CN117290718B - Geological mineral exploration data extraction method and system - Google Patents
Geological mineral exploration data extraction method and system Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 72
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- 239000011707 mineral Substances 0.000 title claims abstract description 36
- 238000013075 data extraction Methods 0.000 title claims abstract description 19
- 238000011835 investigation Methods 0.000 claims abstract description 194
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- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000006837 decompression Effects 0.000 abstract description 4
- 238000000513 principal component analysis Methods 0.000 description 11
- 230000005484 gravity Effects 0.000 description 4
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- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
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Abstract
The invention relates to the technical field of data coding compression, in particular to a geological mineral exploration data extraction method and system. The method comprises the steps of obtaining investigation data at different moments and constructing a data matrix; acquiring a change degree value of the investigation data according to the difference of the investigation data at every two adjacent moments; acquiring the segmentation strength of the investigation data according to the difference of the change degree value between each investigation data and other investigation data, determining the segmentation time, and segmenting the data matrix to obtain an interval matrix; acquiring a corrected covariance matrix according to the division intensity of the investigation data in the interval matrix, and further acquiring one-dimensional data of the interval matrix; and determining the termination time of the revolving door compression algorithm, and determining the duration of each section of compressed survey data. According to the method, the termination time of the revolving door compression algorithm is determined, so that the actual change trend of the compressed investigation data is still reserved after decompression, and further, the analysis of geological mineral products is more accurate.
Description
Technical Field
The invention relates to the technical field of data coding compression, in particular to a geological mineral exploration data extraction method and system.
Background
Geological mineral survey data refers to various data used to study and evaluate the potential of geological mineral resources during the survey, including ground electricity data, magnetic method data, gravity data, conductivity data, potential data, and electromagnetic method data. Because the geological exploration equipment has weaker storage performance and smaller storage space, the obtained various exploration data needs to be compressed first and then stored.
In the existing method, the survey data under each time is compressed and stored through a revolving door compression algorithm, the compression efficiency is improved as much as possible while the change trend of the mineral survey data is maintained, but due to the fact that the different compression durations of the survey data under different time points, the obvious change trend of the survey data under part of time points is influenced by the change trend of the survey data under other time points after the compression of the survey data is decompressed, the obvious change trend is linearly fitted, and errors exist when the geological mineral is analyzed according to the coordination of the decompressed survey data, and the potential evaluation of geological mineral resources is inaccurate.
Disclosure of Invention
In order to solve the technical problem that errors exist between decompressed investigation data and actual investigation data due to inaccurate compression termination time of a revolving door compression algorithm, the invention aims to provide a geological mineral investigation data extraction method and system, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a geological mineral survey data extraction method, the method comprising the steps of:
acquiring investigation data at different moments in a set time period, and constructing a data matrix;
acquiring a change degree value of each piece of investigation data at each time according to the difference between the same piece of investigation data at each two adjacent times; acquiring the segmentation strength of each piece of investigation data at each moment according to the difference of the change degree value between each piece of investigation data at each moment and other pieces of investigation data in a preset time period before and after each moment;
determining a segmentation moment according to the segmentation strength; dividing the data matrix according to the dividing time to obtain an interval matrix;
acquiring a correction covariance matrix of each interval matrix according to the segmentation intensity of each exploration data in each interval matrix; acquiring one-dimensional data of each interval matrix according to the corrected covariance matrix;
determining the termination time of a revolving door compression algorithm according to the data distribution in the one-dimensional data; and determining the duration of each section of compressed survey data according to the termination time.
Further, the method for obtaining the change degree value comprises the following steps:
normalizing each investigation data at each time as a target result;
sequencing any two adjacent moments according to the time sequence to obtain a moment sequence;
and taking the difference value between the target result of the ith investigation data at the later time in the time sequence and the target result of the ith investigation data at the former time as the change degree value of the ith investigation data at the later time.
