CN115422821A - Data processing method and device for rock mass parameter prediction - Google Patents
Data processing method and device for rock mass parameter prediction Download PDFInfo
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Abstract
The application discloses a data processing method and device for rock mass parameter prediction. The method comprises the following steps: acquiring data to be processed; preprocessing the data to be processed based on data analysis to obtain tunneling characteristic data; and performing rock mass parameter prediction processing on the tunneling characteristic data based on a preset rock mass parameter prediction model to obtain target rock mass parameter data. Preprocessing the acquired data to be processed based on data analysis to obtain tunneling characteristic data; and performing rock mass parameter prediction processing on the tunneling characteristic data based on a preset rock mass parameter prediction model to obtain target rock mass parameter data. The rock mass parameter prediction is carried out on the tunneling characteristic data through the preset rock mass parameter model, the technical problem that in the prior art, the accuracy of rock mass parameter judgment is low in the TBM tunnel construction process is solved, and the technical effect of improving the accuracy of rock mass parameter prediction is achieved.
Description
Technical Field
The application relates to the field of computers, in particular to a data processing method and device for rock mass parameter prediction.
Background
The method is characterized in that safe and efficient tunneling of a Tunnel Boring Machine (TBM) is realized, and the key point is that the Tunnel Boring Machine accurately senses the rock mass characteristics of a Tunnel face in real time in the tunneling process of the TBM. After the site constructor accurately judges the condition of the tunnel face rock mass, the most reasonable TBM tunneling strategy can be adopted to realize safe and efficient TBM tunneling. However, the TBM cutter disc and the shield almost isolate all rock mass information in the nearly 2m of face and the surrounding tunnel wall, though partial face surrounding rock can be directly observed through the space of the hob support of the manual cutter changing bin, the cutter changing bin often splashes slag pieces in the tunneling process, the slag ash seriously reduces the internal visibility of the cutter changing bin, the judgment efficiency on rock mass parameters is low, and the judgment on the rock mass parameters is inaccurate.
Therefore, the problem that the accuracy of rock mass parameter judgment is low in the TBM tunnel construction process in the prior art exists.
Disclosure of Invention
The main object of the application is to provide a data processing method and device for rock mass parameter prediction, so that the technical problem that the accuracy of rock mass parameter judgment is lower in the TBM tunnel construction process in the prior art is solved, the accuracy of rock mass parameter prediction is improved, TBM tunnel construction strategies can be conveniently adjusted according to the rock mass parameters, and further the improvement of the TBM tunnel construction efficiency is realized.
In order to achieve the above object, a first aspect of the present application provides a data processing method for rock mass parameter prediction, including:
acquiring data to be processed, wherein the data to be processed is related data used for representing the rock mass tunneling construction of tunneling equipment to be predicted;
preprocessing the data to be processed based on data analysis to obtain tunneling characteristic data, wherein the tunneling characteristic data is used for representing the tunneling characteristics of the tunneling equipment; and
and performing rock mass parameter prediction processing on the tunneling characteristic data based on a preset rock mass parameter prediction model to obtain target rock mass parameter data.
Optionally, performing rock mass parameter prediction processing on the tunneling characteristic data based on a preset rock mass parameter prediction model, and obtaining target rock mass parameter data includes:
performing first prediction processing on the tunneling characteristic data based on a preset first rock mass parameter model to obtain first rock mass parameter data, wherein the first rock mass parameter data is used for representing data of a first process prediction parameter of the rock mass to be predicted;
performing characteristic analysis processing on the tunneling characteristic data and the first rock mass parameter data to obtain process characteristic data;
performing second prediction processing on the process characteristic data based on a preset second rock mass parameter model to obtain second rock mass parameter data, wherein the second rock mass parameter data is used for representing data of a second process prediction parameter of the rock mass parameter to be predicted; and
and carrying out verification processing on the second rock mass parameter data to obtain the target rock mass parameter data.
Optionally, performing feature analysis processing on the tunneling feature data and the first rock mass parameter data to obtain process feature data, wherein the obtaining process feature data includes:
identifying rock integrity characteristics of the first rock parameters to obtain rock integrity characteristic data, wherein the rock integrity characteristic data is used for representing the rock integrity characteristics to be predicted;
carrying out rock integrity classification prediction processing on the rock integrity characteristic data to obtain classification characteristic data, wherein the classification characteristic data comprises rock integrity classes and class probabilities corresponding to the rock integrity classes; and
and matching the classified characteristic data with the tunneling characteristic data to obtain the process characteristic data.
