Intelligent measurement method and system for nickel content of concealed nickel ore body based on big data

Abstract

The present application relates to big data measurement technical field, specifically for the intelligent measurement method and system of concealed nickel ore body nickel content based on big data, including the following steps, obtains response sequence and constructs difference structure, extracts boundary mutation analysis trend increment, purifies path ratio and determines the generation of measurement record.In the present application, the mutation characteristics in response change are identified by constructing difference direction trend, the effective data segment is screened by combining numerical difference and path response stability, the noise optimization and trend purification of complex change path are realized, the abnormal area of nickel content distribution is accurately positioned based on the comprehensive comparison of space distance and response amplitude ratio, the matching ability of space information and response relationship is enhanced, the identification precision of nickel content abnormal area in deep invisible area is improved, the measurement deviation caused by single numerical dependence is avoided, the measurability and practicality of the result are ensured, and the continuity intelligent identification and efficient measurement of unexposed target are realized.

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 concealed nickel ore bodies 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 concealed nickel ore bodies 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 concealed nickel ore bodies 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 concealed nickel ore bodies based on big data, comprising the following steps:

[0006] S1: Obtain the nickel content response value sequence of the 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;

[0007] 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 the boundary candidate point, calculate the difference between the corresponding response value and the response value average, 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;

[0008] 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;

[0009] S4: According to the trend segment average response amplitude data, setting a sliding window and calculating the absolute value of the difference value of each point in the window in turn, eliminating the corresponding points and adjacent points exceeding the fluctuation amplitude threshold and reconstructing the path, generating the purified response trend path data;

[0010] 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.

[0011] As a further scheme of the 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 average amplitude value of each path segment, 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.

[0012] As a further scheme of the application, the difference value direction structure data is obtained by the following steps:

[0013] S111: A portable X-ray fluorescence analyzer is installed to obtain a nickel content response value sequence of spatial continuous sampling points, nickel content response values of two continuous sampling points are extracted according to spatial position indexes and the index order is recorded, the response value of the current point is subtracted from the response value of the previous sampling point, the response difference value and the positive and negative change direction are recorded, and a nickel content difference value direction sequence is generated;

[0014] S112: According to the nickel content difference value direction sequence, the spatial index order and the corresponding difference value direction symbol between adjacent sampling points are extracted, the direction correlation pair is constructed and integrated with the spatial position, the spatial coordinates in the position sequence are read, the index order and the direction change are matched synchronously, and the difference value direction structure data basic item is generated;

[0015] S113: Based on the difference direction structure data basis, the adjacent coordinate point difference, the direction change symbol and the space sequence displacement are integrated, the difference change, the corresponding direction symbol and the index sequence under the direction disturbance amplitude of each group are extracted, the trend change strength of each group of sampling points is obtained by operation, the uniform sequence is constructed with the sampling point position sequence index, the direction change expression of each sampling point is obtained, and the difference direction structure data is established.

[0016] As a further scheme of the present application, the acquisition step of the boundary response mutation data is specifically:

[0017] S211: Based on the difference direction structure data, the section with continuous less than zero in the difference sequence is screened, the starting point and the end point of each group of continuous decreasing sections are extracted according to the position index, and the corresponding response path is determined, the first negative difference point of the continuous negative value section in the response path is read as the decreasing starting point, the index interval from the corresponding point to the minimum value point is calculated, and the decreasing path interval value sequence is generated;

[0018] S212: According to the decreasing path interval value sequence, the minimum value point of the response value in each sequence is screened, the corresponding space position and the response value are extracted, which are taken as the candidate boundary point, the difference amplitude is calculated by comparing the nickel content value of the corresponding point with the average value of the whole section, and the minimum value point is taken as the structure candidate point, and the boundary candidate point difference set is established;

[0019] S213: According to the boundary candidate point difference set, 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 is counted, the relative change ratio index sequence is constructed, the boundary mutation ratio of each candidate point is obtained by operation, if the ratio is greater than the cumulative frequency threshold, the point is retained and belongs to the structure response set, all points meeting the condition are counted and selected to construct the target structure, and the boundary response mutation data is obtained.

[0020] As a further scheme of the present application, the acquisition step of the trend section average response amplitude data is specifically:

[0021] 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 falling back point where the response value first decreases is determined, and the analysis section is divided according to the continuous path between the maximum point and the falling back point, the sampling point number and the corresponding response data in each path section are summarized, and the maximum value to the falling back section response path information set is generated;

[0022] S312: According to the maximum value to the falling section response path information set, the response values of the sampling points in each path section are extracted in turn, 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 section set is generated.

