CN116245020A - Cutter head abrasion and energy consumption optimization method and system based on shield tunneling machine - Google Patents
Cutter head abrasion and energy consumption optimization method and system based on shield tunneling machine Download PDFInfo
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Abstract
The invention discloses a cutter head abrasion and energy consumption optimization method and system based on shield tunneling, wherein the method comprises the following steps: acquiring operation data of the TBM and carrying out data preprocessing, wherein the operation data comprises adjustable operation parameters; based on the preprocessed data, a TBM efficiency prediction model is established by adopting a LightGBM, wherein the LightGBM is subjected to super-parameter optimization by Bayesian optimization, and the TBM efficiency prediction model is evaluated by taking a prediction error as a loss function until the TBM efficiency prediction model meeting the preset requirement is obtained; establishing a multi-objective optimization model based on NSGA-II by taking cutter wear and cutter energy consumption minimization as objectives to obtain a pareto front; and solving the pareto front edge to obtain the optimal adjustable operation parameters of the TBM. The invention can reduce cutter abrasion and cutter head energy consumption, improve the performance of the tunnel boring machine, guide tunnel construction and promote the construction of tunnel engineering projects.
Description
Technical Field
The invention belongs to the technical field of shield tunneling optimization, and particularly relates to a cutter head abrasion and energy consumption optimization method and system based on shield tunneling.
Background
TBM receives extensive attention along with large-scale underground engineering construction due to its strong capability of low construction cost, high propulsion speed and high safety, but because TBM operates underground and is buried outside the daily line of sight, geological conditions and construction processes have great uncertainty and complexity, and still face many challenges. With the expansion of cities and the release of traffic pressure, underground construction activities are continuously increased, and Tunnel Boring Machines (TBM) have been widely applied to tunnel excavation mechanization to replace the traditional drilling and blasting. A key component of TBM is the disc cutter, which rotates and inserts itself into the rock, while broken rock soil is transported and moved forward through the cooperation of a series of devices. That is, TBM-based tunneling relies on direct interaction between the cutterhead and hard rock, which is ground for excavation by thrust and torque effects created by the cutterhead rolling. In general, when the cutterhead is used excessively under the condition of no attention, mechanical accidents are easy to cause, and thus the tunneling progress is influenced. Therefore, the abrasion of the cutter disc determines the performances of good safety, high thrust speed, low cost and the like of the TBM to a great extent. However, wear of the cutterhead is affected by a plurality of factors such as rock mass properties, installation positions and the like, which is a complex evolution process and presents challenges for measuring and understanding wear of the cutterhead in tunnel construction.
In the field of tunnel engineering, field inspection based on expert knowledge and experience still plays a critical role in TBM operation, but subjectivity and randomness are inevitably present, and errors may induce potential risks. On-site inspection of cutterhead wear requires a TBM shutdown, which can affect tunnel progress, thereby increasing costs. As an alternative scheme, the monitoring system is arranged on the TBM, can generate mass data records of key parameters of TBM operation in real time, and provides reference for tunnel engineering safety construction. However, the resulting data is typically non-linear and fluctuates significantly with noise, which makes cutterhead wear prediction and optimization challenging. Therefore, it is necessary to develop a reliable method for tunnel engineering, data mining, performance prediction and optimization.
In recent years, cutter wear prediction can be divided into three major categories, namely an empirical method, a theoretical method and a statistical method, wherein the empirical method is used for analyzing TBM performance based on full-size laboratory tests, the empirical method is used for evaluating the performance based on field observation of TBM performance and geological conditions, and the statistical method is mainly used for constructing a performance prediction method by means of mathematical rules. These methods help to predict TBM performance and enrich the domain knowledge base. However, empirical methods require the design and performance of multiple experiments, are costly, require long time, and require given test conditions and some physical assumptions, in which case the results obtained may only be suitable for a particular situation. Theoretical methods tend to be applicable in simple scenarios, with many assumptions, which may not be appropriate for practical situations, and reliability of the results is doubtful. Statistical methods are inconsistent in robustness to nonlinear and complex systems, and their predictive ability may be impaired by outliers and extrema. That is, these methods have drawbacks that may be challenging to apply in practice.
Empirical theory/statistical methods cannot provide accurate and reliable predictions due to assumptions and limitations in method capabilities. In contrast, machine learning methods can provide reliable predictions through data mining modeling patterns, but current research has focused little on cutterhead wear predictions.
The present invention is therefore directed to developing a machine learning method to predict and optimize cutter wear and cutter energy consumption with high accuracy.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a cutter head abrasion and energy consumption optimizing method and system based on shield tunneling machine tunneling, which are used for predicting and optimizing cutter head abrasion and cutter head energy consumption with high precision.
In order to achieve the above object, according to an aspect of the present invention, there is provided a method for predicting and optimizing wear and energy consumption of a TBM cutterhead, comprising:
acquiring operation data of the TBM and carrying out data preprocessing, wherein the operation data comprises adjustable operation parameters and fixed operation parameters;
based on the preprocessed data, a TBM efficiency prediction model is established by adopting a LightGBM, wherein the LightGBM is subjected to super-parameter optimization by Bayesian optimization, and the TBM efficiency prediction model is evaluated by taking a prediction error as a loss function until the TBM efficiency prediction model meeting the preset requirement is obtained;
Establishing a multi-objective optimization model based on NSGA-II by taking cutter wear and cutter energy consumption minimization as objectives to obtain a pareto front;
and solving the pareto front edge to obtain the optimal adjustable operation parameters of the TBM.
