CN115758527A - Training method, determining method, device and equipment of support parameter prediction model - Google Patents
Training method, determining method, device and equipment of support parameter prediction model Download PDFInfo
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Abstract
The embodiment of the application discloses a training method of a support parameter prediction model, a support parameter determination method, a device and electronic equipment, belongs to the technical field of surrounding rock support of geotechnical engineering and mining engineering, and can solve the problems that in the prior art, the determination of roadway support parameters depends on a large amount of manpower, and the determined support parameters are inaccurate. The method comprises the following steps: constructing an initial support parameter prediction model; acquiring a plurality of first sample data, each of the first sample data including: actual roadway parameters and actual support parameters; dividing a plurality of first sample data into a training set and a test set; training the initial support parameter prediction model through a training set to obtain a trained support parameter prediction model; inputting the test set into a trained support parameter prediction model to obtain a predicted support parameter; and optimizing the trained support parameter prediction model based on the predicted support parameter to obtain the trained support parameter prediction model.
Description
Technical Field
The application relates to the technical field of surrounding rock support of geotechnical engineering and mining engineering, in particular to a training method of a support parameter prediction model, a support parameter determination method, a device and electronic equipment.
Background
The coal mine in China is mainly mined underground, a large number of roadways need to be excavated underground, and the smoothness of the roadways and the stability of surrounding rocks are kept, so that the method has important significance for coal mine construction and production. With the increasing of mining depth, mining range and mining intensity, geological conditions are complicated, roadway support conditions are more and more complex, the requirements and standards for support are higher and higher, and the support difficulty is increased continuously.
Because roadway support parameters are difficult to express by using an accurate calculation formula, in the prior art, the support parameters are mainly determined by an engineering classification method, a dynamic information design method and a numerical simulation method, the process requires construction technicians to have abundant field construction experience, but the uncertain factors brought by manpower are more, and the support parameters are usually determined only by rough calculation and analysis, so that the coal mine roadway support quality determined according to the support parameters can not meet the engineering requirements.
Disclosure of Invention
The embodiment of the application provides a training method of a support parameter prediction model, a support parameter determination method, a device and electronic equipment, and aims to solve the problems that in the prior art, the determination of roadway support parameters depends on a large amount of manpower and the determined support parameters are inaccurate.
In a first aspect of the embodiments of the present application, a method for training a support parameter prediction model is provided, where the method includes: constructing an initial support parameter prediction model; acquiring a plurality of first sample data, each of the first sample data including: actual roadway parameters and actual support parameters; dividing a plurality of first sample data into a training set and a test set; training the initial support parameter prediction model through a training set to obtain a trained support parameter prediction model; inputting the test set into a trained support parameter prediction model to obtain a predicted support parameter; and optimizing the trained support parameter prediction model based on the predicted support parameter to obtain the trained support parameter prediction model.
In some embodiments of the application, before obtaining the plurality of first sample data, the method further includes: constructing a roadway support database based on the acquired historical roadway support data; obtaining a plurality of first sample data, comprising: and acquiring a plurality of first sample data from a roadway support database.
In some embodiments of the present application, before obtaining the plurality of first sample data from the roadway support database, the method further includes: constructing a plurality of random separation models through a random forest algorithm, wherein each support parameter corresponds to one random separation model; obtaining a plurality of second sample data, each second sample data comprising: actual roadway parameters and actual support parameters; respectively inputting a plurality of second sample data into a plurality of random separation models to obtain a plurality of first sequences, wherein each first sequence indicates the importance degree sequence of a plurality of roadway parameters to a support parameter; determining a target roadway parameter based on the plurality of first orderings; obtaining a plurality of first sample data from a roadway support database, comprising: and acquiring a plurality of first sample data corresponding to the target roadway parameters from a roadway support database.
In some embodiments of the present application, training the initial support parameter prediction model through a training set to obtain a trained support parameter prediction model includes: training the initial support parameter prediction model through a training set to obtain a plurality of trained support parameter prediction submodels, wherein each support parameter corresponds to one trained support parameter prediction submodel; and fusing the plurality of trained support parameter prediction submodels to obtain a trained support parameter prediction model.
In some embodiments of the present application, the types of support parameters include: factor type and numerical type; the plurality of trained support parameter prediction submodels comprise: the device comprises a classification prediction model and a regression prediction model, wherein the type of the support parameter corresponding to the classification prediction model is a factor type, and the type of the support parameter corresponding to the regression prediction model is a numerical value type.
In some embodiments of the present application, the obtaining of the trained support parameter prediction model based on the prediction support parameter optimization trained support parameter prediction model includes: determining the accuracy of the trained support parameter prediction model based on the predicted support parameters and the actual support parameters; under the condition that the accuracy is within the accuracy threshold range, obtaining a trained support parameter prediction model; and under the condition that the accuracy is not within the accuracy threshold range, adjusting the parameters of the initial support parameter prediction model to continue training until the accuracy is within the accuracy threshold range, and obtaining the trained support parameter prediction model.
In a second aspect of the embodiments of the present application, a method for determining support parameters is provided, where the method includes: acquiring roadway parameters of a roadway to be supported; inputting the roadway parameters of the roadway to be supported into the trained support parameter prediction model to obtain the support parameters of the roadway to be supported; the trained support parameter prediction model is obtained by training through the training method of the support parameter prediction model according to the first aspect.
