A mine design planning method, device and medium based on multiple types of data

Abstract

The application discloses a mine design planning method and device based on multiple types of data and a medium, relates to the technical field of mine design planning, and comprises the following steps: establishing a mine design planning parameter library by integrating geological data, remote sensing data, geophysical data and equipment operation data; performing space-time alignment and abnormal marking according to the mine design planning parameter library, obtaining standardized mine planning data and mine planning data residuals, and calculating data quality indexes and error variances by using the mine planning data residuals, an abnormal proportion and a time-effect amount; reading a slow update window and a fast update window, and dynamically updating a mine three-dimensional geology, mine block attributes, mine deformation intensity and equipment availability based on the data quality indexes; generating a cross-time mark constraint scaling coefficient, correcting a safety distance and an upper bound of capacity, and generating an exclusion domain. The application provides dynamic adjustment capability and accurate correction of mine planning, and ensures real-time response and continuous optimization of a mine design scheme.

Description

Technical Field

[0001] This invention relates to the field of mine design and planning technology, and in particular to a mine design and planning method, equipment and medium based on multi-type data. Background Technology

[0002] Mine design and planning involve a large amount of geological data, remote sensing data, geophysical data, and equipment operation data. This data typically requires processing and analysis to support efficient and safe mine operation. With technological advancements, especially in the application of big data and artificial intelligence, mine design and planning methods based on multi-type data have gradually been proposed and applied. Modern mine design no longer relies solely on a single data source but integrates multiple types of data, including geological, remote sensing, geophysical, and equipment operation data, utilizing data fusion and analysis techniques for more precise and efficient planning. This method can update planning schemes in real time based on dynamic monitoring data, improving mine efficiency, safety, and resource utilization.

[0003] However, existing mine design and planning methods still have certain limitations. First, because these methods often rely on static data and lack dynamic feedback mechanisms, they cannot adjust the planning in a timely manner according to changes in the mine environment or equipment failures. Second, existing technologies mostly lack cross-timescale data update mechanisms, making it difficult to achieve real-time updates in a continuously changing production environment, especially posing a significant challenge in the synchronous update of geological models and equipment status models. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a mine design and planning method based on multi-type data to solve the problems of slow response to changes in the mine environment and insufficient real-time data updates in existing technologies.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a mine design and planning method based on multi-type data, comprising,

[0008] Acquire geological data, remote sensing data, geophysical data, and equipment operation data; unify coordinates and time benchmarks; and establish a version registration list and a mine design and planning parameter library.

[0009] Spatiotemporal alignment and anomaly marking are performed based on the mine design and planning parameter library to obtain standardized mine planning data and mine planning data residuals;

[0010] Using the residuals of mine planning data, the proportion of abnormalities in mine planning data, and the timeliness of mine planning data, calculate the data quality indicators and error variance, and write them back to the registration list;

[0011] Read the slow update window and fast update window from the mine design and planning parameter library, and define the slow variable, fast variable and triggering conditions;

[0012] When triggered by a slow update window, the three-dimensional geology and block attributes of the mine are updated using data quality indicators. When triggered by a fast update window, the deformation intensity and equipment availability of the mine are updated using data quality indicators.

[0013] Based on the mine deformation intensity and equipment availability, a cross-timescale constraint scaling factor is generated to correct the safety distance and upper limit of production capacity, and a no-entry domain is generated.

[0014] Under modified constraints, mine design elements are generated and the planning is solved in a rolling manner.

[0015] As a preferred embodiment of the mine design and planning method based on multi-type data described in this invention, the specific steps for establishing the version registration list and the mine design and planning parameter library are as follows:

[0016] Acquire geological data, remote sensing data, geophysical data, and equipment operation data;

[0017] By reading the source coordinates and time reference identifiers of all data, the coordinates and time of each record are converted into a unified coordinate and time reference.

[0018] Record all data and their coordinate and time transformations, and create a version registration list;

[0019] Based on the established version registration list, benchmark entries are created in the mine design planning parameter library.

[0020] As a preferred embodiment of the mine design and planning method based on multi-type data described in this invention, the specific steps for obtaining standardized mine planning data and mine planning data residuals are as follows:

[0021] Perform spatial alignment on the recorded parameters of the spatial coordinates and calculate the spatial registration residuals;

[0022] Perform time alignment on the record parameters of the timestamp and calculate the time alignment residual;

[0023] A joint anomaly score is constructed based on the amplitude changes in space and time, and the score is calculated using a sliding window.

[0024] Map the joint anomaly scores to anomaly tags to obtain the anomaly set;

[0025] The parameters after spatial registration and temporal alignment are uniformly encapsulated into standardized mine planning data, and the mine planning data residuals bound to the record identifier are output.