[0023] S313: Based on the response data of all path sections in the continuous low increase amplitude trend path section set, the response value sequence of the sampling points in each path section is extracted, the starting point and the ending point corresponding to the value of each response value change are recorded, the overall response change trend is determined in combination with the path point position information, the classification is labeled according to the section number, and the trend section average response increase amplitude data is generated.

[0024] As a further scheme of the present application, the acquisition step of the purification response trend path data is specifically:

[0025] S411: According to the path section involved in the trend section average response increase amplitude data, the numbering and corresponding response value information of all sampling points in each path section are acquired, a sliding window structure is constructed, each window is composed of a center sampling point and two adjacent points on the left and right, all windows are indexed and numbered according to the center point number, and a response sliding window sequence set is generated.

[0026] S412: Based on the response value sequence of each window in the response sliding window sequence set, the response change of the sampling points is identified, the difference characteristics of the center point response value and the values of other points in the same window are sequentially judged, whether the fluctuation amplitude elimination reference condition is met is judged, and the corresponding sampling point number is recorded, and an abnormal fluctuation elimination point number set is obtained.

[0027] S413: According to the abnormal fluctuation elimination point number set, all sampling point sequences are numbered and matched, the labeled sampling points and adjacent points are removed from the path, and the unremoved sampling point information is retained to reconstruct a continuous numbered sampling path sequence in the original number order, and purification response trend path data is acquired.

[0028] As a further scheme of the present application, the acquisition step of the intelligent measurement and recording of the concealed nickel ore body nickel content is specifically:

[0029] S511: According to the termination point number of each path in the purification response trend path data, the corresponding termination point response value is extracted, and the spatial position coordinates corresponding to the number are matched in the spatial coordinate library, and the response values and spatial coordinate information of all mutation points in the boundary response mutation data are acquired, to form a response and spatial index mapping between the termination points and the boundary points, and to obtain response comparison and spatial index pairing data.

[0030] S512: Based on the combination of the termination point and the boundary point in the response index pairing data, the response value difference information and the spatial coordinate information between each point pair are extracted, the corresponding start and end point numbers, coordinate information and path segment identifiers are uniformly archived based on the combination relationship between the response change amplitude and the spatial displacement distance, and the measurable area point information set is established;

[0031] S513: According to the measurable area point information set, the combination relationship of all boundary points and termination points in each path segment that meet the conditions is extracted, the number correspondence, response value, spatial displacement information and path attribution number between the point pairs are integrated, the structured record content is uniformly generated, and the intelligent measurement record of the concealed nickel ore body nickel content is generated.

[0032] The intelligent measurement system for the nickel content of the concealed nickel ore body based on big data comprises:

[0033] The difference direction construction module is used to perform S1: obtaining the nickel content response value sequence of the sampling point, sequentially extracting the response value between the current point and the previous sampling point, subtracting each item and recording the positive and negative signs and establishing the trend change structure, and generating the difference direction structure data;

[0034] The mutation boundary identification module is used to perform S2: according to the difference direction structure data, extracting the response path between the first decreasing point and the minimum value point, screening the minimum value point as a boundary candidate point, calculating the difference between the corresponding response value and the average value of the response value, and calculating the ratio of the difference between the maximum value and the average value, if the ratio is greater than the cumulative frequency threshold, the boundary response mutation data is generated;

[0035] The trend path extraction module is used to perform S3: based on the boundary response mutation data, detecting all paths between the maximum response point and the first falling point, calculating the continuous response value difference and the average difference value, screening the continuous path segment with the difference value less than the increment stability threshold, and calculating the average increment to obtain the trend segment average response increment data;

[0036] The fluctuation removal purification module is used to perform S4: according to the trend segment average response increment data, setting a sliding window and sequentially calculating the absolute value of the difference value of each point in the window, removing the corresponding points and adjacent points that exceed the fluctuation amplitude threshold and reconstructing the path, and generating the purified response trend path data;

[0037] The measurement interval output module is used to perform 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 and distance ratio, if the ratio is greater than the ratio of the variable range and the base station, the measurable area is recorded, and the intelligent measurement record of the nickel content of the concealed nickel ore body is generated.

