CN112948445A - Method and electronic equipment for predicting target area of rare earth mineral resource in coal - Google Patents
Method and electronic equipment for predicting target area of rare earth mineral resource in coal Download PDFInfo
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Abstract
The invention discloses a method and electronic equipment for predicting a target area of rare earth mineral resources in coal. The method comprises the following steps: taking sample acquisition information of coal samples in an area to be predicted and sample rare earth content values in the coal samples, wherein the sample acquisition information comprises sample acquisition coordinates and sample coal seam numbers; retrieving a spatial database of rare earth elements in the coal based on the sample acquisition information; if a reference typical coal sample matched with the sample acquisition information exists in the spatial database of the rare earth elements in the coal, taking the rare earth content value of the reference typical coal sample as a reference value; if the rare earth content value of the sample is not less than the minimum value in the reference values, inputting the sample collection coordinates into a coal rare earth element mining area model; if the acquisition range represented by the sample acquisition coordinates and the mining area zone have an overlapping area, predicting the target area of the rare earth mineral resources in the area to be predicted based on the rare earth content value of the sample, the acquisition coordinates and the coal quality basic data of a typical coal sample in the overlapping area.
Description
Technical Field
The invention relates to the field of geological exploration, in particular to a method and electronic equipment for predicting a target area of rare earth mineral resources in coal.
Background
Currently, coal-based mineral resources are a focus and a front direction of attention in the field of coal geology. The target area prediction of the rare earth mineral resources in the coal usually needs large-scale geological exploration, sample testing and comprehensive research in a region to be predicted to obtain basic data in the region, the content of various rare earth elements in the coal sample is analyzed and calculated on the basis of the basic data, a contour map of the content of the rare earth elements in the coal in the region is drawn, then the source conditions, the deposition environment and the later structural thermal evolution history of the rare earth elements in the coal are analyzed by integrating professional knowledge in the fields of sedimentology, constructology, coal geology and the like, and the target area of mineral exploration of the rare earth mineral resources in the coal is determined by combining experience.
In the prior art, the method for predicting the target area of the ore exploration in the fields of sedimentology, constructology, coal geology and the like has higher learning and application thresholds, and the prediction accuracy has higher dependence on the professional experience of researchers.
Disclosure of Invention
In view of the above, the present invention provides a method and an electronic device for predicting a target area of a rare earth mineral resource in coal, which can improve efficiency and accuracy of predicting the target area of the rare earth mineral resource in coal.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for predicting a target area of a rare earth mineral resource in coal, the method comprising: acquiring sample acquisition information of a coal sample in an area to be predicted and a sample rare earth content value in the coal sample, wherein the sample acquisition information comprises a sample acquisition coordinate and a sample coal seam number; based on sample acquisition information, retrieving a spatial database of rare earth elements in coal, wherein the spatial database of rare earth elements in coal is constructed based on coal quality basic data of typical coal samples in a preset area, and the coal quality basic data comprises acquisition information and rare earth content values of the typical coal samples; if a reference typical coal sample matched with the sample acquisition information exists in the spatial database of the rare earth elements in the coal, taking the rare earth content value of the reference typical coal sample as a reference value, wherein the reference typical coal sample meets the following conditions: the preset well field range or the preset mining area range represented by the collection information of the reference typical coal sample is the same as the preset well field range or the preset mining area range where the sample collection coordinate is located, and the difference value between the coal seam number in the collection information of the reference typical coal sample and the sample coal seam number is smaller than a first preset threshold value; if the rare earth content value of the sample is not less than the minimum value in the reference values, inputting the sample acquisition coordinates into a pre-constructed coal rare earth element mining area model, wherein the coal rare earth element mining area model comprises a mining area predicted based on coal-series mineral data and coal field geological data; if the acquisition range represented by the sample acquisition coordinates and the mining area zone have an overlapping area, predicting the target area of the rare earth mineral resources in the area to be predicted based on the rare earth content value of the sample, the acquisition coordinates and the coal quality basic data of a typical coal sample in the overlapping area.
