Machine Learning-Based Mining Volume Prediction Method and System

Abstract

The present invention discloses a mining volume prediction method and system based on machine learning, which relates to the technical field of data processing for prediction purposes. It includes obtaining multiple data sources corresponding to the mining volume, and preprocessing the data sources through a distributed computing framework. The processing process includes removing noise, standardizing, and filling missing values. Based on the preprocessed data, a machine learning model is constructed and trained. The machine learning model learns the rules from the data and outputs the mining volume prediction result. The future mining volume is predicted through the trained machine learning model, and the prediction result is updated in real time. The present invention not only improves the accuracy and efficiency of mining volume prediction, but also can adapt to complex dynamic environments, and has strong practicality and application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing for prediction purposes, and in particular to a mining volume prediction method and system based on machine learning. Background Art

[0002] With the continuous advancement of mineral resource mining technology, the prediction of mining volume has become an important part of mine management and resource optimization. Traditional mining volume prediction methods mainly rely on historical data and empirical formulas. These methods usually ignore the complex spatiotemporal dependencies in the data and cannot dynamically adapt to the real-time changing mining environment. Although there are some mining volume prediction methods based on statistical models and machine learning in the prior art, these methods mostly rely on static model assumptions and have limited mining volume prediction effects in large-scale and complex environments. Existing methods face great challenges in processing large-scale data, spatiotemporal changes, and real-time updates, and it is difficult to meet the needs of high-precision and real-time predictions in the mining production process. Therefore, developing a mining volume prediction method that can effectively integrate spatial and temporal characteristics and adapt to dynamic changes has become a technical problem that needs to be solved urgently in the current mining field. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a mining volume prediction method based on machine learning, which solves several key technical problems in the existing mining volume prediction technology. First, traditional methods usually deal with spatial and temporal characteristics separately, and fail to effectively combine spatiotemporal characteristics, so as to be unable to fully capture the complex laws of mining volume changes in mining areas. Secondly, most of the existing technologies rely on static models, lack the ability to update in real time and adapt to dynamic changes in data, resulting in the inability to accurately reflect real-time fluctuations in the mining process of mining areas. In addition, traditional methods are inefficient in processing large-scale data and it is difficult to provide fast and accurate predictions. Finally, when facing changing data, existing machine learning models are often prone to overfitting, resulting in poor generalization ability and inability to adapt to new environments or emergencies. The present invention fundamentally solves these technical problems by combining spatiotemporal convolutional networks (ST-CNN) with long short-term memory networks (LSTM), and performing dynamic learning and real-time prediction based on a distributed computing framework.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions, a mining volume prediction method based on machine learning, comprising:

[0006] Obtain multiple data sources corresponding to the mining volume, including historical mining data, geological data, equipment operation data, environmental data and other factors that may affect the mining volume;

[0007] Preprocess the data source through a distributed computing framework, and the processing process includes removing noise, standardizing, and filling missing values;

[0008] Based on the preprocessed data, construct and train a machine learning model. The machine learning model learns the rules from it and outputs the prediction result of the mining volume. Use the trained machine learning model to predict the future mining volume and update the prediction result in real time.

[0009] As a preferred solution of the mining volume prediction method based on machine learning according to the present invention, wherein: the data source includes sensor data, equipment status data, environmental parameter data, and historical mining volume data of the mining area; among them, the sensor data of the mining area includes temperature, humidity, air pressure, and vibration data in the mine; the equipment status data includes the operating status, fault records, and maintenance records of the equipment in the mine; the environmental parameter data includes temperature, precipitation, wind speed, and seismic activity data outside the mining area; the historical mining volume data includes the mining volume of the mining area over the years or months, the usage of mining equipment, and the mining speed data.

[0010] As a preferred solution of the mining volume prediction method based on machine learning according to the present invention, wherein: the distributed computing framework uses Apache Spark for data parallel processing, and at the same time, the distributed computing framework performs distributed storage and parallel computing of data through Apache Spark.

[0011] As a preferred solution of the mining volume prediction method based on machine learning according to the present invention, wherein: the preprocessing includes denoising, standardizing, and filling missing values of the data source;

[0012] The denoising step processes the noise in the data source by using a filtering algorithm; the standardization is to independently standardize each feature, convert the feature data with different dimensions and units into a unified range, and the specific method is to normalize according to the minimum and maximum values and compress the data values into the range of [0, 1]; the missing value filling step fills the missing values by using an interpolation method, and the interpolation method includes linear interpolation and spline interpolation. Linear interpolation generates an estimated value by connecting the data points before and after the missing value, while spline interpolation uses a polynomial function for smooth fitting.