Further, the method for obtaining the segmentation strength of each piece of investigation data at each moment according to the difference of the change degree value between each piece of investigation data and other investigation data at each moment in a preset time period before and after each moment comprises the following steps:
the formula for acquiring the first difference characteristic value of the ith investigation data in the adjacent preset time period before the ith moment is as follows:；
the formula for acquiring the second difference characteristic value of the ith investigation data in the adjacent preset time period after the ith moment is as follows:；
the formula for acquiring the segmentation strength of the ith investigation data at the t moment is as follows:；
in the method, in the process of the invention,a first difference characteristic value of the ith investigation data at the t-th moment; />Is->A variation degree value of the u-th investigation data at each moment; u is the total amount of survey data at each moment; />Is->Time of day->The degree of change values of the individual survey data; h is the total number of times of acquiring the investigation data respectively in the adjacent preset time periods before and after the t moment, wherein the acquisition of the investigation data for one time corresponds to one moment; />A second difference characteristic value of the ith investigation data at the t-th moment; />Is->A variation degree value of the u-th investigation data at each moment; />Is->Time of day->The degree of change values of the individual survey data; />The segmentation strength of the ith investigation data at the t moment; />As a function of absolute value.
Further, the method for acquiring the segmentation time comprises the following steps:
the result of accumulating the segmentation intensity of each investigation data at each time is used as the integral segmentation intensity at each time;
normalizing the integral segmentation intensity at each moment to obtain an integral result;
when the overall result is greater than or equal to a preset segmentation threshold, the corresponding moment is taken as the segmentation moment.
Further, the method for constructing the data matrix comprises the following steps:
arranging each exploration data under each time according to the same sequence to obtain an exploration data sequence under each time;
and according to the time sequence, taking the investigation data sequence at each moment as each column of the data matrix, and sequentially putting the investigation data sequence into the data matrix to construct the data matrix.
Further, the method for dividing the data matrix according to the dividing time to obtain the interval matrix comprises the following steps:
the column of the investigation data sequence under each dividing moment in the data matrix is used as the ending column of the dividing of the data matrix, and the divided data matrix is obtained;
and taking the data matrix after each block is divided as an interval matrix.
Further, the method for obtaining the correction covariance matrix comprises the following steps:
normalizing the segmentation intensity of each investigation data in each interval matrix to obtain a first result;
acquiring a covariance matrix of each interval matrix, and acquiring a product of first results of the same kind of investigation data between two columns of elements of the interval matrix corresponding to each element of the covariance matrix as a first value;
acquiring the average value of all the first values corresponding to each element of the covariance matrix, and taking the average value as the correction weight of the corresponding element in the covariance matrix;
obtaining the product of each element of the covariance matrix and the corresponding correction weight to be used as the correction value of the corresponding element in the covariance matrix;
and replacing each element in the covariance matrix with a corresponding correction value to obtain a matrix formed by the correction values, and taking the matrix as a correction covariance matrix of each interval matrix.
Further, the one-dimensional data acquisition method comprises the following steps:
acquiring a characteristic value and a characteristic vector of each correction covariance matrix, and sequentially taking the characteristic value and the characteristic vector as a correction characteristic value and a correction characteristic vector;
and acquiring one-dimensional data of the corresponding interval matrix through a PCA dimension reduction algorithm according to the correction eigenvalue and the correction eigenvector of each correction covariance matrix.
Further, the method for determining the termination time of the revolving door compression algorithm according to the data distribution in the one-dimensional data comprises the following steps:
acquiring abnormal data in each piece of one-dimensional data through an outlier detection algorithm;
and taking the moment corresponding to the abnormal data as the termination moment of the revolving door compression algorithm.
In a second aspect, another embodiment of the present invention provides a geological mineral survey data extraction system comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of any one of the methods described above when executing the computer program.