In a third aspect of the embodiments of the present application, a training device for a support parameter prediction model is provided, where the training device includes: the system comprises a building module, an acquisition module, a division module, a training model, an input module and an optimization module; the construction module is used for constructing an initial support parameter prediction model; an obtaining module, configured to obtain a plurality of first sample data, where each first sample data includes: actual roadway parameters and actual support parameters; the dividing module is used for dividing the plurality of first sample data into a training set and a test set; the training model is used for training the initial support parameter prediction model through a training set to obtain a trained support parameter prediction model; the input module is used for inputting the test set into a trained support parameter prediction model to obtain a predicted support parameter; the optimization module is used for optimizing the trained support parameter prediction model based on the predicted support parameter to obtain the trained support parameter prediction model.
In some embodiments of the application, the construction module is further configured to construct a roadway support database based on the acquired historical roadway support data before the plurality of first sample data are acquired; the acquisition module is specifically used for acquiring a plurality of first sample data from a roadway support database.
In some embodiments of the present application, the apparatus further comprises a determining module; the construction module is also used for constructing a plurality of random separation models through a random forest algorithm before a plurality of first sample data are acquired from a roadway support database, wherein each support parameter corresponds to one random separation model; the obtaining module is further configured to obtain a plurality of second sample data, where each second sample data includes: actual roadway parameters and actual support parameters; the input module is further used for respectively inputting the plurality of second sample data into the plurality of random separation models to obtain a plurality of first sequences, and each first sequence indicates the importance degree sequence of the plurality of roadway parameters to one support parameter; the determining module is used for determining a target roadway parameter based on a plurality of first sequences; the acquisition module is specifically used for acquiring a plurality of first sample data corresponding to the target roadway parameters from the roadway support database.
In some embodiments of the present application, the apparatus further comprises: a fusion module; the training module is specifically used for training an initial support parameter prediction model through a training set to obtain a plurality of trained support parameter prediction submodels, wherein each support parameter corresponds to one trained support parameter prediction submodel; the fusion module is used for fusing the plurality of trained support parameter prediction submodels to obtain a trained support parameter prediction model.
In some embodiments of the present application, the types of the supporting parameters include: a factor type and a numerical type; the plurality of trained support parameter prediction submodels comprise: the device comprises a classification prediction model and a regression prediction model, wherein the type of the support parameter corresponding to the classification prediction model is a factor type, and the type of the support parameter corresponding to the regression prediction model is a numerical value type.
In some embodiments of the present application, the optimization module is specifically configured to determine accuracy of a trained support parameter prediction model based on a predicted support parameter and an actual support parameter; under the condition that the accuracy is within the accuracy threshold range, obtaining a trained support parameter prediction model; and under the condition that the accuracy is not within the accuracy threshold range, adjusting the parameters of the initial support parameter prediction model to continue training until the accuracy is within the accuracy threshold range, and obtaining the trained support parameter prediction model.
In a fourth aspect of the embodiments of the present application, a support parameter determining device is provided, where the device includes: the device comprises an acquisition module and an input module; the acquisition module is used for acquiring roadway parameters of a roadway to be supported; the input module is used for inputting the roadway parameters of the roadway to be supported into the trained support parameter prediction model to obtain the support parameters of the roadway to be supported; the trained support parameter prediction model is obtained by training through the training method of the support parameter prediction model.
In a fifth aspect of the embodiments of the present application, an electronic device is provided, which includes a processor, a memory, and a program or instructions stored on the memory and executable on the processor, and when the program or instructions are executed by the processor, the method for training a support parameter prediction model according to the first aspect or the method for determining support parameters according to the above aspect is implemented.
A sixth aspect of the embodiments of the present application provides a readable storage medium, on which a program or instructions are stored, which when executed by a processor implement the steps of the training method of the support parameter prediction model according to the first aspect or the steps of the support parameter determination method as described above.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
in the embodiment of the application, an initial support parameter prediction model is constructed; acquiring a plurality of first sample data, each of the first sample data including: actual roadway parameters and actual support parameters; dividing a plurality of first sample data into a training set and a test set; training the initial support parameter prediction model through a training set to obtain a trained support parameter prediction model; inputting the test set into a trained support parameter prediction model to obtain a predicted support parameter; and optimizing the trained support parameter prediction model based on the predicted support parameter to obtain the trained support parameter prediction model. The finally obtained trained support parameter prediction model is trained on the basis of a large number of training sets and is obtained by optimizing a test set, so that the accuracy of support parameter prediction is ensured, meanwhile, the trained support parameter prediction model can be used for predicting support parameters of a roadway, the dependence on the experience of technicians is reduced (the uncertainty caused by different technicians according to the experience is reduced), the process of determining the support parameters is greatly simplified, the coal mine roadway surrounding rock can be stably supported more reasonably, quickly and fully, and the design efficiency of the support scheme of the whole coal mine roadway is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the embodiments and the drawings used in the description of the prior art, and obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained according to the drawings.
Fig. 1 is a schematic flowchart of a training method of a support parameter prediction model according to an embodiment of the present disclosure;
fig. 2 is a second flowchart illustrating a method for training a support parameter prediction model according to an embodiment of the present disclosure;
fig. 3 is a third schematic flowchart of a method for training a support parameter prediction model according to an embodiment of the present disclosure;
fig. 4 is a fourth flowchart illustrating a training method of a support parameter prediction model according to an embodiment of the present disclosure;
fig. 5 is a fifth flowchart illustrating a training method of a support parameter prediction model according to an embodiment of the present disclosure;
fig. 6 is a schematic flowchart of a method for determining support parameters according to an embodiment of the present disclosure;
fig. 7 is a structural block diagram of a training device for a support parameter prediction model according to an embodiment of the present disclosure;
fig. 8 is a structural block diagram of a support parameter determining device according to an embodiment of the present application;
fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present disclosure.
