshold value and the exploitation quantity reduction corresponding to the exploitation points of the corresponding determined mining areas does not exceed the exploitation quantity reduction threshold value, the exploitation trend of the exploitation points of the current determined mining areas is set to be longitudinal exploitation, namely the exploitation depth can be continuously increased in the exploitation process.
As a preferred embodiment of the invention, analyzing the mining area in real time in the mining distributed area, collecting the mining amount reduction speed difference value of the adjacent determined mining areas under each equal mining working hour of the mining distributed area, if the mining amount reduction speed difference value of the adjacent determined mining areas under the equal mining working hour exceeds the speed difference value threshold, reducing the mining working hour of the determined mining areas with high reduction speed value in the adjacent determined mining areas, and setting the mining trend of the current subarea as the successively reduced mining working hour; and if the reduction speed difference value of the mining quantity of the mining points of the adjacent determined mining areas under the same mining working hours does not exceed the speed difference value threshold value, setting the mining trend of the current subarea as stable mining working hours.
As a preferred embodiment of the present invention, the operation of the mining area potential analysis unit is as follows:
acquiring a regional terrain height reduction span value of a real-time execution region in a mining process and a mining transportation cost increase span in a corresponding regional terrain height reduction span value reduction process; collecting the mining frequency of continuous and stable mining quantity of a real-time execution area in the mining process; acquiring mining area potential analysis coefficients of a real-time execution area through analysis;
comparing the mining area potential analysis coefficient of the real-time execution area with a mining area potential analysis coefficient threshold value:
if the mining area potential analysis coefficient of the real-time execution area exceeds the mining area potential analysis coefficient threshold, judging that the mining area potential analysis of the real-time execution area is abnormal, generating a low mining potential signal and sending the low mining potential signal to an acquisition platform; if the mining area potential analysis coefficient of the real-time execution area does not exceed the mining area potential analysis coefficient threshold value, judging that the mining area potential analysis of the real-time execution area is normal, generating a high mining potential signal and sending the high mining potential signal to an acquisition platform.
As a preferred embodiment of the invention, the operation of the mining area prediction evaluation unit is as follows:
the average reduction of the mining amount of the mining points in the mining area and the peak reduction of the mining points in the mining convergent area are collected, and compared with an average reduction threshold and a peak reduction threshold respectively:
if the average reduction of the mining quantity of the mining points in the mining area distributed area exceeds the average reduction threshold of the mining quantity, or the peak reduction of the mining points in the mining convergent area exceeds the peak reduction threshold of the mining quantity, the prediction of the corresponding mining area is set as a reduction trend, and the mining equipment scheduling is carried out on the corresponding mining area while the synchronous operation quantity is reduced; if the average reduction of the mining amount of the mining points in the mining area and the mining area in the mining distributed area does not exceed the average reduction of the mining amount threshold, and the peak reduction of the mining point in the mining convergent area does not exceed the peak reduction of the mining amount threshold, and setting the prediction of the corresponding mining area as a stable trend, and scheduling the mining equipment in the corresponding mining area to improve the synchronous operation time.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, deposit distribution analysis is carried out on a mining area, and the spatial distribution of deposits in the current mining area is judged through data acquisition analysis, so that targeted acquisition is carried out according to the spatial distribution type, the feasibility of mineral acquisition in the mining area is ensured, the condition that the mineral acquisition efficiency in the mining area does not meet the current requirement is prevented, and the mining efficiency of the current mining area is unqualified, so that unnecessary cost is increased; the sub-area is subjected to real-time exploitation trend analysis, so that real-time control of exploitation points of the sub-area is facilitated, exploitation efficiency is reduced due to untimely exploitation points in the sub-area, exploitation cost of the corresponding sub-area is easily increased, and exploitation of mineral products of the whole mining area is not facilitated;
