A method and system for dynamically correcting geological resource data based on production data

Abstract

The present invention relates to a method and system for dynamically correcting geological resource data based on production data. The method comprises: firstly, constructing an initial geological resource prediction model by collecting geological exploration data and production data; then, based on the geological resource prediction model, fitting with historical data to obtain a regression coefficient matrix, and constructing a resource deviation function; solving to obtain an optimal regression coefficient matrix by minimizing the deviation function, and constructing an optimal geological resource prediction model based on the matrix. Subsequently, new data generated in the production process is collected in real time, input into the optimal geological resource prediction model, and the model is dynamically corrected, thereby generating a resource prediction result that is more in line with the actual situation. The present invention can improve the accuracy of geological resource data prediction, provide reliable support for production planning and resource management, and is particularly suitable for the field of mineral resource exploration and development.

Description

Technical Field

[0001] The present invention relates to the field of geological resource management and data processing technology, and in particular to a method and system for dynamically correcting geological resource data based on production data. Background Art

[0002] With the development of modern mining industry, accurate assessment and rational exploitation of underground resources have become important issues to improve production efficiency and reduce resource waste. Traditional geological exploration methods usually rely on static data collected regularly, which can only be used to guide production decisions after a long period of analysis and processing. However, due to the complexity of geological conditions and the dynamic variability of underground resources, it is often difficult to reflect the real-time distribution of underground resources by relying solely on static data, resulting in a large deviation between the geological model and the actual situation. This deviation not only affects the effective utilization of resources, but may also cause waste of resources and reduce mining efficiency.

[0003] In recent years, with the rapid development of sensor technology and data analysis technology, technologies that can acquire and analyze geological data in real time have gradually been applied to resource management and mining. However, there is still a lack of a system on the market that can fully utilize production data to dynamically correct geological resources. Traditional systems can usually only process historical data and cannot dynamically update geological models based on real-time data generated during the production process, which makes them incapable of coping with complex geological conditions and changing production environments.

[0004] In order to solve the above problems, there is an urgent need for a system that dynamically corrects geological resource data based on production data. The system can collect and process various types of data in the production process in real time, build and dynamically correct the geological resource prediction model, so as to more accurately evaluate the distribution and changes of underground resources, and then guide various decisions in the production process to improve resource utilization and production efficiency. Summary of the invention

[0005] In order to solve the problem that most existing technical solutions rely only on initial exploration data for resource prediction and planning, ignoring the actual impact of dynamic data generated in the production process on resource status; due to the complexity of geological conditions and the dynamic changes in the production process, only using initial exploration data often leads to inaccurate and untimely resource management, thus affecting the efficiency and benefits of resource development.

[0006] The present invention provides:

[0007] A method for dynamically correcting geological resource data based on production data, the method comprising the following steps:

[0008] S1. Establish a real-time data acquisition system through sensors and monitoring equipment to collect historical geological data and historical resource data generated during geological exploration and production, and obtain a historical geological data set and a historical resource data set;

[0009] S2, preprocessing the historical geological data set and the historical resource data set, constructing a geological resource prediction model, solving the parameters to be fitted of the geological resource prediction model based on the historical geological data set and the historical resource data set, and obtaining the optimal geological resource prediction model;

[0010] S3. Set up a real-time data monitoring system to continuously collect real-time geological data through sensors and monitoring equipment;

[0011] S4. Based on the optimal geological resource prediction model, the real-time geological data obtained during the production process is incorporated into the model to dynamically correct the fitted geological resource prediction model;

[0012] S5. Combine real-time geological data with fitted geological resource prediction models to dynamically evaluate geological resources and optimize production parameters.