The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the application may be practiced in sequences other than those illustrated or described herein, and that the terms "first," "second," and the like are generally used herein in a generic sense and do not limit the number of terms, e.g., the first term can be one or more than one. In addition, "and/or" in the specification and claims means at least one of connected objects, a character "/", and generally means that the former and latter related objects are in an "or" relationship.
The electronic device in the embodiment of the present application may be a mobile electronic device, and may also be a non-mobile electronic device. The mobile electronic device may be a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), etc.; the non-mobile electronic device may be a Personal Computer (PC), a Television (TV), a teller machine, a self-service machine, or the like; the embodiments of the present application are not particularly limited.
The execution subject of the training method or the support parameter determination method for the support parameter prediction model provided in the embodiment of the present application may be the electronic device (including a mobile electronic device and a non-mobile electronic device), or may also be a functional module and/or a functional entity capable of implementing the training method or the support parameter determination method for the support parameter prediction model in the electronic device, which may be specifically determined according to actual use requirements, and the embodiment of the present application is not limited.
The training method and the support parameter determination method of the support parameter prediction model provided in the embodiment of the present application are described in detail below with reference to the accompanying drawings through specific embodiments and application scenarios thereof.
As shown in fig. 1, an embodiment of the present application provides a training method for a support parameter prediction model, and the following takes an execution subject as an electronic device as an example to exemplarily explain the training method for the support parameter prediction model provided in the embodiment of the present application. The method may include steps 101 to 106 described below.
101. And constructing an initial support parameter prediction model.
It can be understood that the initial Support parameter prediction model is obtained by modeling based on a Machine learning algorithm, and the specifically used Machine learning algorithm is not limited in the embodiment of the present application, and may be, for example, a Support Vector Machine (SVM) algorithm, a neural network algorithm, a gradient lifting regression tree, a random forest algorithm, or the like.
102. A plurality of first sample data is acquired.
Wherein each first sample data includes: actual roadway parameters and actual support parameters.
It is to be understood that the roadway parameters include at least one of: the method comprises the following steps of coal seam thickness, coal seam inclination angle, coal seam number, coal seam strength, immediate roof thickness, immediate roof strength, basic roof thickness, basic roof strength, direct bottom thickness, direct bottom strength, old bottom thickness, old bottom strength, surrounding rock fracture development condition, roadway service life, section shape, roadway burial depth, roadway height and roadway width, the roadway environment of a coal mine is complex, roadway parameters can also comprise others, and the method is not limited in the embodiment of the application.
It is understood that the support parameters include at least one of: the diameter, the length, the spacing and the row spacing of the roof anchor rods and the anchor rods on the two sides, the diameter, the length, the spacing, the row spacing and the arrangement mode of the roof anchor cables, and the support parameters can also comprise other parameters known in the art, and the embodiment of the application is not limited.
It can be understood that the plurality of first sample data may be obtained based on the obtained original data in the actual coal mine project, or may be obtained based on the data obtained in the third-party database, which is determined specifically according to an actual situation, and the embodiment of the present application is not limited.
It can be understood that, in the data analysis process, the dimension of the variable has a greater influence on the calculation of the coefficient, the distance and the weight, so that the variable with larger number has a greater proportion, and therefore, the standardization of the data is of great importance, and the data is subjected to dimensionless processing by the standardization, so that the data with different dimensions can be transversely compared on the same order of magnitude, and errors caused by data level differences are reduced.
Optionally, obtaining a plurality of first sample data comprises: acquiring a plurality of original sample data; and carrying out standardization processing on the plurality of original sample data to obtain a plurality of first sample data. In this way, each first sample data is obtained through a normalization process, the normalization process may be a range method, a Z-score normalization, or the like, as long as the purpose of comparing data of different dimensions on the same order of magnitude to reduce errors caused by data level differences can be achieved, which is not limited in the embodiment of the present application.
103. The plurality of first sample data is divided into a training set and a test set.
It can be understood that the plurality of first sample data can be divided into a training set and a test set according to a preset proportion; or randomly dividing a plurality of first sample data into a training set and a test set; the embodiment of the present application is not limited to the specific division manner.
Illustratively, the 100 first sample data are scaled to the training set to the test set by 7: and 3, dividing, collecting multiple times (one first sample data can be collected at a time, or multiple first sample data can be collected at a time) randomly and not replaced from 100 first sample data, taking the obtained 70 first sample data as a training set, and taking the rest 30 first sample data which are not collected as a test set.
104. And training the initial support parameter prediction model through a training set to obtain a trained support parameter prediction model.
105. And inputting the test set into the trained support parameter prediction model to obtain the predicted support parameters.
106. And optimizing the trained support parameter prediction model based on the predicted support parameter to obtain the trained support parameter prediction model.
It can be understood that the training process of the initial support parameter prediction model comprises the following steps: selection of kernel functions used in machine learning algorithms, and setting of hyper-parameters, etc.