[0038] Compared with the prior art, the advantages and positive effects of the present application are that:

[0039] In the present application, by constructing the difference direction trend to identify the mutation characteristics in the response change, combining the numerical difference with the path response stability to screen the effective data segment, the denoising optimization and trend purification of the complex change path are realized, the abnormal area of nickel content distribution is accurately positioned based on the comprehensive comparison of spatial distance and response amplitude ratio, the matching ability of spatial information and response relationship is enhanced, the identification precision of the nickel content abnormal area in the deep invisible area is improved, the measurement deviation caused by single numerical dependence is avoided, the measurability and practicality of the result are ensured, the continuous intelligent identification and efficient measurement of the unexposed target are realized. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The present application is a main step flow chart;

[0041] Figure 2 The present application is a difference direction structure data acquisition flow chart;

[0042] Figure 3 The present application is a boundary response mutation data acquisition flow chart;

[0043] Figure 4 The present application is a trend segment average response amplitude data acquisition flow chart;

[0044] Figure 5 The present application is a purified response trend path data acquisition flow chart;

[0045] Figure 6 The present application is a hidden nickel ore body nickel content intelligent measurement record acquisition flow chart. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the present application more clear and obvious, the present 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 present application, and are not used to limit the present application.

[0047] Please refer to Figure 1 The present application is a hidden nickel ore body nickel content intelligent measurement method based on big data, which comprises the following steps:

[0048] S1: install a portable X-ray fluorescence analyzer to obtain a nickel content response value sequence of spatial 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;

[0049] S2: According to the difference value segment of the difference value direction structure data, the first decreasing point to the minimum point is extracted, the minimum point is selected as the boundary candidate point, the numerical difference between the corresponding response value and the average response value is calculated, and the ratio of the difference between the maximum value and the average value is calculated. If it is greater than the cumulative frequency threshold (according to GB / T17412.3-2010 "Rock Chemical Analysis Method", set to ≥85% quantile value), the boundary response mutation data is generated;

[0050] S3: Based on the response sequence after the corresponding point of the boundary response mutation data, all paths between the maximum response point and the first falling point are detected, the continuous response value difference is calculated point by point and the difference value is judged with the average difference value, the continuous path segment with the difference value less than the increase amplitude stability threshold is selected, the increase amplitude of each point is calculated and the corresponding path average increase amplitude is recorded, and the trend segment average response increase amplitude data is generated;

[0051] S4: According to the path segment involved in the trend segment average response increase amplitude data, a sliding window is set as the center point and the adjacent two points, the absolute value of the difference value of each point in the window is calculated in turn, and it is detected whether the center point exceeds the fluctuation amplitude threshold. If it exceeds, the corresponding point and the adjacent point are removed and the path is reconstructed, and the purified response trend path data is generated;

[0052] S5: Based on the numerical difference between the response value of the termination point of the purified 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. Compared with the range and base station ratio (according to the principle of variation function model of geological statistics, the empirical threshold is ≥0.7), if it is greater than, the spatial position of the corresponding start and end point is recorded as the measurable area, and the intelligent measurement record of the concealed nickel ore body nickel content is generated.

[0053] The difference value direction structure data includes the difference value size trend, the direction change characteristics, and the spatial index relationship. The boundary response mutation data includes the boundary point response intensity difference, the response change ratio, and the mutation occurrence position index. The trend segment average response increase amplitude data includes the average increase amplitude value of each path segment, the response change gradient, and the increase amplitude stability index. The purified response trend path data includes the denoising path segment, the stable response point sequence, and the removed point position index. The intelligent measurement record of the concealed nickel ore body nickel content includes the measurable area start and end position, the response value difference and spatial distance ratio, and the effective spatial range meeting the measurement standard.

[0054] Please refer to Figure 2 , S1 step is:

[0055] S111: Install the portable X-ray fluorescence analyzer to obtain a sequence of nickel content response values of spatially continuous sampling points, extract the nickel content response values of two consecutive 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 direction sequence;

[0056] Install the portable X-ray fluorescence analyzer to obtain a sequence of nickel content response values of spatially continuous sampling points, based on the topographic features of the survey area, arrange 5 equally spaced sampling points in the test area, the spacing is set to 3 meters, the corresponding point numbers are P1 to P5, the coordinates of each point are measured as (10, 20), (13, 20), (16, 20), (19, 20), (22, 20), respectively, and 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 difference, and the response difference values ΔN1 to ΔN4 corresponding to P2-P1, P3-P2, P4-P3, P5-P4 are calculated in turn. The corresponding calculation is: 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, ΔN4 = 119.6-121.0 = -1.4 mg / kg, according to the difference value positive and negative direction symbol S1 to S4 are +1, -1, +1, -1, and the direction disturbance amplitude D1 to D4 are 0.08, 0.10, 0.12, 0.09 respectively. The direction disturbance amplitude is set as 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 follows: 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 regularized 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 table below:

[0057] Table 1 Sampling point parameter table

[0058]

[0059] As shown in Table 1, the difference value direction sequence is constructed by calculating the difference value and direction information for each pair, and the nickel content difference value direction sequence is obtained.

