CN117189071A - Automatic control method for core drilling rig operation - Google Patents
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Abstract
An automatic control method for the operation of a core drilling rig is disclosed. Firstly, acquiring rotating speed values, pressure values, temperature values and vibration amplitude values of a drill bit at a plurality of preset time points in a preset time period, then arranging the rotating speed values, the pressure values, the temperature values and the vibration amplitude values at the preset time points into a multi-parameter time sequence coordination matrix according to a time dimension and a sample dimension, then carrying out feature extraction on the multi-parameter time sequence coordination matrix through a parameter coordination time sequence feature extractor based on a deep neural network model to obtain a multi-parameter time sequence coordination feature map, then carrying out autocorrelation correlation reinforcement analysis on the multi-parameter time sequence coordination feature map to obtain autocorrelation reinforcement multi-parameter time sequence coordination features, and finally, determining whether the working state of the drill bit is normal or not and determining whether to suspend drilling or not based on the autocorrelation reinforcement multi-parameter time sequence coordination features. Thus, the safety and reliability of the core drilling rig operation can be improved.
Description
Technical Field
The application relates to the field of core drilling rigs, and more particularly, to an automatic control method for the operation of a core drilling rig.
Background
A core drilling rig is an apparatus for geological exploration that drills a sample of subterranean rock by rotating a drill bit and applying pressure. However, the traditional drilling machine operation needs constructors to ascend a tower to lift, add and subtract, core taking and the like, and certain risks exist in the operation mode, such as personnel falling can be caused by high-altitude operation, and serious injury and even life danger are caused. Meanwhile, constructors need to lift, add and subtract, core-taking and other operations of the drilling tool at high positions, the operations are complex, mistakes are easy to occur, and the intelligent degree is low. Moreover, the operators need to tie safety equipment such as safety ropes and waist-hung falling protectors, the complexity and inconvenience of work are increased, and the operators need to spend a great deal of time and effort in the process of lifting and lowering towers and operating, so that the construction progress is slow, and the overall efficiency of engineering is affected.
Accordingly, an automated control scheme for core drilling rig operation is desired.
Disclosure of Invention
In view of the above, the application provides an automatic control method for the operation of a core drilling rig, which can monitor the working state of the drilling rig in real time and automatically stop the operation of the drilling rig according to preset conditions, thereby reducing the number of ascending operations of constructors and reducing the safety risk.
According to an aspect of the present application, there is provided an automated control method of core drilling rig operation, comprising: acquiring rotating speed values, pressure values, temperature values and vibration amplitude values of a drill bit at a plurality of preset time points in a preset time period; arranging the rotating speed values, the pressure values, the temperature values and the vibration amplitude values of the plurality of preset time points into a multi-parameter time sequence cooperative matrix according to the time dimension and the sample dimension; extracting the characteristics of the multi-parameter time sequence cooperative matrix by a parameter cooperative time sequence characteristic extractor based on a deep neural network model to obtain a multi-parameter time sequence cooperative characteristic map; carrying out autocorrelation correlation reinforcement analysis on the multi-parameter time sequence cooperative characteristic diagram to obtain autocorrelation reinforcement multi-parameter time sequence cooperative characteristics; and determining whether the working state of the drill bit is normal or not and determining whether to pause drilling or not based on the self-correlation enhanced multi-parameter time sequence cooperative characteristic.
Compared with the prior art, the automatic control method for the operation of the core drilling rig provided by the application has the advantages that the working state parameter data of the drill bit is monitored and collected in real time through the sensor group, the data processing and analyzing algorithm is introduced into the rear end to carry out time sequence collaborative analysis on the working state parameter data of the drill bit, so that the working state of the drill bit is detected, and when the abnormal condition of the drill bit is detected, the controller automatically stops the operation of the drill bit and sends out an alarm signal. By the mode, the working state of the drilling machine can be monitored in real time, and the operation of the drilling machine can be automatically stopped according to preset conditions, so that the number of ascending operations of constructors is reduced, and the safety risk is reduced.
Other features and aspects of the present application will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features and aspects of the application and together with the description, serve to explain the principles of the application.
Fig. 1 shows a flow chart of an automated control method of core drilling rig operation according to an embodiment of the application.
Fig. 2 shows a schematic architecture of an automated control method of core drilling rig operation according to an embodiment of the application.
Fig. 3 shows a flow chart of substep S150 of the method for automating the control of the operation of a core drilling rig according to an embodiment of the application.
Fig. 4 shows a block diagram of an automated control system of core drilling rig operation according to an embodiment of the application.
Fig. 5 shows an application scenario diagram of an automated control method of core drilling rig operation according to an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are also within the scope of the application.