[0013] As a preferred solution of the mining volume prediction method based on machine learning according to the present invention, wherein: the construction and training of the machine learning model includes incorporating the time dimension into the convolution calculation and capturing the local features of space and time at the same time, specifically as follows:

[0014] ;

[0015] Wherein, Denotes the data feature at the time step and the spatial position ; is the convolution kernel of space and time, representing the convolution operation; is the length of the time dimension, is the number of spatial dimensions; b is the bias term, representing the offset of the convolution result; is the output feature after the spatio-temporal convolution operation;

[0016] To further enhance the model's ability, the prediction result of the previous time step is introduced as the input of the LSTM, and the historical prediction information is used to adjust the modeling of the current moment, as follows:

[0017] ;

[0018] Among them, is the hidden state of the current time step; is the hidden state of the previous time step; is the input feature of the current time step; is the prediction result of the previous time step; the cell state of the current time step; is the output gate of the current time step, representing the element-wise product, is the activation function;

[0019] The extracted from the ST-CNN is fused with the extracted from the LSTM, and an adaptive weighting mechanism and a spatio-temporal correlation matrix are introduced, enabling the spatial and temporal features to be dynamically weighted according to their importance, and capturing the interaction between the spatial and temporal features through the Hadamard product:

[0020] ;

[0021] Among them, and are matrices used to weight the spatial and temporal features; is the spatio-temporal correlation matrix, used to capture the dependence between the spatial and temporal features; represents the Hadamard product;

[0022] The result after spatio-temporal feature fusion is input to the output layer, and finally the predicted value of the extraction volume is generated. To prevent overfitting and improve the generalization ability of the model, an L2 regularization term is added to the output layer:

[0023] ;

[0024] Wherein, is the predicted mining volume; is the fused spatio-temporal feature, is the weight matrix of the output layer, is the bias term, is the L2 regularization term, is the L2 norm of the weight matrix, is the regularization coefficient.

[0025] As a preferred embodiment of the mining volume prediction method based on machine learning according to the present invention, wherein: the prediction result includes combining the prediction result with a Geographic Information System (GIS) to generate a three-dimensional visualization image including mining volume prediction and risk areas, and pushing the three-dimensional visualization image to a decision support system.

[0026] As a preferred embodiment of the mining volume prediction method based on machine learning according to the present invention, wherein: the decision support system includes a visualization interface for displaying the mining volume prediction results of different regions and time periods. Among them, the visualization interface of the decision support system is used to display the change trends of the mining volume prediction results in different regions and time periods. Users can select regions, adjust time, and adjust the input parameters of the machine learning model according to their needs.

[0027] As a preferred embodiment of the mining volume prediction system based on machine learning according to the present invention, wherein: it includes a data acquisition module, an algorithm module, and a decision support system; the data acquisition module is used for real-time data acquisition; the algorithm module is used for carrying algorithms required for operation and a Geographic Information System (GIS); the decision support system is used for outputting and feedback of the prediction results.

[0028] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the mining volume prediction method based on machine learning.

[0029] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, it implements the steps of the mining volume prediction method based on machine learning.

[0030] Advantages of the present invention: The present invention provides a prediction method for the mining volume based on a spatio-temporal convolutional network and a long short-term memory network, which has significant advantages. First of all, by introducing the spatio-temporal convolutional network (ST-CNN) and the long short-term memory network (LSTM), the present invention can extract complex features in the mining area data from both spatial and temporal dimensions, comprehensively capture the spatio-temporal laws of the mining volume change, and improve the prediction accuracy. Secondly, the present invention adopts an online learning mechanism, which can adapt to the dynamic changes of data in real time, ensuring timely adjustment and prediction of the mining volume in the production environment. Furthermore, combined with the distributed computing framework, the present invention can efficiently process large-scale data sets and update the model parameters in real time, thus effectively solving the problem of low computing efficiency of large-scale data. Finally, the model introduces regularization techniques and spatio-temporal feature fusion mechanisms, enhancing the generalization ability and avoiding overfitting, ensuring high prediction accuracy in different mining area environments. In summary, the present invention not only improves the accuracy and efficiency of the mining volume prediction, but also can adapt to complex dynamic environments, with strong practicality and application prospects. Description of the Drawings

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0032] Figure 1 It is a schematic flowchart of a prediction method for the mining volume based on machine learning provided by an embodiment of the present invention. Detailed Embodiments

[0033] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways than those specifically described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0035] Second, as used herein, "one embodiment" or "an embodiment" refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0036] The present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally not in accordance with the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0037] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner, and outer" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0038] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, and coupled" should be understood in a broad sense. For example: it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0039] Embodiment 1, referring to Figure 1 , is the first embodiment of the present invention. This embodiment provides a prediction method for the extraction volume based on machine learning, including:

[0040] S1: Obtain various data sources corresponding to the extraction volume, including historical extraction data, geological data, equipment operation data, environmental data, and other factor data that may affect the extraction volume in the mining area.