Illustratively, taking an example that the initial support parameter prediction model is obtained by modeling based on an SVM algorithm, the target for training the initial support parameter prediction model includes: the method comprises the following steps of using a kernel function and a hyper-parameter in the SVM algorithm, wherein the hyper-parameter comprises a penalty coefficient of an objective function and a coefficient of the kernel function. The kernel function can be selected from a radial basis kernel function, a linear kernel function, a polynomial kernel function and a Sigmoid kernel function, 4 kinds of kernel functions are sequentially substituted into the initial support parameter prediction model, errors of the model are respectively calculated, and the kernel function with the minimum error is selected as the optimal kernel function of the initial support parameter prediction model; the punishment coefficient of the target function and the coefficient of the kernel function can be determined by any one of three optimization algorithms such as a genetic algorithm, a grid search method or an artificial ant colony algorithm, and the basis of the finally selected optimization algorithm is that the punishment coefficient and the kernel function coefficient which enable the model error to be minimum are used as the optimal hyper-parameters of the final initial support parameter prediction model. Through the optimization training, the nonlinear fitting capability of the initial support parameter prediction model is better.
In the embodiment of the application, an initial support parameter prediction model is constructed; acquiring a plurality of first sample data, each of the first sample data including: actual roadway parameters and actual support parameters; dividing a plurality of first sample data into a training set and a test set; training the initial support parameter prediction model through a training set to obtain a trained support parameter prediction model; inputting the test set into a trained support parameter prediction model to obtain a predicted support parameter; and optimizing the trained support parameter prediction model based on the predicted support parameter to obtain the trained support parameter prediction model. The finally obtained trained support parameter prediction model is trained on the basis of a large number of training sets and is obtained by optimizing a test set, so that the accuracy of support parameter prediction is ensured, meanwhile, the trained support parameter prediction model can be used for predicting support parameters of a roadway, the dependence on the experience of technicians is reduced (the uncertainty caused by different technicians according to the experience is reduced), the process of determining the support parameters is greatly simplified, the coal mine roadway surrounding rock can be stably supported more reasonably, quickly and fully, and the design efficiency of the support scheme of the whole coal mine roadway is improved.
In some embodiments of the present application, as shown in fig. 2 with reference to fig. 1, before the step 102, the method further includes the following step 107, and the step 102 may be specifically implemented by the following step 102 a.
107. And constructing a roadway support database based on the acquired historical roadway support data.
It can be understood that the database is a basis for data analysis and model establishment, and for convenience of model training and optimization, existing coal mine supported roadway engineering technical data are collected to form an original data set containing a large number of samples, the collected supported roadway engineering technical data specifically include roadway parameters and support parameters, all the parameters can be expressed in two forms of numbers and characters, wherein the parameters expressed in the character form need to be quantized or converted into the digital form so as to facilitate the following data processing and analysis. The case sample in the collected original data set is required to be a coal mine roadway which is already subjected to roadway support and has a good support effect.
It can be understood that, in order to ensure the validity of the data stored in the database, the original data set needs to be subjected to data cleaning to obtain coal roadway support data, and the coal roadway support data is stored in the roadway support database. Specifically, the data distribution condition in the original data set is determined, and the data included in the original data set is cleaned according to the distribution condition. The cleaning mode comprises at least one of deleting the abnormal data, replacing the abnormal data and supplementing the missing data.
It can be understood that, in order to facilitate the use of the subsequent model, the coal roadway support data may be standardized and then stored in the roadway support database, or may be standardized again during use, which is specifically determined according to actual needs, and the embodiment of the present application is not limited.
102a, acquiring a plurality of first sample data from a roadway support database.
In the embodiment of the application, a roadway support database is constructed based on the acquired historical roadway support data; and acquiring a plurality of first sample data from a roadway support database. The roadway support database is established, so that subsequent use can be facilitated, new support parameters and roadway parameters can be stored in the roadway support database, the support parameter prediction model can be trained continuously by using new data, and the model can be predicted more accurately.
In some embodiments of the present application, as shown in fig. 3 with reference to fig. 2, before the step 102a, the method further includes the following steps 102b to 102e, and the step 102a may be specifically implemented by the following step 102 f.
102b, constructing a plurality of random separation models through a random forest algorithm.
Wherein each support parameter corresponds to a random separation model.
It can be understood that the number of support parameters to be predicted is the same as the number of random separation models.
Exemplary support parameters include: the diameter, the length, the interval, the row spacing and the arrangement mode of the roof anchor rods and the anchor rods on the two sides, and the diameter, the length, the interval, the row spacing and the arrangement mode of the anchor cables on the roof; the stochastic separation model then has: the random separation model for the diameter of the roof anchor rods, the random separation model for the length of the roof anchor rods, the random separation model for the distance between the roof anchor rods, the random separation model for the row spacing of the roof anchor rods, the random separation model for the diameter of the anchor rods at two sides, the random separation model for the length of the anchor rods at two sides, the random separation model for the distance between the anchor rods at two sides, the random separation model for the row spacing of the anchor rods at two sides, the random separation model for the diameter of the anchor cables at the roof, the random separation model for the length of the anchor cables at the roof, the random separation model for the row spacing of the anchor cables at the roof, and the random separation model for the arrangement mode of the anchor cables at the roof.
102c, acquiring a plurality of second sample data.
Wherein each second sample data comprises: actual roadway parameters and actual support parameters.