As used in the specification and in the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
Various exemplary embodiments, features and aspects of the application will be described in detail below with reference to the drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
In addition, numerous specific details are set forth in the following description in order to provide a better illustration of the application. It will be understood by those skilled in the art that the present application may be practiced without some of these specific details. In some instances, well known methods, procedures, components, and circuits have not been described in detail so as not to obscure the present application.
According to the technical scheme, the automatic control method for the operation of the core drilling rig is provided, and the drilling tool can be quickly and safely detached from a wellhead under the condition that personnel are not required to ascend. Specifically, the method comprises the following steps: 1. a rotatable clamping ring is arranged at the wellhead, and the clamping ring can fix or release the upper end of the drilling tool; 2. inserting the upper end of the drilling tool into the clamping ring to enable the clamping ring to be tightly connected with the drilling tool; 3. starting a rotating mechanism of the clamping ring to enable the clamping ring to rotate along the axis of the drilling tool, so that the upper end of the drilling tool is unscrewed; 4. stopping the rotation of the clamping ring and removing the clamping ring when the upper end of the drilling tool is completely separated from the clamping ring; 5. lifting the upper end of the drilling tool by using a crane and moving the drilling tool to a safe area; 6. repeating the steps 2-5 until all drilling tools are disassembled.
Accordingly, in consideration of the scheme, although the drilling tool can be quickly and safely automatically detached from the wellhead under the condition that personnel are not required to ascend, so that the automatic control of the core drilling machine is completed, the safety problem caused by the manual ascending in the traditional scheme is avoided, but abnormal conditions such as clamping, overheating and abrasion of the drilling bit can also occur in the drilling process, and the conditions can cause equipment damage or operator injury. Therefore, timely detection and handling of these anomalies is important to ensure proper operation of the drilling rig and safety of the operator. However, the traditional manual ascending detection method has a plurality of problems, so that the efficiency is low, potential safety hazards exist, meanwhile, the operation abnormality of the drilling machine is difficult to find in time, and the safety and the reliability of the system are difficult to guarantee.
Aiming at the technical problems, the technical concept of the application is that the working state parameter data of the drill bit, such as a rotating speed value, a pressure value, a temperature value and a vibration amplitude value, are monitored and collected in real time through a sensor group, a data processing and analyzing algorithm is introduced at the rear end to carry out time sequence collaborative analysis of the working state parameter data of the drill bit, so that the working state of the drill bit is detected, and when abnormal conditions of the drill bit, such as the drill bit is blocked, overheated, worn and the like, are detected, the controller automatically stops the operation of the drill bit and sends out an alarm signal. By the mode, the working state of the drilling machine can be monitored in real time, and the operation of the drilling machine can be automatically stopped according to preset conditions, so that the number of ascending operations of constructors is reduced, and the safety risk is reduced.
Fig. 1 shows a flow chart of an automated control method of core drilling rig operation according to an embodiment of the application. Fig. 2 shows a schematic architecture of an automated control method of core drilling rig operation according to an embodiment of the application. As shown in fig. 1 and 2, the method for automatically controlling the operation of the core drilling rig according to the embodiment of the application comprises the following steps: s110, acquiring rotating speed values, pressure values, temperature values and vibration amplitude values of a drill bit at a plurality of preset time points in a preset time period; s120, arranging the rotating speed values, the pressure values, the temperature values and the vibration amplitude values of the plurality of preset time points into a multi-parameter time sequence cooperative matrix according to the time dimension and the sample dimension; s130, extracting characteristics of the multi-parameter time sequence coordination matrix through a parameter coordination time sequence characteristic extractor based on a deep neural network model to obtain a multi-parameter time sequence coordination characteristic diagram; s140, carrying out autocorrelation correlation reinforcement analysis on the multi-parameter time sequence cooperative characteristic diagram to obtain autocorrelation reinforcement multi-parameter time sequence cooperative characteristics; and S150, determining whether the working state of the drill bit is normal or not based on the self-correlation enhanced multi-parameter time sequence cooperative characteristic, and determining whether to pause drilling or not. It should be understood that the purpose of step S110 is to obtain, by using a sensor or other monitoring device, parameter values such as rotational speed, pressure, temperature, and vibration amplitude of the drill bit at a plurality of predetermined time points, where the parameter values reflect the working state and performance of the drill bit, and are basic data for analysis and control in the subsequent steps. Step S120 arranges the parameter values obtained from step S110 according to the time dimension and the sample dimension to form a multi-parameter time sequence cooperative matrix, and the arrangement mode can keep the time sequence relation of time sequence data and combine the values of different parameters together to provide input for the feature extraction and analysis of the subsequent steps. Step S130 processes the multi-parameter time sequence coordination matrix by using a parameter coordination time sequence feature extractor based on the deep neural network model, extracts feature information therein to obtain a multi-parameter time sequence coordination feature map, where the features may include modes, trends, correlations, etc. in the time sequence data, and are used for capturing feature representations of the working state of the drill bit. Step S140 processes the multi-parameter time sequence collaborative feature map through autocorrelation association strengthening analysis, enhances the correlation between the features and extracts time sequence collaborative information, and the autocorrelation association strengthening can capture the time sequence relationship and the mutual influence between the features, thereby improving the expression capacity of the features and the discrimination capacity of the working state of the drill bit. Step S150 judges whether the working state of the drill bit is normal by utilizing the multi-parameter time sequence cooperative feature subjected to feature extraction and autocorrelation correlation reinforcement, and can judge whether the drill bit is in a normal state according to the feature value of the autocorrelation correlation reinforcement multi-parameter time sequence cooperative feature map by setting a certain threshold or using a classifier and other methods, and if abnormality or failure is detected, corresponding control actions such as suspending drilling operation can be triggered.