2. according to the mining area potential analysis method, mining area potential analysis is carried out on each sub-area, whether the real-time potential of each sub-area is qualified or not is judged, so that balanced distribution of mining resources of the mining area is carried out, and the reduction of the overall mining efficiency of the mining area caused by unreasonable distribution of mining equipment is prevented; the real-time mineral exploitation quantity in the mining area is predicted and evaluated, so that the mining area is accurately predicted, construction progress control is conveniently carried out on the exploitation building site, exploitation quantity in the mining area is controlled in real time, and corresponding cost of exploitation equipment is prevented from being controlled in time when exploitation quantity is reduced, so that production cost is increased.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a schematic block diagram of an intelligent management system for mineral resources based on big data analysis.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, an intelligent management system for mineral resources based on big data analysis includes an acquisition platform, wherein the acquisition platform is in communication connection with a mining area distribution analysis unit, a real-time mining trend analysis unit, a mining area potential analysis unit and a mining area prediction evaluation unit, and the acquisition platform is in two-way communication connection with the mining area distribution analysis unit, the real-time mining trend analysis unit, the mining area potential analysis unit and the mining area prediction evaluation unit;
in the mining process of the mining area, real-time data acquisition is required to be carried out on the mining area through an integrated acquisition platform, so that the mining direction and the mining state of the mining area are predicted in real time, the acquisition platform generates mining area distribution analysis signals and sends the mining area distribution analysis signals to a mining area distribution analysis unit, after the mining area distribution analysis unit receives the mining area distribution analysis signals, deposit distribution analysis is carried out on the mining area, the spatial distribution of mineral deposits in the current mining area is judged through data acquisition analysis, and therefore targeted acquisition is carried out according to the spatial distribution type, the feasibility of mineral acquisition in the mining area is ensured, the condition that the mining acquisition efficiency in the mining area does not meet the current requirement is prevented, and the mining efficiency of the current mining area is unqualified, so that unnecessary cost is increased;
dividing a mining area into i subareas, wherein i is a natural number greater than 1, presetting mining area mining points for each subarea in the mining area, analyzing the subareas, collecting real-time mining quantity corresponding peak value of each subarea mining point in the subarea, setting the mining point corresponding to the peak value as a determined mining point, taking the determined mining point as a center, collecting mining quantity excess values of the determined mining point and the mining points of the adjacent preset mining areas, and withdrawing the mining points of the corresponding adjacent preset mining areas if the mining quantity excess values do not exceed the excess value threshold; otherwise, if the extraction quantity excessive value exceeds the excessive value threshold, constructing the extraction points of the corresponding adjacent preset mining areas and the determined extraction points into an extraction main group;
collecting the mining quantity floating trend of the mining area mining points except the center, which are centered on the mining main group, in the subarea and the average floating span value of the corresponding mining quantity, and analyzing the mining quantity floating trend of the mining area mining points except the center, which are centered on the mining main group, in the subarea and the average floating span value of the corresponding mining quantity:
if the mining amount floating trend of the mining area mining points except the center in the subarea is a continuous reducing trend or the average floating span value of the corresponding mining amount exceeds a floating span value threshold, the corresponding subarea is determined and set by taking the mining main group as the center and taking the distance between the center and the nearest preset mining area mining point as a set interval distance, the next interval distance after the mining area mining point is determined in the resetting process is set to be 1.5 times of the set interval distance, and meanwhile the corresponding subarea is marked as a mineral distributed area and the corresponding area number is sent to an acquisition platform;
