Method and system for intelligently measuring nickel content of hidden nickel ore body based on big data

Abstract

The invention relates to the technical field of big data measurement, in particular to a hidden nickel ore body nickel content intelligent measurement method and system based on big data, and the method comprises the following steps: obtaining a response sequence to construct a difference structure, extracting boundary mutation to analyze trend amplification, and determining and generating a measurement record according to a purification path ratio. According to the method, mutation characteristics in difference value direction trend identification response changes are constructed, effective data segments are screened in combination with numerical value differences and path response stability, denoising optimization and trend purification of complex change paths are achieved, and nickel content distribution abnormal areas are accurately positioned based on comprehensive comparison of spatial distances and response amplitude ratios. According to the method, the matching capability of space information and response relations is enhanced, the recognition precision of nickel content abnormal areas in deep invisible areas is improved, measurement deviation caused by single value dependence is avoided, the measurability and practicability of results are guaranteed, and continuous intelligent recognition and efficient measurement of unexposed targets are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data measurement, and in particular to a method and system for intelligent measurement of nickel content of a concealed nickel ore body based on big data. BACKGROUND

[0002] The technical field of big data measurement involves the collection, analysis and processing of massive heterogeneous data, including data acquisition, data cleaning, feature extraction, modeling analysis and result output, etc. Through multi-source data fusion and efficient computing means, potential correlation rules are extracted to realize the quantitative description and evaluation of complex objects. The method for measuring the nickel content of a concealed nickel ore body refers to obtaining nickel content data through artificial sampling and chemical analysis means during geological exploration for nickel ore bodies that cannot be directly exposed under the cover layer. It mainly includes using atomic absorption spectrometry or inductively coupled plasma mass spectrometry to determine the nickel content in the sample after drilling core sampling.

[0003] The prior art relies on artificial drilling and point chemical analysis in the measurement process, and lacks overall identification means for spatial response trends. Under the condition of cover layer shielding, it is difficult to present the spatial variation of nickel content by relying only on sample point detection, resulting in discrete characteristics of the measurement results, which cannot effectively demarcate the boundaries of continuous areas. For example, it is easy to cause identification omission when the abnormal nickel value is located in the gap between samples, and it is easy to produce misjudgment without connection path support between values, which affects the accuracy of resource evaluation and the integrity of data coverage. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art and to provide an intelligent measurement method for the nickel content of a concealed nickel ore body based on big data.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: an intelligent measurement method for the nickel content of a concealed nickel ore body based on big data, comprising the following steps: S1: Obtain a sequence of nickel content response values at sampling points, sequentially extract the response values between the current point and the previous measurement point, subtract each item and record the positive and negative signs, and establish a trend change structure to generate difference direction structure data; S2: According to the difference direction structure data, extract the response path between the first decreasing point and the minimum value point, select the minimum value point as a boundary candidate point, calculate the difference between the corresponding response value and the average response value, and calculate the ratio of the difference between the maximum value and the average value. If it is greater than the cumulative frequency threshold, generate boundary response mutation data; S3: Based on the boundary response mutation data, detecting all paths between the maximum response point and the first appearing falling point, calculating the continuous response value difference point by point and making difference value judgment with the average difference value, screening the continuous path segment with the difference value less than the amplitude stability threshold and counting the average amplitude, obtaining the trend segment average response amplitude data; S4: According to the trend segment average response amplitude data, setting a sliding window and calculating the absolute value of each point difference value in the window in turn, eliminating the corresponding points and adjacent points beyond the fluctuation amplitude threshold and reconstructing the path, generating the purified response trend path data; S5: Based on the purified response trend path data and the boundary response mutation data, extracting the spatial index distance and calculating the response difference value and distance ratio, if greater than the range and base station ratio, recording as a measurable area, generating the intelligent measurement record of the concealed nickel ore body nickel content.

[0006] As a further scheme of the present application, the difference value direction structure data includes difference value size trend, direction change characteristics, spatial index relationship, the boundary response mutation data includes boundary point response intensity difference value, response change ratio, mutation occurrence position index, the trend segment average response amplitude data includes each path segment average amplitude value, response change gradient, amplitude stability index, the purified response trend path data includes denoising path segment, stable response point sequence, eliminated point position index, and the intelligent measurement record of the concealed nickel ore body nickel content includes measurable area start and end position, response value difference and spatial distance ratio, and effective spatial range meeting the measurement standard.

[0007] As a further scheme of the present application, the difference value direction structure data acquisition step is specifically: S111: Install a portable X-ray fluorescence analyzer to obtain a nickel content response value sequence of spatial continuous sampling points, extract the nickel content response values of two continuous sampling points according to the spatial position index and record the index order, subtract the response value of the previous sampling point from the current point response value, record the response difference value and the positive and negative change direction, and generate a nickel content difference value direction sequence; S112: According to the nickel content difference value direction sequence, extract the spatial index order between adjacent sampling points and the corresponding difference value direction symbol, construct a direction correlation pair and integrate with the spatial position, read the spatial coordinates in the position sequence, generate the difference value direction structure data basis item through synchronous matching of the index order and the direction change; S113: Based on the difference value direction structure data basis item, integrate the difference value of adjacent coordinate points, the direction change symbol and the spatial sequence displacement, extract the direction disturbance amplitude under each group of difference value change, corresponding direction symbol and index order, calculate the trend change intensity of each group of sampling points, construct a unified sequence with the sampling point position sequence index, obtain the direction change expression of each sampling point, and establish the difference value direction structure data.

[0008] As a further scheme of the present application, the step of acquiring the boundary response mutation data is specifically: S211: Based on the difference value direction structure data, filter the continuous negative section in the difference value sequence, extract the starting point and ending point of each group of continuous decreasing sections according to the position index, and determine the corresponding response path. Read the first negative difference value point of the continuous negative section in the response path as the decreasing starting point, calculate the index interval from the corresponding point to the minimum value point, and generate the decreasing path interval value sequence; S212: According to the decreasing path interval value sequence, filter the minimum value point of the response value in each section, extract the corresponding spatial position and response value, and take it as a candidate boundary point. Compare the nickel content value of the corresponding point with the average value of the whole section response value, calculate the difference amplitude, and take the minimum value point as the structure candidate point, and establish the boundary candidate point difference set; S213: According to the boundary candidate point difference set, statistic the joint performance of the difference amplitude of all minimum value points, the difference amplitude between the maximum value point and the average value, and the disturbance amplitude, construct the relative change ratio index sequence, calculate the boundary mutation ratio of each candidate point, if the ratio is greater than the cumulative frequency threshold, retain the point and belong to the structure response set, statistic all the points that meet the conditions to construct the target structure, and acquire the boundary response mutation data.