[0026] As a preferred embodiment of the mine design and planning method based on multi-type data described in this invention, the specific steps for calculating data quality indicators and error variance using mine planning data residuals, mine planning data anomaly ratios, and mine planning data timeliness, and then writing them back to the registration list, are as follows:

[0027] Read spatial registration residuals and temporal alignment residuals from the mine planning data residuals, read outliers from the outlier set and count the outlier ratio, and calculate the timeliness from the current time and the record collection termination time;

[0028] The anomaly ratio is obtained by comparing the set of recorded anomalies with the standardized sequence length.

[0029] Subtract the recorded collection termination time from the current time to obtain the time efficiency.

[0030] The spatial registration residual and temporal alignment residual are normalized and synthesized into a comprehensive residual index;

[0031] After normalizing the anomaly ratio and timeliness, together with the comprehensive residual, a quality decay term is formed, and the data quality index is calculated using an exponential decay method.

[0032] In the calculation of error variance, the calibration noise variance and the additional variance resulting from the combination of residuals, anomalies and time aging are added together to obtain the error variance;

[0033] Write the recorded data quality metrics and error variance into the quality and variance fields of the register.

[0034] As a preferred embodiment of the mine design and planning method based on multi-type data described in this invention, the specific steps for reading the slow update window and the fast update window from the mine design and planning parameter library are as follows:

[0035] Read the configuration items for the slow update window and the fast update window from the mine design and planning parameter library;

[0036] Based on the actual needs of the mine site, set the time window length for fast and slow update windows;

[0037] Read the entries related to slow variables from the mine design and planning parameter library, and assign a slow update window to each slow variable;

[0038] Retrieve entries related to fast variables from the mine design and planning parameter library, and assign a fast update window to each fast variable.

[0039] As a preferred embodiment of the mine design and planning method based on multi-type data described in this invention, the following steps are taken: when triggered by a slow update window, the three-dimensional geology and mine block attributes are updated through data quality indicators; when triggered by a fast update window, the mine deformation intensity and equipment availability are updated through data quality indicators.

[0040] When the update cycle of slow variables in the mine planning reaches the set slow update window, the update of the three-dimensional geology of the mine and the attributes of the mine block is triggered.

[0041] When the update cycle of fast variables in the mine planning reaches the set fast update window, the mine deformation intensity and equipment availability are updated.

[0042] When the slow update window is triggered, data quality indicators related to the three-dimensional geological model of the mine and the attributes of the mine block are read from the mine design and planning data, and the data quality indicators are normalized.

[0043] When the quick update window is triggered, read the data quality indicators related to the mine deformation intensity and equipment availability from the real-time monitoring data of the mine, and perform normalization processing.

[0044] When the slow update window is triggered, the 3D geological model of the mine is updated using data quality indicators, and the block attributes are updated using ore body exploration data.

[0045] When the quick update window is triggered, the deformation intensity is updated in real time based on the deformation monitoring data and data quality indicators, and the equipment availability is updated in real time based on the equipment operation data and the data quality indicators of the equipment operation data.

[0046] As a preferred embodiment of the mine design and planning method based on multi-type data described in this invention, the steps of generating cross-timescale constraint scaling factors, correcting safety distances and upper limits of production capacity, and generating prohibited areas based on mine deformation intensity and equipment availability are as follows:

[0047] Based on data on mine deformation intensity and equipment availability, generate cross-timescale constraint scaling factors;

[0048] The safety distance of the mining operation area is corrected by using a cross-timescale constraint scaling factor;

[0049] The upper limit of mining operation capacity is dynamically adjusted based on the cross-timescale constraint scaling factor;

[0050] A new forbidden zone is generated based on the revised safety distance;

[0051] The restricted area refers to a region where entry is prohibited.

[0052] The forbidden domain boundary is dynamically updated by adjusting the safety distance and the cross-timescale constraint scaling factor.

[0053] As a preferred embodiment of the mine design and planning method based on multi-type data described in this invention, the specific steps of generating mine design elements and solving the planning problem on a rolling basis under modified constraints are as follows:

[0054] The revised safety distance and upper limit of production capacity are used as constraints to define mine design elements;

[0055] By utilizing the revised safety distance and upper limit of production capacity, combined with the three-dimensional geological model of the mine and block attributes, new mine design elements are generated and updated in real time during each design cycle.

[0056] Within each planning cycle, starting from the current time, the mine design elements are optimized and solved based on the current correction constraints, mine production data, and actual operating conditions to achieve the production target.

[0057] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements any step of the mine design and planning method based on multi-type data as described in the first aspect of the present invention.

[0058] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the mine design and planning method based on multi-type data as described in the first aspect of the present invention.

[0059] The beneficial effects of this invention are as follows: by integrating geological data, remote sensing data, geophysical data, and equipment operation data, multi-dimensional and precise planning of mine design schemes is achieved; by combining data quality indicators, mine planning data residuals, and anomaly ratios, real-time monitoring and dynamic updating of mine design data are achieved; by combining real-time feedback of deformation intensity and equipment availability, dynamic adjustment capability of mine planning is achieved; and by generating cross-timescale constraint scaling coefficients, precise correction of mine safety distances and upper limits of production capacity is achieved. Attached Figure Description

[0060] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a flowchart of a mine design and planning method based on multi-type data.