Further, the data preprocessing includes:
determining actual cutter wear according to the operation data of the TBM:
wherein O is 1 The abrasion loss of the single-disc cutting machine; k is the wear coefficient; d is the diameter of the cutting path of the disc cutter; n is the revolving speed of the cutterhead; l is TBM thrust distance; v is the thrust speed of the TBM.
Further, the data preprocessing includes:
determining the actual cutter head energy consumption according to the operation data of the TBM:
O 2 ＝E 1 +E 2
wherein O is 2 For total energy consumption of cutterhead, E 1 Energy power consumed for hydraulic thrust system, E 2 Energy power consumed by the knife system, F i Is thrust, T i Is the torque of the cutter head, t i For the time of sequence occurrence, ω i Is the revolving speed of the cutterhead.
Further, the performing the super-parametric optimization on the LightGBM with bayesian optimization includes:
adopting a Bayesian optimization algorithm based on a sequential model optimization technology, and performing super-parameter optimization by using a Parzen estimator tree algorithm; the Bayesian optimization algorithm takes EI criteria as a function of an optimization target:
Wherein x is a hyper-parameter candidate; y is the output of the objective function; y is * P (y|x) is a proxy model representing the probability of y occurrence given x, which is the threshold of the objective function;
the EI is maximized relative to x by searching for the optimal superparameter combination under proxy function p (y|x).
Further, the pre-processing data is used for establishing a TBM efficiency prediction model by adopting a LightGBM, wherein the LightGBM is subjected to super-parameter optimization by using bayesian optimization, and the TBM efficiency prediction model is evaluated by using a prediction error as a loss function until the TBM efficiency prediction model meeting the preset requirement is obtained, and the method comprises the following steps:
obtaining a prediction error based on the actual cutter wear, the actual cutter energy consumption and a predicted value of a TBM efficiency prediction model;
if the prediction error meets the preset requirement, the TBM efficiency prediction model is trained.
Further, the establishing a multi-objective optimization model based on NSGA-II with the aim of minimizing cutter wear and cutter energy consumption to obtain the pareto front comprises the following steps:
determining an optimization target of a multi-target optimization model:
minimizing F (x) = [ F 1 (x)，f 2 (x)，f 3 (x)，...，f m (x)]
Wherein F (x) is based on n independent variables x 1 To x n A set of m objective functions;
determining constraint conditions of the multi-objective optimization model:
g j (x)≤0， j＝1，2，...，k
Wherein x is l And x u Is each variable x i Lower and upper limits of (2); g (x) is an inequality constraint;
and obtaining the pareto front based on the optimization target, the constraint condition and the NSGA-II.
Further, the solving the pareto front to obtain the optimal adjustable operation parameters of the TBM includes:
determining the scores of all candidate solutions in the pareto front by adopting an optimal solution quality distance method:
wherein S is i The value range of the score of the ith solution is [0,1]；
Is the euclidean distance from the ith solution to the most negative solution,
is the Euclidean distance from the ith solution to the most ideal solution;
by S i The maximum solution is the optimal solution in the pareto front edge, and the optimal adjustable operation parameters of the TBM are obtained.
According to a second aspect of the present invention, there is provided a cutter head wear and energy consumption optimization system based on shield tunneling machine, comprising:
the first main module is used for collecting the operation data of the TBM and preprocessing the data, wherein the operation data comprises adjustable operation parameters and fixed operation parameters;
the second main module is used for establishing a TBM efficiency prediction model by adopting a LightGBM based on the preprocessed data, wherein the LightGBM is subjected to super-parameter optimization by Bayesian optimization, and the TBM efficiency prediction model is evaluated by taking the mean square error as a loss function until the TBM efficiency prediction model meeting the preset requirement is obtained;
The third main module is used for establishing a multi-objective optimization model based on NSGA-II by taking cutter head abrasion and cutter head energy consumption minimization as targets to obtain a pareto front;
and the fourth main module is used for solving the pareto front edge to obtain the optimal adjustable operation parameters of the TBM.
According to a third aspect of the present invention, there is provided an electronic terminal comprising:
at least one processor, at least one memory, a communication interface, and a bus; wherein,,
the processor, the memory and the communication interface complete the communication with each other through the bus;
the memory stores program instructions executable by the processor that the processor invokes to implement the method.
According to a fourth aspect of the present invention there is provided a non-transitory computer readable storage medium storing computer instructions which cause the computer to implement the method.
In general, the above technical solutions conceived by the present invention, compared with the prior art, enable the following beneficial effects to be obtained:
1. according to the cutter head abrasion and energy consumption optimization method, an improved LightGBM based on Bayesian optimization is developed, and TBM performance prediction is performed based on TBM working parameters under specific geological conditions. And solving a multi-objective optimization (MOO) problem of the pareto front search by adopting a non-dominant ordering genetic algorithm II (NSGA-II), and then obtaining a multi-objective optimal solution by utilizing an optimal solution superior-inferior distance search (TOPSIS). The method can reduce cutter abrasion and cutter energy consumption, improve the performance of the tunnel boring machine, guide tunnel construction and promote the construction of tunnel engineering projects.
2. According to the cutter head abrasion and energy consumption optimization method, the super parameters of the LightGBM are optimized through the Bayesian method, and compared with other machine learning methods such as Random Forest (RF) and support vector regression (support vector regression, SVR), the improved LightGBM has better fitting precision and more accurate prediction in the aspects of predicting TBM cutter abrasion and cutter power.