The invention has the following beneficial effects:
acquiring the investigation data at different moments in a set time period, constructing a data matrix, and connecting the investigation data at each moment so as to facilitate the subsequent acquisition of an interval matrix; according to the difference between the same investigation data at every two adjacent moments, the change degree value of each investigation data at each moment is obtained, the change trend of the investigation data is reflected preliminarily, and the acquisition of the segmentation moment is taken as accurate; therefore, according to the difference of the change degree value between each piece of investigation data and other pieces of investigation data at each moment in a preset time period before and after each moment, the division strength of each piece of investigation data at each moment is obtained, the change condition of each piece of investigation data is determined, the division moment is further determined, a data matrix is divided, a section matrix is obtained, so that investigation data with similar change trend are placed in the same section, the influence of different pieces of investigation data at different moments is avoided, and the one-dimensional data of the section matrix is inaccurate; further, according to the segmentation strength of each investigation data in each interval matrix, a correction covariance matrix of each interval matrix is obtained, one-dimensional data of each interval matrix is accurately obtained, and the one-dimensional data accurately represents the change trend of the investigation data in the interval matrix; and further, according to the data distribution in the one-dimensional data, the termination time of the revolving door compression algorithm is determined, so that the important investigation data is not influenced by other investigation data after being compressed, the error between the compressed investigation data and the actual investigation data is reduced, the decompressed investigation data can still accurately analyze the geological mineral products, and the potential of the geological mineral products is accurately estimated.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a geological mineral exploration data extraction method according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of a specific implementation, structure, characteristics and effects of a geological mineral exploration data extraction method according to the invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the geological mineral exploration data extraction method provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of a geological mineral exploration data extraction method according to an embodiment of the invention is shown, the method includes the following steps:
step S1: and acquiring investigation data at different moments in a set time period, and constructing a data matrix.
In particular, the survey data collected during the geological mineral exploration process is limited by small storage capacity of the exploration equipment, so that the survey data needs to be compressed and stored. The revolving door compression algorithm can better reserve the trend of the investigation data, so that the compressed data can better restore the change trend of the original data, but the investigation data is different at each moment, therefore, the duration of the revolving door compression algorithm for compressing the investigation data is inaccurate, the change trend of the investigation data at partial moments is easily influenced by the change trend of the investigation data at other moments, the obvious change trend of the investigation data is linearly fitted, errors exist between the decompressed investigation data and the practically collected investigation data, and further, the potential evaluation of geological mineral resources is inaccurate. Therefore, the embodiment of the invention obtains the one-dimensional data in a certain time period through the PCA (Principal Component Analysis) dimension reduction algorithm, obtains the abnormal data by using the outlier detection algorithm on the one-dimensional data, and takes the moment corresponding to the abnormal data as the termination moment of the revolving door compression algorithm to adaptively obtain the duration of each section of compressed investigation data, so that the compressed investigation data is closer to the actually collected investigation data after decompression. The revolving door compression algorithm, the PCA (Principal Component Analysis) dimension reduction algorithm and the outlier detection algorithm are all existing algorithms, and are not described herein.
In order to better illustrate the time length of acquiring each section of compressed survey data, the embodiment of the invention takes the time length of 3 hours as an example, namely, the time length of setting the time period to be 3 hours, and adaptively acquiring the time length of the compressed survey data within 3 hours, and an implementer can set the time length of the time period according to actual conditions without limitation. In order to improve efficiency, survey data are acquired at corresponding moments every 3 minutes, and each acquisition of the survey data comprises ground electricity data, magnetic method data, gravity data, conductivity data, potential data and electromagnetic method data. The time interval for acquiring the survey data can be set by the practitioner according to the actual situation, and is not limited herein. In order to better analyze the change trend of the investigation data and further determine the duration of compressing the investigation data, the embodiment of the invention arranges each investigation data under each time according to the same sequence to obtain the investigation data sequence under each time. For example, the survey data at each time is sorted in the order of the ground data, the magnetic data, the gravity data, the conductivity data, the potential data and the electromagnetic data, and the obtained survey data sequence at each time is { ground data, magnetic data, gravity data, conductivity data, potential data, electromagnetic data }. And according to the time sequence, taking the investigation data sequence at each moment as each column of the data matrix, and sequentially putting the investigation data sequence into the data matrix to construct the data matrix.
Step S2: acquiring a change degree value of each piece of investigation data at each time according to the difference between the same piece of investigation data at each two adjacent times; and acquiring the segmentation strength of each piece of investigation data at each moment according to the difference of the change degree value between each piece of investigation data at each moment and other pieces of investigation data in a preset time period before and after each moment.