In some embodiments, predicting the rare earth mineral resource target area within the area to be predicted based on the sample rare earth content values, the sample acquisition coordinates, and the coal quality base data for a typical coal sample in the overlapping area comprises: retrieving coal quality basic data of typical coal samples collected in the overlapping region from a rare earth element spatial database; generating a rare earth content element contour map based on the rare earth content value of the sample, the sample acquisition coordinates and the coal quality basic data of the typical coal sample in the overlapping area; and determining a target area of the rare earth mineral resource from the contour map of the rare earth content elements based on a preset prediction threshold, wherein the prediction threshold is not greater than the rare earth content value of the sample.
In some embodiments, the rare earth element spatial database is constructed based on the following steps: acquiring the acquisition information and the coal quality test result of a historical coal sample in a preset area; extracting the rare earth content value of each historical coal sample from the coal quality test result; if the rare earth content value of the historical coal sample is larger than a preset rare earth content threshold value, determining the historical coal sample as a typical coal sample; combining the collected information of the typical coal sample and the rare earth content value into coal quality basic data of the typical coal sample; and constructing a rare earth element spatial database based on the corresponding relation between the coal quality basic data and the typical coal sample.
In some embodiments, the method further comprises: if the reference typical coal sample matched with the sample acquisition information does not exist in the coal rare earth element spatial database, and the rare earth content value of the sample is greater than the rare earth content threshold value, executing the following steps: repeatedly sampling for many times within the collection range represented by the collection coordinates, and measuring the rare earth content value in the coal sample obtained by repeated sampling for many times; and if the difference value between the rare earth content value in the coal sample obtained by repeated sampling for multiple times and the rare earth content value of the sample is smaller than a second preset threshold value, updating the rare earth element spatial database based on the sample acquisition information and the rare earth content value of the sample.
In some embodiments, the method further comprises: if the acquisition range represented by the sample acquisition coordinate and the mining zone do not have an overlapping area, repeatedly sampling for many times in the acquisition range represented by the acquisition coordinate, and measuring the rare earth content value in the coal sample obtained by repeated sampling for many times; and if the difference value between the rare earth content value in the coal sample obtained by repeated sampling for multiple times and the rare earth content value of the sample is smaller than a second preset threshold value, updating the rare earth element spatial database and the coal rare earth element mining area model based on the sample acquisition information and the rare earth content value of the sample.
In some embodiments, the method further comprises: and if the sample rare earth content values of all the coal samples in the area to be predicted are smaller than the minimum value in the reference values, determining that the target area of the rare earth mineral resources does not exist in the area to be predicted.
In some embodiments, the coal sample includes coal in a mineable coal seam, gangue and coal seam roof, organic shale, and co-associated mineral production-bearing formation rock.
In some embodiments, the sample acquisition information further includes images and descriptive information of the coal sample.
In a second aspect, the present invention provides an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement the method for predicting a rare earth mineral resource target in coal of any of the embodiments described above.
In a third aspect, the present invention provides a computer readable medium having a computer program stored thereon, wherein the program when executed by a processor implements a method for predicting a target area of a rare earth mineral resource in coal as in any of the above embodiments.
The method for predicting the target area of the rare earth mineral resources in the coal has the beneficial effects that:
the method provided by the invention is based on the sample acquisition information of the coal samples in the area to be predicted, and a spatial database of rare earth elements in coal is retrieved; if a reference typical coal sample matched with the sample acquisition information exists in the spatial database of the rare earth elements in the coal, taking the rare earth content value of the reference typical coal sample as a reference value; if the rare earth content value of the sample is not less than the minimum value in the reference values, inputting the sample acquisition coordinates into a pre-constructed coal rare earth element mining area model; if the acquisition range represented by the sample acquisition coordinates and the mining area zone have an overlapping area, predicting the target area of the rare earth mineral resources in the area to be predicted based on the rare earth content value of the sample, the acquisition coordinates and the coal quality basic data of a typical coal sample in the overlapping area. The difficulty of predicting the target area of the rare earth mineral resources in the coal and the dependence on the professional experience of researchers are reduced, and the efficiency and the accuracy of predicting the target area of the rare earth mineral resources in the coal are improved.
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The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings.