if the mining amount floating trend of the mining areas except the center of the mining main group in the subarea is a reciprocating decreasing trend, or the average floating span value of the corresponding mining amounts does not exceed a floating span value threshold, acquiring the number of the mining areas from the center of the mining main group to the boundary of the corresponding subarea, taking one third of the corresponding number as the determined mining area mining point number, sorting the mining amounts of the mining areas corresponding to the preset mining area mining point number, taking the sorted front as the determined mining area mining point, and representing the sorted front as the determined mining area mining point number before sorting; meanwhile, marking the corresponding subarea as a mineral convergent area, and transmitting the corresponding area number to an acquisition platform;
after the acquisition platform acquires the types of all the subareas, generating a real-time exploitation trend analysis signal and sending the real-time exploitation trend analysis signal to a real-time exploitation trend analysis unit, and after the real-time exploitation trend analysis unit receives the real-time exploitation trend analysis signal, carrying out real-time exploitation trend analysis on the subareas, so that real-time control on exploitation points of the subareas is facilitated, exploitation efficiency reduction caused by untimely exploitation points in the subareas is prevented, and exploitation cost of the corresponding subareas is easily increased, so that mining exploitation of the whole mining area is not facilitated;
analyzing the mining convergence type region in the real-time mining process, collecting the mining volume deviation spans of the mining areas of adjacent determined mining areas in the mining convergence type region and the reduction of the mining volumes corresponding to the mining areas, and comparing the mining volume deviation spans of the mining areas of adjacent determined mining areas in the mining convergence type region and the reduction of the mining volumes corresponding to the mining areas with a mining volume deviation span threshold value and a mining volume reduction threshold value respectively:
if the exploitation quantity deviation span of adjacent determined exploitation points in the mineral aggregate area exceeds the exploitation quantity deviation span threshold value, or the exploitation quantity reduction corresponding to the determined exploitation points in the mineral aggregate area exceeds the exploitation quantity reduction threshold value, carrying out transverse position deviation on the determined exploitation points of the corresponding exploitation points, namely setting the exploitation trend as transverse exploitation, stopping deviation when the transverse position deviation is a preset distance from the adjacent non-deviated determined exploitation points, and carrying out exploitation frequency reduction when the exploitation quantity reduction speed is reduced, and if the exploitation quantity is not available, carrying out exploitation point withdrawal;
if the exploitation quantity deviation span of the exploitation points of the adjacent determined mining areas in the mining convergence area does not exceed the exploitation quantity deviation span threshold value and the exploitation quantity reduction corresponding to the exploitation points of the determined mining areas does not exceed the exploitation quantity reduction threshold value, the exploitation trend of the exploitation points of the determined mining areas is set to be longitudinal exploitation, namely the exploitation depth can be continuously increased in the exploitation process;
analyzing the mining area in the real-time mining process of the mining area in a distributed manner, collecting the mining amount reduction speed difference value of the mining areas which are adjacently determined under each equal mining time of the mining area, if the mining amount reduction speed difference value of the mining areas which are adjacently determined under the equal mining time exceeds a speed difference value threshold, reducing the mining time of the mining areas which are adjacently determined in the mining areas with high reduction speed value, and setting the mining trend of the current subarea as the subsequent reduction mining time; if the reduction speed difference value of the mining quantity of the mining points of the adjacent determined mining areas does not exceed the speed difference value threshold value under the same mining working hours, setting the mining trend of the current subarea as stable mining working hours;
the mining method comprises the steps that in the mining process of the subareas, a mining area potential analysis signal is generated by an acquisition platform and sent to a mining area potential analysis unit, after the mining area potential analysis unit receives the mining area potential analysis signal, mining area potential analysis is carried out on each subarea, whether the real-time potential of each subarea is qualified or not is judged, so that the mining area mining resources are uniformly distributed, and the reduction of the overall mining efficiency of the mining area caused by unreasonable mining equipment distribution is prevented;