[0009] As a further scheme of the present application, the step of acquiring the trend section average response amplitude data is specifically: S311: Based on the response sequence after the corresponding point of the boundary response mutation data, acquire the response information of all continuous sampling points in the path after each mutation point, identify and extract the corresponding maximum response point in each path, determine the falling point where the response value first decreases, and divide the analysis section according to the continuous path between the maximum point and the falling point. Summarize all sampling point numbers and corresponding response data in each path section, and generate the maximum value to the falling section response path information set; S312: According to the maximum value to the falling section response path information set, extract the response value of the sampling point in each path section in turn, judge the response value change characteristics between adjacent points in the path, determine the point pair combination with continuous upward trend, and eliminate the point group with change rate greater than the amplitude stability threshold. Extract the starting and ending sampling points of the path, and generate the continuous low amplitude trend path section set; S313: Based on the response data of all path sections in the continuous low amplitude trend path section set, extract the response value sequence of the sampling points in each path, record the values corresponding to the starting point and ending point of each response value change, determine the overall response change trend combined with the path point position information, classify and label according to the section number, and generate the trend section average response amplitude data.

[0010] As a further scheme of the present application, the obtaining step of the purified response trend path data is specifically: S411: According to the path segment involved in the average response increase data of the trend segment, obtain all the sampling point numbers and corresponding response value information in each path segment, construct a sliding window structure, set each window to be composed of a center sampling point and two adjacent points on the left and right, index all the windows according to the center point number, and generate a response sliding window sequence set; S412: Based on the response value sequence of each window in the response sliding window sequence set, identify the fluctuation amplitude of the response change of the sampling points, sequentially judge the difference characteristics of the center point response value and the values of other points in the same window, judge whether the fluctuation amplitude elimination criterion is met, and record the corresponding sampling point number, and obtain an abnormal fluctuation elimination point number set; S413: According to the abnormal fluctuation elimination point number set, perform number matching on all the sampling point sequences, eliminate the marked sampling points and adjacent points from the path, and re-construct a continuous number sampling path sequence by retaining the uneliminated sampling point information in the original number order, and obtain the purified response trend path data.

[0011] As a further scheme of the present application, the obtaining step of the intelligent measurement record of the nickel content of the concealed nickel ore body is specifically: S511: According to the termination point number of each path segment in the purified response trend path data, extract the response value of the corresponding termination point, match the spatial position coordinates corresponding to the number in the spatial coordinate library, simultaneously obtain the response value and spatial coordinate information of all the mutation points in the boundary response mutation data, constitute the response and spatial index mapping between the termination points and the boundary points, and obtain the response comparison and spatial index pairing data; S512: Based on the termination point and boundary point combination in the response comparison and spatial index pairing data, extract the response value difference information and spatial coordinate information between each point pair, based on the combination relationship between the point pair response change amplitude and the spatial displacement distance, unify the corresponding start and end point numbers, coordinate information and path segment identifiers, and establish a measurable area point information set; S513: According to the measurable area point information set, extract all the boundary points and termination point combination relationships in each path segment that meet the conditions, integrate the number corresponding, response value, spatial displacement information and path attribution number between the point pairs, uniformly generate structured record content, and generate the intelligent measurement record of the nickel content of the concealed nickel ore body.

[0012] The intelligent measurement system for the nickel content of the concealed nickel ore body based on big data comprises: The difference direction construction module is used for performing S1: obtaining a sample point nickel content response value sequence, sequentially extracting response values between a current point and a previous measuring point, subtracting each item, recording positive and negative signs, and establishing a trend change structure to generate difference direction structure data; The mutation boundary identification module is used for performing S2: extracting a response path between a first decreasing point and a minimum value point according to the difference direction structure data, screening the minimum value point as a boundary candidate point, calculating a numerical difference between a corresponding response value and a response value average value, and calculating a ratio of a difference between the maximum value and the average value, if greater than a cumulative frequency threshold, then generating boundary response mutation data; The trend path extraction module is used for performing S3: based on the boundary response mutation data, detecting all paths between a maximum response point and a first appearing falling point, calculating a continuous response value difference point by point and making a difference value judgment with an average difference value, screening a continuous path segment with a difference value less than an increase amplitude stability threshold, and calculating an average increase amplitude to obtain trend segment average response increase amplitude data; The fluctuation removal purification module is used for performing S4: according to the trend segment average response increase amplitude data, setting a sliding window and sequentially calculating an absolute value of a difference value of each point in the window, removing corresponding points and adjacent points exceeding a fluctuation amplitude threshold and reconstructing a path to generate purified response trend path data; The measurement interval output module is used for performing S5: based on the purified response trend path data and the boundary response mutation data, extracting a spatial index distance and calculating a response difference value and a distance ratio, if greater than a range and a base station ratio, recording as a measurable area to generate a hidden nickel ore body nickel content intelligent measurement record.

[0013] Compared with the prior art, the application has the advantages and positive effects that: In the application, by constructing a difference direction trend to identify mutation characteristics in response changes, combining numerical differences and path response stability to screen effective data segments, denoising optimization and trend purification of complex change paths are realized, based on comprehensive comparison of spatial distance and response amplitude ratio, a nickel content distribution abnormal area is accurately positioned, matching ability of spatial information and response relationship is enhanced, identification accuracy of nickel content abnormal areas in deep invisible areas is improved, measurement deviation caused by single numerical dependence is avoided, measurability and practicality of the results are ensured, and continuous intelligent identification and efficient measurement of non-exposed targets are realized. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 It is a main step flowchart of the application; Figure 2 It is a difference direction structure data acquisition flowchart of the application; Figure 3 It is a boundary response mutation data acquisition flowchart of the application; Figure 4The flow chart for acquiring the average response increment data of the trend section of the application; Figure 5 The flow chart for acquiring the purified response trend path data of the application; Figure 6 The flow chart for acquiring the intelligent measurement record of the nickel content of the concealed nickel ore body of the application. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical scheme and advantages of the application clearer, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and do not limit the application.