[0013] Furthermore, step S2 specifically includes:

[0014] S21, constructing a geological resource prediction model, wherein the geological resource prediction model satisfies the formula:

[0015] ;

[0016] ;

[0017] in, Indicated in k The predicted value of resource concentration obtained by fitting the prediction function at each point; Indicates Items of geological data; is the regression coefficient to be fitted; Indicates The maximum fitting series of exploration data, Indicates the total number of exploration data;

[0018] S22. Construct a resource deviation function, wherein the resource deviation function satisfies:

[0019] ;

[0020] in, Indicated in k The actual value of resource concentration measured at each point;

[0021] S23, construct a solution function to obtain the optimal regression coefficient matrix; the solution function satisfies:

[0022] ;

[0023] Optimal regression coefficient matrix In terms of form:

[0024] ;

[0025] in, is the optimal regression coefficient matrix; represents the minimization algorithm, Represents the resource deviation function The optimal regression coefficient with the minimum value , and then assign the optimal regression coefficient into the optimal regression coefficient matrix ;

[0026] S24. Based on the optimal regression coefficient matrix, an optimal geological resource prediction model is obtained, wherein the optimal geological resource prediction model satisfies:

[0027] ;

[0028] in, Indicated in k The optimal predicted value of resource concentration is obtained at each point through the optimal geological resource prediction model.

[0029] Furthermore, in step S1, the historical geological data set and the historical resource data set contain multi-source data, specifically including: rock sample data obtained by drilling, seismic wave reflection data and mine site measurement data, and the above data are uniformly formatted and stored in a database.

[0030] Furthermore, in step S2, preprocessing the historical geological data set and the historical resource data set specifically includes: preprocessing the historical geological data set and the historical resource data set to remove outliers and noise data; and standardizing the data so that data from different sources have the same dimension.

[0031] Furthermore, in step S3, the real-time geological data includes but is not limited to real-time drilling data, surface subsidence monitoring data and underground hydrological data generated during the mining process. The above data are continuously monitored and collected through an automated data acquisition system and uploaded to the data center in real time for use in dynamic model correction.

[0032] Furthermore, in step S4, dynamically correcting the fitted geological resource prediction model specifically includes:

[0033] S41, calculating the difference between the real-time geological data and the historical geological data during the production process;

[0034] S42, locally adjusting the difference value, inputting the adjusted difference data into the model, performing adaptive update of the model parameters, and completing dynamic correction of the geological resource prediction model;

[0035] S43. Save the modified model and use it for resource assessment in subsequent steps.

[0036] Furthermore, in step S5, based on the revised geological resource prediction model, the current distribution of underground resources is evaluated, and a spatial distribution map of resource concentration is generated. Combined with the evaluation results, the working parameters of the underground mining equipment are adjusted, including at least: drilling depth, mining speed and equipment location.

[0037] The present invention also provides a system for dynamically correcting geological resource data based on production data, which is used to implement the method for dynamically correcting geological resource data based on production data. The system comprises:

[0038] A real-time data acquisition module is used to collect geological exploration data and real-time data in the production process through sensors and monitoring equipment, the data including historical geological data, historical resource data, real-time geological data and real-time resource data;

[0039] The data processing and analysis module is used to receive and integrate the data from the real-time data acquisition module, and pre-process, analyze and model the data, build a geological resource prediction model, and dynamically modify the model based on the real-time data obtained during the production process;

[0040] Resource assessment module, which dynamically assesses underground resources based on the revised geological resource prediction model and generates a spatial distribution map of resource concentration to provide support for production decisions;

[0041] The production control module is used to receive the evaluation results of the resource evaluation module, adjust the production parameters according to the evaluation results, control the operation of underground mining equipment, and continuously optimize the production process.

[0042] Furthermore, the data processing and analysis module also includes a data standardization unit for standardizing historical geological data and real-time geological data, eliminating outliers and noise data, and inputting the standardized data into a geological resource prediction model.

[0043] The method and system for dynamically correcting geological resource data based on production data proposed in the present invention have the following beneficial effects:

[0044] 1. Real-time dynamic correction of geological models: A real-time data collection and monitoring system is established through sensors and monitoring equipment to continuously collect real-time data during geological exploration and production. These real-time data are immediately incorporated into the geological resource prediction model to dynamically correct the geological model to ensure that the model can reflect the actual changes in underground resources and improve the accuracy of the prediction.

[0045] 2. Intelligent decision support: This method and system builds a geological resource prediction model and combines real-time data for dynamic evaluation to achieve intelligent evaluation of resource reserves and mining levels. Based on the evaluation results, production parameters can be dynamically adjusted to optimize resource development strategies, thereby improving production efficiency and the effectiveness of resource development.