marking a sub-region of real-time exploitation as a real-time execution region, acquiring a region terrain height reduction span value and a corresponding region terrain height reduction span value of the real-time execution region in the exploitation process, and marking the region terrain height reduction span value and the corresponding region terrain height reduction span value of the real-time execution region in the exploitation process as GJKi and CBZi respectively; collecting a mining frequency of continuously stabilizing the mining quantity of the real-time execution area in the mining process, and marking the mining frequency of continuously stabilizing the mining quantity of the real-time execution area in the mining process as WDPi;
by the formulaAcquiring mining area potential analysis coefficients Xi of a real-time execution area, wherein, bet1, bet2 and bet3 are preset proportionality coefficients, bet1 is more than bet2 is more than bet3 is more than 0, beta is an error correction factor, and the value is 0.759;
comparing the mining area potential analysis coefficient Xi of the real-time execution area with a mining area potential analysis coefficient threshold value:
if the mining area potential analysis coefficient Xi of the real-time execution area exceeds the mining area potential analysis coefficient threshold value, judging that the mining area potential analysis of the real-time execution area is abnormal, generating a low mining potential signal and sending the low mining potential signal to an acquisition platform; if the mining area potential analysis coefficient Xi of the real-time execution area does not exceed the mining area potential analysis coefficient threshold value, judging that the mining area potential analysis of the real-time execution area is normal, generating a high mining potential signal and sending the high mining potential signal to an acquisition platform;
when the corresponding real-time execution area is mined, the acquisition platform carries out preferential addition of mining man-hour or preferential mining setting scheduling on the real-time execution area of the high mining potential signal;
the mining area prediction evaluation signal is generated by the acquisition platform and sent to the mining area prediction evaluation unit, and after the mining area prediction evaluation unit receives the mining area prediction evaluation signal, the real-time mining exploitation amount in the mining area is predicted and evaluated, so that the accurate prediction of the mining area is convenient for the construction progress management and control of the exploitation building site, the exploitation amount of the mining area is controlled in real time to prevent the corresponding cost of exploitation equipment from being not managed in time when the exploitation amount is reduced, and the production cost is increased;
the average reduction of the mining points in the mining area in a distributed mining area and the peak reduction of the mining points in the mining convergent mining area are collected, and the average reduction of the mining points in the mining area in the distributed mining area and the peak reduction of the mining points in the mining convergent mining area are compared with an average reduction of the mining points and a peak reduction of the mining points in the mining convergent mining area respectively:
if the average reduction of the mining quantity of the mining points in the mining area distributed area exceeds the average reduction threshold of the mining quantity, or the peak reduction of the mining points in the mining convergent area exceeds the peak reduction threshold of the mining quantity, the prediction of the corresponding mining area is set as a reduction trend, and the mining equipment scheduling is carried out on the corresponding mining area while the synchronous operation quantity is reduced;
if the average reduction of the mining amount of the mining points in the mining area and the mining area in the mining distributed area does not exceed the average reduction of the mining amount threshold, and the peak reduction of the mining point in the mining convergent area does not exceed the peak reduction of the mining amount threshold, and setting the prediction of the corresponding mining area as a stable trend, and scheduling the mining equipment in the corresponding mining area to improve the synchronous operation time.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions;
when the mining area mining method is used, ore deposit distribution analysis is carried out on a mining area through a mining area distribution analysis unit, the spatial distribution of ore deposits in a current mining area is judged through data acquisition analysis, the mining area is divided into i subareas, i is a natural number larger than 1, mining areas are preset in each subarea in the mining area, analysis is carried out on a single subarea, and mining point setting and type division are carried out on each subarea; real-time mining trend analysis is carried out on the sub-areas through a real-time mining trend analysis unit, the sub-areas of the corresponding types are set to be mining trends through the real-time mining trend analysis, and the mining trends are divided into transverse mining and longitudinal mining; carrying out mining area potential analysis on each subarea through a mining area potential analysis unit, marking the subarea mined in real time as a real-time execution area, acquiring mining area potential analysis coefficients of the real-time execution area, comparing and generating low mining potential signals or high mining potential signals according to the mining area potential analysis coefficients, and sending the low mining potential signals or the high mining potential signals to an acquisition platform; and predicting and evaluating the real-time mineral exploitation quantity in the mining area through a mining area predicting and evaluating unit, setting the prediction of the mining area as a decrement trend or a stabilization trend through analysis, and controlling the corresponding type trend.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The mineral resource intelligent management system based on big data analysis is characterized by comprising an acquisition platform, wherein the acquisition platform is in communication connection with:
the mining area distribution analysis unit is used for carrying out mining deposit distribution analysis on the mining area, judging the spatial distribution of the mining deposit in the current mining area through data acquisition analysis, dividing the mining area into i subareas, wherein i is a natural number greater than 1, presetting mining area mining points for each subarea in the mining area, analyzing the single subarea, and carrying out mining point setting and type division on each subarea;
the real-time exploitation trend analysis unit is used for carrying out real-time exploitation trend analysis on the sub-areas, setting the sub-areas of the corresponding types as exploitation trends through the real-time exploitation trend analysis, and dividing the exploitation trends into transverse exploitation and longitudinal exploitation;
the mining area potential analysis unit is used for carrying out mining area potential analysis on each subarea, marking the subarea mined in real time as a real-time execution area, acquiring mining area potential analysis coefficients of the real-time execution area, comparing and generating low mining potential signals or high mining potential signals according to the mining area potential analysis coefficients, and sending the low mining potential signals or the high mining potential signals to the acquisition platform;
and the mining area prediction evaluation unit is used for predicting and evaluating the real-time mining yield in the mining area, setting the prediction of the mining area as a decrement trend or a stabilization trend through analysis, and controlling the corresponding type trend.
2. The intelligent mineral resource management system based on big data analysis according to claim 1, wherein the peak value corresponding to the real-time mining amount of each sub mining point in the sub-area is collected, the mining point corresponding to the peak value is set as the determined mining point, the mining point is determined as the center, the mining amount excess value of the determined mining point and the mining points of the adjacent preset mining areas is collected, and if the mining amount excess value does not exceed the excess value threshold, the mining points of the corresponding adjacent preset mining areas are withdrawn; otherwise, if the extraction quantity excessive value exceeds the excessive value threshold, the extraction points of the corresponding adjacent preset mining areas and the determined extraction points are assembled into an extraction main group.
3. The intelligent mineral resource management system based on big data analysis according to claim 2, wherein the mining amount floating trend of mining areas except for the center of the mining main group in the subarea and the average floating span value of the corresponding mining amount are collected and analyzed:
if the mining amount floating trend of the mining area mining points except the center in the subarea is a continuous reducing trend or the average floating span value of the corresponding mining amount exceeds a floating span value threshold, the corresponding subarea is determined and set by taking the mining main group as the center and taking the distance between the center and the nearest preset mining area mining point as a set interval distance, the next interval distance after the mining area mining point is determined in the resetting process is set to be 1.5 times of the set interval distance, and meanwhile the corresponding subarea is marked as a mineral distributed area and the corresponding area number is sent to an acquisition platform;
if the mining amount floating trend of the mining areas except the center of the mining main group in the subarea is a reciprocating decreasing trend, or the average floating span value of the corresponding mining amounts does not exceed a floating span value threshold, acquiring the number of the mining areas from the center of the mining main group to the boundary of the corresponding subarea, taking one third of the corresponding number as the determined mining area mining point number, sorting the mining amounts of the mining areas corresponding to the preset mining area mining point number, taking the sorted front as the determined mining area mining point, and representing the sorted front as the determined mining area mining point number before sorting; and simultaneously marking the corresponding subarea as a mineral convergent area, and sending the corresponding area number to the acquisition platform.