Optionally, the preprocessing the data to be processed based on data analysis to obtain the tunneling characteristic data includes:
acquiring reference data, wherein the reference data is related data of tunneling under the no-load state of the tunneling equipment;
filtering the data to be processed based on the reference data to obtain process data to be processed, wherein the process data to be processed is the data to be processed after the reference data is filtered; and
and performing feature extraction processing on the process data to be processed based on a preset feature extraction rule to obtain the tunneling feature data, wherein the preset feature extraction rule corresponds to the tunneling feature, and the tunneling feature data is the data of the tunneling feature extracted from the process data to be processed.
Optionally, before acquiring the data to be processed, the method further includes:
acquiring training sample data, wherein the training sample data is used for training the preset rock parameter prediction model;
preprocessing the training sample data based on data analysis to obtain a plurality of training characteristic data, wherein the training characteristic data are a plurality of characteristic data associated with rock mass parameters; and
and training a preset neural network model based on the plurality of training characteristic data to obtain the preset rock mass parameter prediction model.
Optionally, training a preset neural network model based on the training feature data, and obtaining the preset rock mass parameter prediction model includes:
classifying the plurality of training feature data to obtain a first training feature data set and a second training feature data set, wherein the first training feature data set comprises a plurality of first training feature data, and the second training feature data set comprises a plurality of second training feature data;
training a preset regression classification model according to the first training characteristic data set to obtain a first prediction model of the rock mass, wherein the first prediction model of the rock mass is used for classifying the integrity of the rock mass;
performing rock integrity characteristic prediction processing on the second training characteristic data set based on the first rock mass prediction model to obtain process training characteristic data, wherein the process training characteristic data is characteristic data used for representing rock integrity; and
and training a preset neural network model according to the second training characteristic data set and the process training characteristic data to obtain the rock mass parameter prediction model.
According to a second aspect of the application, there is provided a data processing apparatus for rock mass parameter prediction, comprising:
the device comprises a data acquisition module, a data prediction module and a data prediction module, wherein the data acquisition module is used for acquiring data to be processed, and the data to be processed is related data used for representing the tunneling construction of tunneling equipment on a rock mass to be predicted;
the preprocessing module is used for preprocessing the data to be processed based on data analysis to obtain tunneling characteristic data, wherein the tunneling characteristic data is used for representing the tunneling characteristics of the tunneling equipment; and
and the prediction module is used for carrying out rock mass parameter prediction processing on the tunneling characteristic data based on a preset rock mass parameter prediction model to obtain target rock mass parameter data.
Optionally, the prediction module comprises:
the first prediction module is used for performing first prediction processing on the tunneling characteristic data based on a preset first rock mass parameter model to obtain first rock mass parameter data, wherein the first rock mass parameter data is used for representing data of a first process prediction parameter of the rock mass to be predicted;
performing characteristic analysis processing on the tunneling characteristic data and the first rock mass parameter data to obtain process characteristic data;
the second prediction module is used for performing second prediction processing on the process characteristic data based on a preset second rock parameter model to obtain second rock parameter data, wherein the second rock parameter data is used for representing data of a second process prediction parameter of the rock parameter to be predicted; and
and the result module is used for verifying the second rock mass parameter data to obtain the target rock mass parameter data.
According to a third aspect of the present application, a computer-readable storage medium is provided, which stores computer instructions for causing the computer to execute the above-mentioned data processing method for rock mass parameter prediction.