Specifically, the change of the same type of investigation data under different moments is analyzed, the segmentation strength of each investigation data under each moment is obtained, the segmentation moment is further determined, the data matrix is segmented, the influence among the investigation data under different moments is avoided, and the change trend of the investigation data is kept. The method for acquiring the segmentation strength of each investigation data at each time is as follows:
(1) And obtaining a change degree value.
Acquiring the difference between the same kind of investigation data at every two adjacent moments, and preliminarily determining the change condition of each kind of investigation data, wherein the smaller the difference between the same kind of investigation data at every two adjacent moments is, the smaller the change degree of the same kind of investigation data is; the larger the difference between the same kind of investigation data at every two adjacent moments, the larger the degree of change of the same kind of investigation data is, and in order to avoid influencing obvious change trend of the investigation data, the less likely the corresponding two adjacent moments are in the same time period of compressing the investigation data.
Preferably, the method for obtaining the change degree value is as follows: normalizing each investigation data at each time as a target result; sequencing any two adjacent moments according to the time sequence to obtain a moment sequence; and taking the difference value between the target result of the ith investigation data at the later time in the time sequence and the target result of the ith investigation data at the former time as the change degree value of the ith investigation data at the later time.
As an example, the normalized result of each survey data at each time is obtained, that is, the target result. Taking the t moment and the t-1 moment as examples, the time sequence corresponding to the t moment and the t-1 moment is { t-1, t }, and the u-th exploration data at the t moment and the u-th exploration data at the t-1 moment are obtained, wherein the u-th exploration data at the t moment and the u-th exploration data at the t-1 moment are the same exploration data. The formula for obtaining the variation degree value of the ith investigation data at the t-th moment is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->The change degree value of the ith investigation data at the t-th moment; />Target results of the ith survey data at the ith time; />Is the target result of the ith survey data at the t-1 th time.
It should be noted that the number of the substrates,the more toward 0, say->And->The more equal, the less the trend of the survey data changes between the t-th time and the t-1 th time; />The farther from 0, say->And->The greater the difference between the time and the time, the greater the trend of the survey data change between the time, t, and the time, t-1, the less likely it is that the time, t, and the time, t-1, will be within the same time period of compressed survey data. Wherein (1)>And->The values of (2) are all 0,1, therefore,/->The value range of (C) is [ -1,1]。
And acquiring the change degree value of each piece of investigation data at each moment according to the method for acquiring the change degree value of the ith investigation data at the t moment. It should be noted that, since the change degree value cannot be obtained for each of the survey data at the first time, the change degree value for each of the survey data at the first time is not calculated.
(2) The segmentation strength is obtained.
In order to determine the change trend of each kind of investigation data, according to the difference of the change degree value between each kind of investigation data and other investigation data in each time in the adjacent preset time period before and after each moment, the embodiment of the invention observes the change difference of each kind of investigation data in the adjacent preset time period before and after, obtains the division strength of each kind of investigation data in each time, and then divides the investigation data with different change trends.
As an example, taking the u-th survey data at the t-th moment as an example, taking the t-th moment as a starting point, sequentially acquiring the moments 20 times from the t-th moment before and after the t-th moment as the first pre-preset moment and the first post-preset moment in sequence. The time period between the first preset time and the t time is the adjacent preset time period before the t time; the time period between the t-th time and the first rear preset time is an adjacent preset time period after the t-th time. The number of times from the t-th time, that is, the preset time periods adjacent before and after the t-th time, may be set by the practitioner according to the actual situation, and is not limited herein. Acquiring the average value of the change degree values of other investigation data except the ith investigation data, namely the ith investigation data, in each time in the adjacent preset time periods before and after the t moment, and taking the average value as the target average value in each time in the adjacent preset time periods before and after the t moment; according to the difference value between the variation degree value of the u-th exploration data at each time in the adjacent preset time period before the t-th time and the target average value, a first difference characteristic value of the u-th exploration data in the adjacent preset time period before the t-th time is obtained. According to the difference value between the variation degree value of the ith investigation data at each time point in the adjacent preset time period after the t moment and the target mean value, acquiring a second difference characteristic value of the ith investigation data at the t moment in the adjacent preset time period after the t moment, thereby acquiring a formula of a first difference characteristic value of the ith investigation data at the t moment as followsThe method comprises the steps of carrying out a first treatment on the surface of the The formula for acquiring the second difference characteristic value of the ith investigation data at the t moment is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->A first difference characteristic value of the ith investigation data at the t-th moment; />Is->A variation degree value of the u-th investigation data at each moment; u is the total amount of survey data at each moment; />Is->Time of day->The degree of change values of the individual survey data; h is the total number of times of acquiring the investigation data respectively in the adjacent preset time periods before and after the t moment, wherein the acquisition of the investigation data for one time corresponds to one moment; />A second difference characteristic value of the ith investigation data at the t-th moment;is->A variation degree value of the u-th investigation data at each moment; />Is->Time of day->The degree of change values of the individual survey data; />And->All are target average values;the first preset time is the first preset time; />Is the first post preset time.