It is to be understood that the plurality of second sample data may be the same as or different from the plurality of first sample data. The plurality of second sample data may be partial data acquired from the roadway support database, or may be all data in the roadway support database, which is not limited in the embodiment of the present application.
102d, respectively inputting the plurality of second sample data into the plurality of random separation models to obtain a plurality of first sequences.
Wherein each first ranking indicates a ranking of the importance of the plurality of roadway parameters to a support parameter.
It can be understood that, for a random separation model corresponding to one support parameter, a plurality of second sample data are input into the model, and the obtained output result is the order of the importance degree of each roadway parameter to the one support parameter.
Illustratively, the second sample data is: the first set of data: lane width: 5, roadway height: 4, roof bolt diameter: 20; the second set of data: lane width: 6, roadway height: 4, roof bolt diameter: 20; third group of data: lane width: 5, roadway height: 5, roof bolt diameter: 20; inputting the sample data into a random classification model aiming at the diameter of the roof bolt, wherein the first group of data comprises the following data: the prediction accuracy of the model is 75%; the second set of data: the prediction accuracy of the model is 74.5%; third group of data: the prediction accuracy of the model is 74%; the error change obtained by changing the roadway width is 0.5%, the error change obtained by changing the roadway height is 1%, and the importance degree of the roadway width is lower than the roadway height for the diameter of the roof bolt.
It can be understood that the specific error analysis may be a mean square error (in actual engineering, a result obtained by using the mean square error is more accurate), and a kini coefficient method may also be used, which is not limited in the embodiment of the present application.
102e, determining target roadway parameters based on the plurality of first orderings.
It can be understood that in the roadway parameters, the influence of some parameters on the support parameters is large, the influence of some parameters on the support parameters is small, and for the roadway parameters with small influence on the support parameters, the roadway parameters can be trained without being used as input in the model training process, so that the parameters in the support parameter prediction model training process can be reduced, the model convergence is faster, and the training process is simpler.
It can be understood that, for each support parameter, a first rank of the roadway parameter is obtained, and the target roadway parameter is determined based on a plurality of first ranks, specifically, the target roadway parameter can be determined by scoring each roadway parameter and according to the result of the summation of the scores of each roadway parameter under each support parameter; or determining the target roadway parameters according to a target rule based on a plurality of first orderings, wherein the target rule may be weighted summation, averaging and the like, and the embodiment of the application is not limited.
It can be understood that all roadway parameters are ranked from high to low according to the importance degree of the support parameters, the former preset roadway parameter is determined as the target roadway parameter, or the roadway parameter with the score larger than or equal to the score threshold value is determined as the target roadway parameter.
Illustratively, for the random separation model of the roof bolt diameter, the obtained importance degrees of the roadway parameters are ranked from high to low as: coal seam thickness, coal seam inclination, coal seam number, coal seam strength, immediate roof thickness, then grade as: coal seam thickness: 5. coal seam dip angle: 4. numbering coal beds: 3. the strength of the coal bed: 2. direct roof thickness: 1; aiming at the random separation model of the length of the roof bolt, the obtained tunnel parameters are ranked from high to low in importance degree as follows: coal seam thickness: 5. numbering coal beds: 4. direct roof thickness: 3. the dip angle of the coal seam: 2. the strength of the coal bed: 1; the total thickness of the coal seam is as follows: 10, coal seam dip angle total score: 6, coal seam number total score: 7, total strength of coal seam: 3, total thickness of the direct roof: 4, therefore, the final determined importance degree is ranked from high to low as: the method comprises the following steps of coal seam thickness, coal seam number, coal seam inclination angle, direct roof thickness and coal seam strength, wherein if three roadway parameters need to be selected, the target roadway parameters are as follows: coal seam thickness, coal seam number, coal seam inclination.
And 102f, acquiring a plurality of first sample data corresponding to the target roadway parameters from the roadway support database.
It can be understood that only the data corresponding to the target roadway parameters are obtained from the roadway support database, the data of the other roadway parameters do not participate in model training, and at the moment, each first sample data comprises: target roadway parameters and support parameters.
In the embodiment of the application, a plurality of random separation models are constructed through a random forest algorithm, and each support parameter corresponds to one random separation model; obtaining a plurality of second sample data, each second sample data comprising: actual roadway parameters and actual support parameters; respectively inputting a plurality of second sample data into a plurality of classification models to obtain a plurality of first sequences, wherein each first sequence indicates the importance degree sequence of a plurality of roadway parameters to a support parameter; determining a target roadway parameter based on the plurality of first orderings; and acquiring a plurality of first sample data corresponding to the target roadway parameters from a roadway support database. Therefore, roadway parameters which have small influence on the support parameters are not used as sample data, and input parameters of the support parameter prediction model can be reduced, so that the calculated amount in the model training process is effectively reduced, the complexity of model training is reduced, and the accuracy of model prediction is improved.
In some embodiments of the present application, as shown in fig. 4 in combination with fig. 1, the step 104 may be specifically implemented by the following steps 104a and 104 b.
104a, training the initial support parameter prediction model through a training set to obtain a plurality of trained support parameter prediction submodels.
Each support parameter corresponds to a trained support parameter prediction submodel.
It can be understood that for each support parameter, an initial support parameter prediction submodel is determined, for the submodel, only one output result is output, the training process is simpler, and the model is more easily converged.
And 104b, fusing the plurality of trained support parameter prediction submodels to obtain a trained support parameter prediction model.