[0062] Figure 2 A flowchart for establishing a database of mine design and planning parameters.

[0063] Figure 3 A flowchart for spatiotemporal alignment and anomaly marking processing of mine design planning data.

[0064] Figure 4 A flowchart for data quality calculation and error variance generation.

[0065] Figure 5 A scatter plot of the proportion of anomalies in the stable segment and data quality indicators.

[0066] Figure 6 This is a scatter plot of the anomaly rate and data quality indicators for the risk segment.

[0067] Figure 7 A scatter plot of the percentage of anomalies in the recovery segment and data quality indicators. Detailed Implementation

[0068] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0069] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0070] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0071] Reference Figures 1-7 This is one embodiment of the present invention, which provides a mine design and planning method based on multi-type data, including the following steps:

[0072] S1. Acquire geological data, remote sensing data, geophysical data, and equipment operation data; unify coordinates and time references; and establish a version registration list and a mine design and planning parameter library.

[0073] Furthermore, the engineering coordinate benchmark for the mining area is defined as the target coordinate reference set, which includes at least the plane projection parameters, the benchmark ellipsoid, and the elevation benchmark; the unified time benchmark is defined as the coordinated time, and the timestamp accuracy and leap second handling rules are specified.

[0074] Establish benchmark entries in the mine design and planning parameter library, and write the benchmark name, benchmark version number, effective time, applicable mining area boundary range, and maintenance responsibility field to form a traceable record.

[0075] A set of registration fields is predefined in the mine design and planning parameter library, and each record is filled in a fixed order in the registration list. The registration list includes record identifier, data type identifier, source identifier, acquisition device identifier, acquisition start time, acquisition end time, source coordinate reference identifier, target coordinate reference identifier, source time reference identifier, target time reference identifier, sampling interval, calibration version number, original file pointer, preprocessing file pointer, version fingerprint, and parameter library reference version number.

[0076] Furthermore, records were created for borehole trajectory points, lithological sections, sample numbers, and test results, and these were linked together using the same borehole identifier to obtain geological data.

[0077] Record entries are created for mine orthophotos, point cloud data, and InSAR displacement sequences, with the entry coverage and sampling interval recorded to acquire remote sensing data; record entries are created for mine observation points, material data preprocessing results, and inversion bodies, with the survey line number and sampling interval recorded to acquire geophysical data; record entries are created for equipment start-up and shutdown, fault codes, energy consumption, positioning trajectory, and cycle time, with the equipment calibration version number recorded to acquire equipment operation data; for each record entry generated, the source field and original file pointer of the record entry are written to the registration list.

[0078] For each record, read the source coordinate reference identifier. If the source coordinates and target coordinates are inconsistent, perform coordinate transformation according to the coordinate transformation entries in the mine design and planning parameter library.

[0079] The coordinate transformation process outputs the coordinates before transformation, the coordinates after transformation, the transformation parameter version number, and the transformation residual statistics for each record; and writes the pointer to the transformed data file into the preprocessed file pointer field of the registration list; for records containing elevation, elevation correction is performed according to the provisions of the elevation benchmark entry in the mine design planning parameter library, and the correction method version number is written into the registration list.

[0080] For each record, read the source time reference identifier and time zone offset rule, convert the source timestamp into a unified Coordinated Time timestamp, and write it into the target time reference identifier of the registration list.

[0081] For device operation data, a drift sequence is generated based on the difference between the device's reporting time and the acquisition gateway's receiving time. The drift sequence is then linearly corrected, and the corrected timestamp is written into the preprocessed data as the final timestamp. At the same time, the drift correction version number is written into the registration list to ensure that the boundary calculation of the fast variable update window is not contaminated by clock drift.

[0082] For the InSAR displacement sequence, the time axis is aligned and marked according to the sampling period specified in the mine design planning parameter library.

[0083] A version fingerprint is generated for each record. The version fingerprint is used to avoid redundant calculations caused by the same data being repeatedly entered into subsequent steps.

[0084] Finally, the record entries are fixed in the mine design and planning parameter library and given a version number. The version number referenced by the mine design and planning parameter library is written into the registration list for each record, and the version registration list and the mine design and planning parameter library are obtained.

[0085] S2. Perform spatiotemporal alignment and anomaly marking based on the mine design and planning parameter library to obtain standardized mine planning data and mine planning data residuals;

[0086] Furthermore, spatial alignment is performed on records containing spatial coordinates in the mine design and planning parameter library. Specifically, if the registration list shows that the source coordinate reference and the target coordinate reference of the record are different, the coordinates of the record are transformed according to the coordinate transformation entries in the mine design and planning parameter library. If the record belongs to a data type that needs to be registered, such as remote sensing image, DEM, geophysical profile or inversion body, the registration reference in the mine design and planning parameter library is read, the spatial transformation parameters of the record are obtained by using the least squares method, and the transformed data is written into a standardized data file. The spatial registration residual is calculated and the residual result is written.