[0060] 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 the direction association 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 the synchronous matching of the index order and the direction change;

[0061] According to the data in Table 1, the difference direction combination sequence is constructed, and the adjacent sampling point position index number 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=5.8mg / kg, S1=+1, (102→103) corresponds to ΔN2=−3.1mg / kg, S2=−1, and the record pair is integrated and constructed in this way, forming the basic record unit, such as {101→102, ΔN=5.8, S=+1}, {102→103, ΔN=−3.1, S=−1}, etc. 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)} spatial path pair. The direction symbol definition rule is ΔN>0, then +1, ΔN<0, then −1, and the direction disturbance amplitude is derived from the numerical record in the previous section D, which is matched into the combination structure. In the structure composed of spatial record 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.

[0062] 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 difference value change, the corresponding direction symbol and the index order under the direction disturbance amplitude are extracted, and the formula is used:

[0063] ;

[0064] The operation obtains the trend change intensity of each group of sampling points, 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 intensity value of the first group of sampling points, is the difference value of the nickel content between the first group of adjacent points, is the positive and negative symbol (±1) of the difference direction, is the average value of all difference values , and indicates the direction disturbance amplitude of the first point, which is used to reflect the direction disturbance degree of the sampling point.

[0065] Based on the difference direction structure data basis, the difference ΔN, the direction symbol S and the direction disturbance amplitude D extracted in each pair of combined 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 is calculated as:

[0066] ;

[0067] Taking the second group (P2 to P3) as an example, ΔN2=-3.1, S2=-1, D2=0.10, and substituting into the formula as follows:

[0068] ;

[0069] The rest of the sample point groups are calculated as follows:

[0070] ;

[0071] ;

[0072] ;

[0073] The calculation result is [2.257, 0.419, 2.341, 0.421], and these T values constitute a trend change strength sequence that represents the difference direction strength degree of each pair of paths. A larger T value indicates a dramatic change trend direction, and the corresponding path is an important change node in the path construction. Finally, the T sequence and the index path are combined to generate the final structure expression sequence, and the difference direction structure data is established. The result shows that the change strength value has a gradient difference in the spatial path, which can be used for subsequent identification of abnormal change aggregation position in the region.

[0074] Trend change strength 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 disturbance fluctuation of nickel content between sampling points in continuous spatial paths. This index not only considers the numerical difference of nickel content itself, but also integrates the directionality (positive or negative) of difference change and the instability degree of such directional change in spatial sequence, so as to accurately describe the intensity and stability of nickel content change in a certain direction in the spatial continuous region. When the trend change strength value is higher, it indicates that there is a significant directional deviation or mutation trend of nickel content in the path segment, otherwise it indicates that the change is relatively flat 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.

[0075] The formula embodies the comprehensive measurement mechanism of the difference trend between sampling points. First, the numerator part contains two addition structures: represents the absolute value of the trend strength formed by combining the difference value with its direction positive or negative, used to reflect the directional change intensity of the trend in the spatial path; represents the trend instability degree measurement constructed by weighted combination of the difference value and the direction disturbance amplitude, which is used to reflect the uneven degree of change, and the two are added to form the superimposed measurement of "trend strength + trend disturbance", embodying 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, while forms a normalized scale to measure the deviation of the current difference value from the overall average difference value, that is, when a certain difference value deviates from the overall mean, the T value decreases due to the larger denominator, inhibiting the abnormal amplification of extreme values, thereby forming the logic of "molecule represents the cumulative trend size and disturbance amplitude, and the denominator is used for normalization control" in the numerical structure, ensuring the scale consistency and sensitivity balance of the trend change intensity calculation.

[0076] Please refer to Figure 3 , S2 step is:

[0077] S211: Based on the difference direction structure data, filter the continuous less than zero section in the difference value sequence, extract the starting point and ending point of each group of continuous decreasing section according to the position index, and determine the corresponding response path, read the first negative difference value point of the continuous negative value 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;

[0078] Based on the difference direction structure data, first traverse the difference value of the 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 them, index 2 to 4 and 6 to 7 constitute continuous decreasing sections, extract the index position to construct the sampling path interval, 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 section, such as the minimum value of D1 section is 115.2 mg / kg, corresponding to P4; the minimum value of D2 section is 109.3 mg / kg, corresponding to P7. This step completes the path section positioning and minimum value point extraction operation of the decreasing trend, and generates the decreasing path interval value sequence after summarizing the extracted section response path and minimum value point number.

