Multi-source data partitioning method and system for bench blasting of open-pit mine

Abstract

A kind of open-pit mine step blasting multi-source data explodability partition method and system, method: explodability evaluation unit division;Establish lithology benchmark explodability evaluation index system;Calculate benchmark explodability index and divide benchmark explodability grade;Collect local correction data;Uncertainty multi-source data processing;Calculate correction explodability index;Build step plane explodability index field;Generate explodability partition and recommend blasting parameter;System: including evaluation unit division module, benchmark index system construction module, lithology benchmark evaluation module, local correction data acquisition module, uncertainty processing module, local correction calculation module, spatial interpolation partition module, blasting parameter recommendation module, through the collaborative work of each module, realize from multi-source data acquisition, benchmark explodability evaluation, local correction, uncertainty processing, spatial interpolation mapping to the complete process of differentiating blasting parameter recommendation.The present application can finely realize the explodability evaluation of mine step blasting.

Description

Technical Field

[0001] This invention belongs to the technical field of open-pit mine blasting design and rock blastability evaluation, specifically involving a multi-source data blastability zoning method and system for bench blasting in open-pit mines. Background Technology

[0002] Bench blasting in open-pit mines is a core process in ore extraction, and its blasting effect directly determines the production efficiency and overall cost of the subsequent loading, transportation, crushing, and grinding processes. Rock and ore blastability is a key indicator characterizing the ease with which rock and ore can be broken under explosive loads. It is influenced by a combination of factors, including rock strength, the degree of development of rock mass structural planes, lithological differences, joint and fracture distribution, interlayer development, and local integrity. Accurately identifying the spatial distribution characteristics of rock and ore blastability within the bench blasting area is of significant engineering guiding importance for optimizing borehole parameters, explosive consumption per unit charge, charge structure, and delay time.

[0003] Current open-pit mine blasting designs are mostly based on lithological distribution, engineering experience, or a small number of single-point strength test results to determine parameters, generally assuming that the blastability of ore and rock in the same lithological area is uniform. However, in actual bench blasting areas, even within the same lithology, the integrity of the rock mass varies significantly at different locations due to factors such as diagenetic conditions, tectonic activity, weathering and alteration, and the degree of joint and fracture development, resulting in marked differences in blasting difficulty. If blasting zones are determined solely based on lithological type or a single strength index, the zoning results will be too coarse, easily leading to numerous problems such as local over-blasting, under-blasting, high proportion of large blocks, residual material at the base, and low utilization rate of explosive energy.

[0004] Meanwhile, open-pit mines can acquire multi-source heterogeneous data, mainly including indoor mechanical test data, bench exposed structural surface survey data, point load test data, and borehole television identification data. Among these, indoor mechanical tests can reflect the intrinsic fracture resistance of materials with different lithologies; bench exposed structural surface surveys can characterize the surface rock mass structure of the bench; point load tests can quickly identify local strength differences within the same lithology; and borehole television can accurately detect fractures, fracture zones, interlayers, and changes in rock mass integrity within the borehole. However, these multi-source data exhibit significant differences in spatial scale, sampling density, accuracy, and integrity. Some evaluation indicators also possess fuzziness and uncertainty, making it difficult to directly integrate them into a unified explosiveness zoning evaluation system.

[0005] Therefore, there is an urgent need for a multi-source data fusion method for blastability zoning of ore and rock. This method can comprehensively utilize the aforementioned multi-source data, distinguish the differences in blastability of different lithological benchmarks, refine the local blastability differences within the same lithology, and generate blastability zoning results of bench plane and blasting zone gridding through spatial interpolation. This will provide a scientific basis for the design of bench blasting zoning and the optimization of differentiated blasting parameters in open-pit mines. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a multi-source data-based explosiveness zoning method and system for bench blasting in open-pit mines. This method integrates multi-source heterogeneous data systems into a continuous explosiveness index field on the bench plane, enabling refined, quantitative, and engineering-practical explosiveness zoning for bench blasting in open-pit mines. This system integrates multi-source heterogeneous data systems into a continuous explosiveness index field on the bench plane, upgrading the explosiveness evaluation of bench blasting in open-pit mines from qualitative, empirical judgment to quantitative, refined zoning.

[0007] To achieve the above objectives, the present invention provides a multi-source data blastability partitioning method for bench blasting in open-pit mines, comprising the following steps: Step 1: Delineation of Explosiveness Evaluation Units; Obtain the spatial distribution information of lithology in the bench blasting area of ​​the open-pit mine to be evaluated, and delineate explosiveness evaluation units according to lithological boundaries, bench range, and blasting zone boundaries; Step 2: Establish a lithological benchmark blastability evaluation index system; collect indoor test data and exposed structural surface data of different lithologies, and construct a lithological benchmark blastability evaluation index system that includes rock material fracture resistance index and rock mass structural integrity index; Step 3: Calculate the benchmark explosiveness index and classify the benchmark explosiveness level; calculate the benchmark explosiveness index of different lithologies based on the lithological benchmark explosiveness evaluation index system, and determine the benchmark explosiveness level of different lithologies; Step 4: Collect local correction data; For different evaluation units within the same lithology, collect point load test data and borehole television data, and construct a local correction index system that includes point load test indexes and borehole television indexes. Step 5: Uncertainty processing of multi-source data; uncertainty processing is performed on indoor test data, exposed structural surface data, point load test data, and borehole television data to form a unified dataset for calculating local correction coefficients; Step 6: Calculate the modified explosiveness index; Based on the benchmark explosiveness index, calculate the local correction coefficient by combining point load test data and borehole television data to obtain the modified explosiveness index of each evaluation unit. Step 7: Construct the explosiveness index field of the step plane; determine the explosiveness level of each evaluation unit according to the modified explosiveness index, and use spatial interpolation method to extend the discrete evaluation results to the step plane to form a continuous or quasi-continuous explosiveness index field. Step 8: Generate explosiveness zones and recommend blasting parameters; Generate gridded explosiveness zones based on the explosiveness index field of the step plane, and recommend differentiated blasting parameters according to different explosiveness zones.

[0008] As a preferred option, the process of dividing the explosiveness evaluation unit in step 1 is as follows: S11: Obtain geological logging data, bench measurement data, blasting area design data, and sampling point coordinate data for the bench blasting area of ​​the open-pit mine to be evaluated, determine the distribution range of different lithologies, lithological boundaries, geological boundaries, bench elevations, and blasting area boundaries, and form lithological spatial distribution information; S12: Based on lithological boundaries, bench ranges, and blast zone boundaries, the area to be evaluated is divided into several bench planar evaluation units or blast zone grid evaluation units, and the spatial coordinates and lithology of each evaluation unit are recorded.

[0009] As a preferred option, the process of establishing the lithological benchmark blastability evaluation index system in step 2 is as follows: S21: For different lithologies within the bench blasting area of ​​the open-pit mine to be evaluated, representative rock samples were collected for indoor testing. Indoor tests included uniaxial compression tests, Brazilian splitting tests, Hopkinson bar impact tests, and physical property tests. Based on the indoor test data, rock material fracture resistance indices were obtained and ultimately categorized into a set of rock material fracture resistance indices. Specifically, the uniaxial compression test was used to obtain uniaxial compressive strength, elastic modulus, and Poisson's ratio; the Brazilian splitting test was used to obtain static tensile strength; the Hopkinson bar impact test was used to obtain dynamic compressive strength, dynamic tensile strength, dynamic failure characteristics, and impact fracture energy consumption; and the physical property tests were used to obtain one or more of the following: density, water content, longitudinal wave velocity, transverse wave velocity, and hardness. S22: Investigate the exposed structural surfaces of bench slopes, exposed faces in mining areas, or newly exposed rock masses, obtain exposed structural surface data, and derive rock mass structural integrity indices based on the exposed structural surface data, ultimately classifying them into a rock mass structural integrity index set; among which, exposed structural surface data includes one or more of the following: joint spacing, number of joint groups, joint dip angle, joint extension length, joint opening, joint roughness, fracture density, structural surface connectivity, and rock mass integrity. S23: Standardize and unify the direction of rock material fracture resistance index and rock mass structural integrity index. It is uniformly stipulated that the larger the standardized index value, the greater the difficulty of blasting. For indices positively correlated with blasting difficulty, a positive standardization formula is adopted. For indicators negatively correlated with blasting difficulty, a reverse standardization formula is used. ,in For the first The standardized values ​​of each indicator For the first The original values ​​of each indicator and The first The maximum and minimum values ​​of each indicator in the corresponding lithology or evaluation sample; the standardized indicators together constitute the lithological benchmark blastability evaluation index set.