3. According to the cutter head abrasion and energy consumption optimization method, the best solution of the output parameters is searched by adopting NSGA-II of TOPSIS, after multi-objective optimization, the total abrasion loss of a TBM cutter is obviously reduced by 16.80%, the cutter head energy consumption power is obviously reduced by 14.91%, and the performance of the TBM is improved.
Drawings
FIG. 1 is a schematic diagram of a TBM performance index prediction and optimization framework according to an embodiment of the present invention;
FIG. 2 is a view and position of four edge rollers (38, 39, 40a, 40 b) of an embodiment of the invention ((a) layout of the cutters, (b) wear information of 40b, (c) wear information of 40a, (d) wear information of 38, (e) wear information of 39);
FIG. 3 is a calculation of a map and specific locations of tool wear for field measurements in accordance with an embodiment of the present invention. (five-pointed star is marked as the field actual measurement value of cutter disc abrasion);
FIG. 4 is a graph of TBM performance index curves for (a) cutterhead wear and (b) cutterhead power consumption in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of (a) total cutter wear cumulative value and (b) total cutter energy consumption cumulative value after and before optimization according to an embodiment of the present invention;
FIG. 6 is a flowchart of a cutter head abrasion and energy consumption optimization method based on shield tunneling according to an embodiment of the invention;
fig. 7 is a schematic diagram of a cutter head abrasion and energy consumption optimization system based on shield tunneling according to an embodiment of the invention;
fig. 8 is a schematic diagram of the physical structure of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
In the description of the present invention, unless explicitly stated and limited otherwise, the terms "connected," "connected," and "fixed" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The cutter head abrasion and energy consumption optimization method based on shield tunneling can be applied to the technical fields of shield tunneling, construction, tunnel excavation and the like.
The invention provides a cutter head abrasion and energy consumption optimization method based on shield tunneling, which applies Bayesian optimization to super-parameter optimization of a lightGBM and predicts TBM performance by using the improved lightGBM; the method can reduce cutter abrasion and cutter energy consumption, improve the performance of a tunnel boring machine, guide tunnel construction and promote the construction of tunnel engineering projects.
As shown in fig. 1 and 6, the method for predicting and optimizing the wear and energy consumption of a TBM cutterhead according to the present invention includes the following steps S100 to S400:
step S100, acquiring operation data of the TBM and preprocessing the data;
wherein the operating data includes adjustable operating parameters and fixed operating parameters.
The electronic sensor arranged on the TBM can record real-time running data of the TBM and monitor the construction state of the TBM. TBM real-time operation data acquired by the electronic sensor comprises: total thrust, cutter torque, left side soil pressure average, right side soil pressure average, average propulsion speed, group a pressure average, group B pressure average, screw conveyor pressure, gear oil temperature, gear oil pressure, conveyor mud transport flow, net excavation time.
And obtaining the original data by carrying out data cleaning on the operation data of the TBM. The original data can not be directly read out of the abrasion and energy consumption of the TBM cutterhead, and the TBM operation data are needed to be calculated.
The method for calculating the cutter head abrasion comprises the following steps:
wherein O is 1 The abrasion loss of the single-disc cutting machine; k is the wear coefficient; d is the diameter of the cutting path of the disc cutter; n is the revolving speed of the cutterhead; l is TBM thrust distance; v is the thrust speed of the TBM.
The thrust speed of the TBM is: v (mm/min) =P (mm/r) ×N (r/min)
Wherein P is permeability (mm/r), and N is cutter head rotating speed.
In tunnel construction, the wear coefficient k is determined by the soil conditions, the wear resistance of the cutter and the cutter arrangement scheme of the cutter head. The wear resistance of the cutter and the arrangement of the cutterhead are determined on a particular type of TBM, so the difference in the wear coefficient k is mainly related to the soil conditions. On the basis, the abrasion coefficient k is determined based on real-time operation parameters of the TBM, actual abrasion values of the cutterhead measured by field staff and experience of tunnel experts.
In another embodiment of the invention, the cutterhead of the TBM has multiple cutters working together to cut soil or rock. Multiple tools are mounted on the same cutting path diameter, resulting in a cutting thickness that is typically split among the multiple tools, the wear coefficient k needs to be modified to 
Cutter head wear O 1 The method comprises the following steps:
wherein k is q To correct the wear coefficient; q is the number of cutters with the same cutting diameter; d is the diameter of the cutting path of the disc cutter; l is TBM thrust distance (km); p is permeability (mm/r).
TBM cutterhead energy consumption is also an important indicator affecting the overall engineering cost and environmental protection carbon emissions. The energy consumption of the cutterhead is closely related to the load generated, including the thrust and torque of the cutterhead. The calculation method of the cutter head energy consumption comprises the following steps:
O 2 ＝E 1 +E 2
wherein O is 2 For total energy consumption of cutterhead, E 1 Energy power consumed for hydraulic thrust system, E 2 Energy power consumed by the knife system, F i Is thrust, T i Is the torque of the cutter head, t i For the time of sequence occurrence, ω i Is the revolving speed of the cutterhead.