[0041] S2: Preprocess the data sources through a distributed computing framework. The processing process includes removing noise, standardizing, and filling in missing values.

[0042] S3: Based on the preprocessed data, construct and train a machine learning model. The machine learning model learns the rules from the data and outputs the prediction result of the extraction volume. Use the trained machine learning model to predict the future extraction volume and update the prediction result in real time.

[0043] The data sources include sensor data, equipment status data, environmental parameter data, and historical production data of the mining area; among them, the sensor data of the mining area includes temperature, humidity, air pressure, and vibration data in the mine; the equipment status data includes the operating status, fault records, and maintenance records of the equipment in the mine; the environmental parameter data includes temperature, precipitation, wind speed, and seismic activity data outside the mining area; the historical production data includes the production volume, usage of mining equipment, and production speed data of the mining area over the years or months.

[0044] The distributed computing framework uses Apache Spark for data parallel processing, and at the same time, the distributed computing framework performs distributed storage and parallel computing of data through Apache Spark.

[0045] The preprocessing includes denoising, standardizing, and filling missing values in the data source;

[0046] The denoising step processes the noise in the data source by using a filtering algorithm; the standardization is to independently standardize each feature, converting feature data with different dimensions and units into a unified range. The specific method is to normalize according to the minimum and maximum values, compressing the data values into the range of [0, 1]; the missing value filling step fills the missing values by using interpolation methods, where the interpolation methods include linear interpolation and spline interpolation. Linear interpolation generates an estimated value by connecting the data points before and after the missing value, while spline interpolation uses a polynomial function for smooth fitting.

[0047] The construction and training of the machine learning model include incorporating the time dimension into the convolution calculation and simultaneously capturing local features in space and time, as follows:

[0048] ;

[0049] Among them, represents the data feature at time step and spatial position ; is the convolution kernel in space and time, represents the convolution operation; is the length of the time dimension, is the number of spatial dimensions; b is the bias term, representing the offset of the convolution result; is the output feature after the spatio-temporal convolution operation;

[0050] To further enhance the model's ability, the prediction result from the previous time step is introduced as the input of the LSTM, and the historical prediction information is used to adjust the modeling of the current moment, as follows:

[0051] ;

[0052] Among them, is the hidden state at the current time step; is the hidden state at the previous time step; is the input feature at the current time step; is the prediction result at the previous time step; is the cell state at the current time step; is the output gate at the current time step, represents element-wise multiplication, is the activation function;

[0053] The extracted from ST-CNN will be fused with the extracted by LSTM, introducing an adaptive weighting mechanism and a spatio-temporal correlation matrix , enabling spatial and temporal features to be dynamically weighted according to their importance, and capturing the interaction between spatial and temporal features through the Hadamard product:

[0054] ;

[0055] Among them, and are matrices used to weight spatial features and temporal features; is the spatio-temporal correlation matrix, used to capture the dependencies between spatial and temporal features; represents the Hadamard product;

[0056] The result after spatio-temporal feature fusion is input into the output layer, and finally the predicted value of the mining volume is generated. To prevent overfitting and improve the generalization ability of the model, an L2 regularization term is added to the output layer:

[0057] ;

[0058] Among them, is the predicted mining volume; is the spatio-temporal feature after fusion, is the weight matrix of the output layer, is the bias term, is the L2 regularization term, is the L2 norm of the weight matrix, is the regularization coefficient.

[0059] The prediction result includes combining the prediction result with the Geographic Information System (GIS) to generate a three-dimensional visualization image containing the mining volume prediction and the risk area, and pushing the three-dimensional visualization image to the decision support system.

[0060] The decision support system includes a visualization interface for displaying the predicted mining volume results for different regions and time periods. Among them, the visualization interface of the decision support system is used to display the change trends of different regions and time periods of the predicted mining volume results. Users can select regions, adjust time according to their needs, and adjust the input parameters of the machine learning model.

[0061] Embodiment 2 is the second embodiment of the present invention, which provides a mining volume prediction system based on machine learning, including: a data acquisition module, an algorithm module, and a decision support system; the data acquisition module is used to collect data in real time; the algorithm module is used to carry the algorithms required for operation and the Geographic Information System (GIS); the decision support system is used to output and feedback the prediction results.

[0062] 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 them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0063] Embodiment 3

[0064] The third embodiment of the present invention is different from the previous two embodiments in that:

[0065] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the essence of the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks, etc., which can store program codes.