According to a fourth aspect of the present application, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the above-described data processing method for rock mass parameter prediction.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the method, the obtained data to be processed is preprocessed based on data analysis to obtain tunneling characteristic data; and performing rock mass parameter prediction processing on the tunneling characteristic data based on a preset rock mass parameter prediction model to obtain target rock mass parameter data. The method has the advantages that the tunneling characteristic data are processed, processed and filtered, the rock parameter data with higher accuracy are predicted by combining the rock parameter prediction model, the technical problem that the accuracy of rock parameter judgment is lower in the TBM tunnel construction process in the prior art is solved, and the technical effect of improving the accuracy of rock parameter prediction is realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
fig. 1 is a schematic flow chart of a data processing method for rock mass parameter prediction provided by the present application;
fig. 2 is a schematic flow chart of a data processing method for rock mass parameter prediction provided by the application;
fig. 3 is a schematic flow chart of a data processing method for rock mass parameter prediction provided by the present application;
fig. 4 is a schematic flow chart of a data processing method for rock mass parameter prediction provided by the present application;
fig. 5 is a schematic structural diagram of a data processing device for rock mass parameter prediction provided by the application;
fig. 6 is a schematic structural diagram of another data processing device for rock mass parameter prediction provided by the application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used in other meanings besides orientation or positional relationship, for example, the term "upper" may also be used in some cases to indicate a certain attaching or connecting relationship. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
Furthermore, the terms "mounted," "disposed," "provided," "connected," and "sleeved" are to be construed broadly. For example, "connected" may be a fixed connection, a detachable connection, or a unitary construction; can be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
Fig. 1 is a data processing method for rock mass parameter prediction provided by the present application, and as shown in fig. 1, the method includes the following steps:
s101: acquiring data to be processed;
the data to be processed is related data used for representing the tunneling construction of tunneling equipment on a rock mass to be predicted, the tunneling equipment generates various types of data in the process of performing the rock mass tunneling construction, the data to be processed comprises first construction data and second construction data, the evaluation data to be processed can be divided into active construction data and passive construction data according to the form of data generation, the active construction data is parameter data used for representing control of the tunneling equipment construction, the passive construction data is data used for representing that the tunneling equipment is influenced by the tunneling construction in the construction process, for example, the active construction data comprises preset construction parameters of the tunneling equipment, such as cutter head rotating speed, cutter head torque, construction power and the like, and the passive construction data is data acquired for the vibration characteristics of the tunneling equipment in the construction process, such as acquired vibration data of a cutter head. When the tunneling equipment is used for collecting, the TBM vibration monitoring system arranged on the tunneling equipment is used for collecting cutter head vibration data, the sensor module in the TBM vibration monitoring system is used for collecting and storing the data in real time, and the data collected and stored in real time by the sensor module is transmitted to the server.
S102: preprocessing the data to be processed based on data analysis to obtain tunneling characteristic data;
the tunneling characteristic data is data representing tunneling characteristics of tunneling equipment, the acquired data to be processed is acquired related data generated in a preset time period of current construction in the predicted rock mass, the data of tunneling construction of the tunneling equipment corresponding to any time in the preset time period is difficult to visually represent the tunneling characteristics in the current time period, and the data generated in the tunneling construction of the tunneling equipment in a certain time period needs to be processed based on data analysis to obtain the tunneling characteristic data, so that the tunneling characteristics in the current rock mass tunneling construction process to be predicted can be directly represented, the data processing efficiency in the rock mass parameter prediction process is improved, and the accuracy of rock mass parameter prediction of a rock mass parameter model is improved.
Fig. 2 is a data processing method for rock mass parameter prediction provided by the present application, and as shown in fig. 3, the method includes the following steps:
s201: acquiring reference data;
the reference data is related data of tunneling under an idle state of the tunneling equipment, for example, the reference data includes a host frequency of the tunneling equipment, the host frequency of the tunneling equipment is frequency data of vibration at a cutterhead under the idle state of the tunneling equipment, and the host frequency is vibration frequency generated in a non-tunneling construction process.
S202: filtering the data to be processed based on the reference data to obtain process data to be processed;
and filtering reference data in the data to be processed, wherein the data to be processed comprises construction parameter data to be processed and tunneling vibration data to be processed, filtering the tunneling vibration data to be processed through host frequency in the reference data to obtain vibration data of a cutterhead of the tunneling construction with the host frequency filtered, and taking the data to be processed with the reference data filtered as process data to be processed.
S203: performing feature extraction processing on the process data to be processed based on a preset feature extraction rule to obtain tunneling feature data;
the preset feature extraction rule corresponds to the tunneling feature, the tunneling feature data is obtained by extracting tunneling feature data from process data to be processed, the tunneling feature parameters comprise tunneling vibration feature parameters and tunneling construction feature parameters, data analysis is carried out on the TBM cutter head vibration data obtained after filtering, TBM cutter head vibration data in a preset time period are counted, and the average amplitude value of cutter head vibration is obtained
Peak value X p ，X p ＝max{|x i L, effective value of vibration acceleration X RMS ，
Average extreme value, calculating the average value of the absolute values of the vibration maximum value and the vibration minimum value of the preset number under the rock mass state, wherein x i Is the ith cutter head vibration data in a preset time period, T is the time length in the preset time period,n is the number of vibration data which are acquired by the TBM in a preset time period, and the average amplitude, the peak value, the effective value and the average extreme value of the vibration of the cutter head are tunneling vibration characteristics in the tunneling characteristic data; and (4) extracting the characteristics of the construction characteristic data in the parameters to be processed to obtain the cutter head rotating speed, the cutter head torque, the penetration degree and the cutting depth index FPI, which are the tunneling construction characteristic data in the tunneling characteristic data. By means of data preprocessing of feature extraction of data to be processed, data normalization and accuracy of a rock mass parameter prediction model are improved, and accuracy of target rock mass parameter data prediction is improved.