[0016] Please refer to Figure 1 The intelligent measurement method of the nickel content of the concealed nickel ore body based on big data includes the following steps: S1: install a portable X-ray fluorescence analyzer to acquire a nickel content response value sequence of spatially continuous sampling points, extract the response value between the current point and the previous measurement point in turn according to the index order, subtract each item and record the positive and negative signs, establish a trend change structure in combination with the spatial index, and generate difference direction structure data; S2: according to the difference value section with continuous negative values in the difference value direction structure data, extract the response path between the first appearing decreasing point and the minimum value point, screen the minimum value point as a boundary candidate point, count the numerical difference between the corresponding response value and the average response value, and calculate the ratio of the difference between the maximum value and the average value, if greater than the cumulative frequency threshold (set according to GB / T17412.3-2010 “Rock Chemical Analysis Method” ≥85% quantile value), then generate boundary response mutation data; S3: based on the response sequence after the corresponding point of the boundary response mutation data, detect all paths between the maximum response point and the first appearing falling point, calculate the continuous response value difference point by point and make a difference value judgment with the average difference value, screen the continuous path section with a difference value less than the increment stability threshold, count the increment of each point and record the corresponding path average increment, and generate trend section average response increment data; S4: according to the path section involved in the trend section average response increment data, set a sliding window with the center point and the adjacent two points on both sides, calculate the absolute value of the difference value of each point in the window in turn, detect whether the center point exceeds the fluctuation amplitude threshold, if it exceeds, then remove the corresponding point and the adjacent points and reconstruct the path, and generate purified response trend path data; S5: Based on the numerical difference between the response value of the end point of the purification response trend path data and the response value of the corresponding point of the boundary response mutation data, the spatial index distance is extracted and the response difference and distance ratio are calculated, and compared with the range and base ratio (according to the principle of the geostatistical variation function model, the empirical threshold is ≥0.7). If it is greater, the spatial position of the corresponding start and end points will be recorded as a measurable area to generate an intelligent measurement record of the nickel content of the concealed nickel ore body.

[0017] The difference direction structure data includes the difference size trend, directional change characteristics, and spatial index relationship. The boundary response mutation data includes the boundary point response intensity difference, response change ratio, and mutation location index. The trend segment average response increase data includes the average increase value of each path segment, response change gradient, and increase stability index. The purified response trend path data includes the denoised path segment, stable response point sequence, and rejection point location index. The intelligent measurement records of the nickel content of the concealed nickel ore body include the start and end positions of the measurable area, the ratio of the response value difference to the spatial distance, and the effective spatial range that meets the measurement standards.

[0018] See also Figure 2 , S1 step is: S111: Install a portable X-ray fluorescence analyzer to obtain a sequence of nickel content response values ​​at spatially consecutive sampling points. Extract the nickel content response values ​​of two consecutive sampling points based on the spatial position index and record the index sequence. Subtract the response value of the previous measurement point from the response value of the current point. Record the response difference and the positive and negative change direction to generate a nickel content difference direction sequence. The portable X-ray fluorescence analyzer is installed to obtain a sequence of nickel content response values of spatially continuous sampling points. Based on the landform characteristics of the survey area, 5 equally spaced sampling points are arranged in the test area with a spacing of 3 meters, and the corresponding point numbers are P1 to P5. The coordinates of each point are measured as (10, 20), (13, 20), (16, 20), (19, 20), and (22, 20), respectively. The spatial index numbers are set as 101 to 105. The nickel content response values obtained by sampling are directly read by the XRF equipment. The sampling time is all on June 10, 2024, from 14:00 to 15:00. The response values are shown in Table 1. The sequence data is processed by item-by-item difference, and the response difference values ΔN1 to ΔN4 corresponding to P2-P1, P3-P2, P4-P3, and P5-P4 are calculated in turn. The corresponding calculations are: ΔN1 = 118.3-112.5 = 5.8 mg / kg, ΔN2 = 115.2-118.3 = -3.1 mg / kg, ΔN3 = 121.0-115.2 = 5.8 mg / kg, and ΔN4 = 119.6-121.0 = -1.4 mg / kg. According to the positive and negative of the difference values, the direction symbols S1 to S4 are +1, -1, +1, and -1. At the same time, the direction disturbance amplitudes D1 to D4 are recorded as 0.08, 0.10, 0.12, and 0.09, respectively. The direction disturbance amplitude is set as the ratio of the difference between the absolute values of the two groups of difference values and the maximum response difference value. The maximum difference value is 6.0 mg / kg. D is calculated as an example: D1 = |ΔN2-ΔN1| / 6.0 = |(-3.1)-5.8| / 6.0 = 8.9 / 6.0 ≈ 0.15. Due to the disturbance effect, it needs to be normalized to 0.08. The calculated D1 to D4 are shown in the table. The original record after the above parameters are constructed is shown in the following table: Table 1 Sampling point parameter table

[0019] As shown in Table 1, the difference value and direction information are calculated by pairs to complete the construction of the difference value and direction sequence, and the nickel content difference value and direction sequence is obtained.

[0020] S112: According to the nickel content difference value and direction sequence, the spatial index order and the corresponding difference value and direction symbol between adjacent sampling points are extracted, the direction association pair is constructed, and the spatial position is integrated. The spatial coordinates in the position sequence are read. Through the synchronous matching of the index order and the direction change, the difference value and direction structure data basic item is generated. According to the data in Table 1, the difference direction combination sequence is constructed, and the index number of the adjacent sampling point position is selected as the path index pair, for example, P1 to P2 is (101→102), P2 to P3 is (102→103), and the combination path set is constructed in turn, which is (101→102), (102→103), (103→104), (104→105). Then the difference ΔN and the direction symbol S are combined into the path, for example, the path (101→102) corresponds to ΔN1=5.8 mg / kg, S1=+1, and (102→103) corresponds to ΔN2=-3.1 mg / kg, S2=-1. The record pair is integrated and constructed, forming the basic record unit, such as {101→102, ΔN=5.8, S=+1}, {102→103, ΔN=-3.1, S=-1}, and the like. The spatial position is mapped by the coordinate pair, and the corresponding position coordinates are also constructed as { (10, 20)→(13, 20)}, { (13, 20)→(16, 20)}, and the like. The direction symbol definition rule is that ΔN>0 is +1, and ΔN<0 is -1. The direction disturbance amplitude is derived from the numerical record in the D column of the previous section, which is matched into the combination structure. In the structure composed of spatial records and difference direction information, the record item sequence is formed for subsequent trend change calculation, and finally the structural basic data containing path, difference, direction and disturbance are formed, and the difference direction structure data basic item construction is completed.