4. The intelligent management system for mineral resources based on big data analysis according to claim 1, wherein the real-time exploitation trend analysis unit operates as follows:
analyzing the mining area in real time in the mining process of the mining area, collecting the mining amount deviation span of the mining areas adjacent to the determined mining areas in the mining area and the reduction of the mining amount corresponding to the determined mining areas, and comparing the mining area deviation span with the mining amount deviation span threshold and the mining amount reduction threshold respectively:
if the exploitation quantity deviation span of adjacent determined exploitation points in the mineral aggregate area exceeds the exploitation quantity deviation span threshold value, or the exploitation quantity reduction corresponding to the determined exploitation points in the mineral aggregate area exceeds the exploitation quantity reduction threshold value, carrying out transverse position deviation on the determined exploitation points of the corresponding exploitation points, namely setting the exploitation trend as transverse exploitation, stopping deviation when the transverse position deviation is a preset distance from the adjacent non-deviated determined exploitation points, and carrying out exploitation frequency reduction when the exploitation quantity reduction speed is reduced, and if the exploitation quantity is not available, carrying out exploitation point withdrawal;
if the exploitation quantity deviation span of the exploitation points of the adjacent determined mining areas in the mining convergence area does not exceed the exploitation quantity deviation span threshold value and the exploitation quantity reduction corresponding to the exploitation points of the corresponding determined mining areas does not exceed the exploitation quantity reduction threshold value, the exploitation trend of the exploitation points of the current determined mining areas is set to be longitudinal exploitation, namely the exploitation depth can be continuously increased in the exploitation process.
5. The intelligent management system for mineral resources based on big data analysis according to claim 4, wherein the intelligent management system is characterized in that the intelligent management system is analyzed in a real-time mining process of a distributed mineral area, a mining amount reduction speed difference value of adjacent determined mining areas under each equal mining time of the distributed mineral area is acquired, if the mining amount reduction speed difference value of the adjacent determined mining areas under the equal mining time exceeds a speed difference value threshold, the mining time of the determined mining areas with high reduction speed value in the adjacent determined mining areas is reduced, and the mining trend of the current subarea is set as the subsequent reduction mining time; and if the reduction speed difference value of the mining quantity of the mining points of the adjacent determined mining areas under the same mining working hours does not exceed the speed difference value threshold value, setting the mining trend of the current subarea as stable mining working hours.
6. The mineral resource intelligent management system based on big data analysis according to claim 1, wherein the operation process of the mining area potential analysis unit is as follows:
acquiring a regional terrain height reduction span value of a real-time execution region in a mining process and a mining transportation cost increase span in a corresponding regional terrain height reduction span value reduction process; collecting the mining frequency of continuous and stable mining quantity of a real-time execution area in the mining process; acquiring mining area potential analysis coefficients of a real-time execution area through analysis;
comparing the mining area potential analysis coefficient of the real-time execution area with a mining area potential analysis coefficient threshold value:
if the mining area potential analysis coefficient of the real-time execution area exceeds the mining area potential analysis coefficient threshold, judging that the mining area potential analysis of the real-time execution area is abnormal, generating a low mining potential signal and sending the low mining potential signal to an acquisition platform; if the mining area potential analysis coefficient of the real-time execution area does not exceed the mining area potential analysis coefficient threshold value, judging that the mining area potential analysis of the real-time execution area is normal, generating a high mining potential signal and sending the high mining potential signal to an acquisition platform.
7. The intelligent management system for mineral resources based on big data analysis according to claim 1, wherein the operation process of the mining area prediction evaluation unit is as follows:
the average reduction of the mining amount of the mining points in the mining area and the peak reduction of the mining points in the mining convergent area are collected, and compared with an average reduction threshold and a peak reduction threshold respectively:
if the average reduction of the mining quantity of the mining points in the mining area distributed area exceeds the average reduction threshold of the mining quantity, or the peak reduction of the mining points in the mining convergent area exceeds the peak reduction threshold of the mining quantity, the prediction of the corresponding mining area is set as a reduction trend, and the mining equipment scheduling is carried out on the corresponding mining area while the synchronous operation quantity is reduced; if the average reduction of the mining amount of the mining points in the mining area and the mining area in the mining distributed area does not exceed the average reduction of the mining amount threshold, and the peak reduction of the mining point in the mining convergent area does not exceed the peak reduction of the mining amount threshold, and setting the prediction of the corresponding mining area as a stable trend, and scheduling the mining equipment in the corresponding mining area to improve the synchronous operation time.
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