FIG. 1 is an exemplary system architecture diagram in which some embodiments of the present invention may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for predicting a rare earth mineral resource target in coal in accordance with the present invention;
FIG. 3 is a schematic illustration of the pay zone in the model of the pay zone for rare earth elements in coal in the embodiment of the method for predicting the target area of rare earth resources in coal shown in FIG. 2;
FIG. 4 is a flow chart of predicting a target area of a rare earth mineral resource in coal in one embodiment of a method for predicting a target area of a rare earth mineral resource in coal in accordance with the present invention;
FIG. 5 is a flow chart of a construction of a rare earth element spatial database in one embodiment of a method for predicting rare earth mineral resource targets in coal according to the present invention;
FIG. 6 is a schematic block diagram of an electronic device suitable for use in implementing embodiments of the present invention.
Detailed Description
The present invention is described below based on embodiments, and it will be understood by those of ordinary skill in the art that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout the description and the claims, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
FIG. 1 illustrates an exemplary system architecture 100 for a method for predicting rare earth mineral resource targets in coal to which embodiments of the invention may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages and the like, for example, the sample collection information and the sample rare earth content value of the coal sample can be sent to the server, and the target area of the rare earth mineral resource in the area to be predicted can be received from the server.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, and 103 are hardware, they may be electronic devices with communication functions, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
The server 105 may be a server providing various services, such as a background data server that processes data of the coal samples uploaded by the terminal devices 101, 102, 103 (e.g., retrieving a spatial database of rare earth elements in coal based on sample collection information). The background data server can compare the received sample acquisition information and the sample rare earth content value of the coal sample with coal quality basic data of a reference typical coal sample, input the sample acquisition information into a coal rare earth element mining area model to predict a target area of rare earth mineral resources in the coal and the like, and feed back a processing result (such as the predicted target area of the rare earth mineral resources in the area to be predicted) to the terminal equipment.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules, for example, to provide distributed services, or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the method for predicting the target area of the rare earth mineral resource in coal provided by the embodiment of the present invention may be executed by the terminal devices 101, 102, 103, or may be executed by the server 105. And is not particularly limited herein.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for predicting a target area of a rare earth mineral resource in coal in accordance with the present invention is shown. The method for predicting the target area of the rare earth mineral resources in the coal comprises the following steps:
In this embodiment, the sample rare earth content value represents the content of each rare earth element in the coal sample, and may be, for example, the sum of the contents of each rare earth element in the coal sample.
In practice, a researcher can determine sampling points in a region to be predicted by adopting a diagonal sampling strategy or a quincunx sampling strategy, then respectively collect coal samples at different depths of each sampling point, number each coal sample, record the collection coordinate (such as a Global Positioning System (GPS) coordinate) and the collection depth of each coal sample, and then determine the sample coal seam number of each coal sample according to the collection depth, so that the sample collection information of each coal sample can be obtained. And then, performing element analysis on each coal sample to determine the sum of the content of each rare earth element in the coal sample, thereby obtaining the rare earth content value of each coal sample.
As an example, the executing body may be a terminal device shown in fig. 1, and a researcher may store the sample collection information and the sample rare earth content value of each coal sample in the terminal device, and then the terminal device executes the subsequent steps. The execution main body can also be a server shown in fig. 1, and the server can obtain the sample collection information and the sample rare earth content value of each coal sample from the terminal equipment through the network so as to execute the subsequent steps.
In some optional implementation manners of this embodiment, the coal sample includes coal in the mineable coal seam, gangue and coal seam roof and floor, organic shale, and associated mineral production occurrence stratum rock, so that diversity of components in the coal sample can be improved, and improvement of representativeness of the coal sample is facilitated.
Furthermore, the sample collection information also comprises an image and description information of the coal sample, and the description information can be description information of the environment of the collection place, and can also be appearance information of the coal sample, such as hardness, flexibility or gloss, and the characteristic dimension of the coal sample can be expanded.
And step 202, searching a spatial database of the rare earth elements in the coal based on the sample acquisition information.
The method comprises the steps that a spatial database of the rare earth elements in the coal is constructed on the basis of coal quality basic data of typical coal samples in a preset region, wherein the coal quality basic data comprise acquisition information and rare earth content values of the typical coal samples.
In this example, a typical coal sample represents a coal sample having a rare earth content meeting the mineralization standard. The spatial database of the rare earth elements in the coal may include coal quality basic data of typical coal samples at different collection times in a preset region (for example, the region may be a country or an administrative region), and the typical coal samples may be identified by numbers, where each number corresponds to a piece of coal quality basic data. For example, a relational database such as Oracle or MySQL may be employed.