H is substantially the number of times respectively contained in the adjacent preset time periods before and after the t-th time, and is set to 20 in the embodiment of the present invention, so h=20.
It should be noted that the number of the substrates,and (3) withThe more toward 0, say->And->The closer,And->The closer, the%>Time Down and->The more the change degree value of the u-th investigation data at the moment is close to the change degree value in the preset time period adjacent to the t-th moment, the more stable the change trend of the u-th investigation data in the preset time period adjacent to the t-th moment and the next moment is indirectly indicated>And->The more towards 0;and->The farther from 0, explainAnd->The greater the difference between +.>And->The larger the difference between the values is, the more unstable the variation trend of the u-th investigation data in the adjacent preset time period before and after the t-th moment is indirectly indicated, and the more unstable the variation trend of the u-th investigation data is +.>And->The farther from 0. Wherein (1)>And->Are rational numbers.
Acquiring the difference between the first difference characteristic value and the second difference characteristic value of the ith investigation data at the t moment, when the difference between the first difference characteristic value and the second difference characteristic value is larger, the more the difference of the variation trend of the ith investigation data in the adjacent preset time period before and after the t moment is larger, the more likely to be the segmentation moment at the t moment, and avoiding the influence between the investigation data in the adjacent preset time period before and after the t moment, thereby leading to obvious variation of the investigation dataThe trend was linearly fitted. Therefore, according to the difference between the first difference characteristic value and the second difference characteristic value of the ith investigation data at the ith moment, the formula for obtaining the segmentation strength of the ith investigation data at the ith moment is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->The segmentation strength of the ith investigation data at the t moment; />A first difference characteristic value of the ith investigation data at the t-th moment; />A second difference characteristic value of the ith investigation data at the t-th moment; />As a function of absolute value.
It should be noted that the number of the substrates,the bigger the->And->The greater the difference between the first and second points, the more different the variation trend of the ith investigation data in the preset time period adjacent to the first point is from the variation trend of the ith investigation data in the preset time period adjacent to the second point is, the more likely the first point is a division point, and the second point is a division point>The larger; thus (S)>The larger the time t is, the more likely it is the split time.
And acquiring the segmentation strength of each piece of investigation data at each moment according to the segmentation strength of the ith investigation data at the t moment.
Step S3: determining a segmentation moment according to the segmentation strength; and dividing the data matrix according to the dividing time to obtain an interval matrix.
Specifically, in order to accurately acquire the segmentation time, the data matrix is segmented, so that investigation data with similar variation trend are divided into the same interval matrix, one-dimensional data of each interval matrix is acquired, and the termination time of the revolving door compression algorithm is accurately and efficiently acquired.
The embodiment of the invention takes the result of accumulating the segmentation intensity of each investigation data at each moment as the whole segmentation intensity at each moment. Normalizing the integral segmentation intensity at each moment to obtain an integral result; when the overall result is greater than or equal to a preset segmentation threshold, the corresponding moment is taken as the segmentation moment. In the embodiment of the invention, the preset dividing threshold is set to be 0.75, and the practitioner can set the dividing threshold according to the actual situation, so that the method is not limited. And taking the column of the investigation data sequence under each division time in the data matrix as the termination column of the data matrix division to obtain a divided data matrix. And taking the data matrix after each block is divided as an interval matrix. Thus, the interval matrix with similar change trend of the investigation data is obtained, and the relation between the investigation data at different moments is enhanced.