[0046] 3. Improve production efficiency and output: During the implementation process, by combining the dynamically corrected geological model with production data, it is possible to more accurately guide production activities, effectively optimize the production process, reduce resource waste, improve the efficiency and output of resource development, and achieve higher economic benefits.

[0047] 4. Continuous monitoring and improvement mechanism: The method and system of the present invention can not only dynamically correct the current production data, but also establish a continuous monitoring and improvement mechanism. Through continuous tracking and evaluation of the system operation, potential problems can be discovered and adjusted in a timely manner to ensure long-term stable and efficient operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a geological resource management method based on real-time correction of exploration data combined with dynamic production data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] Embodiment 1

[0050] Reference Figure 1 This embodiment provides a method for dynamically correcting geological resource data based on production data, the method comprising the following steps:

[0051] S1. Establish a real-time data acquisition system through sensors and monitoring equipment to collect historical geological data and historical resource data generated during geological exploration and production, and obtain a historical geological data set and a historical resource data set;

[0052] S2. Preprocessing the historical geological data set and the historical resource data set, constructing a geological resource prediction model, solving the parameters to be fitted of the geological resource prediction model based on the historical geological data set and the historical resource data set, and obtaining the optimal geological resource prediction model; specifically including:

[0053] S21, constructing a geological resource prediction model, wherein the geological resource prediction model satisfies the formula:

[0054] ;

[0055] ;

[0056] wherein, represents the predicted value of the resource concentration obtained by fitting the prediction function at the k th point; represents the th item of geological data; is the regression coefficient to be fitted; represents the maximum fitting order of the th exploration data, represents the total number of exploration data;

[0057] S22. Construct a resource deviation function, and the resource deviation function satisfies:

[0058] ;

[0059] wherein, represents the actual value of the resource concentration measured at the k th point;

[0060] S23. Construct a solution function to obtain an optimal regression coefficient matrix; the solution function satisfies:

[0061] ;

[0062] The optimal regression coefficient matrix formally satisfies:

[0063] ;

[0064] wherein, is the optimal regression coefficient matrix; represents a minimization solution algorithm, represents obtaining all the optimal regression coefficients that make the resource deviation function have the minimum value, and then assigning them to the optimal regression coefficient matrix ;

[0065] S24. Based on the optimal regression coefficient matrix, obtain an optimal geological resource prediction model, and the optimal geological resource prediction model satisfies:

[0066] ;

[0067] wherein, represents at the kThe optimal predicted value of resource concentration obtained by the optimal geological resource prediction model at each point;

[0068] S3. Set up a real-time data monitoring system to continuously collect real-time geological data through sensors and monitoring equipment;

[0069] S4. Based on the optimal geological resource prediction model, the real-time geological data obtained during the production process is incorporated into the model to dynamically correct the fitted geological resource prediction model;

[0070] S5. Combine real-time geological data with fitted geological resource prediction models to dynamically evaluate geological resources and optimize production parameters.

[0071] In step S1, the historical geological data set and the historical resource data set contain multi-source data, specifically including: rock sample data obtained by drilling, seismic wave reflection data and mine site measurement data, and the above data are uniformly formatted and stored in a database.

[0072] In step S2, preprocessing the historical geological data set and the historical resource data set specifically includes: preprocessing the historical geological data set and the historical resource data set to remove outliers and noise data; and standardizing the data so that data from different sources have the same dimension.

[0073] In step S3, the real-time geological data includes but is not limited to real-time drilling data, surface subsidence monitoring data and underground hydrological data generated during the mining process. The above data are continuously monitored and collected through an automated data acquisition system and uploaded to the data center in real time for use in dynamic model correction.

[0074] In step S4, dynamically correcting the fitted geological resource prediction model specifically includes:

[0075] S41, calculating the difference between the real-time geological data and the historical geological data during the production process;

[0076] S42, locally adjusting the difference value, inputting the adjusted difference data into the model, performing adaptive update of the model parameters, and completing dynamic correction of the geological resource prediction model;

[0077] S43. Save the modified model and use it for resource assessment in subsequent steps.