[0087] Time alignment is performed on records containing timestamps in the mine design and planning parameter library. Specifically, the source timestamps are uniformly converted according to the target time base of the mine design and planning parameter library; a uniform time grid is constructed according to the resampling step size in the mine design and planning parameter library, and the original sequence is kernel-weighted sampling, that is, weighted averaging with time difference as weight to obtain a standardized time series; the time alignment residual is calculated and written into the residual result.

[0088] Furthermore, a joint anomaly score is constructed based on the amplitude changes in space and time, and calculated using a sliding window method, so that anomalies can be stably labeled and interpreted.

[0089] The joint anomaly score is calculated using the following formula:

[0090] ;

[0091] in, Representing records At any moment The joint anomaly score, Indicates the length of the sliding window. This represents the integration time variable within the sliding window. Represents the magnitude function of the rate of change. This represents the magnitude of the normalized rate of change. Represents a very small positive number. This represents the spatial registration residual. Indicates the spatial registration residual reference. Indicates time alignment residuals. This indicates the time-aligned residual baseline.

[0092] It should be noted that the spatial registration residual benchmark is obtained by using historical operational data of mine control point registration as a sample, statistically analyzing the stable operating condition distribution of the registration residual, and taking the upper quantile value, such as the 95th percentile, as the spatial registration residual benchmark; the time alignment residual benchmark is obtained by using the historical time sequence of the same sensor after clock calibration as a sample, statistically analyzing the distribution of the time alignment residual under a stable sampling period, and taking the upper quantile value, such as the 95th percentile, as the time alignment residual benchmark.

[0093] ;

[0094] in, Representing records The standardized time series obtained by time alignment and resampling.

[0095] Furthermore, the joint anomaly score is mapped to anomaly markers according to the anomaly threshold in the mine design and planning parameter library. Specifically, when the joint anomaly score is not less than the anomaly threshold, the observation corresponding to the time point is written into the anomaly set; the anomaly set and record identifier are bound and written into the registration list.

[0096] It should be noted that the anomaly threshold is calculated by analyzing the distribution of the joint anomaly score based on historical normal operating data, and the upper quantile value, such as the 95th percentile, is taken as the anomaly threshold to control the false alarm rate.

[0097] The spatially and temporally aligned data is uniformly encapsulated into standardized mine planning data, and consistent output fields are used for different types of data, such as target coordinates, target timestamps, observation values, sampling intervals, record identifiers, and parameter library version numbers.

[0098] Output the mine planning data residuals bound to the record identifier, including at least spatial registration residuals, temporal alignment residuals, and anomaly sets, and write them into the registration list.

[0099] S3. Calculate data quality indicators and error variance using the residuals of mine planning data, the proportion of abnormalities in mine planning data, and the timeliness of mine planning data, and write them back to the registration list;

[0100] Furthermore, the preprocessing file pointer and parameter library version number corresponding to the record are read from the registration list according to the record identifier; the spatial registration residual and temporal alignment residual are read from the mine planning data residual; the abnormal points are read from the abnormal set and the abnormal ratio is calculated; the time efficiency is calculated from the current time and the record acquisition termination time; at the same time, the reference constant and noise calibration information used for normalization and stability calculation are read from the mine design planning parameter library.

[0101] Furthermore, the ratio of the anomaly set corresponding to the record to the standardized sequence length is used to obtain the anomaly ratio, and the anomaly ratio is temporarily stored as the input for the quality calculation of the current record.

[0102] Abnormal proportions are expressed as:

[0103] ;

[0104] in, Representing records abnormal proportions This indicates the number of outlier data points. This indicates the total number of all data.

[0105] Subtract the recorded collection termination time from the current time to obtain the time efficiency.

[0106] Spatial and temporal residual benchmarks are retrieved from the mine design and planning parameter library. Spatial registration residuals and temporal alignment residuals are normalized and synthesized into a comprehensive residual index.

[0107] The comprehensive residual index is expressed as:

[0108] ;

[0109] in, This represents the comprehensive residual index.

[0110] Furthermore, the abnormality ratio benchmark and the time-dependent quantity benchmark are read from the mine design and planning parameter library. After normalizing the abnormality ratio and the time-dependent quantity, they are combined with the comprehensive residual to form a quality decay term. The data quality index is calculated using the exponential decay method. In the error variance calculation, the calibration noise variance and the additional variance composed of residual, abnormality and time-dependent quantity are superimposed to obtain the error variance.

[0111] Data quality metrics are expressed as follows:

[0112] ;

[0113] in, Indicates data quality indicators, Indicates the abnormal proportion benchmark. Indicates time-sensitive quantity. Indicates the timeliness standard.

[0114] Error variance, expressed as:

[0115] ;

[0116] in, Representing records The error variance, Indicates the calibration noise variance. It represents a very small positive number.