Step S4: acquiring a correction covariance matrix of each interval matrix according to the segmentation intensity of each exploration data in each interval matrix; and acquiring one-dimensional data of each interval matrix according to the corrected covariance matrix.
Specifically, a covariance matrix of each interval matrix is obtained, in order to accurately obtain one-dimensional data of each interval matrix, in the embodiment of the invention, each element in the covariance matrix is corrected according to the segmentation strength of each element in the interval matrix, a corrected covariance matrix of each interval matrix is obtained, and one-dimensional data of each interval matrix is obtained through a PCA (Principal Component Analysis) dimension reduction algorithm according to the eigenvalue and eigenvector of the corrected covariance matrix.
Preferably, the method for obtaining the corrected covariance matrix is as follows: normalizing the segmentation intensity of each investigation data in each interval matrix to obtain a first result; acquiring a covariance matrix of each interval matrix, and acquiring a product of first results of the same kind of investigation data between two columns of elements of the interval matrix corresponding to each element of the covariance matrix as a first value; acquiring the average value of all the first values corresponding to each element of the covariance matrix, and taking the average value as the correction weight of the corresponding element in the covariance matrix; obtaining the product of each element of the covariance matrix and the corresponding correction weight to be used as the correction value of the corresponding element in the covariance matrix; and replacing each element in the covariance matrix with a corresponding correction value to obtain a matrix formed by the correction values, and taking the matrix as a correction covariance matrix of each interval matrix.
Taking the S-th interval matrix as an example, the segmentation intensity of each element in the S-th interval matrix, that is, each piece of investigation data, is normalized by maximum and minimum normalization, that is, the first result of each piece of investigation data in the S-th interval matrix is obtained, and in another embodiment of the present invention, the segmentation intensity of each piece of investigation data in the S-th interval matrix may be normalized by normalization methods such as sigmoid function, function transformation, and the like, which is not limited herein. Acquiring a covariance matrix of an S-th interval matrix, wherein each two columns in the S-th interval matrix corresponding to each element of the covariance matrix are known, and the same column can be taken, so that a product, namely a first value, of a first result of the same kind of investigation data between two columns of elements of the interval matrix corresponding to each element of the covariance matrix is acquired; and acquiring the average value of all the first values corresponding to each element of the covariance matrix, and taking the average value as the correction weight of the corresponding element in the covariance matrix. And obtaining the product of each element of the covariance matrix and the corresponding correction weight, namely the correction value of the corresponding element in the covariance matrix. Taking the x-th row and y-th column elements in the covariance matrix of the S-th interval matrix as an example, the formula for obtaining the correction value of the x-th row and y-th column elements in the covariance matrix of the S-th interval matrix is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the invention,correction values for the x-th row and y-th column elements in the covariance matrix of the S-th interval matrix;the x row and y column elements in the covariance matrix of the S interval matrix; u is the total amount of survey data at each moment; />A first result of the survey data for the ith row and the xth column in the sth interval matrix; />A first result of the data for the ith row and the jth column of the ith interval matrix; />Is a first value;to correct the weights.
The first value is thatThe larger the correction weight +.>The greater the by correction weight +.>For->Make adjustments, and/or>The larger; thus (S)>The larger the influence degree of the elements of the x row and the y column in the covariance matrix of the S interval matrix on the acquisition of the one-dimensional data of the S interval matrix is larger.
And acquiring the correction value of each element in the covariance matrix of the S interval matrix according to the correction value of the x row and y column elements in the covariance matrix of the S interval matrix. And replacing each element in the covariance matrix of the S interval matrix with a corresponding correction value, wherein the matrix formed by the correction values is the corrected covariance matrix of the S interval matrix.
And according to the method for acquiring the corrected covariance matrix of the S interval matrix, acquiring the corrected covariance matrix of each interval matrix. Acquiring a characteristic value and a characteristic vector of each correction covariance matrix, and sequentially taking the characteristic value and the characteristic vector as a correction characteristic value and a correction characteristic vector of each correction covariance matrix; substituting the correction eigenvalue and the correction eigenvector of each correction covariance matrix into a PCA (Principal Component Analysis) dimension reduction algorithm to obtain one-dimensional data of the corresponding interval matrix. Thus, one-dimensional data of each section matrix is acquired.