In the embodiment of the application, the initial support parameter prediction model is trained through a training set to obtain a plurality of trained support parameter prediction submodels. And each trained support parameter prediction submodel corresponds to one support parameter, and a plurality of trained support parameter prediction submodels are fused to obtain the trained support parameter prediction model. Compared with a model with a plurality of input parameters and a plurality of output parameters, the initial support parameter prediction sub-model only has one output result, so that the training of the model is simpler, the calculated amount is small, and the model is easier to converge.
In some embodiments of the present application, the types of support parameters include: factor type and numerical type; the plurality of trained support parameter prediction submodels comprise: the device comprises a classification prediction model and a regression prediction model, wherein the type of the support parameter corresponding to the classification prediction model is a factor type, and the type of the support parameter corresponding to the regression prediction model is a numerical value type.
It can be understood that the type of support parameters is factor type, such as: if the arrangement mode of the roof anchor cables is not a specific numerical value, the output result of the trained support parameter prediction sub-model is a classification result, for example, the arrangement mode of the roof anchor cables is a mode 1; the types of the support parameters are numerical types, such as: and (4) the row spacing of the anchor cables of the top plate, wherein the output result of the trained support parameter prediction sub-model is a specific numerical value, such as 3 meters.
Optionally, training the initial support parameter prediction model through a training set to obtain a trained first sub-model and a trained second sub-model, wherein support parameters corresponding to the first sub-model are numerical models, and support parameters corresponding to the second sub-model are factor types; and fusing the trained first sub-model and the trained second sub-model to obtain a trained support parameter prediction model. Thus, the number of submodels and the type of the output result of the training process can be comprehensively considered.
In the embodiment of the present application, the types of the support parameters include: a factor type and a numerical type; the plurality of trained support parameter prediction submodels comprise: the support parameter type corresponding to the classification prediction model is a factor type, and the support parameter type corresponding to the regression prediction model is a numerical value type.
In some embodiments of the present application, as shown in fig. 5 in combination with fig. 1, the step 106 may be specifically implemented by the following steps 106a to 106 c.
106a, determining the accuracy of the trained support parameter prediction model based on the prediction support parameters and the actual support parameters.
106b, under the condition that the accuracy is within the accuracy threshold range, obtaining a trained support parameter prediction model.
And 106c, under the condition that the accuracy is not in the accuracy threshold range, adjusting the parameters of the initial support parameter prediction model to continue training until the accuracy is in the accuracy threshold range, and obtaining the trained support parameter prediction model.
It can be understood that, in the case that the type of the support parameter is a numerical type, the accuracy refers to the proportion of samples in the test set, the output value of the model of which is the same as the true value; in the case where the type of support parameter is a factor type, then the goodness-of-fit R is used 2 And evaluating the model, wherein the degree of closeness of the correlation is determined by the degree of goodness of fit, the greater the degree of goodness of fit is, the higher the interpretation degree of the independent variable on the dependent variable is, the higher the percentage of the variation caused by the independent variable in the total variation is, and the better the degree of fit of the model is.
It can be understood that in the case that the accuracy is not within the accuracy threshold range, the parameters of the initial support parameter prediction model are adjusted, including adjusting the selection of the kernel function, setting the hyper-parameters, and the like.
It can be understood that in the case that the accuracy is not within the accuracy threshold range, the parameters of the initial support parameter prediction model are adjusted to continue training, that is: and continuously training the adjusted support parameter prediction model through the training set, inputting the test set into the trained support parameter prediction model to obtain a prediction support parameter, determining the accuracy of the trained support parameter prediction model based on the prediction support parameter and the actual support parameter, and repeatedly executing until the accuracy is within the accuracy threshold range to obtain the trained support parameter prediction model.
In the embodiment of the application, the accuracy of the trained support parameter prediction model is determined based on the predicted support parameters and the actual support parameters; under the condition that the accuracy is within the accuracy threshold range, obtaining a trained support parameter prediction model; and under the condition that the accuracy is not within the accuracy threshold range, adjusting the parameters of the initial support parameter prediction model to continue training until the accuracy is within the accuracy threshold range, and obtaining the trained support parameter prediction model. Therefore, the accuracy of the trained support parameter prediction model is ensured to be within the threshold range, namely the accuracy of the model prediction is ensured, so that the accuracy of the support parameters determined by the model is higher, and the support quality is more reliable.
The embodiment of the application provides a method for determining support parameters, as shown in fig. 6, including the following steps 201 and 202.
201. And acquiring roadway parameters of the roadway to be supported.
202. And inputting the roadway parameters of the roadway to be supported into the trained support parameter prediction model to obtain the support parameters of the roadway to be supported.
And the trained support parameter prediction model is obtained by training through the training method of the support parameter prediction model.
It should be noted that, the relevant description of step 201 to step 202 may refer to the relevant description of the training method of the support parameter prediction model, and is not described herein again.
In the embodiment of the application, roadway parameters of the roadway to be supported are obtained, and the roadway parameters of the roadway to be supported are input into the trained support parameter prediction model to obtain the support parameters of the roadway to be supported. Therefore, dependence on experience of technicians is reduced (uncertainty caused by experience of different technicians is reduced), the process of determining support parameters is greatly simplified, and more reasonable, rapid and sufficient stable support for surrounding rocks of the coal mine roadway is facilitated, so that the design efficiency of the support scheme of the whole coal mine roadway is improved.