As an example, the spatial database of the rare earth elements in the coal may be stored in a cloud server, the execution main body may input the sample collection information as a search condition into the cloud server through a service port, perform a search operation by the cloud server, and receive a search result returned by the cloud server.
It is understood that the spatial database of the rare earth elements in the coal may also be stored in the local space of the execution body, which is not limited by the present invention.
In some optional implementations of this embodiment, the coal quality basic data may further include the following information of the coal sample: location information, for example, may include administrative areas, mine areas, and fields where the coordinates were acquired; geological information, for example, may include various types of test indicators, such as coal seam thickness, coal age, and coal novelty; the content of various components and elements in the coal sample; physical properties of the coal sample, for example, may include density, ignition point, and the like; the chemical properties of the coal sample may include, for example, ash fusion characteristics calorific value, and the like.
And step 203, if the reference typical coal sample matched with the sample acquisition information exists in the spatial database of the rare earth elements in the coal, taking the rare earth content value of the reference typical coal sample as a reference value.
The reference typical coal sample meets the following conditions: the preset well field range or the preset mining area range represented by the collection information of the reference typical coal sample is the same as the preset well field range or the preset mining area range where the sample collection coordinate is located, and the difference value between the coal seam number in the collection information of the reference typical coal sample and the sample coal seam number is smaller than a first preset threshold value.
In the present embodiment, the preset field area or the preset mining area is a field area and a mining area divided according to the national coal geological survey. The geographical positions of the reference typical coal sample and the coal sample in step 201 are similar, and the geological environments of the reference typical coal sample and the coal sample are similar to the mining conditions, so that the rare earth content of the reference typical coal sample can be used as a reference value to predict whether the position of the coal sample has the mining conditions.
As an example, the execution main body may use the sample collection coordinates in the sample collection information as search conditions, search a typical coal sample located in a preset wellsite range or a preset mining area range where the sample collection coordinates are located from a spatial database of rare earth elements in coal, then use the sample coal seam number and a first preset threshold as screening conditions, filter out a typical coal sample whose coal seam number difference is not less than the first preset threshold, and obtain the remaining typical coal sample as a reference typical coal sample. Then, extracting the rare earth content value of the reference typical coal sample from the coal quality basic data corresponding to the reference typical coal sample as a reference value.
And 204, inputting the sample acquisition coordinates into a pre-constructed coal rare earth element mining area model if the rare earth content value of the sample is not less than the minimum value of the reference values.
The model of the coal rare earth element mining area comprises a mining area predicted based on coal-series mineral data and coal field geological data.
In this embodiment, the value of the rare earth content of the sample is not less than the minimum value of the reference values, which indicates that the region where the coal sample is located in step 201 has the basic condition of rare earth mineralization, and further analysis and prediction can be performed. The model of the mining area of the rare earth elements in the coal can represent the general rule of the distribution of the rare earth mineral resources, and correspondingly, the mining area can represent the area with higher probability of the rare earth mineral resources.
With further reference to fig. 3, fig. 3 illustrates the mineralization zones in the rare earth element mineralization zone model in the coal in an embodiment of the method for predicting a rare earth mineral resource target zone in the coal of fig. 2. As shown in fig. 3, the model of the rare earth element mining area in coal can be in the form of an image, and the mining area zone predicted based on the coal-based mineral data and the geological data of the coal field is marked in the image.
In some optional implementations of this embodiment, the method further includes: and if the sample rare earth content values of all the coal samples in the area to be predicted are smaller than the minimum value in the reference values, determining that the target area of the rare earth mineral resources does not exist in the area to be predicted.
In this implementation manner, the fact that the sample rare earth content value of the coal sample is smaller than the minimum value in the reference values indicates that the position of the coal sample in step 201 does not have the basic condition of rare earth mineralization, and it can be determined that the target region of the rare earth mineral resource does not exist in the position of the coal sample. And if the sample rare earth content values of all coal samples in the area to be predicted are smaller than the minimum value in the reference values, determining that the target area of the rare earth mineral resource does not exist in the area to be detected.