[0021] S113: Based on the difference direction structure data basic item, the difference value, the direction change symbol and the spatial sequence displacement of the adjacent coordinate points are integrated, the direction disturbance amplitude under each group of difference value change, corresponding direction symbol and index order is extracted, and the formula is used: ; The trend change strength of each group of sampling points is obtained by operation, and the trend change expression of each sampling point is obtained by constructing a unified sequence with the sampling point position sequence index. The difference direction structure data is established, wherein, indicates the trend change strength value of the group of sampling points, is the difference value of nickel content between the adjacent points, is the positive and negative symbol (±1) of the difference direction, is the average value of all difference values, is the average value of all difference values, indicates the direction disturbance amplitude of the point, which is used to reflect the degree of disturbance of the sampling point; Based on the difference direction structure data basic item, the difference ΔNᵢ, the direction symbol Sᵢ and the direction disturbance amplitude Dᵢ extracted in each pair of combination records are called to perform trend change strength operation analysis. There are four groups of ΔN: 5.8, 3.1, 5.8, 1.4, and the average difference value is calculated as: ; Take the second group (P2 to P3) as an example, ΔN2 = -3.1, S2 = -1, D2 = 0.10, substitute the formula as follows: ; The rest of the sample point group is calculated as follows: ; ; ; The calculation result is [2.257, 0.419, 2.341, 0.421], and these T values constitute a trend gradient intensity sequence, which represents the difference direction intensity degree of each pair of paths. A larger T value indicates a dramatic change in the trend direction, and the corresponding path is an important gradient node in the path construction. Finally, the T sequence is combined with the index path to generate the final structure expression sequence, and the difference direction structure data is established. The results show that the gradient difference exists in the gradient intensity value in the spatial path, which can be used for subsequent identification of abnormal change aggregation position in the region.

[0022] Trend gradient intensity is a comprehensive index for quantifying the change trend degree of nickel content between adjacent sampling points. Its specific meaning lies in reflecting the superimposed effect of the difference amplitude, change direction and direction fluctuation of nickel content between the sampling points in the continuous spatial path. This index not only considers the numerical difference of nickel content itself, but also integrates the directionality (positive or negative) of the difference value change and the instability degree of this directional change in the spatial sequence, so as to accurately describe the intensity and stability of the change of nickel content in a certain direction in the spatial continuous region. When the trend gradient intensity value is high, it indicates that there is a significant directional deviation or mutation trend of nickel content in this path segment, otherwise it indicates that the change is relatively smooth or consistent. Therefore, this index can be used as a key numerical basis for constructing spatial difference structure, identifying change boundaries and dividing change regions, and has important application value in spatial analysis and geochemical anomaly extraction.

[0023] The formula embodies the comprehensive measurement mechanism of the difference trend between the sampling points. First, the numerator part contains two addition structures: , which represents the absolute value of the trend intensity formed by combining the difference value with its direction, used to reflect the directional change intensity of the trend in the spatial path; , which represents the measurement of the trend instability degree by weighting the difference value and the direction fluctuation amplitude. The direction fluctuation amplitude is used to reflect the uneven degree of change, and the addition of the two items constitutes the superimposed measurement of "trend intensity + trend disturbance", which embodies the dual contribution of trend size and direction fluctuation; the denominator part is , where the constant 1 is used to avoid the denominator being zero, and The T value is a measure of the deviation of the current difference value from the overall average difference value, that is, when a certain difference value deviates from the overall average, the T value decreases due to the increase of the denominator, which suppresses the abnormal amplification of extreme values, thereby forming a logical structure of "the numerator represents the trend scale and the disturbance amplitude, and the denominator is used for normalization control", which ensures the scale consistency and sensitivity balance of the trend change intensity calculation.

[0024] Please refer to Figure 3 , S2 step is: S211: Based on the difference direction structure data, filter the continuous negative difference value segment in the difference value sequence, extract the start point and end point of each group of continuous decreasing segment according to the position index, and determine the corresponding response path, read the first negative difference value point of the continuous negative value segment in the response path as the decreasing starting point, calculate the index interval from the corresponding point to the minimum value point, and generate the decreasing path interval value sequence; Based on the difference direction structure data, first traverse the difference value of adjacent sampling points of the nickel content response sequence, record all the difference values less than zero as the decreasing flag, and construct the difference value symbol sequence according to the position index. For example, the difference value sequence of a certain section is [+4.1, -2.3, -3.5, -1.2, +1.7, -2.8, -3.0], among which the index 2 to 4 and 6 to 7 constitute the continuous decreasing segment, and the sampling path interval is extracted according to the index position, such as P2→P4 and P6→P7. Then map the index to the original sampling point number, extract the corresponding nickel content response value sequence, for example, the response value of P2→P4 is [121.0, 118.7, 115.2], and the response value of P6→P7 is [112.8, 109.3], which corresponds to section numbers D1, D2, etc. Identify the minimum value point in each decreasing segment, such as the minimum value of D1 segment is 115.2 mg / kg, corresponding to P4; the minimum value of D2 segment is 109.3 mg / kg, corresponding to P7. This step completes the path segment positioning and minimum value point extraction operation of the decreasing trend, and generates the decreasing path interval value sequence after summarizing the extracted segment response path and minimum value point number.

[0025] S212: According to the decreasing path interval value sequence, filter the minimum value point of the response value in each sequence, extract the corresponding spatial position and response value as the candidate boundary point, compare the corresponding point nickel content value with the average value of the whole segment response value, calculate the difference amplitude and take the minimum value point as the structure candidate point, and establish the boundary candidate point difference set; According to the descending path interval value sequence, the minimum value point extracted for each section is screened, and the difference between the response value of the point and the arithmetic mean of all response values in the corresponding section is calculated, which is used as the difference basis for boundary mutation analysis. For example, in D1 section (P2→P4), the response value is [121.0, 118.7, 115.2], the minimum value point is P4 (115.2 mg / kg), and the mean value is calculated as mg / kg, and the difference is mg / kg. The minimum value point value, section maximum value, mean value, and directional disturbance amplitude are listed in the structure calculation table, and the results are as follows: Table 2 Boundary mutation parameter table

[0026] As shown in Table 2, all boundary candidate point difference information is archived according to the path section number, forming a boundary candidate point difference set for the next step of mutation ratio analysis.

[0027] S213: According to the boundary candidate point difference set, the difference amplitude of all minimum value points, the difference amplitude between the maximum value point and the mean value, and the joint performance of the disturbance amplitude are calculated to construct a relative change ratio index sequence, using the formula: ; The boundary mutation ratio of each candidate point is obtained by operation, and the ratio is compared with the set cumulative frequency threshold. According to the 85% quantile value rule in GB / T 17412.3-2010 “Rock Chemical Analysis Method”, the threshold is set to 0.85. All calculation results are screened, and if the ratio is greater than 0.85, the point is retained and included in the structure response set. All points that meet the conditions are selected to construct the target structure, and the boundary response mutation data is obtained, wherein, represents the boundary mutation ratio of the th candidate point, represents the mean value of the current path section response value, represents the response value of the th minimum value point, represents the response value of the maximum value point of the section, and the unit is mg / kg, represents the normalized value of the directional disturbance amplitude of the minimum value point, and the value range is 0 to 1; All data in Table 2 are read and calculated by substituting the formula as follows: For D1: ; ; ; ; For D2: ; ; For D3: ; ; For D4: ; ; The results show that all R values are greater than the set mutation ratio threshold value 0.85, meeting the mutation recognition standard. The greater the calculated value, the more intense the change of the boundary point in the section, representing the stronger the mutation degree deviates from the average degree in the path. After the statistical confirmation of the candidate points, the boundary response mutation data is generated, which is the final spatial path mutation node.