Step S5: determining the termination time of a revolving door algorithm according to the data distribution in the one-dimensional data; and determining the duration of each section of compressed survey data according to the termination time.
Specifically, the PCA (Principal Component Analysis) dimension reduction algorithm converts multidimensional data in the interval matrix into one-dimensional data, reduces the data quantity to be processed, can remove redundant features, reduces noise interference, enables the change trend of investigation data in the interval matrix to be more obvious, and is easier to identify data with large change degree, further determines the termination time of the revolving door compression algorithm, and obtains the time length of each compression of the revolving door compression algorithm.
Preferably, the method for determining the termination time of the revolving door compression algorithm is as follows: acquiring abnormal data in each piece of one-dimensional data through an outlier detection algorithm; and taking the moment corresponding to the abnormal data as the termination moment of the revolving door algorithm.
Starting from the first moment of the set time period, the method compresses the investigation data under each moment by using the revolving door compression algorithm until the ending moment appears, and takes the investigation data under the ending moment as the last group of data compressed for the first time by the revolving door compression algorithm, so that the duration between the first moment of the set time period and the ending moment appearing first is the duration of the first compression of the investigation data by the revolving door compression algorithm. According to the method for acquiring the time length of the first compression of the investigation data by the revolving door compression algorithm, the time length of each compression of the investigation data by the revolving door compression algorithm is acquired, so that the change trend of the actually acquired investigation data can be reserved after decompression of each section of compressed investigation data, and further, the analysis and evaluation of geological mineral resources are more accurate.
The present invention has been completed.
In summary, the embodiment of the invention acquires the investigation data at different moments and constructs the data matrix; acquiring a change degree value of the investigation data according to the difference of the investigation data at every two adjacent moments; acquiring the segmentation strength of the investigation data according to the difference of the change degree value between each investigation data and other investigation data, determining the segmentation time, and segmenting the data matrix to obtain an interval matrix; acquiring a corrected covariance matrix according to the division intensity of the investigation data in the interval matrix, and further acquiring one-dimensional data of the interval matrix; and determining the termination time of the revolving door compression algorithm, and determining the duration of each section of compressed survey data. According to the method, the termination time of the revolving door compression algorithm is determined, so that the actual change trend of the compressed investigation data is still reserved after decompression, and further, the analysis of geological mineral products is more accurate.
Based on the same inventive concept as the above method embodiment, the embodiment of the present invention further provides a geological mineral exploration data extraction system, which includes: the system comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of one of the embodiments of the geological mineral survey data extraction method described above, such as the steps shown in fig. 1. The method for extracting geological mineral exploration data is described in detail in the above embodiments, and will not be described again.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (9)
1. A method for extracting geological mineral exploration data, the method comprising the steps of:
acquiring investigation data at different moments in a set time period, and constructing a data matrix;
acquiring a change degree value of each piece of investigation data at each time according to the difference between the same piece of investigation data at each two adjacent times; acquiring the segmentation strength of each piece of investigation data at each moment according to the difference of the change degree value between each piece of investigation data at each moment and other pieces of investigation data in a preset time period before and after each moment;
determining a segmentation moment according to the segmentation strength; dividing the data matrix according to the dividing time to obtain an interval matrix;
acquiring a correction covariance matrix of each interval matrix according to the segmentation intensity of each exploration data in each interval matrix; acquiring one-dimensional data of each interval matrix according to the corrected covariance matrix;
determining the termination time of a revolving door compression algorithm according to the data distribution in the one-dimensional data; determining the duration of each section of compressed survey data according to the termination time;
the method for acquiring the segmentation strength of each piece of investigation data at each moment according to the difference of the change degree value between each piece of investigation data and other investigation data at each moment in a preset time period before and after each moment comprises the following steps:
the formula for acquiring the first difference characteristic value of the ith investigation data in the adjacent preset time period before the ith moment is as follows:；
the formula for acquiring the second difference characteristic value of the ith investigation data in the adjacent preset time period after the ith moment is as follows:；
the formula for acquiring the segmentation strength of the ith investigation data at the t moment is as follows:；
in the method, in the process of the invention,a first difference characteristic value of the ith investigation data at the t-th moment; />Is->A variation degree value of the u-th investigation data at each moment; u is the total amount of survey data at each moment; />Is->Time of day->The degree of change values of the individual survey data; h is the total number of times of acquiring the investigation data respectively in the adjacent preset time periods before and after the t moment, wherein the acquisition is onceThe investigation data corresponds to a moment; />A second difference characteristic value of the ith investigation data at the t-th moment; />Is->A variation degree value of the u-th investigation data at each moment; />Is->Time of day->The degree of change values of the individual survey data; />The segmentation strength of the ith investigation data at the t moment; />As a function of absolute value.