[0078] In step S5, the current distribution of underground resources is evaluated based on the revised geological resource prediction model and a spatial distribution map of resource concentration is generated. Combined with the evaluation results, the working parameters of the underground mining equipment are adjusted, including at least: drilling depth, mining speed and equipment location.

[0079] Embodiment 2

[0080] In order to verify the effectiveness of the method of dynamically correcting geological resource data based on production data of the present invention, actual production data and geological exploration data of a mining area were selected for experimental verification. The specific steps of the experiment are as follows:

[0081] Step S1: Data collection

[0082] A real-time data acquisition system has been established through sensors and monitoring equipment installed in the mining area. The system collects historical geological data and resource data from the geological exploration process, including rock type, ore grade, porosity, density and other parameters. The data collected ranges as follows:

[0083] Rock types: sandstone, shale, limestone

[0084] Ore grade: 0.5% ~ 3.0%;

[0085] Porosity: 5% ~ 25%;

[0086] Density: 2.5 g / cm³ ~ 3.0 g / cm³;

[0087] These data constitute the historical geological data set and the historical resource data set, which serve as the basic data for subsequent model construction.

[0088] Step S2: Constructing geological resource prediction model

[0089] Based on the collected historical geological data and historical resource data, a geological resource prediction model was constructed. The specific steps of model construction are as follows:

[0090] Model expression: According to the actual situation, the polynomial regression model was selected as the geological resource prediction model. The model expression is:

[0091] ;

[0092] in, Indicated in k The predicted value of resource concentration obtained by fitting the prediction function at each point Indicates Items of geological data; is the regression coefficient to be fitted; Indicates The maximum fitting series of exploration data, Represents the total number of exploration data.

[0093] in, Indicates the ore grade, represents the porosity, represents density. The maximum fitting order is set to 2 in the model, that is, , the total number of exploration data is 3, that is, m=3.

[0094] Calculation function:

[0095] ;

[0096] Get the optimal geological resource prediction model: Based on the optimal regression coefficient matrix , we get the optimal geological resource prediction model, the expression is:

[0097] ;

[0098] Step S3: Real-time data monitoring: During the experiment, the real-time monitoring system set up in the mining area continuously collects newly generated real-time geological data in the mining area. The collected real-time data includes the following parameters:

[0099] New ore grade: 2.5%;

[0100] New porosity: 15%;

[0101] New density: 2.8 g / cm³;

[0102] Step S4: Dynamically modify the model: Incorporate the collected real-time data into the optimal geological resource prediction model and dynamically modify the model.

[0103] Step S5: Dynamic evaluation: Combined with the dynamically corrected model, calculate the resource reserves of the mining area and optimize production parameters. By comparing the models before and after correction, it is found that the dynamically corrected model is more consistent with the actual measurement results, the resource evaluation of the mining area is more accurate, and the final production efficiency is increased by 15%.

[0104] Conclusion: This experiment verifies the effectiveness of the method of dynamically correcting geological resource data based on production data, and proves that dynamic correction of geological resource prediction models through real-time data can significantly improve the accuracy of resource prediction and provide effective support for production optimization.

[0105] This experiment verifies the effectiveness of the method of dynamically correcting geological resource data based on production data, and proves that dynamic correction of geological resource prediction models through real-time data can significantly improve the accuracy of resource prediction and provide effective support for production optimization.