Specifically, in the technical scheme of the application, firstly, the rotation speed value, the pressure value, the temperature value and the vibration amplitude value of the drill bit at a plurality of preset time points in a preset time period are obtained. Then, considering the running state parameters of the drill bit, such as the rotation speed value, the pressure value, the temperature value and the vibration amplitude value, have time sequence dynamic change rules in the time dimension, and have time sequence cooperative association relations among the running state parameters of the drill bit, the time sequence cooperative association characteristics of the running state parameters of the drill bit have important significance for the detection of the working state of the drill bit. Therefore, in the technical solution of the present application, in order to perform timing collaborative analysis on the rotation speed value, the pressure value, the temperature value, and the vibration amplitude value, the rotation speed value, the pressure value, the temperature value, and the vibration amplitude value at the plurality of predetermined time points need to be arranged as a multi-parameter timing collaborative matrix according to a time dimension and a sample dimension, so as to integrate distribution information of the rotation speed value, the pressure value, the temperature value, and the vibration amplitude value in the time dimension and the sample dimension.
And then, carrying out feature mining on the multi-parameter time sequence coordination matrix by using a parameter coordination time sequence feature extractor based on a deep neural network model, wherein the parameter coordination time sequence feature extractor has excellent performance in the aspect of implicit correlation feature extraction, so as to extract time sequence coordination correlation feature distribution information related to the rotating speed value, the pressure value, the temperature value and the vibration amplitude value in the multi-parameter time sequence coordination matrix, and be beneficial to carrying out abnormal detection on the working state of a drill bit.
Accordingly, in step S130, the deep neural network model is a convolutional neural network model. It is worth mentioning that convolutional neural network (Convolutional Neural Network, CNN) is a deep learning model, mainly used for processing data with grid structure, such as images and video. The core idea of CNN is local awareness and weight sharing. It extracts features of the input data by using a convolution layer and a pooling layer and classifies or regresses through a fully connected layer. The following are the main components of CNN: 1. convolution layer (Convolutional Layer): the convolutional layer is the core component of the CNN. It extracts features by applying a series of learnable filters (also called convolution kernels) on the input data. Each filter performs a convolution operation on the input to generate a feature map. These feature maps capture different features of the input data, such as edges, textures, etc. 2. Activation function (Activation Function): the convolutional layer typically applies an activation function after the convolutional operation to introduce a nonlinear characteristic. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, tanh, and the like. 3. Pooling Layer (Pooling Layer): the pooling layer serves to reduce the spatial dimensions of the feature map while retaining important features. Common Pooling operations include Max Pooling and Average Pooling. 4. Full tie layer (Fully Connected Layer): the fully connected layer connects the output of the pooling layer to one or more neurons for final classification or regression. The fully attached layer is typically a multi-layer perceptron (Multilayer Perceptron) structure. In training CNNs, the weights in the network are typically updated using a back-propagation algorithm to minimize the gap between the predicted output and the real labels. In addition, to prevent overfitting, regularization techniques (such as L2 regularization) and data enhancement (Data Augmentation) are often used to increase the diversity of the training data. In general, convolutional neural networks extract the features of the input data through a layer-by-layer convolutional operation and a pooling operation, and perform final classification or regression prediction through fully connected layers. This structure allows CNNs to have excellent performance and expressive power in processing mesh structure data such as images.
Further, in the automatic control method for the operation of the core drilling machine, considering that a certain correlation exists among the working state parameters of the drill bit, that is, the rotation speed value, the pressure value, the temperature value and the vibration amplitude value have time sequence cooperative correlation characteristics, more meaningful information of the working state characteristics of the drill bit can be further extracted by utilizing the correlation characteristics, so that the abnormal working state of the drill bit can be better understood and detected. Therefore, in order to better capture the characteristic mode and the change trend of the working state of the drilling machine, in the technical scheme of the application, the multi-parameter time sequence collaborative feature map is further subjected to a characteristic autocorrelation correlation strengthening module to obtain an autocorrelation strengthening multi-parameter time sequence collaborative feature map. It should be appreciated that the feature autocorrelation enhancement module can further enhance the correlation between the time series features of the drill bit operating state parameters by performing autocorrelation operation on the multi-parameter time series collaborative feature map. Through the autocorrelation operation, time sequence important characteristics related to relevant state parameters in the working process of the drill bit in the multi-parameter time sequence collaborative characteristic diagram can be highlighted, the influence of noise and redundant information is reduced, the expression capacity and the distinguishing degree of the characteristics are improved, and the abnormal mode is more obvious and identifiable.