[0010] As a preferred embodiment, in step 3, the process of calculating the baseline explosiveness index and classifying the baseline explosiveness level is as follows: S31: Calculate the benchmark blastability index for different lithologies based on the standardized values ​​and weights of each index in the lithological benchmark blastability evaluation index set. ,in, For the first A standardized evaluation index value, For the first The weight of each evaluation indicator, The number of indicators participating in the benchmark evaluation, and the weights satisfy the following conditions: ; S32: Based on the benchmark explosiveness index, the benchmark explosiveness levels of different rock types are divided into five levels, and quantified using a percentage system; among them, Level I is extremely explosive. Class II explosive Class III, moderately explosive Class IV, difficult to explode Points, Level V, extremely difficult to obtain. The higher the score, the greater the difficulty of blasting the ore and rock.

[0011] As a preferred option, the process of acquiring local correction data in step 4 is as follows: S41: Point load test data is obtained by conducting point load tests on field rock blocks, borehole rock samples, or representative rock samples from blast areas collected from different evaluation units within the same lithology. Point load test indices are then formed based on this data. The point load test data includes the point load strength index, the corrected point load strength index, and the point load strength deviation coefficient within the same lithology. The point load strength deviation coefficient... The formula used to characterize the degree of deviation of the strength of a certain evaluation unit from the average strength level of that lithology is as follows: ,in For the first The point load strength index or modified point load strength index of each evaluation unit For the first The average point load strength index or the average modified point load strength index for lithological types; when When the local strength of the evaluation unit is higher than the average level of the same lithology, the blastability index is corrected towards the direction of difficult blasting; when When the strength of the local area of ​​the evaluation unit is lower than the average level of the same lithology, the explosiveness index is corrected towards the direction of increased explosiveness; when When the value is close to 0, it indicates that the local strength of the evaluation unit is close to the average level of the same lithology. The benchmark blastability index should be kept unchanged or a small correction should be made within the set range. S42: Use borehole television to identify the internal structure of different evaluation units within the same lithology, obtain borehole television data, and form borehole television indices based on the borehole television data; the borehole television data includes one or more of the following: number of fractures, fracture spacing, fracture width, fracture dip angle, fracture zone length, interlayer location, length of intact section, and rock mass integrity variation information.

[0012] As a preferred option, the process of processing uncertain multi-source data in step 5 is as follows: S51: Defuzzify the qualitative indicators; convert the qualitative indicators into quantitative scores using a rank mapping method; S52: Standardize quantitative indicators; For quantitative indicators, standardize them according to their positive or negative relationship with the difficulty of blasting, so that indicators of different dimensions are converted into a unified scoring range. S53: Identify and correct abnormal data; based on field experience thresholds, reasonable experimental ranges, or statistical ranges of similar samples, identify index values ​​that significantly exceed reasonable ranges or have abnormal differences from data in adjacent evaluation units; correct abnormal data by methods such as removal, replacement with the mean of adjacent evaluation units, replacement with the mean of samples of the same lithology, or re-verification; S54: Redistribute weights for missing indicators; when any evaluation unit has missing indoor test data, exposed structural surface data, point load test data, or borehole television data, remove the corresponding weights from the weight set and normalize the remaining weights according to their original weight proportions; the formula for calculating the corrected weights under missing indicator conditions is as follows: ,in For the first Adjusted weights for the remaining indicators, For the first The original weights of the remaining indicators The number of remaining indicators to be evaluated; S55: Perform spatial registration; map indoor test data to its corresponding lithological distribution area, map exposed structural surface data to the spatial location of bench slope or exposed surface in the stope, map point load test data to the evaluation unit where the sampling point is located, map borehole television data to the evaluation unit where the borehole is located or its neighboring evaluation unit, so that data from different sources are uniformly matched to the bench plane evaluation unit or blasting area grid evaluation unit.

[0013] As a preferred embodiment, the process for calculating the corrected explosiveness index in step 6 is as follows: S61: Determine the strength correction factor from point load test data. The strength correction factor is determined based on the degree of deviation of the load strength at the evaluation unit point from the average load strength at the same lithology: when the load strength at the evaluation unit point is higher than the average value at the same lithology, A positive value indicates that the local strength of the evaluation unit is relatively high, and a positive correction is made towards the direction of difficult blasting; when it is lower than the average value of the same lithology, A negative value indicates that the local strength of the evaluation unit is low, and a negative correction is made in the direction of explosiveness. S62: Determine the borehole structure correction factor from borehole television data. The borehole structure correction factor is determined comprehensively based on the degree of fracture development, fracture zone development, interlayer development, and intact section development obtained from borehole television identification. When the borehole fractures are underdeveloped, the fracture zone is not obvious, the interlayer is few, and the intact section is long, A positive value indicates that the rock mass of the evaluation unit has good integrity, and a positive correction is made towards the direction of difficult blasting; when there are well-developed fractures, obvious fracture zones, well-developed interlayers, or short intact sections, the value is negative. A negative value indicates that the rock mass integrity of the evaluation unit is poor, and a negative correction is made in the direction of blastability. S63: Based on the baseline explosiveness index, calculate the modified explosiveness index by combining the strength correction factor and the pore structure correction factor. ,in The blastability index is based on lithology. and These are the correction weights for the strength correction factor and the internal structure correction factor, respectively.

[0014] As a preferred embodiment, the process of constructing the explosiveness index field of the stepped plane in step 7 is as follows: S71: The spatial coordinates of each evaluation unit are used as the interpolation point positions, and the corresponding modified explosiveness index is used as the interpolation attribute value. The discrete evaluation results are spatially interpolated in combination with the lithological boundary, bench range and blasting zone boundary to obtain the explosiveness index field that is continuously or quasi-continuously distributed in the bench plane. During the interpolation process, the spatial coordinates of the evaluation unit, the lithological boundary, and the blast zone boundary are used as constraints to ensure that the interpolation results do not cross unreasonable lithological boundaries or blast zone boundaries. For areas with significant differences between different lithologies, interpolation is performed within the corresponding lithological regions, or lithological boundary constraints are introduced during the interpolation process to avoid mutual interference between evaluation results of different lithologies. Spatial interpolation methods include one or more of inverse distance weighted interpolation, ordinary kriging interpolation, radial basis function interpolation, and natural neighborhood interpolation. When the number of evaluation units is small and the sample distribution is uneven, inverse distance weighted interpolation or natural neighborhood interpolation is used. When the number of evaluation units is large and there is obvious spatial correlation, ordinary kriging interpolation is used. When a smoother explosiveness index field is required, radial basis function interpolation is used. S72: Determine the explosiveness level according to the index value of each grid point or grid unit in the explosiveness index field of the step plane, in accordance with the explosiveness level classification standard; the classification standard includes Level I extremely explosive, Level II easily explosive, Level III moderately explosive, Level IV difficult to explode and Level V extremely difficult to explode.

[0015] As a preferred option, in step 8, the process of generating explosiveness zones and recommending blasting parameters is as follows: S81: Based on the blastability index field of the bench plane, the blasting area to be evaluated is divided into several grid units. The blastability index and blastability level corresponding to each grid unit are extracted to generate the blasting area gridded blastability partitioning result. The blasting area gridded blastability partitioning result includes the blastability index of each blasting area grid, the spatial location of each blasting area grid, the lithology to which it belongs, the blastability level, and the corresponding blasting parameter recommendation results. S82: Differentiated blasting parameters are recommended based on different explosiveness zones; blasting parameters include one or more of the following: hole spacing, row spacing, explosive consumption per unit, charge structure, plugging length, and delay time; for Class I extremely explosive zones, reduce explosive consumption per unit and appropriately increase hole spacing or row spacing; for Class II easily explosive zones, use lower explosive consumption per unit and maintain conventional hole mesh parameters; for Class III moderately explosive zones, use standard explosive consumption per unit and standard hole mesh parameters; for Class IV difficult-to-explode zones, increase explosive consumption per unit and appropriately decrease hole spacing or row spacing; for Class V extremely difficult-to-explode zones, increase blasting energy input and optimize charge structure and delay time.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention divides the explosiveness evaluation into two levels: lithological benchmark evaluation and local correction within the same lithology. First, a benchmark explosiveness index for different lithologies is established based on indoor test data and exposed structural surface data. Then, for different evaluation units within the same lithology, positive and negative local corrections are made using point load test data and borehole television data. This approach considers both the overall blasting difficulty differences between different lithologies and the heterogeneity within the same lithology caused by local strength variations and differences in concealed structures, effectively improving the precision of the explosiveness zoning for bench blasting.

[0017] 2. This invention integrates two types of information in the benchmark blastability evaluation: rock material fracture resistance index and rock mass structural integrity index. The rock material's fracture resistance performance is obtained through uniaxial compression test, Brazilian splitting test, Hopkinson bar impact test and physical property test. At the same time, the structural surface development characteristics such as joint spacing, joint group number, fracture density, structural surface penetration and rock mass integrity are obtained by combining the investigation of exposed structural surfaces, making the benchmark blastability evaluation of different lithologies more comprehensive and objective.