[0106] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for dynamically correcting geological resource data based on production data, characterized in that: The method comprises the following steps: S1. Establish a real-time data acquisition system through sensors and monitoring equipment to collect historical geological data and historical resource data generated during geological exploration and production, and obtain a historical geological data set and a historical resource data set; S2. Preprocessing the historical geological data set and the historical resource data set, constructing a geological resource prediction model, solving the parameters to be fitted of the geological resource prediction model based on the historical geological data set and the historical resource data set, and obtaining the optimal geological resource prediction model; specifically including: S21, constructing a geological resource prediction model, wherein the geological resource prediction model satisfies the formula: ； ； in, Indicated in k The predicted value of resource concentration obtained by fitting the prediction function at each point; Indicates i Items of geological data; is the regression coefficient to be fitted; Indicates The maximum fitting series of exploration data, Indicates the total number of exploration data; S22. Construct a resource deviation function, wherein the resource deviation function satisfies: ; in, Indicated in k The actual value of resource concentration measured at each point; S23, construct a solution function to obtain the optimal regression coefficient matrix; the solution function satisfies: ; Optimal regression coefficient matrix In terms of form: ； in, is the optimal regression coefficient matrix; represents the minimization algorithm, Represents the resource deviation function The optimal regression coefficient with the minimum value , and then assign the optimal regression coefficient into the optimal regression coefficient matrix ; S24. Based on the optimal regression coefficient matrix, an optimal geological resource prediction model is obtained, wherein the optimal geological resource prediction model satisfies: ; in, Indicated in k The optimal predicted value of resource concentration obtained by the optimal geological resource prediction model at each point; S3. Set up a real-time data monitoring system to continuously collect real-time geological data through sensors and monitoring equipment; S4. Based on the optimal geological resource prediction model, the real-time geological data obtained during the production process is incorporated into the model to dynamically correct the fitted geological resource prediction model; S5. Combine real-time geological data with fitted geological resource prediction models to dynamically evaluate geological resources and optimize production parameters.

2. A method for dynamically correcting geological resource data based on production data according to claim 1, characterized in that: In step S1, the historical geological data set and the historical resource data set contain multi-source data, specifically including: rock sample data obtained through drilling, seismic wave reflection data and mine site measurement data, and the above data are uniformly formatted and stored in a database.

3. A method for dynamically correcting geological resource data based on production data according to claim 2, characterized in that: In step S2, preprocessing the historical geological data set and the historical resource data set specifically includes: preprocessing the historical geological data set and the historical resource data set to remove outliers and noise data; and standardizing the data so that data from different sources have the same dimension.

4. A method for dynamically correcting geological resource data based on production data according to claim 3, characterized in that: In step S3, the real-time geological data includes but is not limited to real-time drilling data, surface subsidence monitoring data and underground hydrological data generated during the mining process. The above data are continuously monitored and collected through an automated data acquisition system and uploaded to the data center in real time for use in dynamic model correction.

5. A method for dynamically correcting geological resource data based on production data according to claim 4, characterized in that: In step S4, dynamically correcting the fitted geological resource prediction model specifically includes: S41, calculating the difference between the real-time geological data and the historical geological data during the production process; S42, locally adjusting the difference value, inputting the adjusted difference data into the model, performing adaptive update of the model parameters, and completing dynamic correction of the geological resource prediction model; S43. Save the modified model and use it for resource assessment in subsequent steps.

6. A method for dynamically correcting geological resource data based on production data according to claim 5, characterized in that: In step S5, based on the revised geological resource prediction model, the current distribution of underground resources is evaluated, and a spatial distribution map of resource concentration is generated. Combined with the evaluation results, the working parameters of the underground mining equipment are adjusted, including at least: drilling depth, mining speed and equipment location.

7. A system for dynamically correcting geological resource data based on production data, used for implementing a method for dynamically correcting geological resource data based on production data according to any one of claims 1 to 6, characterized in that: The system comprises: A real-time data acquisition module is used to collect geological exploration data and real-time data in the production process through sensors and monitoring equipment, the data including historical geological data, historical resource data, real-time geological data and real-time resource data; The data processing and analysis module is used to receive and integrate the data from the real-time data acquisition module, and pre-process, analyze and model the data, build a geological resource prediction model, and dynamically modify the model based on the real-time data obtained during the production process; Resource assessment module, which dynamically assesses underground resources based on the revised geological resource prediction model and generates a spatial distribution map of resource concentration to provide support for production decisions; The production control module is used to receive the evaluation results of the resource evaluation module, adjust the production parameters according to the evaluation results, control the operation of underground mining equipment, and continuously optimize the production process.

8. A system for dynamically correcting geological resource data based on production data according to claim 7, characterized in that: The data processing and analysis module also includes a data standardization unit for standardizing historical geological data and real-time geological data, eliminating abnormal values ​​and noise data, and inputting the standardized data into the geological resource prediction model.

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