Accordingly, in step S140, performing autocorrelation enhancement analysis on the multi-parameter timing coordination feature map to obtain an autocorrelation enhanced multi-parameter timing coordination feature, including: and the multi-parameter time sequence cooperative characteristic diagram is used for obtaining an autocorrelation reinforced multi-parameter time sequence cooperative characteristic diagram serving as the autocorrelation reinforced multi-parameter time sequence cooperative characteristic through a characteristic autocorrelation correlation reinforcing module.
It should be noted that the process of obtaining the self-correlation enhanced multi-parameter time sequence collaborative feature map by the multi-parameter time sequence collaborative feature map through the feature self-correlation enhancement module mainly refers to that the multi-parameter time sequence collaborative feature map is processed by the feature self-correlation enhancement module to obtain the self-correlation enhanced multi-parameter time sequence collaborative feature map. The purpose of this process is to enhance the correlation between features and extract more rich timing synergy information. Through the characteristic autocorrelation association, the time sequence relation and the mutual influence between the characteristics can be captured, and then the understanding and modeling capability of the model on time sequence data are improved. The autocorrelation reinforced multi-parameter time sequence collaborative feature map plays an important role in time sequence data analysis and modeling tasks. The following are some application scenarios and uses: 1. modeling time sequence data: in the tasks of time sequence prediction, behavior recognition, action recognition and the like, the self-correlation enhanced multi-parameter time sequence collaborative feature map can provide more accurate feature representation, so that the prediction performance of a model is improved. 2. And (3) time sequence data analysis: for analysis of time series data, the autocorrelation enhanced multi-parameter time series collaborative feature map can help discover long-term dependencies and related patterns in the data, thereby providing more insight and analysis. 3. Visualization of time series data: by visualizing the autocorrelation reinforced multi-parameter time sequence collaborative feature map, the association and the mode in the time sequence data can be displayed more intuitively, and people are helped to understand the evolution and the change trend of the data. In other words, the autocorrelation reinforced multi-parameter timing collaborative feature map can provide richer timing related information, and has important roles in modeling, analysis and visualization of timing data. Such a characterization can help improve the performance of the model and provide more insight and insight into the data.
More specifically, the multi-parameter time sequence cooperative feature map is passed through a feature autocorrelation correlation strengthening module to obtain an autocorrelation strengthening multi-parameter time sequence cooperative feature map as the autocorrelation strengthening multi-parameter time sequence cooperative feature, and the method comprises the following steps: the multi-parameter time sequence collaborative feature map passes through a first convolution layer of the feature autocorrelation correlation strengthening module to obtain a first feature map; the first characteristic diagram passes through a second convolution layer of the characteristic autocorrelation correlation strengthening module to obtain a second characteristic diagram; expanding each feature matrix of the second feature map along the channel dimension into feature vectors to obtain a sequence of first feature vectors; calculating cosine similarity between any two first feature vectors in the sequence of the first feature vectors to obtain a cosine similarity feature map; normalizing the cosine similarity feature map through a Softmax function to obtain a normalized cosine similarity feature map; multiplying the normalized cosine similarity feature map and the cosine similarity feature map according to position points to obtain a similarity mapping optimization feature map; the similarity mapping optimization feature map passes through a first deconvolution layer of the feature autocorrelation correlation strengthening module to obtain a first deconvolution feature map; calculating element-by-element sums of the first deconvolution feature map and the first feature map to obtain a first fusion feature map; the first fusion feature map passes through a second deconvolution layer of the feature autocorrelation correlation strengthening module to obtain a second deconvolution feature map; and calculating element-by-element sums of the second deconvolution feature map and the multi-parameter timing synergy feature map to obtain the autocorrelation enhanced multi-parameter timing synergy feature map.
And then, the self-correlation enhanced multi-parameter time sequence collaborative feature map is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the working state of the drill bit is normal or not. That is, the working state of the drill bit is detected by classifying the multi-parameter time sequence collaborative correlation characteristic information of the working state of the drill bit after the reinforcement of the self-correlation characteristic, and when abnormal conditions of the drill bit such as drill bit jamming, overheating, abrasion and the like are detected, the controller automatically stops the operation of the drill bit and sends out an alarm signal. By the mode, the working state of the drilling machine can be monitored in real time, and the operation of the drilling machine can be automatically stopped according to preset conditions, so that the number of ascending operations of constructors is reduced, and the safety risk is reduced.