Fig. 7 is a block diagram of a training apparatus for a support parameter prediction model according to an embodiment of the present application, and as shown in fig. 7, the training apparatus includes: the system comprises a construction module 701, an acquisition module 702, a division module 703, a training model 704, an input module 705 and an optimization module 706; the building module 701 is used for building an initial support parameter prediction model; an obtaining module 702, configured to obtain a plurality of first sample data, where each first sample data includes: actual roadway parameters and actual support parameters; the dividing module 703 is configured to divide the plurality of first sample data into a training set and a test set; the training model 704 is used for training the initial support parameter prediction model through a training set to obtain a trained support parameter prediction model; the input module 705 is configured to input the test set into the trained support parameter prediction model to obtain a predicted support parameter; the optimizing module 706 is configured to optimize the trained support parameter prediction model based on the predicted support parameter, so as to obtain the trained support parameter prediction model.
In some embodiments of the application, the constructing module 701 is further configured to, before the obtaining of the plurality of first sample data, construct a roadway support database based on the obtained historical roadway support data; the acquisition module is specifically used for acquiring a plurality of first sample data from a roadway support database.
In some embodiments of the present application, the apparatus further comprises a determining module; the building module 701 is further configured to build a plurality of random separation models through a random forest algorithm before obtaining a plurality of first sample data from a roadway support database, wherein each support parameter corresponds to one random separation model; the obtaining module 702 is further configured to obtain a plurality of second sample data, where each second sample data includes: actual roadway parameters and actual support parameters; the input module 705 is further configured to input a plurality of second sample data into a plurality of random separation models respectively to obtain a plurality of first ranks, where each first rank indicates an order of importance of a plurality of roadway parameters to a support parameter; the determining module is used for determining a target roadway parameter based on a plurality of first sequences; the obtaining module 702 is specifically configured to obtain a plurality of first sample data corresponding to target roadway parameters from a roadway support database.
In some embodiments of the present application, the apparatus further comprises: a fusion module; the training module 704 is specifically configured to train an initial support parameter prediction model through a training set to obtain a plurality of trained support parameter prediction submodels, where each support parameter corresponds to one trained support parameter prediction submodel; the fusion module is used for fusing the plurality of trained support parameter prediction submodels to obtain a trained support parameter prediction model.
In some embodiments of the present application, the types of support parameters include: a factor type and a numerical type; the plurality of trained support parameter prediction submodels comprise: the device comprises a classification prediction model and a regression prediction model, wherein the type of the support parameter corresponding to the classification prediction model is a factor type, and the type of the support parameter corresponding to the regression prediction model is a numerical value type.
In some embodiments of the present application, the optimization module 706 is specifically configured to determine accuracy of a trained support parameter prediction model based on a predicted support parameter and an actual support parameter; under the condition that the accuracy is within the accuracy threshold range, obtaining a trained support parameter prediction model; and under the condition that the accuracy is not within the accuracy threshold range, adjusting the parameters of the initial support parameter prediction model to continue training until the accuracy is within the accuracy threshold range, and obtaining the trained support parameter prediction model.
It should be noted that the training device of the support parameter prediction model may be an electronic device in the foregoing method embodiment of the present application, or may also be a functional module and/or a functional entity capable of implementing a function of the embodiment of the device in the electronic device, and the embodiment of the present application is not limited.
In the embodiment of the present application, each module may implement the training method of the support parameter prediction model provided in the above method embodiment, and may achieve the same technical effect, and for avoiding repetition, details are not described here again.
The beneficial effects of the various implementation manners in this embodiment may specifically refer to the beneficial effects of the corresponding implementation manners in the training embodiment of the support parameter prediction model, and are not described herein again to avoid repetition.
Fig. 8 is a structural block diagram of a support parameter determining device according to an embodiment of the present application, and as shown in fig. 8, the device includes: an acquisition module 801 and an input module 802; the acquisition module 801 is used for acquiring roadway parameters of a roadway to be supported; the input module 802 is configured to input roadway parameters of the roadway to be supported into the trained support parameter prediction model to obtain support parameters of the roadway to be supported; the trained support parameter prediction model is obtained by training through the training method of the support parameter prediction model.
It should be noted that the support parameter determining apparatus may be an electronic device in the foregoing method embodiment of the present application, or may also be a functional module and/or a functional entity capable of implementing a function of the apparatus embodiment in the electronic device, and the embodiment of the present application is not limited.
In the embodiment of the present application, each module may implement the method for determining support parameters provided in the above method embodiment, and may achieve the same technical effects, and in order to avoid repetition, the details are not repeated here.
The beneficial effects of the various implementation manners in this embodiment may specifically refer to the beneficial effects of the corresponding implementation manners in the above-mentioned support parameter determination method embodiment, and in order to avoid repetition, details are not described here again.
As shown in fig. 9, there is further provided an electronic device for an embodiment of the present application, where the electronic device may include: the processor 901, the memory 902, and a program or an instruction stored in the memory 902 and executable on the processor 901, where the program or the instruction, when executed by the processor 901, may implement each process of the training method for a support parameter prediction model provided in the foregoing method embodiment, or each process of the support parameter determination method provided in the foregoing method embodiment, and may achieve the same technical effect, and in order to avoid repetition, details are not described here again.