[0079] S212: According to the sequence of decreasing path interval values, the minimum value point of the response value in each sequence is screened, the corresponding spatial position and response value are extracted, and the corresponding point nickel content value is compared with the average value of the whole segment response value. The difference value is calculated and the minimum value point is taken as the structure candidate point. The difference value set of the boundary candidate point is established;

[0080] According to the sequence of decreasing path interval values, the minimum value point extracted from 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 taken as the difference value basis for boundary mutation analysis. For example, in D1 segment (P2→P4), the response value is [121.0, 118.7, 115.2], the minimum value point is P4 (115.2 mg / kg), the average value is calculated as mg / kg, and the difference value is mg / kg. The minimum value point value, the maximum value of the section, the average value and the disturbance amplitude are listed in the structure calculation table, and the results are as follows:

[0081] Table 2 Boundary mutation parameter table

[0082]

[0083] As shown in Table 2, all the difference value information of the boundary candidate points is archived according to the path segment number, forming the boundary candidate point difference value set for the next step of mutation ratio analysis.

[0084] S213: According to the boundary candidate point difference value set, the joint performance of the difference value amplitude of all minimum value points, the difference value amplitude between the maximum value point and the average value, and the disturbance amplitude is calculated, and the relative change ratio index sequence is constructed. The formula is:

[0085]

[0086] The boundary mutation ratio of each candidate point is obtained by operation, the ratio is compared with the set cumulative frequency threshold, the threshold is set to 0.85 according to the 85% quantile value rule in GB / T 17412.3-2010 "Rock chemical analysis method", all calculation results are screened, if the ratio is greater than 0.85, the point is retained and belongs to 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 average value of the current path segment response value, represents the response value of the th minimum value point, represents the response value of the maximum value point of the segment, and the unit is mg / kg, ​The direction perturbation amplitude normalized value representing the minimum point, ranging from 0 to 1;

[0087] Read all the data in Table 2, substitute into the formula for calculation, as follows:

[0088] For D1:

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] For D2:

[0094] ;

[0095] ;

[0096] For D3:

[0097] ;

[0098] ;

[0099] For D4:

[0100] ;

[0101] ;

[0102] 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 in the path. After the statistical confirmation of the candidate points, the boundary response mutation data is generated as the final spatial path mutation node.

[0103] The boundary mutation ratio is a numerical judgment index for measuring whether a minimum point of a response value constitutes a structural mutation boundary in a spatial path. The specific meaning is to reflect the deviation intensity of the minimum value point relative to the overall average of the path segment, and to comprehensively consider the contribution of the maximum value point to the trend range of the segment and the interference effect of the path disturbance amplitude. The larger the ratio is, 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, which has the characteristics of boundary mutation; if the ratio is close to or lower than the set threshold, it means that the minimum value point is low in numerical value, but it does not constitute an abnormal inflection point in the path trend and disturbance background, 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.

[0104] 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 path disturbance amplitude", and constructs a normalized mutation ratio judgment index, the core of which is to comprehensively consider the relative abnormal degree of the candidate point under the statistical average background. The numerator part 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 overall trend center, and is a direct measure of the mutation trend; the denominator part is the square root of the sum of squares of two terms, in which reflects the upper limit fluctuation degree of the response value distribution of the segment, represents the amplified intensity index of the minimum value point deviating from the center considering the disturbance effect, and the square sum is used to unify the dimension of various influence factors after the square sum is squared and then squared, to form a comprehensive fluctuation evaluation benchmark in the form of Euclidean norm, so that the formula establishes a nonlinear harmonic relationship between the mutation value amplification and the path disturbance adjustment, so that the ratio structure has the quantitative characteristics of amplifying the strong mutation segment and balancing the edge disturbance, and therefore the multiplication is used to express the amplification effect of the disturbance degree, the square sum is used to unify the dimension of various influence factors, and the square root is used to restore the original numerical dimension and realize the scale normalization, so as to construct a stable and complete quantitative trend mutation system.

[0105] Please refer to Figure 4 , the S3 step is:

[0106] 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 falling point where the response value first decreases, and divide the analysis segment between the maximum point and the falling point according to the continuous path, summarize all sampling point numbers and corresponding response data in each path segment, and generate the maximum value to the falling segment response path information set;

[0107] 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 backward for the first point where the response value decreases, which is 127.5 mg / kg here, as the falling point. Then extract the sampling point number between the maximum value point and the falling point, record the corresponding response value sequence and distance sequence, and constitute a response path. According to the path segment information, each sampling point sequence is numbered and labeled to generate a path segment information structure, and the numbering sequence of the path segment corresponding to multiple boundary points and the response data are integrated to constitute the basis data group for trend judgment. In actual implementation, for example, the path segment numbers P12 to P17 are extracted after the boundary point B1, and the response values are [126.1, 127.8, 128.3, 127.5, 126.2, 124.7] in turn. The segment number, start and end sampling point number and response value sequence are recorded, and the path coordinates of these points are collected to obtain the following path segment response information shown in Table 3:

[0108] Table 3 Path segment response information table

[0109]

[0110] As shown in Table 3, in the constructed path segment structure information, each segment takes the sequence between the maximum response point and the falling point as the sampling interval, which is used as the basis for subsequent path trend judgment, and the maximum value to falling segment response path information set is finally obtained.