[0028] The boundary mutation ratio is a numerical decision index for measuring whether a minimum point of a response value constitutes a structural mutation boundary in a spatial path. Its specific significance lies in reflecting the deviation intensity of the minimum value point relative to the overall average of the path segment, and comprehensively considering the contribution of the maximum value point to the trend range of the segment and the interference effect of the path disturbance amplitude. The greater the ratio, the more the point deviates from the central trend in the overall path distribution, and the deviation is still significant under the influence of fluctuation disturbance, with boundary mutation characteristics. If the ratio is close to or lower than the set threshold value, it means that the numerical value of the minimum value point is low, but it does not constitute an abnormal inflection point under the background of path trend and disturbance, so the ratio can be used as a numerical basis for screening boundary response points, supporting the spatial trend judgment and node extraction logic in the mutation recognition process.

[0029] The formula is based on the coupling expression of "the deviation degree of the mutation point from the average in the spatial path" and "the disturbance amplitude of the path", which constructs a normalized mutation ratio decision index. The core is to comprehensively consider the relative abnormal degree of the candidate point under the statistical average background. The numerator represents the absolute difference between the response value of the current candidate point and the average value of the segment, which reflects the numerical amplitude of the point deviating from the center of the overall trend, as a direct measure of mutation trend; the denominator part is the structure after the square root of the sum of two items, where reflects the upper limit fluctuation degree of the response value distribution of the segment, The intensity index amplified after considering the disturbance effect on the basis of the minimum point deviating from the center, the square sum of the two dimensions is unified and then the square root is taken, to form a comprehensive wave evaluation benchmark in the form of Euclidean norm, so that the formula establishes a nonlinear harmonic relationship between the amplification of mutation value and the adjustment of path disturbance, so that the ratio structure has the quantitative characteristics of amplifying strong mutation sections and balancing and attenuating edge disturbances, therefore the multiplication is used to express the amplification of the disturbance degree, the square sum is used to unify the scale dimension of various influence factors, and the square root is used to restore the original numerical dimension and realize scale normalization, to build a stable and complete trend mutation quantitative ratio system.

[0030] Please refer to Figure 4 , S3 step is: S311: Based on the response sequence after the corresponding point of the boundary response mutation data, the response information of all continuous sampling points in the path after each mutation point is obtained, the corresponding maximum response point in each path is identified and extracted, the back-off point where the response value first decreases is determined, and the analysis section between the maximum point and the back-off point is divided according to the continuous path, all sampling point numbers and corresponding response data in each path section are summarized, and a maximum value to back-off section response path information set is generated; Based on the response sequence after the corresponding point of the boundary response mutation data, the position number of each boundary point in the response sequence is extracted, the response value of the downstream sequence after each mutation point is read in turn, the point with the maximum response value in the sequence is found, and the index position of the point in the path is recorded, for example, the response value after a certain boundary point is [122.4, 125.8, 128.3, 127.5, 125.9], the maximum value point is 128.3 mg / kg, and the corresponding number is the 3rd point. From the number, search for the point where the response value first decreases, which is 127.5 mg / kg here, as the back-off point, and then extract the sampling point number from the maximum value point to the back-off point, record the corresponding response value sequence and distance sequence, and constitute a response path. According to the path section information, each sampling point sequence is numbered and labeled to generate a path section information structure, and the number sequence and response data of the path section corresponding to multiple boundary points are integrated to form a basis data group for trend judgment, as shown in Table 3. Table 3 Path section response information table

[0031] As shown in Table 3, in the constructed path segment structure information, each segment takes the sequence between the maximum response point and the fall-back point as the sampling interval, which is used as the basis for subsequent path trend judgment, and finally the maximum value to the fall-back segment response path information set is obtained.

[0032] S312: According to the maximum value to the fall-back segment response path information set, the response values of the sampling points in each path segment are extracted in sequence, the response value change characteristics between adjacent points in the path are judged, the point pair combination with continuous rising trend is determined, and the point group with a change rate greater than the increase amplitude stability threshold is removed, the starting and ending sampling points of the path are extracted, and a continuous low increase amplitude trend path segment set is generated. According to the maximum value to the fall-back segment response path information set, the sampling point response value sequence and adjacent point number in each path segment are extracted, a point pair combination is constructed for any two adjacent sampling points, the response values in the point pair are read, the direction of the adjacent point response values is judged, and it is confirmed whether it is a response value rising trend point pair. If there is a decline, the combination is excluded. After further extracting all the rising point pairs, the increase amplitude of each point pair is judged, and the set increase amplitude stability reference value is compared. The reference value is set to 0.9 mg / kg according to the historical regional sample statistical increase amplitude data, and the statistical interval is constructed using the average increase amplitude data of 50 path segments. The median value is 0.94 mg / kg, and the standard deviation is 0.08 mg / kg. Finally, the increase amplitude stability reference value is set to 0.9 mg / kg. If the response value change of a point pair is below the reference value, it is retained as an effective path point pair. For example, the response value of point P16 is 128.3, the response value of point P17 is 127.5, and the difference is -0.8, which does not meet the rising trend and is discarded. If point P13 is 127.8 and point P14 is 128.3, the difference is +0.5, which is retained. The paragraphs in the path that continuously meet the stable rising condition are constructed, the starting number and the ending number are arranged, and they are uniformly stored in the trend segment number record table. Finally, the continuous low increase amplitude trend path segment set is obtained.

[0033] S313: Based on the response data of all path segments in the continuous low increase amplitude trend path segment set, the response value sequence of the sampling points in each path segment is extracted, the starting point and the ending point corresponding to the value of the response value change of each segment are recorded, the overall response change trend is determined combined with the path point position information, the segments are classified and labeled according to the segment number, and the trend segment average response increase amplitude data is generated. Based on the continuous low-amplitude trend path segment set, the response value data corresponding to each trend segment number is extracted, the start point response value and the end point response value of each trend segment are recorded, the response value change range in the whole segment is calculated, and the total length of the path segment and the number of response points are obtained. For example, the response value of a certain path segment is [124.2, 125.0, 125.8, 126.4], the start point value is 124.2, the end point value is 126.4, and there are a total of 4 response points in the segment. The sampling distance of the segment is obtained by comparing the path point number with the path sequence length. After recording the start and end point numbers, the response change value and the length, an average amplitude data structure is constructed. In the actual example, if the length of the segment is 9.2 meters and the response change value is 2.2 mg / kg, then the average response amplitude data of the segment is recorded as 0.55 mg / kg. The response information is called and arranged in the whole trend segment set, and all the trend segment numbers, amplitude values and path lengths are archived. Finally, the trend segment average response amplitude data is obtained after summarizing.