And step 205, if the acquisition range represented by the sample acquisition coordinates and the mining area zone have an overlapping area, predicting the target area of the rare earth mineral resources in the area to be predicted based on the rare earth content value of the sample, the sample acquisition coordinates and the coal quality basic data of a typical coal sample in the overlapping area.
In this embodiment, the collection range represented by the sample collection coordinates may be determined according to the number of sampling points and the interval between adjacent sampling points. The overlapping area of the collection range and the mineralization zone represented by the sample collection coordinates represents an area with a high probability of rare earth mineral resources.
In an example of the constraint, the execution subject may search a rare earth element spatial database for a typical coal sample with a sampling point located in an overlapping region, then mark the acquisition coordinates of the searched typical coal sample and the sample acquisition coordinates in the overlapping region, and determine an overlapping portion of a region surrounded by the acquisition coordinates of the typical coal sample with the highest rare earth content value and the sample acquisition coordinates and the region to be detected as a target region of the rare earth mineral resource in the region to be predicted.
In some optional implementation manners of the embodiment, if there is no overlapping area between the collection range represented by the sample collection coordinate and the mining zone, repeatedly sampling for many times in the collection range represented by the collection coordinate, and measuring the rare earth content value in the coal sample obtained by repeatedly sampling for many times; and if the difference value between the rare earth content value in the coal sample obtained by repeated sampling for multiple times and the rare earth content value of the sample is smaller than a second preset threshold value, updating the rare earth element spatial database and the coal rare earth element mining area model based on the sample acquisition information and the rare earth content value of the sample.
In this implementation manner, the difference between the rare earth content value in the coal sample obtained by repeated sampling for multiple times and the rare earth content value of the sample is smaller than the second preset threshold value, which indicates that the coal sample in step 201 can represent the overall characteristics of the coal in the area, and at this time, the collected data of the coal sample can be imported into the rare earth element spatial database to enrich the data amount in the rare earth element spatial database, and the mineralization zone in the coal rare earth element mineralization zone model is corrected according to the sample collection information, so as to improve the precision of the mineralization zone in the coal rare earth element mineralization zone model, and further improve the accuracy of predicting the target area of the rare earth mineral resource in the coal.
If the difference between the rare earth content value in the coal sample obtained by repeated sampling for multiple times and the rare earth content value of the sample is not less than the second preset threshold, it indicates that the coal sample in step 201 is not enough to characterize the overall characteristics of the coal in the area, and at this time, if the collected data of the coal sample is imported into the rare earth element spatial database, noise data may be introduced into the rare earth element spatial database, thereby affecting the accuracy of the data in the rare earth element spatial database.
According to the method for predicting the target area of the rare earth mineral resources in the coal, provided by the embodiment of the invention, a spatial database of rare earth elements in the coal is retrieved based on the sample acquisition information of coal samples in the area to be predicted; if a reference typical coal sample matched with the sample acquisition information exists in the spatial database of the rare earth elements in the coal, taking the rare earth content value of the reference typical coal sample as a reference value; if the rare earth content value of the sample is not less than the minimum value in the reference values, inputting the sample acquisition coordinates into a pre-constructed coal rare earth element mining area model; if the acquisition range represented by the sample acquisition coordinates and the mining area zone have an overlapping area, predicting the target area of the rare earth mineral resources in the area to be predicted based on the rare earth content value of the sample, the acquisition coordinates and the coal quality basic data of a typical coal sample in the overlapping area. The difficulty of predicting the target area of the rare earth mineral resources in the coal and the dependence on the professional experience of researchers are reduced, and the efficiency and the accuracy of predicting the target area of the rare earth mineral resources in the coal are improved.
With further reference to FIG. 4, step 205 in the above-described embodiment may also employ the flow 400 shown in FIG. 4. The process 400 includes the following steps:
As an example, the execution subject may retrieve coal quality basic data of typical coal samples collected in the overlap region from the rare earth element spatial database using the longitude range and the latitude range of the overlap region as retrieval conditions.
And 403, determining the target area of the rare earth mineral resource from the contour map of the rare earth content element based on a preset prediction threshold.