[0066] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0067] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0068] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0069] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0070] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A mining volume prediction method based on machine learning, characterized in that: include, Obtain multiple data sources corresponding to the amount of mining; Preprocessing the data source through a distributed computing framework, including removing noise, standardizing, and filling missing values; Based on the preprocessed data, a machine learning model is constructed and trained, rules are learned from the machine learning model and a mining volume prediction result is output, future mining volumes are predicted through the trained machine learning model, and the prediction result is updated in real time; The construction and training of the machine learning model includes incorporating the time dimension into the convolution calculation and capturing the local features of space and time, as follows: ； in, Indicates that at time step and spatial location The data characteristics on is the spatial and temporal convolution kernel, Represents the convolution operation; is the length of the time dimension, is the number of spatial dimensions; b is the bias term, which indicates the offset of the convolution result; It is the output feature after the spatiotemporal convolution operation; In order to further enhance the model's capabilities, the prediction results of the previous time step are introduced As the input of LSTM, historical prediction information is used to adjust the modeling at the current moment, as follows: ； in, is the hidden state at the current time step; is the hidden state at the previous time step; is the input feature of the current time step; is the prediction result of the previous time step; is the cell state at the current time step; is the output gate of the current time step, represents element-wise product, is the activation function; Extracted from ST-CNN Extracted with LSTM Fusion, introducing adaptive weighting mechanism and spatiotemporal correlation matrix , which enables spatial and temporal features to be dynamically weighted according to their importance, and captures the interaction between spatial and temporal features through the Hadamard product: ； in, and is a matrix used to weight spatial and temporal features; is the spatiotemporal correlation matrix, which is used to capture the dependencies between spatial and temporal features; represents the Hadamard product; The result of spatiotemporal feature fusion is input into the output layer to finally generate the predicted value of mining volume. In order to prevent overfitting and improve the generalization ability of the model, an L2 regularization term is added to the output layer: ； in, is the predicted mining volume; is the fused spatiotemporal feature, is the weight matrix of the output layer, is the bias term, is the L2 regularization term, is the L2 norm of the weight matrix, is the regularization coefficient; The data sources include sensor data, equipment status data, environmental parameter data and historical mining volume data of the mining area; among them, the sensor data of the mining area includes temperature, humidity, air pressure and vibration data in the mine; the equipment status data includes the operating status, fault records and maintenance records of the equipment in the mine; the environmental parameter data includes temperature, precipitation, wind speed and seismic activity data outside the mining area; the historical mining volume data includes the mining volume of the mining area over the years or months, the usage of mining equipment and mining speed data.

2. The mining volume prediction method based on machine learning according to claim 1, characterized in that: The distributed computing framework uses Apache Spark to perform data parallel processing, and the distributed computing framework performs distributed storage and parallel computing of data through Apache Spark.

3. The mining volume prediction method based on machine learning as claimed in claim 2, characterized in that: The preprocessing includes denoising, standardizing and filling missing values ​​on the data source; The denoising step processes the noise in the data source by using a filtering algorithm; the standardization independently standardizes each feature and converts feature data of different dimensions and units into a unified range. The specific method is to normalize according to the minimum and maximum values ​​and compress the data values ​​into the range of [0, 1]; the missing value filling step fills the missing values ​​by using interpolation methods, wherein the interpolation methods include linear interpolation and spline interpolation. Linear interpolation generates an estimated value by connecting the data points before and after the missing value, while spline interpolation uses a polynomial function for smooth fitting.

4. The mining volume prediction method based on machine learning as claimed in claim 3, characterized in that: The prediction results include combining the prediction results with a geographic information system (GIS) to generate a three-dimensional visualization image including mining volume prediction and risk areas, and pushing the three-dimensional visualization image to a decision support system.

5. The mining volume prediction method based on machine learning according to claim 4, characterized in that: The decision support system includes a visualization interface for displaying the mining volume forecast results for different regions and time periods, wherein the visualization interface of the decision support system is used to display the changing trends of the mining volume forecast results for different regions and time periods, and the user selects the region and adjusts the time according to needs, and adjusts the input parameters of the machine learning model.

6. A mining yield prediction system based on machine learning, applied to the mining yield prediction method based on machine learning as claimed in any one of claims 1 to 5, characterized in that: Including data acquisition module, algorithm module and decision support system; The data acquisition module is used to collect data in real time; The algorithm module is used to carry the algorithms and geographic information system GIS required for calculation; The decision support system is used to output and provide feedback on the prediction results.

7. 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, the steps of the production volume prediction method based on machine learning described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the mining volume prediction method based on machine learning described in any one of claims 1 to 5 are implemented.

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