[0117] It should be noted that the anomaly ratio benchmark is obtained by statistically analyzing the anomaly ratio distribution of historical data under normal operating conditions, and the upper quantile value, such as the 95th percentile, is taken as the anomaly ratio benchmark; the timeliness benchmark is obtained by statistically analyzing the collection period of historical data, and the upper quantile value, such as the 95th percentile, is taken as the timeliness benchmark.

[0118] Write the data quality metrics and error variance of the currently recorded data into the quality and variance fields of the register, and simultaneously write back the decomposition field.

[0119] S4. Read the slow update window and fast update window from the mine design and planning parameter library, and define the slow variable, fast variable and triggering conditions;

[0120] Furthermore, the configuration items for slow update windows and fast update windows are read from the mine design and planning parameter library; whereby, the slow update window refers to the update cycle of slow variables such as geological features and ore body distribution in the mine design and planning; and the fast update window refers to the update cycle of fast variables such as equipment operating status and deformation monitoring in the mine design.

[0121] Based on the actual needs of the mine site, set the time window length for fast and slow update windows; the time window length setting must ensure that there is a clear time boundary between fast and slow update windows. For example, if the slow update window is 30 days, the fast update window can be daily or weekly.

[0122] Furthermore, we define slow variables. Specifically, slow variables refer to parameters in mine design that change slowly over time and are updated infrequently, such as ore body distribution, geological characteristics, grade distribution, and mining area topography.

[0123] Retrieve entries related to slow variables from the mine design and planning parameter library, including but not limited to the boundary of the ore body, the geometric parameters of the fault, and the grade of the ore body; define the slow variables as a set of slow variables, and assign a slow update window to each slow variable.

[0124] Define fast variables. Specifically, fast variables refer to parameters in mine design that change rapidly over time and are updated frequently, such as equipment operating status, transportation cycle time, and mine deformation.

[0125] Retrieve entries related to fast variables from the mine design and planning parameter library, including but not limited to equipment operating status, deformation rate, geophysical data, and production system efficiency; define fast variables as a set of fast variables, and assign a fast update window to each fast variable.

[0126] Define trigger conditions. Specifically, trigger conditions refer to the automatic triggering of corresponding variable updates when specific data or events meet preset thresholds in mine design and planning.

[0127] It should be noted that for slow variables, the triggering conditions are usually related to the progress of mining exploration, grade prediction errors, etc. For example, when new borehole data is entered or the exploration progress reaches a predetermined milestone, the ore body boundary and grade distribution are updated. For fast variables, the triggering conditions are directly related to real-time monitoring data. For example, when the equipment availability rate is lower than the set threshold, the displacement rate exceeds the standard, or the production cycle is delayed, the equipment operating status, deformation monitoring, and other fast variables are updated.

[0128] It should be noted that the definitions, update cycles, and triggering conditions of all slow and fast variables are stored in the mine design and planning parameter library and are bound to each record in the registration list.

[0129] S5. When triggered by a slow update window, update the three-dimensional geology and block attributes of the mine through data quality indicators. When triggered by a fast update window, update the deformation intensity and equipment availability of the mine through data quality indicators.

[0130] Furthermore, when the update cycle of slow variables such as geological features, ore body distribution, and grade distribution in the mine planning reaches the set slow update window, the update of the three-dimensional geology of the mine and the attributes of the mine block is triggered; when the update cycle of fast variables such as deformation monitoring and equipment operating status in the mine operation reaches the set fast update window, the update of the mine deformation intensity and equipment availability is triggered.

[0131] When the slow update window is triggered, data quality indicators related to the three-dimensional geological model of the mine and the attributes of the mine block are read from the mine design and planning data, and the data quality indicators are normalized.

[0132] When the quick update window is triggered, data quality indicators related to mine deformation intensity and equipment availability are read from the real-time mine monitoring data and normalized.

[0133] Furthermore, when the slow update window is triggered, the three-dimensional geological model of the mine is updated through data quality indicators. Specifically, the geological data is weighted using normalized data quality indicators, and the normalized data quality indicators are used as the update weights to fuse the updated geological data. Then, a new three-dimensional geological model of the mine is generated using three-dimensional modeling technology.

[0134] When the slow update window is triggered, the block attributes are updated through the ore body exploration data. Specifically, the ore body exploration data is weighted using data quality indicators, and the normalized data quality indicators are used as the update weights to merge the updated ore body exploration data and obtain new block attributes.

[0135] Furthermore, when the fast update window is triggered, the deformation intensity is updated in real time based on the deformation monitoring data and data quality indicators. Specifically, a deformation intensity calculation model is adopted to combine the monitored displacement data and the weighted data quality indicators to update the mine deformation intensity.

[0136] When the quick update window is triggered, the device availability rate is updated in real time based on the device's operating data (such as power-on time, downtime, etc.) and the data quality indicators of the device's operating data.

[0137] Finally, the updated 3D geological model, block attributes, mine deformation intensity, and equipment availability are written back to the mine planning database to generate a new mine design scheme.