[0018] 3. This invention utilizes point load test data to calculate the deviation coefficient of point load strength within the same lithology, quantitatively characterizing the degree of deviation of the local strength of each evaluation unit from the average level of the same lithology. Simultaneously, it uses borehole television data to identify concealed fractures, fracture zones, interlayers, and intact rock sections, comprehensively determining the strength correction coefficient and the borehole structure correction coefficient. This achieves positive and negative local corrections for different evaluation units within the same lithology. It can identify both difficult-to-blast areas caused by locally high strength or good borehole integrity, and easily blastable areas caused by well-developed fractures, obvious fracture zones, or interlayers. This overcomes the limitation of single-benchmark evaluation ignoring the heterogeneity within the same lithology, making the quantitative basis for local corrections more comprehensive.

[0019] 4. This invention systematically processes the uncertainty of four types of multi-source heterogeneous data: indoor test data, exposed structural surface data, point load test data, and borehole television data. This includes defuzzification of qualitative indicators, standardization of quantitative indicators, identification and correction of abnormal data, weight redistribution of missing indicators, and spatial registration. It effectively solves the inconsistency problems of different sources of data in terms of dimensions, scale, completeness, and spatial location, and improves the stability and reliability of explosiveness evaluation results under the conditions of incomplete and inconsistent multi-source data.

[0020] 5. This invention uses a spatial interpolation method to extend the modified explosiveness index of discrete evaluation units to the bench plane, constructing a continuous or quasi-continuous explosiveness index field. During the interpolation process, lithological boundaries and blast zone boundaries are used as constraints to generate gridded explosiveness zoning results for the blast zone. This extends the explosiveness evaluation results from discrete points to a continuous surface, which can directly serve the zoning design and blasting parameter optimization of bench blasting in open-pit mines.

[0021] 6. This invention recommends differentiated blasting parameters based on different explosiveness zones. It provides adjustment suggestions for parameters such as hole spacing, row spacing, explosive consumption per unit, charge structure, plugging length, and delay time for zones ranging from Class I extremely explosive to Class V extremely difficult to blast. This forms a complete technical closed loop from explosiveness evaluation to blasting design guidance, improving the pertinence and engineering practicality of blasting design.

[0022] This method can integrate multi-source heterogeneous data systems into a continuous explosiveness index field of the bench plane, realizing the refinement, quantification, and engineering application of explosiveness zoning for bench blasting in open-pit mines.

[0023] This invention also provides a multi-source data explosiveness zoning system for bench blasting in open-pit mines, used to implement a multi-source data explosiveness zoning method for bench blasting in open-pit mines, comprising: The evaluation unit division module is used to obtain the lithological spatial distribution information of the bench blasting area in the open-pit mine to be evaluated, and to divide the blastability evaluation units according to the lithological boundaries, bench range and blasting zone boundaries; The benchmark index system construction module is used to collect indoor test data and exposed structural surface data of different lithologies, and to establish a lithological benchmark blastability evaluation index system that includes rock material fracture resistance index and rock mass structure integrity index. The lithological benchmark evaluation module is used to calculate the benchmark explosiveness index of different lithologies based on the standardized values ​​and weights of each index in the lithological benchmark explosiveness evaluation index system, and to determine the benchmark explosiveness level of different lithologies based on the benchmark explosiveness index. The local correction data acquisition module is used to collect point load test data and borehole television data for different evaluation units within the same lithology, and to obtain information on local strength differences and borehole structure development. The uncertainty processing module is used to perform uncertainty processing on indoor test data, exposed structural surface data, point load test data, and borehole television data to form a unified dataset for calculating local correction coefficients. The local correction calculation module is used to calculate the local correction coefficient based on the benchmark explosiveness index, combined with point load test data and borehole television data, to obtain the corrected explosiveness index of each evaluation unit. The spatial interpolation partitioning module is used to determine the explosiveness level of each evaluation unit based on the modified explosiveness index, and to perform spatial interpolation to extend the modified explosiveness index of the discrete evaluation unit to the step plane, forming a continuous or quasi-continuous distribution of the explosiveness index field, and generating the gridded explosiveness partitioning result of the explosion zone. The blasting parameter recommendation module is used to recommend differentiated blasting parameters based on different explosiveness zones.

[0024] In this invention, the evaluation unit division module divides the area to be evaluated into several stepped planar evaluation units or blasting zone grid evaluation units, providing a clear spatial analysis framework for subsequent spatial registration of multi-source data and blastability evaluation, ensuring the spatial consistency between the evaluation results and the actual mining units in the mine. The benchmark index system construction module organically integrates indoor test data with exposed structural surface survey data, thereby constructing a two-dimensional benchmark blastability evaluation index system covering rock material fracture resistance and rock mass structural integrity. By unifying the direction and standardizing the two types of indicators, the incommensurability between indicators from different sources and with different dimensions is eliminated, providing a unified and standardized quantitative basis for horizontal comparison of different lithologies. The lithology benchmark evaluation module calculates the benchmark blastability index for different lithologies based on standardized indicators and their weights, and divides the evaluation results into five benchmark levels from extremely blastable to extremely difficult to blast, achieving a quantitative distinction between the blasting difficulty of different lithologies and providing a reliable reference benchmark for subsequent local corrections. Based on the local correction data acquisition module, local strength deviation information can be obtained through point load tests and information on the development of hidden structures within boreholes can be obtained for different evaluation units within the same lithology. This effectively compensates for the shortcomings of exposed structural surface surveys, which can only reflect surface characteristics. It enables the evaluation system to penetrate the rock mass surface and identify heterogeneity caused by local strength variations and differences in hidden structures within the same lithology. Based on the uncertainty processing module, systematic processing of four types of multi-source heterogeneous data—indoor test data, exposed structural surface data, point load test data, and borehole television data—can be performed. Through qualitative index defuzzification, quantitative index standardization, anomaly data identification and correction, weight redistribution of missing indexes, and spatial registration, it effectively solves problems such as diverse data sources, inconsistent dimensions, inconsistent spatial scales, and missing data, ensuring the stability and reliability of explosiveness evaluation results under conditions of incomplete and inconsistent multi-source data. Based on the local correction calculation module, a strength correction coefficient determined by point load test data and a borehole structure correction coefficient determined by borehole television data can be introduced on top of the baseline blastability index. Through weighted synthesis, bidirectional correction is achieved. This can correct upwards for difficult-to-blast areas caused by excessive local strength or good integrity, and downwards for easily blasted areas caused by well-developed fractures or obvious broken zones, overcoming the limitation of single-benchmark evaluation neglecting the heterogeneity within the same lithology. Based on the spatial interpolation zoning module, using the spatial coordinates of the evaluation unit as interpolation points and the corrected blastability index as interpolation attribute values, the discrete evaluation results can be extended into a continuous or quasi-continuous stepped plane blastability index field under the constraints of lithological boundaries and blasting zone boundaries. This generates a gridded blastability zoning result for the blasting zone, realizing a leap from discrete points to a continuous surface in blastability evaluation, providing a spatially continuous zoning basis for blasting design.Based on the settings of the blasting parameter recommendation module, differentiated blasting parameters such as hole spacing, row spacing, explosive consumption, charge structure, plugging length, and delay time can be recommended according to the results of the five-level explosiveness zoning. This forms a complete technical closed loop from multi-source data acquisition, benchmark evaluation, local correction, uncertainty handling, spatial interpolation to blasting parameter optimization, which improves the pertinence and engineering practicality of blasting design.

[0025] Based on a multi-level progressive architecture, the system can integrate multi-source heterogeneous data systems into a continuous explosiveness index field of the bench plane, realizing the upgrade of the explosiveness evaluation of bench blasting in open-pit mines from qualitative experience judgment to quantitative and precise zoning, and providing a scientific basis for the design of differentiated blasting parameters. Attached Figure Description

[0026] Figure 1 This is a flowchart of the explosiveness partitioning method in this invention; Figure 2 This is a flowchart illustrating the construction of the lithological benchmark blastability evaluation index system and the local correction index system in this invention; Figure 3 This is a schematic diagram of the interpolation of the explosiveness index field of the step plane and the gridded partitioning of the blast zone in this invention; where (a) is the result of the discrete evaluation unit, (b) is the explosiveness index field of the step plane, and (c) is the result of the gridded explosiveness partitioning of the blast zone. Figure 4 This is a schematic diagram of the explosiveness zoning system in this invention. Detailed Implementation

[0027] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0028] Example 1:

[0029] like Figure 1 and Figure 2 As shown in the figure, this embodiment provides a multi-source data explosiveness zoning method for bench blasting in open-pit mines. This method is applicable to the evaluation of the explosiveness of ore and rock in bench blasting areas of open-pit mines, the construction of bench plane explosiveness index fields, the gridded explosiveness zoning of blasting areas, and the recommendation of differentiated blasting parameters. Specifically, it includes the following steps: Step 1: Delineation of Explosiveness Evaluation Units; Obtain the spatial distribution information of lithology in the bench blasting area of ​​the open-pit mine to be evaluated, and delineate explosiveness evaluation units according to lithological boundaries, bench range, and blasting area boundaries; The explosiveness evaluation unit is either a bench planar evaluation unit or a blasting area grid evaluation unit, and each evaluation unit records its spatial coordinates, lithology, bench, and blasting area range.