In this embodiment, the prediction threshold is not greater than the sample rare earth content value. The prediction threshold can be set according to empirical data, and it can be understood that the higher the prediction threshold is, the smaller the predicted range of the target area of the rare earth mineral resource is, the larger the proportion of the distribution range of the rare earth mineral in the corresponding target area of the rare earth mineral resource is and the higher the content of the rare earth element is. The lower the prediction threshold value is, the larger the range of the target area of the predicted rare earth mineral resource is, the smaller the proportion of the distribution range of the rare earth mineral in the target area of the rare earth mineral resource is, and the higher the content of the rare earth element is. ,
as can be seen from fig. 4, the process 400 of the method for predicting the target area of the rare earth mineral resource in coal in this embodiment represents a step of drawing a contour map of rare earth content elements based on the rare earth content value of the sample, the collection coordinates, and the coal quality basic data of a typical coal sample in the overlap area, and using the predicted target area of the rare earth mineral resource, the efficiency and accuracy of predicting the target area of the rare earth mineral resource in coal can be further improved.
Referring next to fig. 5, a flow 500 of constructing a spatial database of rare earth elements in one embodiment of a method for predicting a target area of a rare earth mineral resource in coal is shown. The process 500 includes the following steps:
and 501, acquiring the acquisition information and the coal quality test result of the historical coal sample in the preset area.
In this embodiment, the collected information includes collected coordinates and a coal seam number, and may further include an image and description information. The coal quality test result can comprise the content of various components and elements in the coal sample, and can also comprise position information, geological information, physical properties, chemical properties and the like of the coal sample. As an example, the collected information and coal quality test results of all historical coal samples nationwide may be obtained.
In this embodiment, a rare earth content threshold may be preset according to experience data of rare earth mineralization, the rare earth content being greater than the rare earth content threshold indicates that basic conditions of rare earth mineralization are met, and the rare earth content being not greater than the rare earth content threshold indicates that basic conditions of rare earth mineralization are not met, so that historical coal samples without basic conditions of rare earth mineralization may be filtered out, so as to avoid introducing noise data into a rare earth element spatial database, thereby affecting accuracy of data.
And step 504, combining the collected information of the typical coal sample and the rare earth content value into coal quality basic data of the typical coal sample.
And 505, constructing a rare earth element spatial database based on the corresponding relation between the coal quality basic data and the typical coal sample.
In a specific example, the execution subject may adopt MySQL, and the coal quality basic data is imported into MySQL based on a corresponding relationship between the coal quality basic data and a typical coal sample to obtain a rare earth element spatial database.
In an optional implementation manner of this embodiment, the method further includes: if the reference typical coal sample matched with the sample acquisition information does not exist in the coal rare earth element spatial database, and the rare earth content value of the sample is greater than the rare earth content threshold value, executing the following steps: repeatedly sampling for many times within the collection range represented by the collection coordinates, and measuring the rare earth content value in the coal sample obtained by repeated sampling for many times; and if the difference value between the rare earth content value in the coal sample obtained by repeated sampling for multiple times and the rare earth content value of the sample is smaller than a second preset threshold value, updating the rare earth element spatial database based on the sample acquisition information and the rare earth content value of the sample.
In this implementation manner, by comparing the coal sample obtained in step 201 with the coal sample obtained by repeated sampling, it can be determined whether the coal sample obtained in step 201 can represent the overall characteristics of coal in the area to be predicted, and if the coal sample obtained in step 201 can represent the overall characteristics of coal in the area to be predicted, the sample acquisition information and the sample rare earth content value are introduced into the rare earth element spatial database, so that on one hand, the data amount in the rare earth element spatial database can be enriched, and then, the accuracy of the target area of the rare earth mineral resource in coal can be predicted, and on the other hand, the introduction of noise data can be avoided, and the accuracy of data in the rare earth element spatial database can be affected.
As can be seen from fig. 5, the process 500 of the method for predicting the target area of the rare earth mineral resource in coal in the embodiment represents a step of constructing a rare earth element spatial database based on data of historical coal samples, and may integrate data of the historical coal samples with reference values into the database, and may retrieve a reference typical matched with the data according to the collected information of the coal samples, so as to improve the comparison efficiency between the collected data of the coal samples and the typical coal sample data.