[0138] S6. Based on the mine deformation intensity and equipment availability, generate cross-timescale constraint scaling factors, correct the safety distance and upper limit of production capacity, and generate prohibited areas;

[0139] Furthermore, based on the mine deformation intensity and equipment availability data, a cross-timescale constraint scaling factor is generated; among which, the cross-timescale constraint scaling factor is used to dynamically adjust the safety distance and the upper limit of production capacity.

[0140] The calculation of the cross-timescale constraint scaling factor is expressed as:

[0141] ;

[0142] in, This represents the scaling factor across timescale constraints. Indicates a time window. This represents the average time window value of equipment availability. Indicates the length of time the window is updated quickly. Indicates a spatial region. Indicates spatial location and time The deformation rate of the mining area. The standard for representing the deformation rate of a mining area. Indicates spatial location and time The acceleration of deformation in the mining area. An acceleration reference for representing deformation in a mining area. For spatial integration variables, This is the time integration variable.

[0143] The average time window value of equipment availability is expressed as:

[0144] ;

[0145] in, This represents the time average of equipment availability.

[0146] It should be noted that the deformation rate of the mining area is obtained by calculating the displacement change rate of each point in the mining area through InSAR monitoring; the deformation acceleration of the mining area is obtained by analyzing the change of the deformation rate of the mining area over time; the deformation rate benchmark of the mining area is obtained by taking the upper quantile value, such as the 95th percentile, based on the deformation rate distribution of historical monitoring data of the mining area; the deformation acceleration benchmark of the mining area is obtained by taking the upper quantile value, such as the 95th percentile, based on statistical analysis of deformation acceleration in historical data; and the spatial area is obtained through the actual geographical range of the mining area.

[0147] Furthermore, the safety distance of the mining operation area is corrected based on the cross-timescale constraint scaling factor; whereby the safety distance refers to the minimum distance between the mining operation area and the danger zone.

[0148] The correction for the safe distance is expressed as:

[0149] ;

[0150] in, This indicates the corrected safe distance. This represents the initial safe distance baseline.

[0151] The upper limit of mining operations capacity is dynamically adjusted based on the cross-timescale constraint scaling factor; where the upper limit of capacity refers to the maximum production volume that can be completed per unit time.

[0152] The adjustment to the upper limit of production capacity is expressed as:

[0153] ;

[0154] in, This indicates the revised upper limit of production capacity. This represents the upper limit of the initial production capacity.

[0155] Furthermore, based on the revised safety distance, a new restricted area is generated; whereby the restricted area refers to the area where entry is prohibited due to safety, environmental protection, or other reasons, and is usually a buffer zone set up around a known dangerous area.

[0156] The new forbidden domain is generated as follows:

[0157] ;

[0158] in, This indicates a prohibited domain. Indicates the geometric center of the danger zone. Indicates spatial location Distance to the danger zone.

[0159] The forbidden domain boundary is dynamically updated by adjusting the safety distance and the cross-timescale constraint scaling factor.

[0160] S7. Under modified constraints, generate mine design elements and solve the planning problem in a rolling manner;

[0161] Furthermore, the revised safety distance and upper limit of production capacity will be used as constraints within the current time window.

[0162] Based on the constraints, the mine design elements are defined, including stope boundaries, working face arrangement, stripping sequence, equipment dispatch, and transportation calculations. Among them, the stope boundary is defined by geological data and safety distances to define the boundary between the stope area and the restricted area; the working face arrangement is planned by the distribution of ore bodies and grades to allocate working faces in different time periods; the stripping sequence is determined by the mining sequence of each stage of the ore body to ensure optimal resource utilization and mining efficiency; and the equipment dispatch and transportation plan is adjusted by the availability of equipment and the upper limit of production capacity to adjust equipment configuration and transportation routes.

[0163] By utilizing the revised safety distance and upper limit of production capacity, combined with the three-dimensional geological model of the mine and block attributes, new mine design elements are generated and updated in real time during each design cycle.

[0164] Furthermore, define the time window for rolling planning, such as one month, one quarter, etc.

[0165] Within each planning cycle, the rolling window will be automatically updated, meaning that starting from the current time, the optimization solution will be performed based on the current correction constraints, mine production data, and actual operating conditions.

[0166] Within each planning cycle, optimization algorithms, such as integer programming, linear programming, and genetic algorithms, are used to solve the mine design elements to ensure that production targets can be achieved under modified constraints.

[0167] In this embodiment, in order to verify the real-time data feedback and dynamic adjustment mechanism based on mine design and planning, the impact of data quality on the accuracy of mine planning is verified by simulating data input under various real-world environments (such as geological, remote sensing, geophysical exploration, equipment operation, etc.), especially the impact in dynamic environments. The experiment focuses on simulating environmental conditions in three stages: stable stage, risk stage, and recovery stage, which represent the normal, risk, and repair states in mine planning, respectively.