Step S200, based on the preprocessed data, a TBM efficiency prediction model is established by adopting a LightGBM, wherein the LightGBM is subjected to super-parameter optimization by Bayesian optimization, and the TBM efficiency prediction model is evaluated by taking a prediction error as a loss function until the TBM efficiency prediction model meeting the preset requirement is obtained;
the light gradient elevator (LightGBM) is a distributed gradient enhancement method based on a decision tree algorithm, and has the capabilities of supporting efficient parallel training, being faster in training speed, lower in memory consumption and higher in accuracy under the condition of rapidly processing massive data. The LightGBM is based on gradient-based single-sided gradient sampling (GOSS) and mutual Exclusion Feature Bundling (EFB) techniques. The GOSS first orders the training examples in descending order and separates them into two subsets according to their absolute gradient values. Wherein the data gradient of the subset A is larger, and the gradient value is that Ax100%, while subset B is composed of the remaining lower gradients A C Random samples of composition b×|A in size c Gradient value is (1-a). Times.100%. Subsequently, the example features j are gained by their estimated variance over the subset A U B
Segmentation, where V' j (d) is calculated by the following equation.
Wherein A is l ＝[x i ∈A:x ij <d],A r ＝[x i ∈A:x ij >d]；B l ＝[x i ∈B:x ij <d]；B r ＝[x i ∈B:x ij >d]Gi is the negative gradient of the loss function.
Secondly, EFB aims to reduce the feature dimension of the data set, re-group the data set into new bundles, thereby avoiding unnecessary computation time and the cost of zero feature values to a large extent, and thus improving the data processing speed. EPB mainly includes two steps of processing data. First, EFB needs to determine which properties should be bound to the original data properties. It treats this as a graph coloring problem, uses features as vertices, and adds edges for every two non-mutually exclusive features, finding the best binding result by using a greedy algorithm. Next, the EFB needs to create a new package, which is a key step of the method. By setting the offset of the original values of the dataset features using a histogram-based algorithm, it is possible to store the exclusive features in different bundles.
The hyper-parameters of the machine learning model are externally configured values that cannot be estimated from the input data. In order to obtain an accurate trained predictive model, the machine learning algorithm requires super-parametric tuning. The Bayesian optimization algorithm can search candidate super-parameters according to probability estimation, and provide an optimal solution for the super-parameter tuning problem. Compared with automatic tuning of machine learning, the method has the advantage of calculation cost, and particularly, the automatic tuning based on large-scale data. Specifically, for the LightGBM method, the invention adopts a Bayesian optimization method to select the super parameters. And adopting a Bayesian optimization algorithm based on a sequential model optimization (SMBO) technology, and performing super-parametric optimization by using a Parzen estimator Tree (TPE) algorithm. The bayesian optimization algorithm takes the desired improvement (EI) criterion as a function of the optimization objective. EI is given in the following formula.
Wherein x is a hyper-parameter candidate; y is the output of the objective function; y is * As a threshold for the objective function, p (y|x) is a proxy model that represents the probability of y occurrence given x. The EI is maximized relative to x by searching for the optimal superparameter combination under proxy function p (y|x).
And dividing the preprocessed operation data obtained in the step S100 into a training set and a testing set for testing the model for the TBM efficiency prediction model established by the LightGBM after Bayesian optimization and parameter adjustment.
Specifically, step S200 includes steps S201 to S202:
step S201, obtaining a prediction error based on the actual cutter wear, the actual cutter energy consumption and a predicted value of a TBM efficiency prediction model;
step S202, if the prediction error meets the preset requirement, the TBM efficiency prediction model training is completed.
Step S200 further includes step S203:
if the prediction error meets the preset requirement, performing super-parameter optimization based on a Bayesian method, constructing a TBM efficiency prediction model based on the LightGBM, and performing training.
The invention adopts a prediction error as a loss function to evaluate the training effect of a model, wherein the prediction error comprises the following components: mean Absolute Error (MAE), root Mean Square Error (RMSE) and R 2 Three evaluation indexes. The closer the MAE and RMSE values are to zero, the closer the machine learning prediction is to the actual value of the dataset, and R 2 The closer the value is to 1, the higher the correlation of the TBM performance index value and the operation parameter is, namely machine learningThe more accurate the model prediction.
Where N is the total number of samples for testing, f i (x) Is the predictive value of the TBM efficacy predictive model, y i Actual cutter wear and actual cutter energy consumption;
step S300, with the aim of minimizing cutter head abrasion and cutter head energy consumption, establishing a multi-objective optimization model based on NSGA-II to obtain a pareto front;
specifically, step S300 includes steps S301 to S303.
Step S301, determining an optimization target of the multi-target optimization model:
minimizing F (x) = [ F 1 (x)，f 2 (x)，f 3 (x)，...，f m (x)]
Wherein F (x) is based on n independent variables x 1 To x n A set of m objective functions;
step S302, determining constraint conditions of the multi-objective optimization model:
g j (x)≤0，j＝113，...，k
wherein x is l And x u Is each variable x i Lower and upper limits of (2); g (x) is an inequality constraint;
and step S303, obtaining the pareto front based on the optimization target, the constraint condition and NSGA-II.
In order to find the optimal solution of the multi-objective optimization, the pareto front needs to be generated, and the invention adopts NSGA-II algorithm to search the pareto front. The NSGA-II procedure is as follows:
randomly generating an initial population with the size of P, and generating a first offspring population by selection, crossing and mutation based on a genetic algorithm with non-dominant ordering; combining the parent population and the offspring population from the second generation, performing rapid non-dominant ranking, and calculating the Crowding Degree (CD) of each non-dominant layer individual; selecting proper individuals according to the non-dominant relationship of the individuals and the CD, and generating a new parent population; a genetic algorithm is used to generate a new offspring population. Repeating the steps until the population quantity or the maximum offspring meets the preset termination condition, and obtaining the pareto front.