[0111] S312: According to the maximum value to falling segment response path information set, the response values of the sampling points in each path segment are extracted in turn, 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 are extracted, and a continuous low increase amplitude trend path segment set is generated.

[0112] According to the maximum to the falling section response path information set, the response value sequence of each section path and the adjacent point number are extracted, the point pair combination is constructed for any two adjacent sampling points, the response value in the point pair is read, the direction judgment is performed on the response value of the adjacent points, whether it is an upward trend point pair of the response value is confirmed, if there is a decrease, the combination is excluded, after further extracting all the upward point pairs, the amplitude range judgment is performed on each point pair, the set amplitude stability reference value is called for comparison, the reference value is set to 0.9 mg / kg according to the historical regional sample statistical amplitude data, the statistical interval is constructed by using the average amplitude data of 50 path sections, the median value is 0.94 mg / kg, and the standard deviation is 0.08 mg / kg, and finally the 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, the point pair is reserved 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, the difference is -0.8, which does not meet the upward trend, and is discarded; if the response value of point P13 is 127.8, the response value of point P14 is 128.3, and the difference is +0.5, it is reserved; the paragraphs in the path that continuously meet the stable upward condition are constructed, the start number and the end number are arranged, and are uniformly stored in the trend section number record table, and finally the continuous low-amplitude trend path section set is obtained.

[0113] S313: Based on the response data of all path sections in the continuous low-amplitude trend path section set, the response value sequence of the sampling points in each path section is extracted, the start point and the end point corresponding to the value of each section response value change are recorded, the overall response change trend is determined in combination with the path point position information, the classification is labeled according to the section number, and the trend section average response amplitude data is generated;

[0114] Based on the continuous low-amplitude trend path section set, the response value data corresponding to each trend section number is extracted, the start point response value and the end point response value of each section are recorded, the response value change range in the whole section is calculated, and the total length of the path section and the number of response points are obtained, for example, the response value of a certain path section 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 section. The sampling distance of the section is obtained by comparing the path point number with the path sequence length, the start point number, the end point number, the response change value and the length in the section are recorded, and the average amplitude data structure is constructed. In the actual sample, if the length in the section is 9.2 meters and the response change value is 2.2 mg / kg, the average response amplitude data of the section is recorded as 0.55 mg / kg. The response information of each section in the whole trend section set is called and the arrangement operation is completed, all the trend section numbers, the amplitude and the path length are uniformly archived, and finally the trend section average response amplitude data is obtained after being summarized.

[0115] Please refer to Figure 5 , the S4 step is:

[0116] S411: According to the path segment involved in the trend segment average response increase data, obtain all the sample point numbers and corresponding response value information in each path segment, construct a sliding window structure, set each window to be composed of a central sample point and two adjacent points on the left and right, index all the windows by the central point number, and generate a response sliding window sequence set;

[0117] According to the path segment involved in the trend segment average response increase data, first read all the sample point numbers and their corresponding response values in each path segment, extract the sample point number list in order of path segment, and select two numbers to the front and back to form a five-point sliding window with each sample point as the center within each path segment. 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 data missing points. Construct the window sequence and record the sample point numbers and their corresponding response value sequences in each window. For example, path segment P1 contains sample points P01 to P10, and the center point P05 corresponds to the window P03 to P07. If its response value is [128.4, 129.6, 130.3, 130.1, 129.5], the window structure is valid and recorded in the sequence set. Traverse all the sample points in the path segment to generate the window set, sort its number, window number and path attribution information, establish a complete sliding window index structure, and obtain the response sliding window sequence set.

[0118] 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 sample points, sequentially judge the difference characteristics of the response value of the center point and other points in the same window, and judge whether it meets the baseline conditions for fluctuation amplitude elimination, and record the corresponding sample point number, to obtain the abnormal fluctuation elimination point number set;

[0119] 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.

[0120] 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 retained in the original number order to reconstruct a continuous number sampling path sequence, and the purified response trend path data is obtained.

[0121] 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 retained points are extracted and arranged, for example, the removal points of the path segment P1 are P05, P04 and P06, and the retained 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.