[0030] As a preferred option, the process of dividing the explosiveness evaluation unit is as follows: S11: Obtain geological logging data, bench measurement data, blasting area design data, and sampling point coordinate data for the bench blasting area of ​​the open-pit mine to be evaluated, determine the distribution range of different lithologies, lithological boundaries, geological boundaries, bench elevations, and blasting area boundaries, and form lithological spatial distribution information; S12: Based on lithological boundaries, bench ranges, and blast zone boundaries, the area to be evaluated is divided into several bench planar evaluation units or blast zone grid evaluation units, and the spatial coordinates and lithology of each evaluation unit are recorded.

[0031] Through the above division, indoor test data, exposed structural surface data, point load test data, and borehole television data are uniformly matched into specific evaluation units, providing a basis for subsequent calculation of the benchmark explosiveness index, local correction, and spatial interpolation.

[0032] Step 2: Establish a lithological benchmark blastability evaluation index system; For different lithologies in the bench blasting area of ​​the open-pit mine to be evaluated, collect indoor test data of different representative lithologies. At the same time, conduct an investigation of exposed structural surfaces on bench slopes, exposed surfaces of the stope, or newly exposed rock surfaces to obtain exposed structural surface data, and construct a lithological benchmark blastability evaluation index system that includes rock material fracture resistance index and rock mass structural integrity index. As a preferred option, the process of establishing a lithological benchmark blastability evaluation index system is as follows: S21: For different lithologies within the bench blasting area of ​​the open-pit mine to be evaluated, representative rock samples were collected for indoor testing. Indoor tests included uniaxial compression tests, Brazilian splitting tests, Hopkinson bar impact tests, and physical property tests. Based on the indoor test data, rock material fracture resistance indices were obtained and ultimately categorized into a set of rock material fracture resistance indices. Specifically, the uniaxial compression test was used to obtain uniaxial compressive strength, elastic modulus, and Poisson's ratio; the Brazilian splitting test was used to obtain static tensile strength; the Hopkinson bar impact test was used to obtain dynamic compressive strength, dynamic tensile strength, dynamic failure characteristics, and impact fracture energy consumption; and the physical property tests were used to obtain one or more of density, water content, longitudinal wave velocity, transverse wave velocity, and hardness. Uniaxial compressive strength, elastic modulus, Poisson's ratio, static tensile strength, dynamic compressive strength, dynamic tensile strength, impact fracture energy consumption, density, wave velocity, and hardness constitute the rock material fracture resistance indices. S22: Investigate exposed structural surfaces on bench slopes, exposed mining faces, or newly exposed rock masses to obtain exposed structural surface data. Based on this data, derive rock mass structural integrity indices, which are then incorporated into a rock mass structural integrity index set. The exposed structural surface data includes one or more of the following: joint spacing, number of joint groups, joint dip angle, joint extension length, joint opening, joint roughness, fracture density, structural surface connectivity, and rock mass integrity. These factors—joint spacing, number of joint groups, joint dip angle, joint extension length, joint opening, joint roughness, fracture density, structural surface connectivity, and rock mass integrity—form the rock mass structural integrity index. S23: To ensure that indicators from different sources and with different dimensions can participate in a unified evaluation, the rock material fracture resistance index and rock mass structural integrity index are standardized and directionally unified. It is uniformly stipulated that the larger the standardized index value, the greater the difficulty of blasting the ore and rock. For indicators positively correlated with blasting difficulty, such as uniaxial compressive strength, static tensile strength, dynamic compressive strength, dynamic tensile strength, impact crushing energy consumption, elastic modulus, wave velocity, hardness, joint spacing, joint roughness, and rock mass integrity, a positive standardization formula is adopted. For indicators negatively correlated with blasting difficulty, such as fracture density, number of joint groups, joint opening, and structural surface continuity, a reverse standardization formula is used. ,in For the first The standardized values ​​of each indicator For the first The original values ​​of each indicator and The first The maximum and minimum values ​​of each indicator in the corresponding lithology or evaluation sample are obtained; after standardization, the standardized evaluation index values ​​of each lithology are obtained, and the standardized evaluation index values ​​of each lithology are used to form a lithological benchmark blastability evaluation index set, which provides input for subsequent calculation of the benchmark blastability index of different lithologies.

[0033] The rock material fracture resistance index is used to characterize the ability of rock materials to resist fracture under static and dynamic loading conditions, and the rock mass structural integrity index is used to characterize the control effect of the structural surface on the propagation of blast cracks and rock mass fracture. The lithological benchmark blastability evaluation index system is shown in Table 1: Table 1. Evaluation Index System for Lithological Baseline Explosiveness

[0034] Step 3: Calculate the benchmark blastability index and classify the benchmark blastability level; Based on the lithological benchmark blastability evaluation index system, after standardizing the indoor test data and exposed structural surface data of different lithologies, the standardized evaluation index values ​​of each lithology are obtained. The index weights are determined according to the degree of influence of each index on the difficulty of blasting ore and rock, and the benchmark blastability index of different lithologies is calculated and the benchmark blastability level of different lithologies is determined. As a preferred method, the process of calculating the baseline explosiveness index and classifying the baseline explosiveness level is as follows: S31: Based on the standardized values ​​of each indicator in the lithological benchmark blastability evaluation index set and the weights determined by the degree of influence of each indicator on the blasting difficulty of ore and rock, calculate the benchmark blastability index for different lithologies. ,in, For the first A standardized evaluation index value, For the first The weight of each evaluation indicator, The number of indicators participating in the benchmark evaluation, and the weights satisfy the following conditions: ; The higher the value, the greater the difficulty of blasting the rock type under bench blasting conditions. The smaller the value, the easier the rock type is to be broken by blasting; In one implementation, the indicator weight The weights can be determined based on expert experience, on-site blasting experience, analytic hierarchy process (AHP), entropy weighting method, coefficient of variation method, or a combination thereof. When historical blasting data is lacking, expert experience or AHP should be given priority in determining the weights. When sufficient experimental data and on-site survey data are available, objective weighting methods can be used to correct the weights.

[0035] As an alternative calculation method, the benchmark blastability index can also be obtained through a two-level weighted synthesis, the specific process of which is as follows: First, the various indices in the rock material fracture resistance index set are weighted to synthesize the rock material fracture resistance index. The rock mass structure integrity index is synthesized by weighting the various indicators within the rock mass structure integrity index set. Then, the two are weighted and combined, that is... ,in and These are the weights of the rock material's resistance to fracture and the rock mass's structural integrity, respectively. This method is mathematically compatible with the direct weighted summation method and can be flexibly selected based on actual data conditions and engineering experience.

[0036] S32: Based on the benchmark explosiveness index, the benchmark explosiveness levels of different rock types are divided into five levels, and quantified using a percentage system; among them, Level I is extremely explosive. Class II explosive Class III, moderately explosive Class IV, difficult to explode Points, Level V, extremely difficult to obtain. The higher the score, the greater the difficulty of blasting the ore and rock, as shown in Table 2.

[0037] Table 2. Classification Standards for Explosiveness Grades Based on Lithology

[0038] Step 4: Collect local correction data; After determining the benchmark blastability index and benchmark blastability level for different lithologies, collect point load test data and borehole television data for different evaluation units within the same lithology to obtain information on local strength differences and borehole structure development, and further construct a local correction index system that includes point load test index and borehole television index. As a preferred option, the process of collecting locally corrected data is as follows: S41: Point load test data is used to reflect local strength differences at different locations within the same lithology; point load test data is obtained by conducting point load tests on field rock blocks, borehole rock samples, or representative rock samples from blast areas collected from different evaluation units within the same lithology, and point load test indices are formed based on the point load test data; point load test data includes point load strength index, modified point load strength index, and point load strength deviation coefficient within the same lithology; point load strength deviation coefficient. The formula used to characterize the degree of deviation of the strength of a certain evaluation unit from the average strength level of that lithology is as follows: ,in For the first The point load strength index or modified point load strength index of each evaluation unit For the first The average point load strength index or the average modified point load strength index for lithological types; when When the local strength of the evaluation unit is higher than the average level of the same lithology, the blastability index is corrected towards the direction of difficult blasting; when When the strength of the local area of ​​the evaluation unit is lower than the average level of the same lithology, the explosiveness index is corrected towards the direction of increased explosiveness; when When the value is close to 0, it indicates that the local strength of the evaluation unit is close to the average level of the same lithology, and the benchmark blastability index can be kept unchanged or a small correction can be made within the set range. S42: Borehole television data is used to reflect the differences in the development of borehole structures at different locations within the same lithology; borehole television data is obtained by identifying the borehole structures of different evaluation units within the same lithology using borehole television data, and borehole television indices are formed based on the borehole television data; the borehole television data includes one or more of the following: number of borehole fractures, fracture spacing, fracture width, fracture dip angle, length of fracture zone, location of interlayer, length of intact section, and rock mass integrity variation information, in order to identify hidden fractures, fracture zones, interlayers, and intact rock sections within the evaluation unit that cannot be directly observed from exposed structural surfaces.