And step S400, solving the pareto front edge to obtain the optimal adjustable operation parameters of the TBM.
The solution of the pareto front to obtain the optimal adjustable operation parameters of the TBM comprises the following steps:
determining the scores of all candidate solutions in the pareto front by adopting an optimal solution quality distance method:
wherein S is i The value range of the score of the ith solution is [0,1]；
Is the euclidean distance from the ith solution to the most negative solution,
is the Euclidean distance from the ith solution to the most ideal solution;
by S i The maximum solution is the optimal solution in the pareto front edge, and the optimal adjustable operation parameters of the TBM are obtained.
The invention provides a TOPSIS method comprising Bayesian optimization of improved LightGBM and NSGA-II, aiming at improving the performance of TBM, comprising the steps of reducing the energy consumption of a cutterhead and reducing the abrasion of the cutterhead, wherein the improved LightGBM is subjected to super-parameter setting by using Bayesian optimization for prediction, the Paretofeng is obtained by using NSGA-II, and then the TOPSIS is applied to select an optimal solution for optimization. The innovations and advantages of the proposed method emphasize the accuracy and efficiency of its prediction and optimization of long-term and large-scale data sets. In practice, cutter head hob wear is reduced from 0.001806 (mm) to 0.001502 (mm), and cutter head power consumption is reduced from 469.2546 (Kw) to 400.3490 (Kw). The reduction of the energy consumption of the cutterhead reduces the carbon emission, and the optimization of the cutter wear reduces the replacement times. The project cost is reduced, and the project profit capability is improved.
Example 1
In order to make the implementation process of the method of the present invention clearer, this example uses a subway No. 6 line outside tunnel engineering as an example, and the implementation process of the method of the present invention will be specifically described.
The process of this embodiment is implemented on a computer work platform for the following device parameters. The specific parameter information is shown as follows [ operating system: windows 10 professional 21H2; CPU, 12 th generation Intel (R) KuRui (TM) i7-12700F 2.10GHz; the internal memory is 16GB; GPU NVIDIA GeForce RTX3070 GB.
step 1.1 data acquisition. The electronic sensor arranged at the key position of the TBM can record real-time construction data of the TBM and monitor the construction state of the TBM. In this example, a subway No. 6 line outside tunnel engineering is taken as an example, and the proposed method is checked. A data set of one interval, about 640 ring tunnel lengths. A data set of total [39040x12] is obtained by data cleaning and preprocessing about every 20mm of data unit columns acquired from the sensor, and data division is performed before and after tunneling time, wherein the training set accounts for 70% of the total data set, and the test set accounts for 30% of the total data set, namely the sizes of the training set and the test set are [27328x12] and [11712x12], respectively.
Step 1.2, determining the cutter head abrasion value. And calculating tool wear according to a JTS formula, and determining a wear coefficient k. The wear coefficient k is generally determined by geological conditions, as shown in table 1. It can be seen that the geological conditions are mainly divided into three types, and the range of the abrasion coefficient k is corresponding to the geological conditions. The engineering project geological conditions applied in this example are mainly SIII type soil, the geological conditions are few, the soil type is SIV, and SIII and SIV are medium to highly weathered rocks (i.e. weathered class III and class IV), and these geological features belong to the columns of sandy cobbles, rocks, etc. On this basis, the tunnel specialist determines the wear coefficient k=0.04 from a great deal of actual engineering, abundant engineering experience and geological data. Four edge rollers (38, 39,40a,40 b) were chosen for verification in this example. Their positions and views are shown with reference to fig. 2, in which the cutting path diameters of the four edge hobs are the same as the TBM diameter (d=6.68 m), and their wear is accurately determined.
Table 1. Wear coefficient value k under different geological conditions.
| Clay, silt, etc | Sha Tulei | Sandy pebbles, rocks, etc. -SII and SIV types |
| 0.001～0.003 | 0.01～0.02 | 0.03～0.05 |
The actual wear values of the four edge hobs are measured on the rings 103, 271, 353 and 556, respectively, based on in situ manual measurements. On the basis of the real-time operating data of the tunnel specialist, the number of cutting teeth q=4 and the TBM, it can be determined that the wear coefficient k is approximately 0.04. Referring to fig. 3 and table 2, the actual measured value and the calculation result based on k are respectively. The calculated value is close to the measured value, the average error is 0.21%, and the abrasion coefficient k determined by an expert is accurate and accords with the field geological condition of the example. This estimate may further be used for tool wear calculations in the engineering project to which the present example applies.
Table 2. Comparison of calculated wear values of the tool at a particular location with measured values in the field.
The present example selects 12 key operating parameters x collected by the TBM sensor 1 To x 12 As input data of training model, the cutter abrasion loss is O 1 The cutter power is O 2 As an output target, the details are shown in table 3. In addition, table 4 shows the data distribution with parameters. The detailed trend of the output parameters of the tunnel boring machine in the driving process is shown with reference to fig. 4.
Table 3. Details the selected TBM parameters and performance metrics.
Table 4. Data descriptive of selected TBM parameters and performance metrics.