Referring now to fig. 6, a schematic diagram of an electronic device (e.g., the server or terminal device of fig. 1) 600 suitable for use in implementing embodiments of the present invention is shown. The terminal device in the embodiment of the present invention may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 6 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the invention include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the invention. It should be noted that the computer readable medium in the embodiments of the present invention may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present invention, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring sample acquisition information of a coal sample in an area to be predicted and a sample rare earth content value in the coal sample, wherein the sample acquisition information comprises a sample acquisition coordinate and a sample coal seam number; based on sample acquisition information, retrieving a spatial database of rare earth elements in coal, wherein the spatial database of rare earth elements in coal is constructed based on coal quality basic data of typical coal samples in a preset area, and the coal quality basic data comprises acquisition information and rare earth content values of the typical coal samples; if a reference typical coal sample matched with the sample acquisition information exists in the spatial database of the rare earth elements in the coal, taking the rare earth content value of the reference typical coal sample as a reference value, wherein the reference typical coal sample meets the following conditions: the preset well field range or the preset mining area range represented by the collection information of the reference typical coal sample is the same as the preset well field range or the preset mining area range where the sample collection coordinate is located, and the difference value between the coal seam number in the collection information of the reference typical coal sample and the sample coal seam number is smaller than a first preset threshold value; if the rare earth content value of the sample is not less than the minimum value in the reference values, inputting the sample acquisition coordinates into a pre-constructed coal rare earth element mining area model, wherein the coal rare earth element mining area model comprises a mining area predicted based on coal-series mineral data and coal field geological data; if the acquisition range represented by the sample acquisition coordinates and the mining area zone have an overlapping area, predicting the target area of the rare earth mineral resources in the area to be predicted based on the rare earth content value of the sample, the acquisition coordinates and the coal quality basic data of a typical coal sample in the overlapping area.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will readily appreciate that the above-described preferred embodiments may be freely combined, superimposed, without conflict.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for predicting a rare earth mineral resource target in coal, the method comprising:
acquiring sample acquisition information of a coal sample in an area to be predicted and a sample rare earth content value in the coal sample, wherein the sample acquisition information comprises a sample acquisition coordinate and a sample coal seam number;
retrieving a coal rare earth element spatial database based on the sample acquisition information, wherein the coal rare earth element spatial database is constructed based on coal quality basic data of typical coal samples in a preset region, and the coal quality basic data comprises acquisition information and rare earth content values of the typical coal samples;
if a reference typical coal sample matched with the sample acquisition information exists in the coal rare earth element spatial database, taking the rare earth content value of the reference typical coal sample as a reference value, wherein the reference typical coal sample meets the following conditions: the preset well field range or the preset mining area range represented by the acquisition information of the reference typical coal sample is the same as the preset well field range or the preset mining area range where the sample acquisition coordinate is located, and the difference value between the coal seam number in the acquisition information of the reference typical coal sample and the sample coal seam number is smaller than a first preset threshold value;
if the rare earth content value of the sample is not less than the minimum value in the reference values, inputting the sample acquisition coordinates into a pre-constructed coal rare earth element mining area model, wherein the coal rare earth element mining area model comprises a mining area predicted based on coal-series mineral data and coal field geological data;
if the collection range represented by the sample collection coordinate and the mining zone have an overlapping region, predicting the target area of the rare earth mineral resources in the region to be predicted based on the sample rare earth content value, the collection coordinate and the coal quality basic data of a typical coal sample in the overlapping region.
2. The method of claim 1, wherein predicting the rare earth mineral resource target area within the area to be predicted based on the sample rare earth content values, the acquisition coordinates, and coal quality base data of typical coal samples in the overlapping region comprises:
retrieving coal quality basic data of typical coal samples collected in the overlapping region from the rare earth element spatial database;
generating a rare earth content element contour map based on the sample rare earth content value, the sample collection coordinates and coal quality basic data of a typical coal sample in the overlapping area;
and determining a target area of the rare earth mineral resource from the contour map of the rare earth content elements based on a preset prediction threshold, wherein the prediction threshold is not larger than the rare earth content value of the sample.