The embodiment of the present application provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the program or the instruction implements each process of the training method for a support parameter prediction model provided in the above method embodiment, or each process of the support parameter determination method provided in the above method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The embodiment of the present application further provides a computer program product, where the computer program product includes a computer program or an instruction, and when the computer program product runs on a processor, the processor is enabled to execute the computer program or the instruction, so as to implement each process of the training method for a support parameter prediction model provided in the foregoing method embodiment, or each process of the support parameter determination method provided in the foregoing method embodiment, and achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The embodiment of the present application further provides a chip, where the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to run a program or an instruction to implement each process of the training method for the support parameter prediction model, or each process of the support parameter determination method provided in the embodiment of the foregoing method, and the same technical effect can be achieved, and in order to avoid repetition, the details are not repeated here.
It should be understood that the chips mentioned in the embodiments of the present application may also be referred to as system-on-chip, system-on-chip or system-on-chip, etc.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus, server and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, which are essential or part of the technical solutions contributing to the prior art, or all or part of the technical solutions, may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A training method of a support parameter prediction model is characterized by comprising the following steps:
constructing an initial support parameter prediction model;
acquiring a plurality of first sample data, each of the first sample data including: actual roadway parameters and actual support parameters;
dividing the plurality of first sample data into a training set and a test set;
training the initial support parameter prediction model through the training set to obtain a trained support parameter prediction model;
inputting the test set into the trained support parameter prediction model to obtain a predicted support parameter;
and optimizing the trained support parameter prediction model based on the prediction support parameters to obtain the trained support parameter prediction model.
2. The method of claim 1, wherein prior to obtaining the first plurality of sample data, the method further comprises:
constructing a roadway support database based on the acquired historical roadway support data;
the obtaining a plurality of first sample data includes:
and acquiring the plurality of first sample data from the roadway support database.
3. The method of claim 2, wherein prior to said obtaining the first plurality of sample data from the roadway support database, the method further comprises:
constructing a plurality of random separation models through a random forest algorithm, wherein each support parameter corresponds to one random separation model;
obtaining a plurality of second sample data, each second sample data comprising: actual roadway parameters and actual support parameters;
respectively inputting the second sample data into the random separation models to obtain a plurality of first sequences, wherein each first sequence indicates the importance degree sequence of the roadway parameters to a support parameter;
determining a target roadway parameter based on the plurality of first orderings;
the obtaining the plurality of first sample data from the roadway support database includes:
and acquiring the plurality of first sample data corresponding to the target roadway parameters from the roadway support database.
4. The method according to claim 1, wherein the training the initial support parameter prediction model through the training set to obtain a trained support parameter prediction model comprises:
training the initial support parameter prediction model through the training set to obtain a plurality of trained support parameter prediction submodels, wherein each support parameter corresponds to one trained support parameter prediction submodel;
and fusing the plurality of trained support parameter prediction submodels to obtain the trained support parameter prediction model.
5. The method of claim 4, wherein the types of support parameters include: a factor type and a numerical type; the plurality of trained support parameter prediction submodels comprise: the support parameter classification method comprises a classification prediction model and a regression prediction model, wherein the type of the support parameter corresponding to the classification prediction model is a factor type, and the type of the support parameter corresponding to the regression prediction model is a numerical type.
6. The method according to any one of claims 1 to 5, wherein said optimizing said trained support parameter prediction model based on said predicted support parameters to obtain said trained support parameter prediction model comprises:
determining the accuracy of the trained support parameter prediction model based on the predicted support parameters and the actual support parameters;
under the condition that the accuracy is within the accuracy threshold range, obtaining the trained support parameter prediction model;
and under the condition that the accuracy is not within the accuracy threshold range, adjusting the parameters of the initial support parameter prediction model to continue training until the accuracy is within the accuracy threshold range, and obtaining the trained support parameter prediction model.
7. A method for determining support parameters, the method comprising:
acquiring roadway parameters of a roadway to be supported;
inputting the roadway parameters of the roadway to be supported into the trained support parameter prediction model to obtain the support parameters of the roadway to be supported;
wherein the trained support parameter prediction model is trained by the method according to any one of claims 1 to 6.
8. A training device for a support parameter prediction model, the device comprising: the system comprises a building module, an acquisition module, a division module, a training model, an input module and an optimization module;
the construction module is used for constructing an initial support parameter prediction model;
the obtaining module is configured to obtain a plurality of first sample data, where each first sample data includes: actual roadway parameters and actual support parameters;
the dividing module is used for dividing the plurality of first sample data into a training set and a test set;
the training model is used for training the initial support parameter prediction model through the training set to obtain a trained support parameter prediction model;
the input module is used for inputting the test set into the trained support parameter prediction model to obtain a predicted support parameter;
and the optimization module is used for optimizing the trained support parameter prediction model based on the predicted support parameters to obtain the trained support parameter prediction model.
9. A support parameter determination apparatus, the apparatus comprising: the device comprises an acquisition module and an input module;
the acquisition module is used for acquiring roadway parameters of a roadway to be supported;
the input module is used for inputting the roadway parameters of the roadway to be supported into the trained support parameter prediction model to obtain the support parameters of the roadway to be supported;
wherein the trained support parameter prediction model is trained by the method according to any one of claims 1 to 6.
10. An electronic device comprising a processor, a memory and a program or instructions stored on the memory and executable on the processor, the program or instructions when executed by the processor implementing a method of training a support parameter prediction model as claimed in any one of claims 1 to 6 or implementing a method of determining support parameters as claimed in claim 7.
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