S103: performing rock mass parameter prediction processing on the tunneling characteristic data based on a preset rock mass parameter prediction model to obtain target rock mass parameter data;
fig. 3 is a data processing method for rock mass parameter prediction provided by the present application, as shown in fig. 2, the method includes the following steps:
s301: performing first prediction processing on the tunneling characteristic data based on a preset first rock mass parameter model to obtain first rock mass parameter data;
the first rock mass parameter data is used for representing data of a first process prediction parameter of a rock mass to be predicted;
identifying rock integrity characteristics of the first rock parameters to obtain rock integrity characteristic data, wherein the rock integrity characteristic data is used for representing rock integrity characteristics to be predicted; carrying out rock integrity classification prediction processing on the rock integrity characteristic data to obtain classification characteristic data, wherein the classification characteristic data comprises rock integrity classes and class probabilities corresponding to the rock integrity classes;
and matching the classification characteristic data with the tunneling characteristic data to obtain process characteristic data.
And (3) carrying out classification prediction on the integrity of the rock mass to obtain rock mass integrity labels corresponding to the rock mass integrity characteristic data, for example, classifying the integrity of the rock mass to have 5 rock mass integrity labels, according to the plurality of rock mass integrity labels obtained by classification, obtaining probability values of the sample data under the plurality of rock mass integrity labels respectively by returning a prediction function prediction _ proba, and taking the class corresponding to the highest probability as a classification label result of the sample. The prediction _ proba returns an n-row and k-column array, the numerical value in the ith row and the jth column is the probability that the model predicts that the ith prediction sample is a certain label, the sum of the probabilities in each row is 1, and the rock integrity label with the maximum probability value corresponding to the rock integrity label is selected to obtain the classification characteristic data.
S302: performing characteristic analysis processing on the tunneling characteristic data and the first rock mass parameter data to obtain process characteristic data;
and constructing the rock integrity classification characteristic data obtained by the tunneling characteristic data and the first rock parameter model as process characteristic data, and obtaining the process characteristic data according to the corresponding relation between the tunneling characteristic data and the rock integrity classification characteristic.
S303: performing second prediction processing on the process characteristic data based on a preset second rock mass parameter model to obtain second rock mass parameter data;
performing rock mass parameter prediction on the tunneling characteristic data and the rock mass integrity classification characteristic data through a second rock mass parameter model, wherein the tunneling characteristic data comprises tunneling construction characteristic data and tunneling vibration characteristic data, the tunneling construction characteristic data comprises cutter head rotating speed, cutter head torque, penetration and cutting depth index (FPI), and the tunneling vibration characteristic data comprises vibration acceleration effective value X in a rock mass interval to be predicted RMS Average amplitude value
Peak value X p The average extreme value, the rock integrity classification characteristic data are the rock integrity degree in a rock interval to be predicted by a first rock parameter model and are used for representing the rock integrity degree in a certain interval of the rock to be predicted, the rock integrity classification characteristic data correspond to the tunneling characteristic data according to the rock tunneling interval, for example, the first rock integrity classification characteristic data corresponding to the first rock tunneling interval correspond to the first tunneling characteristic data, the second rock integrity classification characteristic data correspond to the second rock integrity classification characteristic data and correspond to the second tunneling characteristic data。
S304: verifying the second rock mass parameter data to obtain target rock mass parameter data;
the rock mass parameter data correspond to a preset rock mass parameter range, the obtained second rock mass parameter is compared with the preset rock mass parameter range, if the second rock mass parameter is within the preset rock mass parameter range, the second rock mass parameter data are judged to be reasonable in prediction, and the second rock mass parameter is output as a target rock mass parameter; if the second rock mass parameter is not in the preset rock mass parameter range, the second rock mass parameter data is judged to be unreasonable in prediction, prompt information of prediction errors is output, the rock mass parameter prediction errors can be prompted conveniently, a user can check the rock mass parameter prediction process conveniently, the problem that the check result is influenced is solved, and the accuracy of rock mass parameter prediction is improved.
Fig. 4 is a data processing method for rock mass parameter prediction provided by the present application, and as shown in fig. 4, the method includes the following steps:
s401: acquiring training sample data;
the training sample data is used for training a preset rock parameter prediction model, and comprises training tunneling parameter data and training rock parameter data.