[0039] The point load test data and borehole television data are used as local correction indices, combined with the lithological benchmark blastability evaluation results, for subsequent calculation of positive and negative local correction coefficients. The local correction index system is shown in Table 3.

[0040] Table 3 Local Correction Index System within the Same Lithology

[0041] By collecting the aforementioned locally corrected data, based on different lithological benchmark blastability levels, it is possible to further identify the differences in blastability within the same lithology caused by local intensity variations and differences in concealed structures.

[0042] Step 5: Uncertainty processing of multi-source data; uncertainty processing is performed on indoor test data, exposed structural surface data, point load test data, and borehole television data to form a unified dataset for calculating local correction coefficients; Uncertainty handling includes defuzzification of qualitative indicators, standardization of quantitative indicators, identification of outliers, weight redistribution of missing indicators, and spatial registration, in order to eliminate the impact of different data sources, different units of measurement, different spatial scales, and missing data on the explosiveness evaluation results. As a preferred approach, the process of deterministic multi-source data processing is as follows: S51: Defuzzify qualitative indicators; Defuzzify qualitative indicators such as lithology, weathering degree, alteration degree, structural surface development degree, fracture zone development degree, interlayer development degree, and rock mass integrity description by using a grade mapping method, and convert them into quantitative scores; For example, the structural surface development degree can be divided into five levels: undeveloped, poorly developed, moderately developed, developed, and extremely developed, and assigned corresponding score values.

[0043] S52: Standardize quantitative indicators; For quantitative indicators such as uniaxial compressive strength, static tensile strength, dynamic compressive strength, impact crushing energy consumption, joint spacing, fracture density, point load strength index, fracture width, and fracture zone length, standardize them according to their positive or negative relationship with blasting difficulty, so that indicators of different dimensions are converted to a unified scoring range; after standardization, the larger the indicator value, the greater the blasting difficulty.

[0044] S53: Identify and correct abnormal data; based on field experience thresholds, reasonable experimental ranges, or statistical ranges of similar samples, identify a certain index value that is significantly outside the reasonable range or has abnormal differences from data in adjacent evaluation units; abnormal data can be corrected by methods such as removal, replacement with the mean of adjacent evaluation units, replacement with the mean of samples of the same lithology, or re-verification. S54: Redistribute weights for missing indicators; when any evaluation unit has missing indoor test data, exposed structural surface data, point load test data, or borehole television data, remove the corresponding weights from the weight set and normalize the remaining weights according to their original weight proportions; the formula for calculating the corrected weights under missing indicator conditions is as follows: ,in For the first Adjusted weights for the remaining indicators, For the first The original weights of the remaining indicators The number of remaining indicators to be evaluated; S55: Perform spatial registration; map indoor test data to its corresponding lithological distribution area, map exposed structural surface data to the spatial location of bench slopes or exposed surfaces in the stope, map point load test data to the evaluation unit where the sampling point is located, and map borehole television data to the evaluation unit where the borehole is located or its neighboring evaluation units. Through spatial registration, data from different sources are uniformly matched to bench plane evaluation units or blasting zone grid evaluation units, so that data from different scales and locations can participate in blastability calculation within the same evaluation unit and form a unified dataset for calculating local correction coefficients and corrected blastability indices.

[0045] Through the above uncertainty processing, a unified dataset is formed for calculating local correction coefficients and corrected explosiveness index.

[0046] Step 6: Calculate the modified blastability index; Based on the benchmark blastability index obtained for different lithologies, calculate the positive and negative local correction coefficients for different evaluation units within the same lithology, combining point load test data and borehole television data, and make corrections based on the benchmark blastability index to obtain the modified blastability index for each evaluation unit. As a preferred option, the process for calculating the modified explosiveness index is as follows: S61: Determine the strength correction factor from point load test data. The strength correction factor is determined by point load test data and is used to characterize the local strength differences among different evaluation units within the same lithology. Specifically, the strength correction factor is determined based on the degree of deviation of the point load strength of the evaluation unit from the average point load strength of the same lithology: when the point load strength of the evaluation unit is higher than the average value of the same lithology, A positive value indicates that the local strength of the evaluation unit is relatively high, and a positive correction is made towards the direction of difficult blasting; when it is lower than the average value of the same lithology, A negative value indicates that the local strength of the evaluation unit is low, and a negative correction is made towards the direction of potential explosion; in one embodiment, the strength correction coefficient can be based on the deviation coefficient of point load strength within the same lithology. Sure.

[0047] S62: Determine the borehole structure correction factor from borehole television data. The borehole structure correction factor, determined by borehole television data, characterizes the differences in the development of fractures, fracture zones, interlayers, and intact sections within different evaluation units of the same lithology. Specifically, the borehole structure correction factor is determined comprehensively based on the degree of fracture development, fracture zone development, interlayer development, and intact section development identified by borehole television data: when borehole fractures are underdeveloped, fracture zones are indistinct, interlayers are few, and intact sections are long, ... A positive value indicates that the rock mass of the evaluation unit has good integrity, and a positive correction is made towards the direction of difficult blasting; when there are well-developed fractures, obvious fracture zones, well-developed interlayers, or short intact sections, the value is negative. A negative value indicates that the rock mass integrity of the evaluation unit is poor, and a negative correction is made in the direction of blastability. S63: Based on the baseline explosiveness index, calculate the modified explosiveness index by combining the strength correction factor and the pore structure correction factor. ,in The blastability index is based on lithology. and These are the correction weights for the strength correction factor and the pore structure correction factor, respectively; the corrected explosiveness index is limited to the range of 0 to 100: 0 is taken when the calculation result is less than 0, and 100 is taken when the calculation result is greater than 100; when... or When it is positive, it indicates a correction towards a less explosive direction. or When the value is negative, it indicates a correction towards an explosive direction.

[0048] In one implementation, the correction direction can be determined according to Table 4.

[0049] Table 4 Positive and Negative Local Correction Rules

[0050] Through the above positive and negative local corrections, based on the blastability evaluation results of different lithological benchmarks, the local strength differences and pore concealed structure differences of different evaluation units within the same lithology are further reflected, thereby obtaining a more refined corrected blastability index.

[0051] Step 7: Construct the explosiveness index field of the step plane; determine the explosiveness level of each evaluation unit according to the modified explosiveness index, and use spatial interpolation method to extend the discrete evaluation results to the step plane to form a continuous or quasi-continuous explosiveness index field. As a preferred method, the process of constructing the explosiveness index field of the stepped plane is as follows: S71: After obtaining the modified explosiveness index of each evaluation unit, the spatial coordinates of each evaluation unit are used as the interpolation point position, and the corresponding modified explosiveness index is used as the interpolation attribute value. Combined with the lithological boundary, bench range and blasting zone boundary, spatial interpolation is performed on the discrete evaluation results to obtain the explosiveness index field that is continuously or quasi-continuously distributed in the bench plane. Spatial interpolation methods include one or more of inverse distance weighted interpolation, ordinary kriging interpolation, radial basis function interpolation, and natural neighborhood interpolation. In one implementation, when the number of evaluation units is small and the sample distribution is uneven, inverse distance weighted interpolation or natural neighborhood interpolation is used; when the number of evaluation units is large and there is obvious spatial correlation, ordinary kriging interpolation is used; when a smoother explosiveness index field is required, radial basis function interpolation is used. During the interpolation process, the spatial coordinates of the evaluation unit, the lithological boundary, and the blast zone boundary are used as constraints to ensure that the interpolation results do not cross unreasonable lithological boundaries or blast zone boundaries. For areas with significant differences between different lithologies, interpolation is performed within the corresponding lithological regions, or lithological boundary constraints are introduced during the interpolation process to avoid mutual interference between evaluation results of different lithologies.