2. The geological mineral exploration data extraction method of claim 1, wherein the variation value obtaining method comprises the following steps:
normalizing each investigation data at each time as a target result;
sequencing any two adjacent moments according to the time sequence to obtain a moment sequence;
and taking the difference value between the target result of the ith investigation data at the later time in the time sequence and the target result of the ith investigation data at the former time as the change degree value of the ith investigation data at the later time.
3. The geological mineral exploration data extraction method of claim 1, wherein the acquisition method of the segmentation time is as follows:
the result of accumulating the segmentation intensity of each investigation data at each time is used as the integral segmentation intensity at each time;
normalizing the integral segmentation intensity at each moment to obtain an integral result;
when the overall result is greater than or equal to a preset segmentation threshold, the corresponding moment is taken as the segmentation moment.
4. A method of geological mineral survey data extraction as claimed in claim 1, wherein said method of constructing a data matrix comprises:
arranging each exploration data under each time according to the same sequence to obtain an exploration data sequence under each time;
and according to the time sequence, taking the investigation data sequence at each moment as each column of the data matrix, and sequentially putting the investigation data sequence into the data matrix to construct the data matrix.
5. The method for extracting geological mineral exploration data according to claim 4, wherein the method for dividing the data matrix according to the dividing time to obtain the interval matrix comprises the following steps:
the column of the investigation data sequence under each dividing moment in the data matrix is used as the ending column of the dividing of the data matrix, and the divided data matrix is obtained;
and taking the data matrix after each block is divided as an interval matrix.
6. The method for extracting geological mineral exploration data according to claim 4, wherein the method for obtaining the corrected covariance matrix comprises the following steps:
normalizing the segmentation intensity of each investigation data in each interval matrix to obtain a first result;
acquiring a covariance matrix of each interval matrix, and acquiring a product of first results of the same kind of investigation data between two columns of elements of the interval matrix corresponding to each element of the covariance matrix as a first value;
acquiring the average value of all the first values corresponding to each element of the covariance matrix, and taking the average value as the correction weight of the corresponding element in the covariance matrix;
obtaining the product of each element of the covariance matrix and the corresponding correction weight to be used as the correction value of the corresponding element in the covariance matrix;
and replacing each element in the covariance matrix with a corresponding correction value to obtain a matrix formed by the correction values, and taking the matrix as a correction covariance matrix of each interval matrix.
7. The geological mineral exploration data extraction method of claim 1, wherein the one-dimensional data acquisition method comprises the following steps:
acquiring a characteristic value and a characteristic vector of each correction covariance matrix, and sequentially taking the characteristic value and the characteristic vector as a correction characteristic value and a correction characteristic vector;
and acquiring one-dimensional data of the corresponding interval matrix through a PCA dimension reduction algorithm according to the correction eigenvalue and the correction eigenvector of each correction covariance matrix.
8. The method for extracting geological mineral exploration data according to claim 1, wherein the method for determining the termination time of the revolving door compression algorithm according to the data distribution in the one-dimensional data is as follows:
acquiring abnormal data in each piece of one-dimensional data through an outlier detection algorithm;
and taking the moment corresponding to the abnormal data as the termination moment of the revolving door compression algorithm.
9. A geological mineral survey data extraction system comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, carries out the steps of a geological mineral survey data extraction method according to any one of the preceding claims 1-8.
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