S402: carrying out data analysis-based preprocessing on training sample data to obtain a plurality of training characteristic data;
the method comprises the steps that a plurality of training characteristic data are a plurality of characteristic data related to rock mass parameters, one-time tunneling construction of TBM tunneling construction equipment in training sample data is divided into an interval, in the interval, sample rock mass parameter data of a sample rock mass correspond to the sample tunneling parameter data, the sample tunneling parameter data comprise sample tunneling vibration characteristic data and sample tunneling construction characteristic data, the sample tunneling vibration characteristic data comprise average amplitude, peak value, vibration acceleration effective value and average extreme value in the sample interval, and the sample tunneling construction characteristic data comprise cutter head rotating speed, cutter head torque, penetration and cutting depth index (FPI) in the sample interval; and constructing a plurality of sample interval data sets based on the sample interval, wherein the plurality of sample interval data sets are a plurality of training data sets comprising sample tunneling construction characteristic data, sample tunneling vibration characteristic data and corresponding sample rock mass parameter data.
S403: training a preset neural network model based on a plurality of training characteristic data to obtain a preset rock mass parameter prediction model;
classifying the plurality of training characteristic data to obtain a first training characteristic data set and a second training characteristic data set; the first training feature data set comprises a plurality of first training feature data and the second training feature data set comprises a plurality of second training feature data; training a preset regression classification model according to the first training characteristic data set to obtain a first rock mass prediction model; the rock mass classification prediction model is used for classifying the integrity of rock masses; performing rock integrity characteristic prediction processing on the second training characteristic data set based on the first rock mass prediction model to obtain process training characteristic data; the process training characteristic data is characteristic data used for representing the integrity of the rock mass; and training the preset neural network model according to the second training characteristic data set and the process training characteristic data to obtain a rock mass parameter prediction model.
In an optional embodiment of the application, training of a rock integrity classification model is performed through a first training characteristic data set, in the training process, the first training characteristic data set comprises sample tunneling vibration characteristic data, sample tunneling construction characteristic data and sample rock integrity classification characteristic data, a preset neural network model is trained and processed based on the first training characteristic data set, the sample tunneling vibration characteristic data and the sample tunneling construction characteristic data are used as model input, the sample rock integrity classification characteristic data are used as model output, and model parameters are trained. In the training process, learning curves of the number, the maximum depth and the maximum features of random forest trees are respectively established, a training set is imported for first training, and when the learning curves start to fluctuate, a model enters a stationary period; taking the first fluctuation range to train for the second time, and performing training iteration again according to the learning curve until the training iteration is completedAnd selecting the model parameters with higher accuracy to obtain the number of target random forest trees, the maximum depth of the target and the maximum characteristics of the target. The method comprises the following steps of substituting the number of target random forest trees, the maximum depth of a target and the maximum characteristic of the target into a model, carrying out model evaluation on the model with the obtained super-parameters to obtain first test sample data, carrying out rock integrity classification prediction on the first test sample data according to a training rock integrity classification model, and evaluating the rock integrity classification model through an accuracy rate P and a Kappa coefficient k, wherein the accuracy rate P is the proportion of a sample for predicting the correctness of the training rock integrity classification model to the first test sample, and the Kappa coefficient calculation formula is as follows:
wherein p is 0 For accuracy, N represents the total number of samples, N i Representing the actual total number of samples of class i,
representing the total number of samples predicted for the ith class.
Performing rock integrity characteristic prediction processing on the second training characteristic data set based on the first rock mass prediction model to obtain process training characteristic data;
and performing rock integrity characteristic data prediction on the second training sample by using the first prediction model to obtain rock integrity characteristic data of the second training sample, and taking the rock integrity characteristic data in the second training sample, the training tunneling construction characteristic data and the training tunneling vibration characteristic data in the second training sample obtained by prediction and the sample training rock parameter of the second training sample as process training characteristic data, wherein the process training characteristic data are characteristic data for training a rock parameter prediction model.
And training the preset neural network model according to the second training characteristic data set and the process training characteristic data to obtain a rock mass parameter prediction model.
The preset neural network model can be BP nerveThe network model is trained to obtain hyper-parameters in the neural network in the model training process, parameters are sequentially adjusted in a set parameter range in a Grid Search (Grid Search) mode, and the model is trained by utilizing the adjusted parameters to find the parameters with the highest precision on the test set from the set parameters; by introducing RMSE (Root Mean Square Error), mean Absolute Error MAE (Mean Absolute Error) and R 2 (R Squared) three evaluation indexes to evaluate the prediction effect of the model,
wherein, in the formula, y i The actual value of the rock mass parameter is represented,
the predicted value of the rock mass parameter is shown,
and (4) representing the average value of the actual values of the rock mass parameters. For example, a plurality of training neural network models corresponding to a plurality of hyper-parameters are obtained by adopting a grid search mode, model evaluation is performed on the plurality of training neural network models corresponding to the plurality of hyper-parameters through model evaluation indexes, hyper-parameter data of a target training neural network model is determined, a rock mass parameter prediction model is obtained, and a model R after grid search is performed 2 And analyzing and comparing values, and determining a target training neural network model according to the obtained hyper-parameters when the learning rate of the data set is 0.3, the number of hidden layers of the neural network is 1 and the number of neurons in each hidden layer is 3, wherein the neural network has the best effect.