Accordingly, as shown in fig. 3, based on the self-correlation enhanced multi-parameter time sequence cooperative characteristic, determining whether the working state of the drill bit is normal or not and determining whether to pause drilling includes: s151, the self-correlation enhanced multi-parameter time sequence collaborative feature map is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the working state of a drill bit is normal or not; and S152, determining whether to pause drilling or not based on the classification result.
It should be appreciated that the role of the classifier is to learn the classification rules and classifier using a given class, known training data, and then classify the unknown data. Logistic regression, support vector machines, etc. are commonly used to solve the classification problem, and for multi-classification problems (multi-class classification), logistic regression or support vector machines can also be used, but multiple two classifications are needed to form multiple classifications, but this is prone to error and inefficient, and the commonly used multiple classification method is the Softmax classification function.
Further, in the technical scheme of the application, the automatic control method for the operation of the core drilling rig further comprises the training steps of: the method is used for training the parameter collaborative time sequence feature extractor, the feature autocorrelation correlation strengthening module and the classifier based on the deep neural network model. It should be understood that in the automatic control method of the core drilling rig operation, the training step aims to train parameters based on the convolutional neural network model, including a parameter collaborative time sequence feature extractor, a feature autocorrelation correlation strengthening module and a classifier. The effect of these training steps is as follows: 1. parameter collaborative timing feature extractor training: the parameter collaborative timing feature extractor is a module for extracting features from sensor data of a core drilling rig. The training parameters cooperate with the time series feature extractor to enable accurate extraction of features associated with the drilling process by learning feature patterns in the data. Training may be performed by supervised learning using labeled data samples or feature learning using an unsupervised learning method. 2. Feature autocorrelation associated reinforcement module training: the feature autocorrelation correlation strengthening module is a module for strengthening correlation between features and extracting timing synergy information. The training feature autocorrelation correlation strengthening module aims to enable the training feature autocorrelation correlation strengthening module to better capture time sequence relations and mutual influences in time sequence data by learning correlation modes among features. Training may be performed by supervised learning using labeled data samples or feature-dependent learning using an unsupervised learning method. 3. Training a classifier: the classifier is a module for classifying the state or behavior of the core drilling rig. The purpose of training the classifier is to enable it to accurately map input features to corresponding classes or labels by learning labeled data samples. Training of the classifier can use a supervised learning approach to update the weights of the classifier by minimizing the gap between the prediction output and the true labels. By training these modules, the automated control system of the core drilling rig can learn the ability to extract useful features from sensor data, enhance feature correlation, and accurately classify. The training process can improve the performance and the robustness of the system, so that the system can better cope with different drilling scenes and conditions, and more accurate and reliable automatic control is realized.
In one example, the training step includes: acquiring training data, wherein the training data comprises training rotating speed values, training pressure values, training temperature values and training vibration amplitude values of a drill bit at a plurality of preset time points in a preset time period, and a true value of whether the working state of the drill bit is normal or not; arranging the training rotating speed values, the training pressure values, the training temperature values and the training vibration amplitude values of the plurality of preset time points into a training multi-parameter time sequence cooperative matrix according to the time dimension and the sample dimension; the training multi-parameter time sequence cooperative matrix passes through the parameter cooperative time sequence feature extractor based on the deep neural network model to obtain a training multi-parameter time sequence cooperative feature map; the training multi-parameter time sequence collaborative feature map passes through the feature autocorrelation associated strengthening module to obtain a training autocorrelation enhanced multi-parameter time sequence collaborative feature map; the training autocorrelation reinforced multi-parameter time sequence cooperative feature diagram passes through the classifier to obtain a classification loss function value; and training the parameter collaborative time sequence feature extractor, the feature autocorrelation correlation strengthening module and the classifier based on the classification loss function value and through gradient descent direction propagation, wherein the training autocorrelation strengthening module performs progressive optimization of a weight matrix based on a training autocorrelation strengthening multi-parameter time sequence collaborative feature vector obtained after the training autocorrelation strengthening multi-parameter time sequence collaborative feature map is unfolded when the weight matrix iterates each time of training.
Wherein the training is performedThe self-correlation enhanced multi-parameter time sequence cooperative feature map is processed through the classifier to obtain a classification loss function value, and the method comprises the following steps: the classifier processes the training autocorrelation enhanced multi-parameter time sequence collaborative feature map by a training classification formula to generate a training classification result; wherein, training classification formula is:wherein (1)>Representing projection of the training autocorrelation enhanced multiparameter timing synergy feature map as a vector, ++>To->Weight matrix for all connection layers of each layer, < ->To->Representing the bias matrix of each fully connected layer; and calculating a cross entropy value between the training classification result and the true value as the classification loss function value.
