CN115268272B - TBM control parameter decision method and device based on tunneling load prediction - Google Patents
TBM control parameter decision method and device based on tunneling load prediction Download PDFInfo
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Abstract
The invention discloses a TBM control parameter decision method and device based on tunneling load prediction. The method comprises the following steps: acquiring a control parameter set value related to a stable section in a circulating section of the current full-section tunnel boring machine; acquiring a control parameter of a set time length before the current time and corresponding response parameter historical data; based on a control parameter set value and the historical data, acquiring a response parameter predicted value corresponding to the control parameter set value according to a preset tunneling load prediction model; meanwhile, collecting the actual value of the response parameter at the current moment; judging whether the current full-face tunnel boring machine reaches a stable boring stage or not based on the predicted value and the actual value of the response parameter; and if the stable tunneling stage is not reached, adjusting the set value of the current control parameter. The method solves the technical problem that the surrounding rock state feedback is lacked in the current TBM tunneling process, and assists in adjusting the control parameters of TBM tunneling by accurately predicting the response parameters of the TBM.
Description
Technical Field
The disclosure relates to data prediction methods, and in particular, to a TBM control parameter decision method and device based on tunneling load prediction.
Background
TBMs have become the sharp tool for long tunnel boring. A complete TBM cycle segment is generally considered to include an idle push segment, a rise segment, a steady segment and a fall segment, wherein the rise segment and the steady segment are closely related to rock breaking. The ascending section rock breaking parameter value is changed rapidly, and the ascending section rock breaking parameter value is an important surrounding rock information sensing stage; the rock breaking parameter value of the stable section generates tiny fluctuation after reaching the stable value, so that the ascending section and the stable section have strong correlation. However, in the existing TBM tunneling process, feedback of the surrounding rock state is lacked, the operation experience of a TBM operator is basically relied on, and no better solution is provided for the situations of driving fatigue of the operator, unskilled operation of a novice operator, complex surrounding rock and the like, so that a tunneling load prediction method needs to be constructed urgently, the surrounding rock information is sensed, and the judgment of the surrounding rock property or the landslide early warning and the like can be realized.
Disclosure of Invention
Aiming at the prior art, the invention solves the technical problem of how to provide a TBM control parameter decision method based on tunneling load prediction, so that the control parameter is optimal, and the TBM can assist in surrounding rock perception.
In order to solve the technical problem, the invention provides a TBM control parameter decision method based on tunneling load prediction, which comprises the following steps:
acquiring a control parameter set value related to a stable section in a circulation section of a current full-face Tunnel Boring Machine (TBM);
acquiring a control parameter of a set time length before the current time and corresponding response parameter historical data;
acquiring a response parameter predicted value corresponding to a control parameter set value according to a preset tunneling load prediction model based on the control parameter set value and the historical data;
meanwhile, collecting the actual value of the response parameter at the current moment;
judging whether the current full-face tunnel boring machine reaches a stable boring stage or not based on the predicted value and the actual value of the response parameter;
if the stable tunneling stage is not reached, adjusting the set value of the current control parameter;
the preset tunneling load prediction model is a model which is trained by adopting a supervised machine learning method and is used for predicting response parameters.
In the technical scheme, the response parameter is the tunneling load and is changed along with the change of the control parameter. The control parameters are set by operators, but the difference between actually acquired control parameter data and set values is small, so that the control parameters and response parameters can be acquired by using sensors for convenient acquisition and processing. By utilizing a machine learning method, the internal timing sequence incidence relation of a control parameter set value, a control parameter and corresponding response parameter historical data is obtained through model training and learning, the accuracy of the response parameter value in the stable tunneling stage is improved, and whether the current TBM reaches the stable tunneling stage is judged based on the response parameter predicted value and the response parameter actual value, so that whether the control parameter is adjusted or not is determined. The control parameters of TBM tunneling are adjusted in an auxiliary mode through accurately predicting TBM response parameters, so that ascending section consumption time is shortened, an efficient stable section rock breaking stage is entered as soon as possible, the tunneling efficiency is improved, and the effect of assisting TBM tunneling is achieved. The problem that the response parameters of the TBM cannot be accurately predicted due to lack of feedback of the surrounding rock state in the current TBM tunneling process is solved.