[0052] S72: After interpolation, the explosiveness level is determined according to the index value of each grid point or grid unit in the explosiveness index field of the step plane, in accordance with the explosiveness level classification standard. The classification standard includes Level I extremely explosive, Level II easily explosive, Level III moderately explosive, Level IV difficult to explode and Level V extremely difficult to explode. The higher the explosiveness index, the greater the difficulty of blasting.

[0053] Through the above spatial interpolation process, point-like or local evaluation results are expanded into area-like distribution results, realizing the transformation from discrete evaluation units to a step-plane explosiveness index field, providing a foundation for subsequent gridded explosiveness zoning of the blast zone. For example... Figure 3 As shown, the discrete evaluation unit results are spatially interpolated to form a step plane explosiveness index field, which is then used for gridded explosiveness partitioning of the blast zone.

[0054] Step 8: Generate explosiveness zones and recommend blasting parameters; Generate gridded explosiveness zones based on the explosiveness index field of the step plane, and recommend differentiated blasting parameters according to different explosiveness zones.

[0055] As a preferred approach, the process of generating explosiveness zones and recommending blasting parameters is as follows: S81: Based on the blastability index field of the bench plane, the blasting area to be evaluated is divided into several grid units. The blastability index and blastability level corresponding to each grid unit are extracted to generate the blasting area gridded blastability partitioning result. The blasting area gridded blastability partitioning result includes the blastability index of each blasting area grid, the spatial location of each blasting area grid, the lithology to which it belongs, the blastability level, and the corresponding blasting parameter recommendation results. S82: Differentiated blasting parameters are recommended based on different explosiveness zones. Blasting parameters include one or more of the following: hole spacing, row spacing, explosive consumption per unit, charge structure, plugging length, and delay time. The recommended blasting parameter rules for different explosiveness zones are shown in Table 5. For Class I extremely explosive zones, reduce explosive consumption per unit and appropriately increase hole spacing or row spacing. For Class II easily explosive zones, use lower explosive consumption per unit and maintain conventional hole mesh parameters. For Class III moderately explosive zones, use standard explosive consumption per unit and standard hole mesh parameters. For Class IV difficult-to-explode zones, increase explosive consumption per unit and appropriately decrease hole spacing or row spacing. For Class V extremely difficult-to-explode zones, increase blasting energy input and optimize charge structure and delay time.

[0056] Table 5. Recommended rules for differentiated blasting parameters in different explosiveness zones.

[0057] In one implementation, a standard parameter set is set based on the existing blasting design parameters of the mine, and the standard parameter set is adjusted by adding or subtracting according to the explosiveness level. For example, the hole spacing, row spacing, and explosive consumption per unit area in a Class III medium explosiveness zone are used as standard values; when the zone level changes towards a more explosive direction, the explosive consumption per unit area is reduced or the hole mesh parameters are increased; when the zone level changes towards a less explosive direction, the explosive consumption per unit area is increased or the hole mesh parameters are decreased.

[0058] The above methods transform the explosiveness evaluation results into gridded blasting zones and differentiated blasting parameter recommendations, providing a basis for the design of bench blasting zones in open-pit mines.

[0059] This invention can take into account both the overall blastability differences of different lithologies and the local variations within the same lithology. Under the conditions of uncertain multi-source data, discrete evaluation points, and obvious spatial heterogeneity of rock masses, it can achieve refined zoning of the blastability of bench blasting in open-pit mines, providing a basis for zonal blasting design and blasting parameter optimization.

[0060] Example 2:

[0061] likeFigure 4 As shown, this embodiment provides a multi-source data explosiveness zoning system for bench blasting in open-pit mines, used to implement a multi-source data explosiveness zoning method for bench blasting in open-pit mines, including: The evaluation unit division module is used to obtain the lithological spatial distribution information of the bench blasting area in the open-pit mine to be evaluated, and to divide the blastability evaluation units according to the lithological boundaries, bench range, and blasting zone boundaries; the lithological spatial distribution information includes the distribution range of different lithologies, lithological boundaries, geological boundaries, bench elevations, and blasting zone boundaries.

[0062] The benchmark index system construction module is used to collect indoor test data and exposed structural surface data of different lithologies to establish a lithological benchmark blastability evaluation index system that includes rock material fracturing resistance index and rock mass structural integrity index. The indoor test data is obtained by conducting uniaxial compression tests, Brazilian splitting tests, Hopkinson bar impact tests, and physical property tests on representative rock samples of different lithologies, and is categorized into the rock material fracturing resistance index set. The exposed structural surface data is obtained by conducting structural surface surveys of bench slopes, exposed surfaces in mining areas, or newly exposed rock masses, and is categorized into the rock mass structural integrity index set. The benchmark index system construction module performs directional unification and standardization processing on both types of indices, so that the larger the standardized index value, the greater the difficulty of blasting the ore and rock. The standardized indices together constitute the lithological benchmark blastability evaluation index set.

[0063] The lithology benchmark evaluation module is used to calculate the benchmark explosiveness index for different lithologies based on the standardized values ​​and weights of each index in the lithology benchmark explosiveness evaluation index system, and to determine the benchmark explosiveness level for different lithologies based on the benchmark explosiveness index. The benchmark explosiveness level includes Level I (extremely easy to blast), Level II (easy to blast), Level III (moderately explosive), Level IV (difficult to blast), and Level V (extremely difficult to blast). The higher the score, the greater the difficulty of blasting the ore and rock.

[0064] The local correction data acquisition module is used to collect point load test data and borehole television data for different evaluation units within the same lithology, and to obtain information on local strength differences and borehole structure development. The point load test data is obtained by conducting point load tests on field rock blocks, borehole rock samples, or representative rock samples from the blast area, and includes the point load strength index, the corrected point load strength index, and the deviation coefficient of point load strength within the same lithology. The borehole television data is obtained by identifying the borehole structure of the evaluation unit, including one or more of the following: number of fractures, fracture spacing, fracture width, fracture zone length, interlayer location, length of intact section, and rock mass integrity variation information.

[0065] An uncertainty processing module is used to process indoor test data, exposed structural surface data, point load test data, and borehole television data to form a unified dataset for calculating local correction coefficients. The uncertainty processing includes: defuzzifying qualitative indicators using a rank mapping method to convert them into quantitative scores; standardizing quantitative indicators according to their positive or negative relationship with blasting difficulty; identifying and correcting abnormal data based on field experience thresholds, reasonable test ranges, or statistical ranges of similar samples; removing the corresponding weights of missing indicators from the weight set and normalizing the remaining weights according to their original weight proportions; and spatially registering multi-source data to uniformly match them to bench plane evaluation units or blasting zone grid evaluation units.

[0066] The local correction calculation module is used to calculate local correction coefficients based on the baseline blastability index, combined with point load test data and borehole television data, to obtain the corrected blastability index for each evaluation unit. The local correction coefficients include a strength correction coefficient determined by the point load test data and a borehole structure correction coefficient determined by the borehole television data. The strength correction coefficient is determined based on the deviation of the evaluation unit's point load strength from the average point load strength of the same lithology. The borehole structure correction coefficient is comprehensively determined based on the degree of fracture development, fracture zone development, interlayer development, and intact section development identified by borehole television. The corrected blastability index is a weighted synthesis of the baseline blastability index, the strength correction coefficient, and the borehole structure correction coefficient, and the corrected blastability index is limited to the range of 0–100.

[0067] The spatial interpolation zoning module is used to determine the explosiveness level of each evaluation unit based on the modified explosiveness index. It uses the spatial coordinates of each evaluation unit as the interpolation point location and the corresponding modified explosiveness index as the interpolation attribute value. It performs spatial interpolation on the discrete evaluation results in combination with lithological boundaries, bench ranges, and blast zone boundaries, extending the modified explosiveness index of the discrete evaluation units to the bench plane to form a continuous or quasi-continuously distributed explosiveness index field and generating gridded explosiveness zoning results for the blast zone. During the spatial interpolation process, the spatial coordinates of the evaluation units, lithological boundaries, and blast zone boundaries are used as constraints to avoid mutual interference between evaluation results of different lithologies.

[0068] The blasting parameter recommendation module is used to recommend differentiated blasting parameters based on different explosiveness zones. These blasting parameters include one or more of the following: hole spacing, row spacing, explosive consumption per unit area, charge structure, plugging length, and delay time. Specifically, for Class I highly explosive zones, explosive consumption per unit area is reduced and hole spacing or row spacing is appropriately increased; for Class II easily explosive zones, lower explosive consumption per unit area is used while maintaining conventional hole mesh parameters; for Class III moderately explosive zones, standard explosive consumption per unit area and standard hole mesh parameters are used; for Class IV difficult-to-explode zones, explosive consumption per unit area is increased and hole spacing or row spacing is appropriately decreased; for Class V extremely difficult-to-explode zones, blasting energy input is increased and charge structure and delay time are optimized.