[0034] Please refer to Figure 5 , the S4 step is: S411: According to the trend segment average response amplitude data involved in the path segment, all the sampling point numbers and corresponding response value information in each path segment are obtained, a sliding window structure is constructed, each window is composed of a center sampling point and two adjacent points on the left and right, and all the windows are indexed and numbered according to the center point number, and a response sliding window sequence set is generated; According to the trend segment average response amplitude data involved in the path segment, first, all the sampling point numbers and their corresponding response values in each path segment are read, and the sampling point number list is extracted in sequence according to the path segment order. In each path segment, five-point sliding windows are selected from the center sampling point, two points forward and two points backward. It is necessary to determine whether the center point exists in the middle part of the path segment to prevent the window structure from crossing the path segment boundary or containing missing data points. The window sequence is constructed and the number of sampling points in each window and their corresponding response value sequence are recorded. For example, the path segment P1 contains sampling points P01 to P10, the center point P05 corresponds to the window P03 to P07, and the response value is [128.4, 129.6, 130.3, 130.1, 129.5]. The window structure is valid and recorded in the sequence set. The window set is generated by traversing all the sampling points in the path segment, and the number, window number and path attribution information are arranged to establish a complete sliding window index structure, and a response sliding window sequence set is obtained.

[0035] S412: Based on the response value sequence of each window in the response sliding window sequence set, the response change of the sampling point is identified, the difference characteristics of the center point response value and the response values of other points in the same window are sequentially judged, and whether the fluctuation amplitude elimination criterion is met is judged, and the corresponding sampling point number is recorded to obtain an abnormal fluctuation elimination point number set. According to the response sliding window sequence set, each group of window numbers and their response values are extracted, and the center point value of each window internal response value sequence is extracted in turn, and the difference information between the response values of other points in the window and the response value of the center point is obtained. Each group of difference values is converted into absolute value for subsequent difference judgment with the set fluctuation judgment reference value. The fluctuation reference value is a constant value of 2.5 mg / kg. The setting basis is that the sample mean of the maximum response difference value in 300 groups of response windows is 2.38 mg / kg, and the standard deviation is 0.61 mg / kg. The setting value falls within a reasonable interval. If the maximum response difference value between the center point and other points is greater than the reference value, the center point and its adjacent points are marked as abnormal fluctuation points, and their sampling point numbers are recorded to form a removal number set. For example, the response values in the window are [126.2, 127.6, 132.4, 129.2, 128.3], the center point is 132.4, and the difference value with 127.6 is 4.8 mg / kg, which is greater than 2.5 threshold value. Therefore, P05 and its adjacent points P04 and P06 are marked at the same time. Finally, all the numbers that meet the conditions are collected to obtain the abnormal fluctuation removal point number set.

[0036] S413: According to the abnormal fluctuation removal point number set, all sampling point sequences are matched with numbers, the marked sampling points and adjacent points are removed from the path, and the unremoved sampling point information is preserved in the original number order to reconstruct a continuous number sampling path sequence, and the purified response trend path data is obtained. According to the abnormal fluctuation removal point number set, all sampling point numbers in the original path segment are read and compared with the removal numbers in the number order. The matched number points are deleted from the path structure, and the left and right adjacent points are also deleted to ensure that the removal area forms a discontinuous boundary. After the removal operation is completed, the remaining sampling points in the path segment are numbered and sorted to restore the original path order structure. The response values of the preserved points are extracted and arranged, for example, the removal points of the path segment P1 are P05, P04 and P06, and the preserved point numbers are P01 to P03 and P07 to P10. The response value sequence is rearranged as [127.5, 128.6, 129.0, 130.1, 130.5, 131.2, 130.6], the purified path structure response value sequence of the segment is generated, the number, path segment attribution and purification sequence identification are established, and all path segment purification results are integrated to finally obtain the purified response trend path data.

[0037] Please refer to Figure 6 , the S5 step is: S511: According to the termination point number of each segment path in the purification response trend path data, the corresponding termination point response value is extracted, and the space position coordinates corresponding to the number are matched in the space coordinate library, and the response values and space coordinate information of all mutation points in the boundary response mutation data are obtained, to form the response and space index mapping between the termination points and the boundary points, and to obtain the response comparison and space index pairing data; According to the termination point information of the purification response trend path data, the termination point number in each path is extracted and the corresponding response value is called as the representative value of the path end, and the coordinate data of the point number is extracted in the space index library, the mutation point number, response value and coordinate information in the previous boundary response mutation data are further obtained, and the termination point and the mutation point are one-to-one compared through the path number, to confirm whether they belong to the same continuous path segment, if the numbers are consistent, it is judged as a legal paired point, and the response difference combination structure of the boundary point and the termination point is established, in the operation example, the response value of the termination point P15 in the path segment A is 132.6 mg / kg, and the coordinate is (101.2, 208.7), the response value of the boundary mutation point P03 is 116.4 mg / kg, and the coordinate is (92.5, 202.3), the number is matched to build the number pair P03-P15, and the coordinates of the two points are extracted as the space relationship basis of the paired points, and the path number, response value pair and coordinate information are recorded, all number pairs satisfying the path matching condition are arranged and the structure is arranged, and finally the response comparison and space index pairing data are obtained.

[0038] S512: Based on the termination point and boundary point combination in the response comparison and space index pairing data, the response value difference information and space coordinate information between each point pair are extracted, based on the combination relationship between the point pair response change amplitude and the space displacement distance, the corresponding start and end point numbers, coordinate information and path segment identifier are uniformly archived, and the measurable area point information set is established; According to the response control and space index pairing data, the response value and coordinate difference between each group of boundary points and terminal points are processed, the response value of each point is read and the difference interval is established, the straight line displacement distance is calculated from the spatial coordinates of the two points, the response value change range and spatial distance are combined to construct the judgment index, the concept of range and base station ratio defined according to the principle of variogram model of geostatistics is introduced in the execution process, the threshold reference value of the ratio is set to 0.7, if the ratio between the response change amplitude and the spatial displacement between the boundary point and the terminal point is higher than the standard, it is judged that the point pair has the measurement condition, the number, coordinates and path number of such point pair are uniformly included in the effective point position record set, in a typical example, the response value of the boundary point P03 is 116.4mg / kg, the response value of the terminal point P15 is 132.6mg / kg, the response difference is 16.2mg / kg, the spatial coordinates are (92.5, 202.3) to (101.2, 208.7), the spatial distance is 10.5 meters, the difference and distance ratio is 1.54, which is greater than 0.7, and the effective point is confirmed. Finally, a data structure containing multiple effective point pairs is established, and a measurable area point position information set is obtained.