In another optional embodiment of the present application, in the training neural network model, there is a determination of an activation function in the neural network model, for example, the activation function includes Sigmoid (S-shaped generation curve), tanh (hyperbolic function), reLU (linear rectification function), randomized leak ReLU (random leakage corrected linear rectification function), and the like, the training neural network model based on different activation functions is screened, for example, the neural network model obtained after model evaluation processing has the best effect by using the Randomized leak ReLU function as the activation function, and the Randomized leak ReLU function is selected as the activation function of the neural network model.
In the optional embodiment of the application, the preset neural network model is trained through the second training characteristic data set and the process training characteristic data to obtain the rock mass parameter prediction model, the integrity classification of the rock mass is predicted through the first training characteristic data set and the process training characteristic data, the rock mass integrity characteristic data obtained after prediction is used as the training data of the training rock mass parameter prediction model, the accuracy of the rock mass parameter model obtained through training is improved through increasing the characteristic dimension of the training data of the training rock mass parameter prediction model, and the accuracy of rock mass parameter prediction is achieved.
Fig. 5 is a schematic structural diagram of a data processing device for rock mass parameter prediction provided by the present application, and as shown in fig. 5, the device includes:
the data acquisition module 51 is configured to acquire data to be processed, where the data to be processed is related data used for indicating that the tunneling equipment is performing tunneling construction on a rock mass to be predicted;
the preprocessing module 52 is configured to perform data analysis-based preprocessing on the data to be processed to obtain tunneling characteristic data, where the tunneling characteristic data is data used for representing tunneling characteristics of tunneling equipment; and
and the prediction module 53 performs rock mass parameter prediction processing on the tunneling characteristic data based on a preset rock mass parameter prediction model to obtain target rock mass parameter data.
Fig. 6 is a schematic structural diagram of a data processing device for rock mass parameter prediction provided by the present application, and as shown in fig. 6, the device includes:
the first prediction module 61 is used for performing first prediction processing on the tunneling characteristic data based on a preset first rock mass parameter model to obtain first rock mass parameter data, wherein the first rock mass parameter data is used for representing data of a first process prediction parameter of a rock mass to be predicted;
performing characteristic analysis processing on the tunneling characteristic data and the first rock mass parameter data to obtain process characteristic data;
the second prediction module 62 is configured to perform second prediction processing on the process characteristic data based on a preset second rock parameter model to obtain second rock parameter data, where the second rock parameter data is used to represent data of a second process prediction parameter of the rock parameter to be predicted; and
and the result module 63 is used for verifying the second rock mass parameter data to obtain target rock mass parameter data.
The specific manner of executing the operations of the units in the above embodiments has been described in detail in the embodiments related to the method, and will not be elaborated herein.
In summary, in the present application, the tunneling characteristic data is obtained by performing data analysis-based preprocessing on the acquired data to be processed; and performing rock mass parameter prediction processing on the tunneling characteristic data based on a preset rock mass parameter prediction model to obtain target rock mass parameter data. The tunneling characteristic data is processed and filtered, the rock parameters are predicted through the rock parameter prediction model, and target rock parameter data are obtained, so that the technical problem that in the prior art, the accuracy of rock parameter judgment is low in the TBM tunnel construction process is solved, and the technical effect of improving the accuracy of rock parameter prediction is achieved.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
It will be apparent to those skilled in the art that the various elements or steps of the present application described above may be implemented by a general purpose computing device, centralized on a single computing device or distributed across a network of multiple computing devices, or alternatively, may be implemented by program code executable by a computing device, such that the program code may be stored in a memory device and executed by a computing device, or may be implemented by individual integrated circuit modules, or by a plurality of modules or steps included in the program code as a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A data processing method for rock mass parameter prediction is characterized by comprising the following steps:
acquiring data to be processed, wherein the data to be processed is related data used for representing the rock mass tunneling construction of tunneling equipment to be predicted;
preprocessing the data to be processed based on data analysis to obtain tunneling characteristic data, wherein the tunneling characteristic data is used for representing the tunneling characteristics of the tunneling equipment; and
and performing rock mass parameter prediction processing on the tunneling characteristic data based on a preset rock mass parameter prediction model to obtain target rock mass parameter data.