[0069] Through the above system, data is transferred and processed sequentially between modules, realizing a complete process from multi-source data acquisition, lithological benchmark evaluation, local correction, uncertainty handling, spatial interpolation zoning to blasting parameter recommendation.

[0070] In this invention, the evaluation unit division module divides the area to be evaluated into several stepped planar evaluation units or blasting zone grid evaluation units, providing a clear spatial analysis framework for subsequent spatial registration of multi-source data and blastability evaluation, ensuring the spatial consistency between the evaluation results and the actual mining units in the mine. The benchmark index system construction module organically integrates indoor test data with exposed structural surface survey data, thereby constructing a two-dimensional benchmark blastability evaluation index system covering rock material fracture resistance and rock mass structural integrity. By unifying the direction and standardizing the two types of indicators, the incommensurability between indicators from different sources and with different dimensions is eliminated, providing a unified and standardized quantitative basis for horizontal comparison of different lithologies. The lithology benchmark evaluation module calculates the benchmark blastability index for different lithologies based on standardized indicators and their weights, and divides the evaluation results into five benchmark levels from extremely blastable to extremely difficult to blast, achieving a quantitative distinction between the blasting difficulty of different lithologies and providing a reliable reference benchmark for subsequent local corrections. Based on the local correction data acquisition module, local strength deviation information can be obtained through point load tests and information on the development of hidden structures within boreholes can be obtained for different evaluation units within the same lithology. This effectively compensates for the shortcomings of exposed structural surface surveys, which can only reflect surface characteristics. It enables the evaluation system to penetrate the rock mass surface and identify heterogeneity caused by local strength variations and differences in hidden structures within the same lithology. Based on the uncertainty processing module, systematic processing of four types of multi-source heterogeneous data—indoor test data, exposed structural surface data, point load test data, and borehole television data—can be performed. Through qualitative index defuzzification, quantitative index standardization, anomaly data identification and correction, weight redistribution of missing indexes, and spatial registration, it effectively solves problems such as diverse data sources, inconsistent dimensions, inconsistent spatial scales, and missing data, ensuring the stability and reliability of explosiveness evaluation results under conditions of incomplete and inconsistent multi-source data. Based on the local correction calculation module, a strength correction coefficient determined by point load test data and a borehole structure correction coefficient determined by borehole television data can be introduced on top of the baseline blastability index. Through weighted synthesis, bidirectional correction is achieved. This can correct upwards for difficult-to-blast areas caused by excessive local strength or good integrity, and downwards for easily blasted areas caused by well-developed fractures or obvious broken zones, overcoming the limitation of single-benchmark evaluation neglecting the heterogeneity within the same lithology. Based on the spatial interpolation zoning module, using the spatial coordinates of the evaluation unit as interpolation points and the corrected blastability index as interpolation attribute values, the discrete evaluation results can be extended into a continuous or quasi-continuous stepped plane blastability index field under the constraints of lithological boundaries and blasting zone boundaries. This generates a gridded blastability zoning result for the blasting zone, realizing a leap from discrete points to a continuous surface in blastability evaluation, providing a spatially continuous zoning basis for blasting design.Based on the settings of the blasting parameter recommendation module, differentiated blasting parameters such as hole spacing, row spacing, explosive consumption, charge structure, plugging length, and delay time can be recommended according to the results of the five-level explosiveness zoning. This forms a complete technical closed loop from multi-source data acquisition, benchmark evaluation, local correction, uncertainty handling, spatial interpolation to blasting parameter optimization, which improves the pertinence and engineering practicality of blasting design.

[0071] Based on a multi-level progressive architecture, the system can integrate multi-source heterogeneous data systems into a continuous explosiveness index field of the bench plane, realizing the upgrade of the explosiveness evaluation of bench blasting in open-pit mines from qualitative experience judgment to quantitative and precise zoning, and providing a scientific basis for the design of differentiated blasting parameters.

[0072] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multi-source data-based blastability partitioning method for bench blasting in open-pit mines, characterized in that, Includes the following steps: Step 1: Delineation of Explosiveness Evaluation Units; Obtain the spatial distribution information of lithology in the bench blasting area of ​​the open-pit mine to be evaluated, and delineate explosiveness evaluation units according to lithological boundaries, bench range, and blasting zone boundaries; Step 2: Establish a lithological benchmark blastability evaluation index system; collect indoor test data and exposed structural surface data of different lithologies, and construct a lithological benchmark blastability evaluation index system that includes rock material fracture resistance index and rock mass structural integrity index; Step 3: Calculate the baseline explosiveness index and classify the baseline explosiveness level; The benchmark explosiveness index for different lithologies is calculated based on the benchmark explosiveness evaluation index system of lithology, and the benchmark explosiveness level for different lithologies is determined. Step 4: Collect local correction data; For different evaluation units within the same lithology, collect point load test data and borehole television data, and construct a local correction index system that includes point load test indexes and borehole television indexes. Step 5: Uncertainty processing of multi-source data; uncertainty processing is performed on indoor test data, exposed structural surface data, point load test data, and borehole television data to form a unified dataset for calculating local correction coefficients; Step 6: Calculate the corrected explosiveness index; Based on the benchmark explosiveness index, local correction coefficients are calculated by combining point load test data and borehole television data to obtain the corrected explosiveness index for each evaluation unit. Step 7: Construct the explosiveness index field of the step plane; determine the explosiveness level of each evaluation unit according to the modified explosiveness index, and use spatial interpolation method to extend the discrete evaluation results to the step plane to form a continuous or quasi-continuous explosiveness index field. Step 8: Generate explosiveness zones and recommend blasting parameters; Generate gridded explosiveness zones based on the explosiveness index field of the step plane, and recommend differentiated blasting parameters according to different explosiveness zones.

2. The multi-source data blastability partitioning method for bench blasting in open-pit mines according to claim 1, characterized in that, In step 1, the process of dividing the explosiveness evaluation unit is as follows: S11: Obtain geological logging data, bench measurement data, blasting area design data, and sampling point coordinate data for the bench blasting area of ​​the open-pit mine to be evaluated, determine the distribution range of different lithologies, lithology boundaries, geological boundaries, bench elevations, and blasting area boundaries, and form lithology spatial distribution information; S12: Based on lithological boundaries, bench ranges, and blast zone boundaries, the area to be evaluated is divided into several bench planar evaluation units or blast zone grid evaluation units, and the spatial coordinates and lithology of each evaluation unit are recorded.

3. The multi-source data blastability partitioning method for bench blasting in open-pit mines according to claim 1, characterized in that, In step 2, the process of establishing the lithological benchmark blastability evaluation index system is as follows: S21: For different lithologies in the bench blasting area of ​​the open-pit mine to be evaluated, representative rock samples were collected for indoor tests, and the rock material fracture resistance index was obtained based on the indoor test data and finally included in the rock material fracture resistance index set. S22: Investigate the exposed structural surfaces of bench slopes, mining exposed surfaces, or newly exposed rock mass surfaces, obtain exposed structural surface data, and obtain rock mass structural integrity indicators based on the exposed structural surface data, and finally classify them into the rock mass structural integrity indicator set; S23: Standardize and unify the direction of rock material fracture resistance and rock mass structural integrity indices. For indices positively correlated with blasting difficulty, adopt a positive standardization formula. For indicators negatively correlated with blasting difficulty, an inverse standardization formula is used. ,in For the first The standardized values ​​of each indicator For the first The original values ​​of each indicator and The first The maximum and minimum values ​​of each indicator in the corresponding lithology or evaluation sample; the standardized indicators together constitute the lithological benchmark blastability evaluation index set.

4. The multi-source data blastability partitioning method for bench blasting in open-pit mines according to claim 1, characterized in that, In step 3, the process of calculating the baseline explosiveness index and classifying the baseline explosiveness level is as follows: S31: Calculate the benchmark blastability index for different lithologies based on the standardized values ​​and weights of each index in the lithological benchmark blastability evaluation index set. ,in, For the first A standardized evaluation index value, For the first The weight of each evaluation indicator, The number of indicators participating in the benchmark evaluation, and the weights satisfy the following conditions: ; S32: Based on the benchmark explosiveness index, the benchmark explosiveness levels of different rock types are divided into five levels, and quantified using a percentage system; among them, Level I is extremely explosive. Class II explosive Class III, moderately explosive Class IV, difficult to explode Points, Level V, extremely difficult to obtain. The higher the score, the greater the difficulty of blasting the ore and rock.