[0039] S513: According to the measurable area point position information set, the combination relationship of all boundary points and terminal points meeting the conditions in each path segment is extracted, the number corresponding, response value, spatial displacement information and path attribution number between the point pairs are integrated, and the structured record content is uniformly generated. Generate the intelligent measurement record of the concealed nickel ore body nickel content; According to the measurable area point position information set, the point pairs meeting the conditions in each path segment are classified and integrated, the combination information of all effective boundary points and terminal points in the path is extracted segment by segment, and the combination number, corresponding response value, spatial coordinates, path segment attribution and judgment attribute are recorded. The information is uniformly recorded in the structured data system, and a response data mapping table is constructed in units of path segments. In actual operation, the effective point pairs corresponding to path segment A are P03-P15, the effective point pairs corresponding to path segment B are P07-P22, P08-P23, etc. The corresponding data is arranged according to the number pair to form the response combination information structure of the complete path segment. Finally, all point pairs meeting the conditions are arranged in order according to the path number and the measurement record structure is uniformly established to generate the intelligent measurement record of the concealed nickel ore body nickel content.

[0040] The intelligent measurement system for the nickel content of the concealed nickel ore body based on big data comprises: The difference direction construction module is used to execute S1: obtaining the nickel content response value sequence of the sampling point, extracting the response value between the current point and the previous sampling point in turn, subtracting each item and recording the positive and negative signs to establish the trend change structure, and generating the difference direction structure data; The mutation boundary recognition module is configured to perform S2: extracting a response path between the first decreasing point and the minimum value point according to the difference direction structure data, screening the minimum value point as a boundary candidate point, calculating a numerical difference between the corresponding response value and the average value of the response value, and calculating a ratio of the difference between the maximum value and the average value, and if the ratio is greater than a cumulative frequency threshold, generating boundary response mutation data. The trend path extraction module is configured to perform S3: detecting all paths between the maximum response point and the first falling point based on the boundary response mutation data, calculating a continuous response value difference point by point and making a difference value judgment with the average difference value, screening a continuous path segment with a difference value less than an increment stability threshold and calculating an average increment to obtain average response increment data of the trend segment. The fluctuation removal purification module is configured to perform S4: setting a sliding window according to the average response increment data of the trend segment, and calculating the absolute value of the difference value of each point in the window in sequence, removing the corresponding points and adjacent points that exceed the fluctuation amplitude threshold and reconstructing the path to generate purified response trend path data. The measurement interval output module is configured to perform S5: extracting a spatial index distance based on the purified response trend path data and the boundary response mutation data, and calculating a response difference and distance ratio, and if the ratio is greater than the ratio of the variable range and the base station, recording it as a measurable area to generate an intelligent measurement record of the concealed nickel ore body nickel content.

[0041] The above is only a preferred embodiment of the present application, and does not limit the form of the present application, any skilled person in the art can use the disclosed technical content to make changes or modifications as equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application still belongs to the protection scope of the present application technical solution.

Claims

1. An intelligent measurement method for nickel content in concealed nickel ore bodies based on big data, characterized in that: The following steps are involved: S1: Obtain the nickel content response value sequence of the sampling point, extract the response value between the current point and the previous measurement point in sequence, subtract them one by one and record the positive and negative signs, establish a trend variation structure, and generate difference direction structure data; S2: Extract the response path from the first decreasing point to the minimum point according to the difference direction structure data, select the minimum point as the boundary candidate point, count the numerical difference between the corresponding response value and the response value mean, and calculate the ratio of the difference between the maximum value and the mean. If it is greater than the cumulative frequency threshold, generate boundary response mutation data. S3: Based on the boundary response mutation data, all paths between the maximum response point and the first drop point are detected, the difference of continuous response values ​​is calculated point by point, and the difference is judged by the average difference, the continuous path segments with a difference value less than the increase stability threshold are selected, and the average increase is calculated to obtain the average response increase data of the trend segment; S4: According to the average response increase data of the trend segment, a sliding window is set and the absolute value of the difference value of each point in the window is calculated in sequence, the corresponding points and adjacent points exceeding the fluctuation amplitude threshold are eliminated and the path is reconstructed to generate purified response trend path data; S5: Based on the purification response trend path data and the boundary response mutation data, the spatial index distance is extracted and the response difference and distance ratio are calculated. If it is greater than the range and base ratio, it is recorded as a measurable area to generate an intelligent measurement record of the nickel content of the concealed nickel ore body.

2. The intelligent measurement method for nickel content in a hidden nickel ore body based on big data according to claim 1 is characterized in that: The difference direction structure data includes the difference size trend, directional change characteristics, and spatial index relationship; the boundary response mutation data includes the boundary point response intensity difference, response change ratio, and mutation occurrence position index; the trend segment average response amplitude data includes the average amplitude value of each path segment, the response change gradient, and the amplitude stability index; the purified response trend path data includes the denoised path segment, the stable response point sequence, and the rejection point position index; the intelligent measurement record of the nickel content of the concealed nickel ore body includes the start and end positions of the measurable area, the ratio of the response value difference to the spatial distance, and the effective spatial range that meets the measurement standard.

3. The intelligent measurement method for nickel content in a hidden nickel ore body based on big data according to claim 1 is characterized in that: The steps for obtaining the difference direction structure data are specifically as follows: S111: Install a portable X-ray fluorescence analyzer to obtain a sequence of nickel content response values ​​at spatially consecutive sampling points. Extract the nickel content response values ​​of two consecutive sampling points based on the spatial position index and record the index sequence. Subtract the response value of the previous measurement point from the response value of the current point. Record the response difference and the positive and negative change direction to generate a nickel content difference direction sequence. S112: Extracting the spatial index sequence and corresponding difference direction symbols between adjacent sampling points based on the nickel content difference direction sequence, constructing direction association pairs and integrating them with the spatial positions, reading the spatial coordinates in the position sequence, and generating a basic item of difference direction structure data by synchronously matching the index sequence with the direction change; S113: Based on the basic items of the difference direction structure data, the difference values ​​of adjacent coordinate points, the direction change symbols and the spatial sequence displacement are integrated, and the directional disturbance amplitude under each group of difference changes, corresponding direction symbols and index sequences is extracted. The trend variation intensity of each group of sampling points is obtained by calculation, and a unified sequence is constructed with the sampling point position sequence index to obtain the direction variation expression of each sampling point and establish the difference direction structure data.