Particularly, in the technical scheme of the application, each feature matrix of the training multi-parameter time sequence collaborative feature map expresses the time sequence-sample cross dimension local association feature of a training rotation speed value, a training pressure value, a training temperature value and a training vibration amplitude value, and the channel distribution of the convolutional neural network model is matched among the feature matrices, so that after the feature self-correlation strengthening module is further passed, the self-correlation strengthening can be carried out on the feature vector of the training multi-parameter time sequence collaborative feature map along the channel based on the distribution of the time sequence-sample cross dimension local association feature on the feature matrix space dimension, thereby improving the feature integral space distribution association effect of the training self-correlation strengthening multi-parameter time sequence collaborative feature map on the channel dimension, namely, improving the expression effect of the training self-correlation strengthening multi-parameter time sequence collaborative feature map on the channel dimension.
However, at the same time, the training autocorrelation enhanced multi-parameter time sequence collaborative feature map has feature representations with dense distribution dimension association on the cross dimension local association feature space distribution in the feature matrix and the feature channel distribution among the feature matrices in the overall feature map distribution dimension, so that the training efficiency of the weight matrix of the classifier is reduced when the training autocorrelation enhanced multi-parameter time sequence collaborative feature map carries out classification regression training through the classifier.
Based on the training self-correlation enhanced multi-parameter time sequence cooperative feature vector, the applicant performs progressive optimization of a weight matrix based on the training self-correlation enhanced multi-parameter time sequence cooperative feature vector obtained after the training self-correlation enhanced multi-parameter time sequence cooperative feature map is developed when the training self-correlation enhanced multi-parameter time sequence cooperative feature map is subjected to classification regression training through a classifier.
Specifically, in one example, during each weight matrix iteration of the training, performing progressive optimization of a weight matrix on training autocorrelation enhanced multi-parameter time sequence collaborative feature vectors obtained after the training autocorrelation enhanced multi-parameter time sequence collaborative feature map is developed according to the following optimization formula to obtain an optimized training autocorrelation enhanced multi-parameter time sequence collaborative feature map; wherein, the optimization formula is:wherein (1)>And->Respectively is upper partWeight matrix of the second and the current iteration, wherein in the first iteration, different initialization strategies are adopted to set +.>And(e.g.)>Set as a unitary matrix->Set as the diagonal matrix of the mean value of the feature vector to be classified),>is training autocorrelation reinforced multi-parameter time sequence cooperative characteristic vector to be classified, < >>And->Respectively represent feature vector +>And->Global mean of (2), and->Is a bias matrix, e.g. initially set as a unity matrix, the vectors being in the form of column vectors, +.>Representing vector multiplication, ++>Representing matrix addition, ++>Representing multiplication by location +.>Representing a transpose operation->Represents a maximum function>And representing the optimized training autocorrelation reinforced multi-parameter time sequence collaborative feature matrix obtained after the optimized training autocorrelation reinforced multi-parameter time sequence collaborative feature diagram is unfolded.
That is, the multi-parameter time sequence cooperative feature vector is enhanced in consideration of training based on the classificationDuring the dense prediction task of (1), the high resolution representation of the weight matrix and the training autocorrelation reinforced multi-parameter time sequence cooperative feature vector to be classified are needed to be added>The feature multidimensional distribution association context is integrated, so that progressive integration (progressive integrity) is realized based on iteration association representation resource cognition (resource-aware) by maximizing a distribution boundary of a weight space in an iteration process, thereby improving the training effect of a weight matrix and improving the training efficiency of the whole model. In this way, the detection of the working state of the drill bit can be automatically performed, and when the drill bit is detectedAbnormal conditions such as drill bit blocking, overheating and abrasion occur, the controller can automatically stop the operation of the drill bit and send out an alarm signal, through the mode, the working state of the drilling machine can be monitored in real time, the operation of the drilling machine can be automatically stopped according to preset conditions, accordingly, the number of ascending operations of constructors is reduced, and safety risks are reduced.
In summary, according to the automatic control method for the operation of the core drilling rig provided by the embodiment of the application, the working state of the drilling rig can be monitored in real time, and the operation of the drilling rig can be automatically stopped according to preset conditions, so that the number of ascending operations of constructors is reduced, and the safety risk is reduced.
Fig. 4 shows a block diagram of an automated control system 100 for core drilling rig operation according to an embodiment of the present application. As shown in fig. 4, an automated control system 100 for core drilling rig operation according to an embodiment of the present application includes: a data acquisition module 110, configured to acquire rotational speed values, pressure values, temperature values, and vibration amplitude values of the drill bit at a plurality of predetermined time points within a predetermined period of time; a matrixing module 120, configured to arrange the rotation speed values, the pressure values, the temperature values, and the vibration amplitude values at the plurality of predetermined time points into a multi-parameter time sequence synergy matrix according to a time dimension and a sample dimension; the parameter collaborative timing feature extraction module 130 is configured to perform feature extraction on the multi-parameter timing collaborative matrix through a parameter collaborative timing feature extractor based on a deep neural network model to obtain a multi-parameter timing collaborative feature map; the reinforcement analysis module 140 is configured to perform autocorrelation correlation reinforcement analysis on the multi-parameter time sequence coordination feature map to obtain an autocorrelation reinforced multi-parameter time sequence coordination feature; and an operating state analysis module 150, configured to determine whether the operating state of the drill bit is normal or not, and determine whether to suspend drilling or not, based on the autocorrelation enhanced multi-parameter timing coordination feature.