[0122] Please refer to Figure 6 , the S5 step is:

[0123] 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;

[0124] 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 path end representative value, and the coordinate data of the point number is extracted in the space index library, and 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 construct 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.

[0125] 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;

[0126] 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 linear 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 measurement conditions, the number, coordinates and path number of such point pairs 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, and the ratio of the difference and the distance is 1.54, which is greater than 0.7, confirming that it is effective, and finally a data structure containing multiple effective point pairs is established, and a measurable area point position information set is obtained.

[0127] 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, and the intelligent measurement record of the concealed nickel ore body is generated.

[0128] 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 the boundary points and terminal points determined to be effective in the path is extracted segment by segment, and the combination number, corresponding response value, spatial coordinates, path segment attribution and determination 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 and 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 the 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.

[0129] The intelligent measurement system for nickel content of a concealed nickel ore body based on big data comprises:

[0130] The difference direction construction module is used to execute S1: obtaining the sequence of nickel content response values of the sampling points, sequentially extracting the response values between the current point and the previous sampling point, subtracting each item and recording the positive and negative signs to establish the trend change structure, and generating the difference direction structure data;

[0131] 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 difference between the corresponding response value and the average value of the response values, 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.

[0132] 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 difference between continuous response values 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.

[0133] 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 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.

[0134] 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 to distance ratio, and if the ratio is greater than a range to base station ratio, recording as a measurable area to generate a nickel content intelligent measurement record of the concealed nickel ore body.

[0135] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any person skilled in the art can modify or change the above disclosed technical content to 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.

Claims

1. A smart method for measuring nickel content in concealed nickel ore bodies based on big data, characterized in that, Includes the following steps: S1: Obtain the nickel content response value sequence of sampling points, extract the response values ​​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 change structure, and generate difference direction structure data. S2: Based on the difference direction structure data, extract the response path between the first decreasing point and the minimum point, select the minimum point as the boundary candidate point, calculate the difference between the corresponding response value and the mean response value, and calculate the ratio between the difference between the maximum value and the mean value. If it is greater than the cumulative frequency threshold, generate boundary response mutation data. S3: Based on the boundary response mutation data, detect all paths from the maximum response point to the first point of fallback, calculate the difference of continuous response values ​​point by point and make a difference judgment with the average difference, filter out continuous path segments with differences less than the growth rate stability threshold and calculate the average growth rate to obtain the trend segment average response growth rate data. S4: Based on the average response increase data of the trend segment, set a sliding window and calculate the absolute value of the difference between each point in the window in turn. Remove the points that exceed the fluctuation amplitude threshold and adjacent points and reconstruct the path to generate purification response trend path data. S5: Based on the purification response trend path data and the boundary response mutation data, extract the spatial index distance and calculate the response difference and distance ratio. If it is greater than the range and sill ratio, it is recorded as a measurable area, and a smart measurement record of nickel content of the concealed nickel ore body is generated.

2. The intelligent measurement method for nickel content of concealed nickel ore bodies based on big data according to claim 1, characterized in that, The difference direction structure data includes the difference magnitude 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 purification response trend path data includes the noise reduction path segment, stable response point sequence, and removal point location index; and the concealed nickel ore body nickel content intelligent measurement record includes the start and end positions of the measurable area, the response value difference to spatial distance ratio, and the effective spatial range that meets the measurement standards.

3. The intelligent measurement method for nickel content of concealed nickel ore bodies based on big data according to claim 1, characterized in that, The specific steps for obtaining the difference direction structure data are as follows: S111: Install a portable X-ray fluorescence analyzer to obtain a sequence of nickel content response values ​​from continuous spatial sampling points. Extract the nickel content response values ​​of two consecutive sampling points according to the spatial location index and record the index order. 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, and generate a sequence of nickel content difference directions. S112: Based on the nickel content difference direction sequence, extract the spatial index order and corresponding difference direction symbol between adjacent sampling points, construct direction association pairs and integrate them with spatial positions, read the spatial coordinates in the position sequence, and generate the difference direction structure data basic item by synchronously matching the index order and direction changes. S113: Based on the basic items of the difference direction structure data, integrate the difference between adjacent coordinate points, the sign of the direction change and the spatial sequence displacement, extract the change of each group of differences, the corresponding direction sign and the direction disturbance amplitude under the 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 direction structure data.