2. The data processing method according to claim 1, wherein rock mass parameter prediction processing is performed on the tunneling characteristic data based on a preset rock mass parameter prediction model, and obtaining target rock mass parameter data comprises:
performing first prediction processing on the tunneling characteristic data based on a preset first rock mass parameter model to obtain first rock mass parameter data, wherein the first rock mass parameter data is used for representing data of a first process prediction parameter of the rock mass to be predicted;
performing characteristic analysis processing on the tunneling characteristic data and the first rock mass parameter data to obtain process characteristic data;
performing second prediction processing on the process characteristic data based on a preset second rock mass parameter model to obtain second rock mass parameter data, wherein the second rock mass parameter data is used for representing data of a second process prediction parameter of the rock mass parameter to be predicted; and
and carrying out verification processing on the second rock mass parameter data to obtain the target rock mass parameter data.
3. The data processing method according to claim 2, wherein the characteristic analysis processing is performed on the tunneling characteristic data and the first rock mass parameter data, and the obtaining of the process characteristic data comprises:
identifying the rock integrity characteristics of the first rock parameters to obtain rock integrity characteristic data, wherein the rock integrity characteristic data is used for representing the rock integrity characteristics to be predicted;
carrying out rock integrity classification prediction processing on the rock integrity characteristic data to obtain classification characteristic data, wherein the classification characteristic data comprises rock integrity classes and class probabilities corresponding to the rock integrity classes; and
and matching the classified characteristic data with the tunneling characteristic data to obtain the process characteristic data.
4. The data processing method according to claim 1, wherein the preprocessing of the data to be processed based on data analysis to obtain the tunneling characteristic data comprises:
acquiring reference data, wherein the reference data is related data of tunneling under the no-load state of the tunneling equipment;
filtering the data to be processed based on the reference data to obtain process data to be processed, wherein the process data to be processed is the data to be processed after the reference data is filtered; and
and performing feature extraction processing on the process data to be processed based on a preset feature extraction rule to obtain the tunneling feature data, wherein the preset feature extraction rule corresponds to the tunneling feature, and the tunneling feature data is the data of the tunneling feature extracted from the process data to be processed.
5. The data processing method of claim 1, wherein prior to obtaining the data to be processed, the method further comprises:
acquiring training sample data, wherein the training sample data is the sample data for training the preset rock mass parameter prediction model;
preprocessing the training sample data based on data analysis to obtain a plurality of training characteristic data, wherein the training characteristic data are a plurality of characteristic data associated with rock mass parameters; and
and training a preset neural network model based on the plurality of training characteristic data to obtain the preset rock mass parameter prediction model.
6. The data processing method of claim 5, wherein training a preset neural network model based on the training feature data to obtain the preset rock mass parameter prediction model comprises:
classifying the plurality of training feature data to obtain a first training feature data set and a second training feature data set, wherein the first training feature data set comprises a plurality of first training feature data, and the second training feature data set comprises a plurality of second training feature data;
training a preset regression classification model according to the first training characteristic data set to obtain a first prediction model of the rock mass, wherein the first prediction model of the rock mass is used for classifying the integrity of the rock mass;
performing rock integrity characteristic prediction processing on the second training characteristic data set based on the first rock mass prediction model to obtain process training characteristic data, wherein the process training characteristic data are characteristic data used for representing rock integrity; and
and training a preset neural network model according to the second training characteristic data set and the process training characteristic data to obtain the rock mass parameter prediction model.
7. A data processing apparatus for rock mass parameter prediction, comprising:
the device comprises a data acquisition module, a data prediction module and a data prediction module, wherein the data acquisition module is used for acquiring data to be processed, and the data to be processed is related data used for representing the tunneling construction of tunneling equipment on a rock mass to be predicted;
the preprocessing module is used for preprocessing the data to be processed based on data analysis to obtain tunneling characteristic data, wherein the tunneling characteristic data is used for representing the tunneling characteristics of the tunneling equipment; and
and the prediction module is used for carrying out rock parameter prediction processing on the tunneling characteristic data based on a preset rock parameter prediction model to obtain target rock parameter data.