3. The method according to claim 1, wherein the rare earth element spatial database is constructed based on the following steps:
acquiring the acquisition information and the coal quality test result of the historical coal sample in the preset area;
extracting the rare earth content value of each historical coal sample from the coal quality test result;
if the rare earth content value of the historical coal sample is larger than a preset rare earth content threshold value, determining the historical coal sample as a typical coal sample;
combining the collected information of the typical coal sample and the rare earth content value into coal quality basic data of the typical coal sample;
and constructing a rare earth element spatial database based on the corresponding relation between the coal quality basic data and the typical coal sample.
4. The method of claim 3, further comprising:
if the reference typical coal sample matched with the sample acquisition information does not exist in the coal rare earth element spatial database, and the sample rare earth content value is greater than the rare earth content threshold value, executing the following steps:
repeatedly sampling for many times in the collection range represented by the collection coordinates, and measuring the rare earth content value in the coal sample obtained by repeatedly sampling for many times;
and if the difference value between the rare earth content value in the coal sample obtained by repeated sampling for multiple times and the rare earth content value of the sample is smaller than a second preset threshold value, updating the rare earth element spatial database based on the sample acquisition information and the rare earth content value of the sample.
5. The method of claim 1, further comprising:
if the acquisition range represented by the sample acquisition coordinate and the mining zone do not have an overlapping area, repeatedly sampling for many times in the acquisition range represented by the acquisition coordinate, and measuring the rare earth content value in the coal sample obtained by repeated sampling for many times;
and if the difference value between the rare earth content value in the coal sample obtained by repeated sampling for multiple times and the rare earth content value of the sample is smaller than a second preset threshold value, updating the rare earth element spatial database and the coal rare earth element mining area model based on the sample acquisition information and the rare earth content value of the sample.
6. The method of claim 1, further comprising:
and if the sample rare earth content values of all the coal samples in the area to be predicted are smaller than the minimum value in the reference values, determining that the target area of the rare earth mineral resources does not exist in the area to be predicted.
7. The method of claim 1, wherein the coal samples comprise coal in a mineable coal seam, gangue and coal seam roof and floor, organic shale, and associated mineral production-bearing formation rock.
8. The method of any one of claims 1 to 7, wherein the sample collection information further comprises an image and descriptive information of the coal sample.
9. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method for predicting rare earth mineral resource targets in coal of any of claims 1-8.
10. A computer-readable medium, on which a computer program is stored, which program, when executed by a processor, implements a method for predicting a rare earth mineral resource target in coal as claimed in any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113588919A (en) * | 2021-07-23 | 2021-11-02 | 昆明理工大学 | Prediction method suitable for ore prospecting of granite type rare metal deposit |
| CN116992339A (en) * | 2023-09-27 | 2023-11-03 | 华御祥茶科学研究院(深圳)有限公司 | Method for detecting trace elements in tea based on element spectrum |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102539194A (en) * | 2010-12-29 | 2012-07-04 | 中国石油天然气集团公司 | Gradient geochemical exploration method |
| WO2018212680A1 (en) * | 2017-05-17 | 2018-11-22 | Mineral Exploration Network (Finland) Ltd. | Geochemical method for searching mineral resource deposits |
| CN111860983A (en) * | 2020-07-06 | 2020-10-30 | 中国煤炭地质总局勘查研究总院 | Method for estimating associated metal resource quantity in coal |
-
2021
- 2021-05-13 CN CN202110520269.2A patent/CN112948445B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102539194A (en) * | 2010-12-29 | 2012-07-04 | 中国石油天然气集团公司 | Gradient geochemical exploration method |
| US20130327125A1 (en) * | 2010-12-29 | 2013-12-12 | Zhanxiang He | Method for geochemical grandient exploration |
| WO2018212680A1 (en) * | 2017-05-17 | 2018-11-22 | Mineral Exploration Network (Finland) Ltd. | Geochemical method for searching mineral resource deposits |
| CN111860983A (en) * | 2020-07-06 | 2020-10-30 | 中国煤炭地质总局勘查研究总院 | Method for estimating associated metal resource quantity in coal |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113588919A (en) * | 2021-07-23 | 2021-11-02 | 昆明理工大学 | Prediction method suitable for ore prospecting of granite type rare metal deposit |
| CN116992339A (en) * | 2023-09-27 | 2023-11-03 | 华御祥茶科学研究院(深圳)有限公司 | Method for detecting trace elements in tea based on element spectrum |
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