Step 2, based on the preprocessed data, a TBM efficiency prediction model is established by adopting a LightGBM, wherein the LightGBM is subjected to super-parameter optimization by Bayesian optimization, and the TBM efficiency prediction model is evaluated by taking a prediction error as a loss function until the TBM efficiency prediction model meeting the preset requirement is obtained;
the method specifically comprises the following steps: cutter wear and cutter energy consumption are key performance indexes of TBM, and accurate prediction of cutter wear and cutter power can provide reliable estimation, which is beneficial to TBM operation decision. The proposed model is capable of providing accurate predictions, demonstrating its strong ability to map nonlinear relationships between input and output variables. To highlight the advantages of the improved LightGBM, a series of system analyses were performed. The specific analysis results are as follows:
(1) Bayesian optimization has good performance in hyper-parametric searching. To achieve high performance of the improved LightGBM, it is necessary to set appropriate super parameters, including the maximum depth D of the tree m Number of leaves N l Learning rate η and number of estimators N e . Table 5 lists f 1 (x i ) And f 2 (x i ) And optimal hyper-parameter results. It can be seen that the bayesian optimization obtains a large number of super parameters to set the model, and the result is also very high, which indicates that the bayesian optimization has good performance in effectively performing super parameter optimization, and is beneficial to obtaining good performance. Among all the super parameters, the best super parameters are obtained as follows: target O 1 Is 0.0268, the number of estimators N e 660, leaf number N i 57, maximum depth D m 81; o (O) 2 The learning rate η of (1) is 0.0181, the number of estimators N e 710 leaf number N l At a maximum depth D of 3 m At 81, the present example sets the best hyper-parameters according to the bayesian optimization result, so that the model can be further explored.
TABLE 5 search space for superparameter and optimal superparameter
(2) Based on the optimal super parameters, the improved LightGBM achieves good effect in predicting TBM cutter wear and cutter power. During the training process, the loss is recorded in real time to demonstrate the training performance of the model. Obviously, the loss values drop rapidly and then flatten out, indicating that the model can be trained well to learn the features in the data. To verify the performance of the model, a well-trained model was used to work on the test set. The results obtained are shown in Table 6. It can be seen that the model can provide accurate real-time predictions, O 1 Is 1.2584E-04, RMSE is 1.7237E-04, R 2 0.9084, O 2 MAE of 31.0081, RMSE of 36.0126, R 2 0.8044. In addition, overall, these results demonstrate that the improved LightGBM performs well, fitting well with the actual measurements in predicting TBM cutting power and cutting wear.
Table 6. Results of comparisons of different machine learning methods.
(3) Compared with other machine learning methods, the improved LightGBM has better performance. The study compares the improved LightGBM with other machine learning methods such as Random Forest (RF), support vector regression (support vector regression, SVR), and the like. Table 6 shows O respectively 1 And O 2 Comparison results of different methods. It can be seen that the improved LightGBM performs best in predicting TBM tool power and tool wear. RF and SVR are worse than the modified LightGBM on all evaluation metrics, while SVR performs worst in all methods. It can be seen that the trend and fluctuation of the improved LightGBM predictions are substantially consistent with the ground truth values, while the differences in other methods are greater. From all comparison results, the improved LightGBM is superior to other methods in predicting TBM cutter wear and cutter power, indicating that the improved LightGBM has better performance and great practical application potential.
Overall, the improved LightGBM based on bayesian optimization performs well in predicting cutting power and cutting wear. In addition, the improved LightGBM has better performance than other machine learning methods. By combining all the results, the conclusion can be drawn that the improved Bayesian optimized LightGBM has excellent performance, effectiveness and reliability in predicting cutter abrasion and cutter power, and has great practical potential.
multi-objective optimization and result analysis. In performing multi-objective optimization, f is used 1 (x i ) And f 2 (x i ) As a function of the fitness of multiple targets, the aim is to reduce the wear of the bed knife disc hob (O 1 ) And reducing the energy consumption power (O) of the cutterhead 2 ) Is a minimum goal. In addition, during TBM tunneling, parameter x 1 ～x 5 Other parameters are considered as fixed factors that cannot be adjusted directly or indirectly, as are considered to be adjustable by manual direct and indirect methods. The main focus of this study is the parameter x 1 To x 5 For O 1 And O 2 Effect of the results. These 5 adjustable decision variables x for multi-objective optimization 1 ～x 5 The change spaces of (2) are shown in Table 7.
TABLE 7 5 adjustment decision variables for Multi-objective optimization x 1 ～x 5 Is provided.
In the training procedure using NSGA-II, the population size was set to 50 and the maximum offspring number was set to 100 as the parameter for NSGA-II.
And 4, solving the pareto front edge to obtain the optimal adjustable operation parameters of the TBM.
To find the optimal result, the present example examines four samples (i.e., samples 7000,17000,27000 and 37000). In addition, in order to find the optimal solution from these pareto fronts, the present example adopts the optimal solution superior-inferior distance method (TOPSIS) method. Based on the results obtained, the analysis was as follows:
(1) The NSGA-II adopting TOPSIS has good performance in searching the optimal solution of the output parameters, and is beneficial to improving the performance of TBM. It is clear that NSGA-II performed well, extracting several pareto fronts, and thus obtaining the best solution. The statistical analysis of the data of the original values and the optimization results is shown in table 8. It can be seen that the average of the tool wear decreases from 0.001806 to 0.001502, the standard deviation decreases from 0.000571 to 0.0001417, the average of the tool power decreases from 469.2546 to 400.3490, and the standard deviation decreases from 96.2401 to 83.9914. The tool wear was reduced by 16.83% and the tool power was reduced by 14.68% compared to the original data. Experimental results show that the method has a good effect on optimizing TBM performance. As can be seen from comparison of the optimized result and the original data, the cutter abrasion is obviously reduced, the cutter power is obviously reduced, and the method is superior in performance and outstanding in optimized result. The result shows that the method can optimize the output parameters of the TBM and improve the performance of the TBM.