[0168] The environmental configuration specifically involved a 30-day simulation with a 10-minute time step, and 144 daily samplings for real-time data updates. The data encompassed various types, including geological data, remote sensing data, geophysical data, and equipment operation data. These data, with varying quality and update frequencies, influenced the mine design process. Based on data quality indicators, anomaly rates, and residual values, parameters such as safety distances, upper limits of production capacity, and restricted access boundaries in the mine planning were dynamically adjusted to simulate the mine's resilience under different environmental conditions. The simulation included three stable states, such as... Figure 5 As shown, the risk section is as follows Figure 6 As shown and the recovery segment Figure 7 As shown.

[0169] like Figure 5 The diagram illustrates the relationship between the anomaly ratio and data quality indicators under stable conditions. As the anomaly ratio increases, the data quality indicators show a significant downward trend, with the scatter cloud gradually tilting to the lower right on the coordinate axis. Each point in the diagram represents a data record. In this case, the data quality indicators remain at a high value (close to 0.9), and the anomaly ratio is low. As the anomaly ratio gradually increases, the data quality decreases significantly, indicating that under stable conditions, a lower anomaly ratio can ensure higher data quality, while an increase in the anomaly ratio indicates a decline in data quality. This reflects that under a stable data environment, the mine design in this embodiment can maintain high planning accuracy.

[0170] like Figure 6 The diagram illustrates the changes in the anomaly ratio and data quality indicators under risk conditions. Unlike the stable phase, the scatter plot in the risk phase shows a more pronounced sloping trend, with a significant decrease in data quality indicators and a marked increase in the anomaly ratio. In this stage, when the anomaly ratio reaches a high value, the data quality indicators decline significantly, and the scatter plot shows denser clustering, especially in areas of low data quality. The diagram depicts potential risks that may occur during mine design. The significant increase in the anomaly ratio gradually exacerbates the impact on the design scheme, reflecting that even with declining data quality, the mine design in this embodiment can maintain planning accuracy through dynamic adjustment measures.

[0171] like Figure 7The diagram illustrates the relationship between the anomaly ratio and data quality indicators under the recovery phase. During the recovery phase, the anomaly ratio gradually decreases, and the data quality indicators gradually recover to a higher level. The distribution of the scatter cloud begins to shift to the upper left, indicating that the data quality indicators tend to improve as the anomaly ratio decreases. Although the anomaly ratio is still higher than in the stable phase, the data quality is gradually restored as the recovery process progresses. This demonstrates that data anomalies are corrected to a certain extent during the processing, reflecting that the mine design implemented in this case can restore the safety and accuracy of the plan through real-time monitoring and correction during the data recovery process.

[0172] This embodiment also provides a computer device applicable to the mine design and planning method based on multi-type data, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the mine design and planning method based on multi-type data as proposed in the above embodiment.

[0173] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0174] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the mine design and planning method based on multi-type data as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0175] In summary, this invention achieves multi-dimensional and precise planning of mine design schemes by integrating geological data, remote sensing data, geophysical data, and equipment operation data; it enables real-time monitoring and dynamic updating of mine design data by combining data quality indicators, mine planning data residuals, and anomaly ratios; it enables dynamic adjustment capabilities of mine planning by combining real-time feedback of deformation intensity and equipment availability; and it achieves precise correction of mine safety distances and upper limits of production capacity by generating cross-timescale constraint scaling factors.

[0176] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A mine design planning method based on multi-type data, characterized in that: include, Acquire geological data, remote sensing data, geophysical data, and equipment operation data; unify coordinates and time benchmarks; and establish a version registration list and a mine design and planning parameter library. Spatiotemporal alignment and anomaly marking are performed based on the mine design and planning parameter library to obtain standardized mine planning data and mine planning data residuals; Using the residuals of mine planning data, the proportion of abnormalities in mine planning data, and the timeliness of mine planning data, calculate the data quality indicators and error variance, and write them back to the registration list; Read the slow update window and fast update window from the mine design and planning parameter library, and define the slow variable, fast variable and triggering conditions; When triggered by a slow update window, the three-dimensional geology and block attributes of the mine are updated using data quality indicators. When triggered by a fast update window, the deformation intensity and equipment availability of the mine are updated using data quality indicators. Based on the mine deformation intensity and equipment availability, a cross-timescale constraint scaling factor is generated to correct the safety distance and upper limit of production capacity, and a no-entry domain is generated. Under modified constraints, mine design elements are generated and the planning is solved in a rolling manner.

2. The mine design and planning method based on multi-type data as described in claim 1, characterized in that: The specific steps for establishing the version registration list and the mine design and planning parameter library are as follows: Acquire geological data, remote sensing data, geophysical data, and equipment operation data; By reading the source coordinates and time reference identifiers of all data, the coordinates and time of each record are converted into a unified coordinate and time reference. Record all data and their coordinate and time transformations, and create a version registration list; Based on the established version registration list, benchmark entries are created in the mine design planning parameter library.