5. A multi-source data blastability partitioning method for bench blasting in open-pit mines according to claim 1, characterized in that, In step 4, the process of acquiring local correction data is as follows: S41: Point load test data is obtained by conducting point load tests on field rock blocks, borehole rock samples, or representative rock samples from blast areas collected from different evaluation units within the same lithology. Point load test indices are then formed based on this data. The point load test data includes the point load strength index, the corrected point load strength index, and the point load strength deviation coefficient within the same lithology. The point load strength deviation coefficient... The formula used to characterize the degree of deviation of the strength of a certain evaluation unit from the average strength level of that lithology is as follows: ,in For the first The point load strength index or modified point load strength index of each evaluation unit For the first The average point load strength index or the average modified point load strength index for lithological types; when When the local strength of the evaluation unit is higher than the average level of the same lithology, the blastability index is corrected towards the direction of difficult blasting; when When the strength of the local area of ​​the evaluation unit is lower than the average level of the same lithology, the explosiveness index is corrected towards the direction of increased explosiveness; when When the value is close to 0, it indicates that the local strength of the evaluation unit is close to the average level of the same lithology. The benchmark blastability index should be kept unchanged or a small correction should be made within the set range. S42: Use borehole television to identify the internal structure of different evaluation units within the same lithology, obtain borehole television data, and form borehole television indices based on the borehole television data; the borehole television data includes one or more of the following: number of fractures, fracture spacing, fracture width, fracture dip angle, fracture zone length, interlayer location, length of intact section, and rock mass integrity variation information.

6. The multi-source data blastability partitioning method for bench blasting in open-pit mines according to claim 1, characterized in that, In step 5, the process of processing uncertain multi-source data is as follows: S51: Defuzzify the qualitative indicators; convert the qualitative indicators into quantitative scores using a grade mapping method; S52: Standardize quantitative indicators; For quantitative indicators, standardize them according to their positive or negative relationship with the difficulty of blasting, so that indicators of different dimensions are converted into a unified scoring range. S53: Identify and correct abnormal data; based on field experience thresholds, reasonable experimental ranges, or statistical ranges of similar samples, identify index values ​​that significantly exceed reasonable ranges or differ abnormally from data in adjacent evaluation units; correct abnormal data by methods such as removal, replacement with the mean of adjacent evaluation units, replacement with the mean of samples of the same lithology, or re-verification; S54: Redistribute weights for missing indicators; when any evaluation unit has missing indoor test data, exposed structural surface data, point load test data, or borehole television data, remove the corresponding weights from the weight set and normalize the remaining weights according to their original weight proportions; the formula for calculating the corrected weights under missing indicator conditions is as follows: ,in For the first Adjusted weights for the remaining indicators, For the first The original weights of the remaining indicators The number of remaining indicators to be evaluated; S55: Perform spatial registration; map indoor test data to its corresponding lithological distribution area, map exposed structural surface data to the spatial location of bench slope or exposed surface in the stope, map point load test data to the evaluation unit where the sampling point is located, map borehole television data to the evaluation unit where the borehole is located or its neighboring evaluation unit, so that data from different sources are uniformly matched to the bench plane evaluation unit or blasting area grid evaluation unit.

7. The multi-source data blastability partitioning method for bench blasting in open-pit mines according to claim 1, characterized in that, In step 6, the process of calculating the corrected explosiveness index is as follows: S61: Determine the strength correction factor from point load test data. ; The strength correction factor is determined based on the degree of deviation of the load strength at the evaluation unit point from the average load strength at the same lithology: when the load strength at the evaluation unit point is higher than the average value at the same lithology, A positive value indicates that the local strength of the evaluation unit is relatively high, and a positive correction is made towards the direction of difficult blasting; when it is lower than the average value of the same lithology, A negative value indicates that the local strength of the evaluation unit is low, and a negative correction is made in the direction of explosiveness. S62: Determine the borehole structure correction factor from borehole television data. The borehole structure correction factor is determined comprehensively based on the degree of fracture development, fracture zone development, interlayer development, and intact section development obtained from borehole television identification. When the borehole fractures are underdeveloped, the fracture zone is not obvious, the interlayer is few, and the intact section is long, A positive value indicates that the rock mass of the evaluation unit has good integrity, and a positive correction is made towards the direction of difficult blasting; when there are well-developed fractures, obvious fracture zones, well-developed interlayers, or short intact sections, the value is negative. A negative value indicates that the rock mass integrity of the evaluation unit is poor, and a negative correction is made in the direction of blastability. S63: Based on the baseline explosiveness index, calculate the modified explosiveness index by combining the strength correction factor and the pore structure correction factor. ,in The blastability index is based on lithology. and These are the correction weights for the strength correction factor and the internal structure correction factor, respectively.

8. A multi-source data blastability partitioning method for bench blasting in open-pit mines according to claim 1, characterized in that, In step 7, the process of constructing the explosiveness index field of the step plane is as follows: S71: The spatial coordinates of each evaluation unit are used as the interpolation point positions, and the corresponding modified explosiveness index is used as the interpolation attribute value. The discrete evaluation results are spatially interpolated in combination with the lithological boundary, bench range and blasting zone boundary to obtain the explosiveness index field that is continuously or quasi-continuously distributed in the bench plane. During the interpolation process, the spatial coordinates of the evaluation unit, the lithological boundary, and the blast zone boundary are used as constraints to ensure that the interpolation results do not cross unreasonable lithological boundaries or blast zone boundaries. For areas with significant differences between different lithologies, interpolation is performed within the corresponding lithological regions, or lithological boundary constraints are introduced during the interpolation process to avoid mutual interference between evaluation results of different lithologies. S72: Determine the explosiveness level according to the explosiveness level classification standard based on the index value of each grid point or grid unit in the explosiveness index field of the step plane. The classification standards include Level I: extremely explosive; Level II: explosive; Level III: moderately explosive; Level IV: difficult to explode; and Level V: extremely difficult to explode.

9. A multi-source data blastability partitioning method for bench blasting in open-pit mines according to claim 1, characterized in that, In step 8, the process of generating explosiveness zones and recommending blasting parameters is as follows: S81: Based on the blastability index field of the bench plane, the blasting area to be evaluated is divided into several grid units. The blastability index and blastability level corresponding to each grid unit are extracted to generate the blasting area gridded blastability partitioning result. The blasting area gridded blastability partitioning result includes the blastability index of each blasting area grid, the spatial location of each blasting area grid, the lithology to which it belongs, the blastability level, and the corresponding blasting parameter recommendation results. S82: Differentiated blasting parameters are recommended based on different explosiveness zones; blasting parameters include one or more of the following: hole spacing, row spacing, explosive consumption per unit, charge structure, plugging length, and delay time; for Class I extremely explosive zones, reduce explosive consumption per unit and appropriately increase hole spacing or row spacing; for Class II easily explosive zones, use lower explosive consumption per unit and maintain conventional hole mesh parameters; for Class III moderately explosive zones, use standard explosive consumption per unit and standard hole mesh parameters; for Class IV difficult-to-explode zones, increase explosive consumption per unit and appropriately decrease hole spacing or row spacing; for Class V extremely difficult-to-explode zones, increase blasting energy input and optimize charge structure and delay time.

10. A multi-source data explosiveness zoning system for bench blasting in open-pit mines, used to implement the multi-source data explosiveness zoning method for bench blasting in open-pit mines as described in any one of claims 1 to 9, characterized in that, include: The evaluation unit division module is used to obtain the lithological spatial distribution information of the bench blasting area in the open-pit mine to be evaluated, and to divide the blastability evaluation units according to the lithological boundaries, bench range and blasting zone boundaries. The benchmark index system construction module is used to collect indoor test data and exposed structural surface data of different lithologies, and to establish a lithological benchmark blastability evaluation index system that includes rock material fracture resistance index and rock mass structure integrity index. The lithological benchmark evaluation module is used to calculate the benchmark explosiveness index of different lithologies based on the standardized values ​​and weights of each index in the lithological benchmark explosiveness evaluation index system, and to determine the benchmark explosiveness level of different lithologies based on the benchmark explosiveness index. The local correction data acquisition module is used to collect point load test data and borehole television data for different evaluation units within the same lithology, and to obtain information on local strength differences and borehole structure development. The uncertainty processing module is used to perform uncertainty processing on indoor test data, exposed structural surface data, point load test data, and borehole television data to form a unified dataset for calculating local correction coefficients. The local correction calculation module is used to calculate the local correction coefficient based on the benchmark explosiveness index, combined with point load test data and borehole television data, to obtain the corrected explosiveness index of each evaluation unit. The spatial interpolation partitioning module is used to determine the explosiveness level of each evaluation unit based on the modified explosiveness index, and to perform spatial interpolation to extend the modified explosiveness index of the discrete evaluation unit to the step plane, forming a continuous or quasi-continuous distribution of the explosiveness index field, and generating the gridded explosiveness partitioning result of the explosion zone. The blasting parameter recommendation module is used to recommend differentiated blasting parameters based on different explosiveness zones.

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