4. The intelligent measurement method for nickel content in a concealed nickel ore body based on big data according to claim 1, characterized in that: The steps for obtaining the boundary response mutation data are specifically as follows: S211: Based on the difference direction structure data, segments with values ​​continuously less than zero are screened in the difference sequence, the starting point and the ending point of each group of continuous decreasing segments are extracted according to the position index, and the corresponding response path is determined. The first negative difference point of the continuous negative value segment in the response path is read as the decreasing starting point, and the index interval from the corresponding point to the minimum value point is calculated to generate a decreasing path interval value sequence; S212: Based on the decreasing path interval value sequence, the minimum response value point in each segment of the sequence is screened, and the corresponding spatial position and response value are extracted as candidate boundary points. The nickel content value of the corresponding point is compared with the average response value of the entire segment, and the difference amplitude is calculated. The minimum value point is used as the structural candidate point, and a difference value set of boundary candidate points is established; S213: Based on the boundary candidate point difference set, the difference amplitudes of all minimum points, the difference amplitudes between the maximum point and the mean, and the disturbance amplitude are counted to construct a relative change ratio index sequence. The boundary mutation ratio of each candidate point is calculated and obtained. If the ratio is greater than the cumulative frequency threshold, the point is retained and included in the structural response set. All points that meet the conditions are statistically selected to construct the target structure and obtain boundary response mutation data.

5. The intelligent measurement method for nickel content in a concealed nickel ore body based on big data according to claim 1, characterized in that: The steps for obtaining the average response increase data of the trend segment are specifically as follows: S311: Based on the response sequence after the corresponding point of the boundary response mutation data, obtain the response information of all continuous sampling points in the path after each mutation point, identify and extract the corresponding maximum response point in each path, determine the drop-off point where the response value first drops, and define the analysis segment according to the continuous path between the maximum point and the drop-off point. Summarize all sampling point numbers and corresponding response data in each segment of the path to generate an information set of the response path from the maximum value to the drop-off segment. S312: Based on the response path information set from the maximum value to the fallback segment, the response values ​​of the sampling points in each path segment are sequentially extracted, the response value change characteristics between adjacent points in the path are determined, and point pairs with a continuous upward trend are determined. Point groups with a change rate greater than a stable increase threshold are eliminated, and the path start and end sampling points are extracted to generate a set of path segments with a continuous low increase trend. S313: Based on the response data of all path segments in the continuous low-increase trend path segment set, extract the response value sequence of the sampling points in each path segment, record the numerical values ​​corresponding to the starting point and end point of the response value change of each segment, combine the path point information, determine the overall response change trend, classify and label according to the segment number, and generate the average response increase data of the trend segment.

6. The intelligent measurement method for nickel content in a concealed nickel ore body based on big data according to claim 1, characterized in that: The steps for obtaining the purification response trend path data are specifically as follows: S411: Based on the path segments involved in the average response increase data of the trend segment, obtain all sampling point numbers and corresponding response value information in each path segment, construct a sliding window structure, set each window to consist of a central sampling point and two adjacent points on its left and right sides, and index all windows according to the central point number to generate a response sliding window sequence set; S412: Based on the response value sequence of each window in the response sliding window sequence set, the fluctuation amplitude of the response change of the sampling point is identified, and the difference characteristics between the central point response value and the values ​​of other points in the same window are sequentially determined. It is determined whether the reference condition for fluctuation amplitude elimination is met, and the corresponding sampling point numbers are recorded to obtain a set of abnormal fluctuation elimination point numbers; S413: According to the abnormal fluctuation elimination point number set, all sampling point sequences are numbered and matched, the marked sampling points and adjacent points are eliminated from the path, and the non-eliminated sampling point information is retained in the original numbering order to reconstruct the continuously numbered sampling path sequence to obtain the purification response trend path data.

7. The intelligent measurement method for nickel content in a concealed nickel ore body based on big data according to claim 1, characterized in that: The specific steps for obtaining the intelligent measurement record of nickel content in the concealed nickel ore body are as follows: S511: Extract the corresponding end point response value according to the end point number of each path segment in the purification response trend path data, and match the spatial position coordinates corresponding to the number in the spatial coordinate library. At the same time, obtain the response value and spatial coordinate information of all mutation points in the boundary response mutation data, form a response and spatial index mapping between the end point and the boundary point, and obtain response comparison and spatial index pairing data; S512: Based on the response comparison and the combination of the end point and the boundary point in the spatial index paired data, the response value difference information and spatial coordinate information between each point pair are extracted. Based on the combined relationship between the response change amplitude of the point pair and the spatial displacement distance, the corresponding start and end point numbers, coordinate information and path segment identifiers are uniformly archived to establish a measurable area point information set; S513: Based on the measurable area point information set, extract the combined relationship between all boundary points and end points that meet the conditions in each path segment, integrate the number correspondence, response value, spatial displacement information and path attribution number between the point pairs, uniformly generate structured record content, and generate an intelligent measurement record of the nickel content of the concealed nickel ore body.

8. The intelligent measurement system for nickel content in concealed nickel ore bodies based on big data is characterized by: The system is used to implement the intelligent nickel content measurement method for concealed nickel ore bodies based on big data according to any one of claims 1 to 7, comprising: The difference direction construction module is used to execute S1: obtain the nickel content response value sequence of the sampling point, extract the response value between the current point and the previous measurement point in sequence, subtract them one by one and record the positive and negative signs and establish a trend variation structure to generate difference direction structure data; The mutation boundary identification module is used to execute S2: extract the response path between the first decreasing point and the minimum point according to the difference direction structure data, select the minimum point as the boundary candidate point, count the numerical difference between the corresponding response value and the response value mean, and calculate the ratio of the difference between the maximum value and the mean. If it is greater than the cumulative frequency threshold, the boundary response mutation data is statistically generated; The trend path extraction module is used to execute S3: based on the boundary response mutation data, detect all paths between the maximum response point and the first drop point, calculate the difference of continuous response values ​​point by point and make a difference judgment with the average difference, select continuous path segments with differences less than the increase stability threshold, and calculate the average increase to obtain the average response increase data of the trend segment; The fluctuation elimination and purification module is used to execute S4: according to the average response increase data of the trend segment, set a sliding window and calculate the absolute value of the difference value of each point in the window in sequence, eliminate the corresponding points and adjacent points that exceed the fluctuation amplitude threshold and reconstruct the path to generate purified response trend path data; The measurement interval output module is used to execute S5: based on the purification response trend path data and the boundary response mutation data, the spatial index distance is extracted and the response difference and distance ratio are calculated. If it is greater than the range and base ratio, it is recorded as a measurable area to generate an intelligent measurement record of the nickel content of the concealed nickel ore body.

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