Table 8 statistical analysis of data between the original values and the optimized results.
Note that 1. Mu. Is the average value and sigma is the standard deviation. 2. Based on a percentage formula
The optimization percentages (hereinafter the same), wherein μ I To improve the average value of the results, mu O Is the average of the original dataset.
(2) The provided optimizing method can optimize the operation parameters of the TBM and is beneficial to the performance of the TBM. The original data distribution is wider, the optimized data distribution is relatively aggregated, and the range is narrower, so that the optimization method provided by the invention improves 5 operation parameters, and is beneficial to the performance of TBM.
(3) After multi-objective optimization, the total abrasion loss of the TBM cutter is obviously reduced by 16.80 percent, the energy consumption power of the cutter disc is obviously reduced by 14.91 percent, and the performance of the TBM is improved. Referring to fig. 5, the cumulative values of the total wear loss of the cutterhead and the total power consumption of the cutterhead under the original data and the optimized data are given. The total grinding loss of the cutterhead hob in the original data is accumulated to 70.502mm, and the total grinding loss of the cutterhead hob after optimization is 58.657mm, so that 16.80% is reduced. In addition, the total power consumption of the cutterhead in the original data is accumulated to 453057 Kw.h, and the total power consumption of the optimized cutterhead is 385485 Kw.h, which is reduced by 14.91%. From these results, it can be concluded that TBM can save significant energy and reduce carbon emissions. In addition, according to practical project experience, the average wear limit of the four edge hobs is assumed to be 15mm, and according to the original data, the number of times of replacing the cutter is assumed to be 
Secondary times; and the optimized number of tool changes is +.>
And twice. The reduction of the energy consumption of the cutterhead reduces the carbon emission, and the optimization of the cutter wear reduces the replacement times. All of which contribute to the benefits of tunneling. That is, the optimized output parameters are obviously improved by the optimizing method provided by the research, which is beneficial to improving the TBM performance, reducing the project cost and improving the project profitability.
From the results of this example, the conclusions drawn are summarized below. (1) The improved LightGBM shows better prediction effect in predicting cutter head abrasion and cutter head power consumption, O 1 Is 1.2584E-04, RMSE is 1.7237E-04, R 2 0.9084, O 2 MAE of 31.0081, RMSE of 36.0126, R 2 0.8044. (2) The NSGA-II algorithm combined with the TOPSIS has better solving performance on multi-objective optimization, wherein the NSGA-II algorithm has the capability of searching the pareto front first and then optimizing the optimal solution by applying the TOPSIS algorithm, thereby being beneficial to improving the performance of TBM and being beneficial to the construction of tunnel engineering projects. Cutter head hob abrasion is reduced from 0.001806 (mm) to 0.001502 (mm), and cutter head energy consumption power is reduced from 469.2546 (Kw) to 400.3490 (Kw). (3) The comparison result shows that the Bayesian optimization has better performance, and can assist the improved LightGBM and NSGA-II to obtain better results.
The implementation basis of the embodiments of the present invention is realized by a device with a processor function to perform programmed processing. Therefore, in engineering practice, the technical solutions and the functions of the embodiments of the present invention can be packaged into various modules. Based on the actual situation, on the basis of the above embodiments, the embodiment of the invention provides a cutter head abrasion and energy consumption optimizing device based on shield tunneling, which is used for executing the cutter head abrasion and energy consumption optimizing method based on shield tunneling in the method embodiment.
As shown in fig. 7, the present invention provides a cutter head abrasion and energy consumption optimizing system based on shield tunneling machine, comprising:
the first main module is used for collecting the operation data of the TBM and preprocessing the data, wherein the operation data comprises adjustable operation parameters and fixed operation parameters; the second main module is used for establishing a TBM efficiency prediction model by adopting a LightGBM based on the preprocessed data, wherein the LightGBM is subjected to super-parameter optimization by Bayesian optimization, and the TBM efficiency prediction model is evaluated by taking the error as a loss function until the TBM efficiency prediction model meeting the preset requirement is obtained; the third main module is used for establishing a multi-objective optimization model based on NSGA-II by taking cutter head abrasion and cutter head energy consumption minimization as targets to obtain a pareto front; and the fourth main module is used for solving the pareto front edge to obtain the optimal adjustable operation parameters of the TBM.
The method of the embodiment of the invention is realized by the electronic equipment, so that the related electronic equipment is necessary to be introduced. To this end, an embodiment of the present invention provides an electronic device, as shown in fig. 8, including: at least one processor (processor), a communication interface (Communications Interface), at least one memory (memory) and a communication bus, wherein the at least one processor, the communication interface, and the at least one memory communicate with each other via the communication bus. The at least one processor may invoke logic instructions in the at least one memory to perform all or part of the steps of the methods provided by the various method embodiments described above.