8. The data processing apparatus of claim 7, wherein the prediction module comprises:
the first prediction module is used for performing first prediction processing on the tunneling characteristic data based on a preset first rock mass parameter model to obtain first rock mass parameter data, wherein the first rock mass parameter data is used for representing data of a first process prediction parameter of the rock mass to be predicted;
performing characteristic analysis processing on the tunneling characteristic data and the first rock mass parameter data to obtain process characteristic data;
the second prediction module is used for performing second prediction processing on the process characteristic data based on a preset second rock mass parameter model to obtain second rock mass parameter data, wherein the second rock mass parameter data are used for representing data of a second process prediction parameter of the rock mass parameter to be predicted; and
and the result module is used for verifying the second rock mass parameter data to obtain the target rock mass parameter data.
9. A computer readable storage medium storing computer instructions for causing a computer to perform the data processing method for rock mass parameter prediction according to any one of claims 1 to 6.
10. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform a data processing method for rock mass parameter prediction according to any one of claims 1 to 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116644799A (en) * | 2023-07-27 | 2023-08-25 | 青岛理工大学 | Stratum vibration acceleration prediction method and related device based on tunneling parameters |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019042483A2 (en) * | 2017-08-30 | 2019-03-07 | 中铁工程装备集团有限公司 | Tbm boring rock state real-time sensing system and method |
| WO2019063024A1 (en) * | 2017-09-30 | 2019-04-04 | 中铁工程装备集团有限公司 | Smart decision making method and system for boring control parameters of hard rock tbm |
| CN110244344A (en) * | 2019-06-05 | 2019-09-17 | 中南大学 | A kind of TBM method for forecasting advanced geology based on deep learning |
| KR102211421B1 (en) * | 2020-06-17 | 2021-02-02 | 에스케이건설 주식회사 | Method and system for determining tbm control parameters based on prediction geological condition ahead of tunnel face |
| CN112926267A (en) * | 2021-03-10 | 2021-06-08 | 山东大学 | TBM tunnel rock burst grade prediction method and system based on tunneling parameter inversion |
| CN113657515A (en) * | 2021-08-20 | 2021-11-16 | 盾构及掘进技术国家重点实验室 | Classification method for judging and improving tunnel surrounding rock grade of FMC model based on rock sensitivity parameters |
-
2022
- 2022-07-22 CN CN202210871892.7A patent/CN115422821B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019042483A2 (en) * | 2017-08-30 | 2019-03-07 | 中铁工程装备集团有限公司 | Tbm boring rock state real-time sensing system and method |
| WO2019063024A1 (en) * | 2017-09-30 | 2019-04-04 | 中铁工程装备集团有限公司 | Smart decision making method and system for boring control parameters of hard rock tbm |
| CN110244344A (en) * | 2019-06-05 | 2019-09-17 | 中南大学 | A kind of TBM method for forecasting advanced geology based on deep learning |
| KR102211421B1 (en) * | 2020-06-17 | 2021-02-02 | 에스케이건설 주식회사 | Method and system for determining tbm control parameters based on prediction geological condition ahead of tunnel face |
| CN112926267A (en) * | 2021-03-10 | 2021-06-08 | 山东大学 | TBM tunnel rock burst grade prediction method and system based on tunneling parameter inversion |
| CN113657515A (en) * | 2021-08-20 | 2021-11-16 | 盾构及掘进技术国家重点实验室 | Classification method for judging and improving tunnel surrounding rock grade of FMC model based on rock sensitivity parameters |
Non-Patent Citations (4)
| Title |
|---|
| B. LIU ET AL.: "Prediction of rock mass parameters in the TBM tunnel based on BP neural network integrated simulated annealing algorithm", 《TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY》, vol. 95, 11 October 2019 (2019-10-11), pages 1 - 12, XP085911189, DOI: 10.1016/j.tust.2019.103103 * |
| ZHIJUN WU ET AL.: "Real-time rock mass condition prediction with TBM tunneling big data using a novel rock–machine mutual feedback perception method", 《JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING》, vol. 13, no. 6, 31 December 2021 (2021-12-31), pages 1311 - 1325 * |
| 张娜等: "TBM掘进参数智能控制系统的研究与应用", 《隧道建设(中英文)》, vol. 38, no. 10, 31 December 2018 (2018-12-31), pages 1734 - 1740 * |
| 张娜等: "基于隧道掘进机掘进过程的岩体状态感知方法", 《浙江大学学报(工学版)》, vol. 53, no. 10, 31 December 2019 (2019-12-31), pages 1977 - 1985 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116644799A (en) * | 2023-07-27 | 2023-08-25 | 青岛理工大学 | Stratum vibration acceleration prediction method and related device based on tunneling parameters |
| CN116644799B (en) * | 2023-07-27 | 2023-10-17 | 青岛理工大学 | Stratum vibration acceleration prediction method and related device based on tunneling parameters |
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