4. The intelligent measurement method for nickel content of concealed nickel ore bodies based on big data according to claim 1, characterized in that, The specific steps for obtaining the boundary response mutation data are as follows: S211: Based on the difference direction structure data, filter the segments in the difference sequence that are continuously less than zero, extract the starting point and ending point of each group of continuously decreasing segments according to the position index, and determine the corresponding response path. Read the first negative difference point of the continuous negative value segment in the response path as the starting point of the decrease, calculate the index interval from the corresponding point to the minimum value point, and generate the 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 location 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 whole segment, the difference amplitude is calculated, and the minimum value point is used as a structural candidate point to establish a boundary candidate point difference set. S213: Based on the boundary candidate point difference set, statistically analyze the combined performance of the difference magnitude of all minimum points, the difference magnitude between the maximum point and the mean, and the disturbance magnitude, construct a relative change ratio index sequence, calculate and obtain the boundary mutation ratio of each candidate point, if the ratio is greater than the cumulative frequency threshold, retain the point and include it in the structural response set, statistically select all points that meet the conditions to construct the target structure, and obtain boundary response mutation data.

5. The intelligent measurement method for nickel content of concealed nickel ore bodies based on big data according to claim 1, characterized in that, The specific steps for obtaining the average response increase data of the trend segment are 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 fallback point where the response value first decreases, and delineate the analysis segment according to the continuous path between the maximum point and the fallback point. Summarize the sampling point numbers and corresponding response data in each segment path to generate the response path information set from the maximum value to the fallback segment. S312: Based on the response path information set from the maximum value to the fallback segment, extract the response values ​​of the sampling points in each path segment in sequence, determine the response value change characteristics between adjacent points in the path, determine the point pair combination with a continuous upward trend, and remove the point group whose change rate is greater than the increase stability threshold, extract the starting and ending sampling points of the path, and 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 values ​​corresponding to the start and end points of the response value change in 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 of concealed nickel ore bodies based on big data according to claim 1, characterized in that, The specific steps for obtaining the purification response trend path data are 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 the left and right, and index and number 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, the difference characteristics between the response value of the center point and the values ​​of other points in the same window are judged in sequence, and it is determined whether the baseline conditions for fluctuation amplitude elimination are met, and the corresponding sampling point number is recorded to obtain the abnormal fluctuation elimination point number set. S413: Based on the abnormal fluctuation removal point number set, perform number matching on all sampling point sequences, remove the marked sampling points and adjacent points from the path, and reconstruct the continuously numbered sampling path sequence by retaining the information of the unremoved sampling points in the original numbering order, thereby obtaining the purification response trend path data.

7. The intelligent measurement method for nickel content of concealed nickel ore bodies 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: Based on the termination point number of each path segment in the purification response trend path data, extract the corresponding termination point response value, 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 to form a response and spatial index mapping between the termination point and the boundary point, and obtain response comparison and spatial index pairing data. S512: Based on the combination of the termination point and boundary point 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 response change amplitude of the point pair and the spatial displacement distance, archive the corresponding start and end point numbers, coordinate information and path segment identifiers in a unified manner to establish a set of measurable area point information. S513: Based on the measurable area point information set, extract the combination relationship of all boundary points and termination points that meet the conditions in each path segment, integrate the number correspondence, response value, spatial displacement information and path belonging number between point pairs, and uniformly generate structured record content to generate intelligent measurement record of nickel content of concealed nickel ore body.

8. A smart measurement system for nickel content in concealed nickel ore bodies based on big data, characterized in that, The system is used to implement the intelligent measurement method for nickel content of concealed nickel ore bodies based on big data as described in any one of claims 1-7, including: 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, establish a trend change structure, and generate difference direction structure data. The mutation boundary identification module is used to perform S2: based on the difference direction structure data, extract the response path between the first decreasing point and the minimum point, select the minimum point as the boundary candidate point, calculate the difference between the corresponding response value and the mean response value, and calculate the ratio between the difference between the maximum value and the mean value. If it is greater than the cumulative frequency threshold, generate boundary response mutation data. The trend path extraction module is used to execute S3: Based on the boundary response mutation data, detect all paths from the maximum response point to the first point of fallback, calculate the difference of continuous response values ​​point by point and make a difference judgment with the average difference, filter the continuous path segments with differences less than the increase stability threshold and count the average increase to obtain the trend segment average response increase data. The fluctuation elimination and purification module is used to execute S4: based on the average response increase data of the trend segment, a sliding window is set and the absolute value of the difference between each point in the window is calculated in turn. Points exceeding the fluctuation amplitude threshold and adjacent points are eliminated and the path is reconstructed to generate purification 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, extract the spatial index distance and calculate the response difference and distance ratio. If it is greater than the range and sill ratio, it is recorded as a measurable area, and a smart measurement record of nickel content of the concealed nickel ore body is generated.

Images