Further, the logic instructions in at least one of the memories described above may be implemented in the form of a software functional unit and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or may be implemented by hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. Based on this knowledge, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The cutter head abrasion and energy consumption optimization method based on shield tunneling machine is characterized by comprising the following steps of:
acquiring operation data of the TBM and carrying out data preprocessing, wherein the operation data comprises adjustable operation parameters;
based on the preprocessed data, a TBM efficiency prediction model is established by adopting a LightGBM, wherein the LightGBM is subjected to super-parameter optimization by Bayesian optimization, and the TBM efficiency prediction model is evaluated by taking a prediction error as a loss function until the TBM efficiency prediction model meeting the preset requirement is obtained;
establishing a multi-objective optimization model based on NSGA-II by taking cutter wear and cutter energy consumption minimization as objectives to obtain a pareto front;
and solving the pareto front edge to obtain the optimal adjustable operation parameters of the TBM.
2. The method for optimizing cutter head wear and energy consumption based on shield tunneling according to claim 1, wherein the data preprocessing comprises:
determining actual cutter wear according to the operation data of the TBM:
wherein O is 1 The abrasion loss of the single-disc cutting machine; k is the wear coefficient; d is the diameter of the cutting path of the disc cutter; n is the revolving speed of the cutterhead; l is TBM thrust distance; v is the thrust speed of the TBM.
3. The method for optimizing cutter head wear and energy consumption based on shield tunneling according to claim 2, wherein said data preprocessing comprises:
Determining the actual cutter head energy consumption according to the operation data of the TBM:
O 2 ＝E 1 +E 2
wherein O is 2 For total energy consumption of cutterhead, E 1 Energy power consumed for hydraulic thrust system, E 2 Energy power consumed by the knife system, F i Is thrust, T i Is the torque of the cutter head, t i For the time of sequence occurrence, ω i Is the revolving speed of the cutterhead.
4. The method for optimizing cutter head abrasion and energy consumption based on shield tunneling according to claim 1, wherein the super-parameter optimization of LightGBM by bayesian optimization comprises:
adopting a Bayesian optimization algorithm based on a sequential model optimization technology, and performing super-parameter optimization by using a Parzen estimator tree algorithm; the Bayesian optimization algorithm takes EI criteria as a function of an optimization target:
wherein x is a hyper-parameter candidate; y is the output of the objective function; y is * P (y|x) is a proxy model representing the probability of y occurrence given x, which is the threshold of the objective function;
the EI is maximized relative to x by searching for the optimal superparameter combination under proxy function p (y|x).
5. The cutter head abrasion and energy consumption optimization method based on shield tunneling machine according to claim 3, wherein the pre-processing data is used for establishing a TBM efficiency prediction model by adopting a LightGBM, wherein the LightGBM is subjected to super-parameter optimization by Bayesian optimization, and the TBM efficiency prediction model is evaluated by taking a prediction error as a loss function until the TBM efficiency prediction model meeting preset requirements is obtained, and the method comprises the following steps:
Obtaining a prediction error based on the actual cutter wear, the actual cutter energy consumption and a predicted value of a TBM efficiency prediction model;
if the prediction error meets the preset requirement, the TBM efficiency prediction model is trained.
6. The method for optimizing cutter wear and energy consumption based on shield tunneling machine according to claim 1, wherein the establishing a multi-objective optimization model based on NSGA-II with the objective of minimizing cutter wear and cutter energy consumption to obtain pareto leading edge comprises:
determining an optimization target of a multi-target optimization model:
minimizing F (x) = [ F 1 (x)，f 2 (x)，f 3 (x)，...，f m (x)]
Wherein F (x) is based on n independent variables x 1 To x n A set of m objective functions;
determining constraint conditions of the multi-objective optimization model:
g j (x)≤0，j＝1，2，...，k
wherein x is l And x u Is each variable x i Lower and upper limits of (2); g (x) is an inequality constraint;
and obtaining the pareto front based on the optimization target, the constraint condition and the NSGA-II.
7. The method for optimizing cutter head abrasion and energy consumption based on shield tunneling according to claim 1, wherein the step of solving the pareto front to obtain the optimal adjustable operation parameters of the TBM comprises the steps of:
determining the scores of all candidate solutions in the pareto front by adopting an optimal solution quality distance method:
Wherein S is i The value range of the score of the ith solution is [0,1]；
Is the Euclidean distance from the ith solution to the most negative solution,>
is the Euclidean distance from the ith solution to the most ideal solution;
by S i The maximum solution is the optimal solution in the pareto front edge, and the optimal adjustable operation parameters of the TBM are obtained.
8. Cutter head abrasion and energy consumption optimizing system based on shield tunneling machine is characterized by comprising:
the first main module is used for collecting the operation data of the TBM and preprocessing the data, wherein the operation data comprises adjustable operation parameters;
the second main module is used for establishing a TBM efficiency prediction model by adopting a LightGBM based on the preprocessed data, wherein the LightGBM is subjected to super-parameter optimization by Bayesian optimization, and the TBM efficiency prediction model is evaluated by taking the prediction error as a loss function until the TBM efficiency prediction model meeting the preset requirement is obtained;
the third main module is used for establishing a multi-objective optimization model based on NSGA-II by taking cutter head abrasion and cutter head energy consumption minimization as targets to obtain a pareto front;
and the fourth main module is used for solving the pareto front edge to obtain the optimal adjustable operation parameters of the TBM.
9. An electronic terminal, comprising:
At least one processor, at least one memory, a communication interface, and a bus; wherein,,
the processor, the memory and the communication interface complete the communication with each other through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to implement the method of any of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions that cause the computer to implement the method of any one of claims 1-7.
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