In one possible implementation, the deep neural network model is a convolutional neural network model.
Here, it will be understood by those skilled in the art that the specific functions and operations of the respective units and modules in the above-described automatic control system 100 for core drilling rig operation have been described in detail in the above description of the automatic control method for core drilling rig operation with reference to fig. 1 to 3, and thus, repetitive descriptions thereof will be omitted.
As described above, the automated control system 100 for core drilling rig operation according to an embodiment of the present application may be implemented in various wireless terminals, such as a server or the like having an automated control algorithm for core drilling rig operation. In one possible implementation, the automated control system 100 for core drilling rig operation according to embodiments of the present application may be integrated into the wireless terminal as a software module and/or hardware module. For example, the automated control system 100 for core drilling rig operation may be a software module in the operating system of the wireless terminal, or may be an application developed for the wireless terminal; of course, the automated control system 100 of core drilling rig operation may likewise be one of the numerous hardware modules of the wireless terminal.
Alternatively, in another example, the core drilling rig operating automation control system 100 and the wireless terminal may be separate devices, and the core drilling rig operating automation control system 100 may be connected to the wireless terminal through a wired and/or wireless network and transmit interactive information in a agreed data format.
Fig. 5 shows an application scenario diagram of an automated control method of core drilling rig operation according to an embodiment of the application. As shown in fig. 5, in this application scenario, first, rotational speed values (e.g., D1 illustrated in fig. 5), pressure values (e.g., D2 illustrated in fig. 5), temperature values (e.g., D3 illustrated in fig. 5), and vibration amplitude values (e.g., D4 illustrated in fig. 5) of a plurality of predetermined time points of a drill bit within a predetermined period of time are acquired, and then the rotational speed values, pressure values, temperature values, and vibration amplitude values of the plurality of predetermined time points are input to a server (e.g., S illustrated in fig. 5) where an automated control algorithm for core drilling rig operation is deployed, wherein the server is capable of processing the rotational speed values, pressure values, temperature values, and vibration amplitude values of the plurality of predetermined time points using the automated control algorithm for core drilling rig operation to obtain a classification result for indicating whether the working state of the drill bit is normal.
It should be understood that the method and the system of the application do not need personnel to ascend, reduce the operation risk and labor intensity, improve the efficiency and quality of the withdrawing drilling tool, reduce the operation time and cost, are suitable for drilling tools of different types and specifications, and have wide applicability and universality.
Further, in another example of the present application, there is provided an automated control method of core drilling rig operation, the method comprising the steps of: 1. detecting the working state of the drilling machine, including parameters such as the rotating speed, pressure, temperature, vibration and the like of the drilling bit, by a sensor, and sending data to a controller; 2. the controller monitors and adjusts the operation of the drilling machine in real time according to a preset working mode and parameters so as to ensure the safety and efficiency of the drilling machine; 3. when the controller detects that the drilling machine has abnormal conditions, such as drill bit clamping, overheating, abrasion and the like, or reaches a preset drilling depth or time, the controller automatically stops the operation of the drilling machine and sends out an alarm signal; 4. the operator can check the working state and alarm information of the drilling machine through a display screen or a remote terminal, and manually intervene or switch the working mode according to the requirement.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of embodiments of the application has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (8)
1. An automatic control method for the operation of a core drilling rig is characterized by comprising the following steps: acquiring rotating speed values, pressure values, temperature values and vibration amplitude values of a drill bit at a plurality of preset time points in a preset time period; arranging the rotating speed values, the pressure values, the temperature values and the vibration amplitude values of the plurality of preset time points into a multi-parameter time sequence cooperative matrix according to the time dimension and the sample dimension; extracting the characteristics of the multi-parameter time sequence cooperative matrix by a parameter cooperative time sequence characteristic extractor based on a deep neural network model to obtain a multi-parameter time sequence cooperative characteristic map; carrying out autocorrelation correlation reinforcement analysis on the multi-parameter time sequence cooperative characteristic diagram to obtain autocorrelation reinforcement multi-parameter time sequence cooperative characteristics; and determining whether the working state of the drill bit is normal or not and determining whether to pause drilling or not based on the self-correlation enhanced multi-parameter time sequence cooperative characteristic.
2. The automated control method of core drilling rig operation of claim 1, wherein the deep neural network model is a convolutional neural network model.