3. The mine design and planning method based on multi-type data as described in claim 2, characterized in that: The specific steps for obtaining standardized mine planning data and mine planning data residuals are as follows: Perform spatial alignment on the recorded parameters of the spatial coordinates and calculate the spatial registration residuals; Perform time alignment on the record parameters of the timestamp and calculate the time alignment residual; A joint anomaly score is constructed based on the amplitude changes in space and time, and the score is calculated using a sliding window. Map the joint anomaly scores to anomaly tags to obtain the anomaly set; The parameters after spatial registration and temporal alignment are uniformly encapsulated into standardized mine planning data, and the mine planning data residuals bound to the record identifier are output.

4. The mine design and planning method based on multi-type data as described in claim 3, characterized in that: The process involves using residuals in mine planning data, the proportion of anomalies in mine planning data, and the timeliness of mine planning data to calculate data quality indicators and error variance, and then writing these data back to the registration list. The specific steps are as follows: Read spatial registration residuals and temporal alignment residuals from the mine planning data residuals, read outliers from the outlier set and count the outlier ratio, and calculate the timeliness from the current time and the record collection termination time; The anomaly ratio is obtained by comparing the set of recorded anomalies with the standardized sequence length. Subtract the recorded collection termination time from the current time to obtain the time efficiency. The spatial registration residual and temporal alignment residual are normalized and synthesized into a comprehensive residual index; After normalizing the anomaly ratio and timeliness, together with the comprehensive residual, a quality decay term is formed, and the data quality index is calculated using an exponential decay method. In the calculation of error variance, the calibration noise variance and the additional variance resulting from the combination of residuals, anomalies and time aging are added together to obtain the error variance; Write the recorded data quality metrics and error variance into the quality and variance fields of the register.

5. The mine design and planning method based on multi-type data as described in claim 4, characterized in that: The specific steps for reading the slow update window and fast update window from the mine design and planning parameter database are as follows: Read the configuration items for the slow update window and the fast update window from the mine design and planning parameter library; Based on the actual needs of the mine site, set the time window length for fast and slow update windows; Read the entries related to slow variables from the mine design and planning parameter library, and assign a slow update window to each slow variable; Retrieve entries related to fast variables from the mine design and planning parameter library, and assign a fast update window to each fast variable.

6. The mine design and planning method based on multi-type data as described in claim 5, characterized in that: When triggered by a slow update window, the 3D geological features and block attributes of the mine are updated using data quality indicators. When triggered by a fast update window, the deformation intensity and equipment availability of the mine are updated using data quality indicators. The specific steps are as follows: When the update cycle of slow variables in the mine planning reaches the set slow update window, the update of the three-dimensional geology of the mine and the attributes of the mine block is triggered. When the update cycle of fast variables in the mine planning reaches the set fast update window, the mine deformation intensity and equipment availability are updated. When the slow update window is triggered, data quality indicators related to the three-dimensional geological model of the mine and the attributes of the mine block are read from the mine design and planning data, and the data quality indicators are normalized. When the quick update window is triggered, read the data quality indicators related to the mine deformation intensity and equipment availability from the real-time monitoring data of the mine, and perform normalization processing. When the slow update window is triggered, the 3D geological model of the mine is updated using data quality indicators, and the block attributes are updated using ore body exploration data. When the quick update window is triggered, the deformation intensity is updated in real time based on the deformation monitoring data and data quality indicators, and the equipment availability is updated in real time based on the equipment operation data and the data quality indicators of the equipment operation data.

7. The mine design and planning method based on multi-type data as described in claim 6, characterized in that: The steps involve generating a cross-timescale constraint scaling factor based on the mine deformation intensity and equipment availability, correcting the safety distance and upper limit of production capacity, and generating a no-entry domain. Based on data on mine deformation intensity and equipment availability, generate cross-timescale constraint scaling factors; The safety distance of the mining operation area is corrected by using a cross-timescale constraint scaling factor; The upper limit of mining operation capacity is dynamically adjusted based on the cross-timescale constraint scaling factor; A new forbidden zone is generated based on the revised safety distance; The restricted area refers to a region where entry is prohibited. The forbidden domain boundary is dynamically updated by adjusting the safety distance and the cross-timescale constraint scaling factor.

8. The mine design and planning method based on multi-type data as described in claim 7, characterized in that: The specific steps for generating mine design elements and solving the planning algorithm under modified constraints are as follows: The revised safety distance and upper limit of production capacity are used as constraints to define mine design elements; By utilizing the revised safety distance and upper limit of production capacity, combined with the three-dimensional geological model of the mine and block attributes, new mine design elements are generated and updated in real time during each design cycle. Within each planning cycle, starting from the current time, the mine design elements are optimized and solved based on the current correction constraints, mine production data, and actual operating conditions to achieve the production target.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the mine design and planning method based on multi-type data as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the mine design and planning method based on multi-type data as described in any one of claims 1 to 8.

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