3. The automated control method of core drilling rig operations of claim 2, wherein performing an autocorrelation correlation enhancement analysis on the multi-parameter time series collaborative feature map to obtain an autocorrelation correlation enhanced multi-parameter time series collaborative feature comprises: and the multi-parameter time sequence cooperative characteristic diagram is used for obtaining an autocorrelation reinforced multi-parameter time sequence cooperative characteristic diagram serving as the autocorrelation reinforced multi-parameter time sequence cooperative characteristic through a characteristic autocorrelation correlation reinforcing module.
4. An automated control method for core drilling rig operations according to claim 3, wherein passing the multi-parameter time-series collaborative feature map through a feature autocorrelation correlation enhancement module to obtain an autocorrelation enhanced multi-parameter time-series collaborative feature map as the autocorrelation enhanced multi-parameter time-series collaborative feature comprises: the multi-parameter time sequence collaborative feature map passes through a first convolution layer of the feature autocorrelation correlation strengthening module to obtain a first feature map; the first characteristic diagram passes through a second convolution layer of the characteristic autocorrelation correlation strengthening module to obtain a second characteristic diagram; expanding each feature matrix of the second feature map along the channel dimension into feature vectors to obtain a sequence of first feature vectors; calculating cosine similarity between any two first feature vectors in the sequence of the first feature vectors to obtain a cosine similarity feature map; normalizing the cosine similarity feature map through a Softmax function to obtain a normalized cosine similarity feature map; multiplying the normalized cosine similarity feature map and the cosine similarity feature map according to position points to obtain a similarity mapping optimization feature map; the similarity mapping optimization feature map passes through a first deconvolution layer of the feature autocorrelation correlation strengthening module to obtain a first deconvolution feature map; calculating element-by-element sums of the first deconvolution feature map and the first feature map to obtain a first fusion feature map; the first fusion feature map passes through a second deconvolution layer of the feature autocorrelation correlation strengthening module to obtain a second deconvolution feature map; and calculating element-by-element sums of the second deconvolution feature map and the multi-parameter timing synergy feature map to obtain the autocorrelation enhanced multi-parameter timing synergy feature map.
5. The automated control method of core drilling rig operations of claim 4, wherein determining whether the operational state of the drill bit is normal and determining whether to pause drilling based on the auto-correlation enhanced multiparameter timing coordination feature comprises: the self-correlation enhanced multi-parameter time sequence collaborative feature diagram is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the working state of a drill bit is normal or not; and determining whether to pause drilling based on the classification result.
6. The method for automated control of core drilling rig operations according to claim 5, further comprising the training step of: the method is used for training the parameter collaborative time sequence feature extractor, the feature autocorrelation correlation strengthening module and the classifier based on the deep neural network model.
7. The method for automated control of core drilling rig operations according to claim 6, wherein the training step comprises: acquiring training data, wherein the training data comprises training rotating speed values, training pressure values, training temperature values and training vibration amplitude values of a drill bit at a plurality of preset time points in a preset time period, and a true value of whether the working state of the drill bit is normal or not; arranging the training rotating speed values, the training pressure values, the training temperature values and the training vibration amplitude values of the plurality of preset time points into a training multi-parameter time sequence cooperative matrix according to the time dimension and the sample dimension; the training multi-parameter time sequence cooperative matrix passes through the parameter cooperative time sequence feature extractor based on the deep neural network model to obtain a training multi-parameter time sequence cooperative feature map; the training multi-parameter time sequence collaborative feature map passes through the feature autocorrelation associated strengthening module to obtain a training autocorrelation enhanced multi-parameter time sequence collaborative feature map; the training autocorrelation reinforced multi-parameter time sequence cooperative feature diagram passes through the classifier to obtain a classification loss function value; and training the parameter collaborative time sequence feature extractor, the feature autocorrelation correlation strengthening module and the classifier based on the classification loss function value and through gradient descent direction propagation, wherein the training autocorrelation strengthening module performs progressive optimization of a weight matrix based on a training autocorrelation strengthening multi-parameter time sequence collaborative feature vector obtained after the training autocorrelation strengthening multi-parameter time sequence collaborative feature map is unfolded when the weight matrix iterates each time of training.
8. The automated control method of core drilling rig operations of claim 7, wherein passing the training autocorrelation enhanced multiparameter timing synergy signature through the classifier to obtain a classification loss function value comprises: the classifier processes the training autocorrelation enhanced multi-parameter time sequence collaborative feature map by a training classification formula to generate a training classification result; wherein, training classification formula is:wherein (1)>Representing projection of the training autocorrelation enhanced multiparameter timing synergy feature map as a vector, ++>To->Weight matrix for all connection layers of each layer, < ->To->Representing the bias matrix of each fully connected layer; calculating the training classification result and the true valueCross entropy values between as the class loss function value.
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