In the technical scheme, the rock breaking control parameter with a set time length before the current moment and the corresponding response parameter historical data are obtained through the sensor, and the data cleaning is carried out on the historical data, so that the data noise is reduced, the data characteristics of the control parameter and the corresponding response parameter data can truly reflect the control time sequence change, and the prediction accuracy is improved. The data cleaning comprises the steps of processing abnormal values, processing empty values and deleting cycle data with short duration; processing the abnormal value, and if the data volume is 1, replacing the abnormal value by an uplink and downlink data mean value; if the data volume is larger than 1, deleting the rows; processing the empty vacancy value, and if the data volume is 1, filling the empty vacancy value with the mean value of uplink and downlink data; if the data volume is larger than 1, deleting the rows; the short-duration cycle segment data is data of which the data length of a rising segment or a stable segment in the cycle segment of the full-face tunnel boring machine is less than the set time.
In the above technical solution, in one embodiment, the method selects, as basic parameters for the application of the method, key control parameters and response parameters that vary with the control parameters from hundreds of signals collected by the sensor. The control parameters comprise the rotating speed and the propelling speed of the cutter head, and the response parameters comprise the thrust of the cutter head and the torque of the cutter head. In the tunneling process, if a TBM operator changes the rotating speed n (r/min) and the propelling speed v (mm/min) of a cutter head, the cutter head torque T (kN · m) and the cutter head thrust F (kN) acquired by the system can be changed accordingly, the cutter head torque T (kN · m) and the cutter head thrust F (kN) can be used as feedback of the surrounding rock state, and the method can be further used for distinguishing the surrounding rock property, pre-warning the tower square and the like. Therefore, the method provided by the invention further mines the internal correlation and time sequence relationship among the 4 types of parameters in the historical data to obtain the data for reflecting the surrounding rock conditions in front of the tunnel face.
As a further improvement of the above technical solution, before the rock breaking control parameter data is input into a preset tunneling load prediction model, further calculating to obtain: the torque depth index (TPI) mean value, the torque depth index fitting value and the torque depth index fitting goodness, the field depth index (FPI) mean value, the field depth index fitting value and the field depth index fitting goodness are obtained by further calculating, and the data obtained by further calculation is used as the input of the model, so that the prediction precision of the model can be improved. Wherein:
the torque cut depth index (TPI) mean is calculated by:
in the formula: t is i Knife for ith second of rising sectionDisc torque value, p i Is the penetration value of ith second of ascending segment, N1 is the ith data of ascending segment, v i For the advancing speed of the i-th second of the rising section, n i The cutter head rotating speed of the ith second of the ascending section;
the torque cutting depth index fitting value is a slope obtained by fitting the torque and the penetration of the cutter head of the ascending section;
the torque depth of cut index goodness of fit is the goodness of fit of the torque and penetration of the cutter head of the ascending section;
the field depth index (FPI) mean value calculation method comprises the following steps:
in the formula: f i The cutter thrust value of the ith second of the ascending section is obtained;
the on-site cutting depth index fitting value is a slope obtained by fitting the thrust and penetration of the cutter head of the ascending section;
and the fitting goodness of the field cutting depth index is the fitting goodness of the thrust and the penetration of the cutter head of the ascending section.
In the technical scheme, the method deletes the circulation section of the full-section tunnel boring machine with the torque cutting depth index goodness of fit or the field cutting depth index goodness of fit smaller than or equal to the set value in one implementation mode, and inputs the control parameter and response parameter historical data after deletion processing into a preset tunneling load prediction model so as to improve the data quality and improve the prediction effect of the model.
In the above technical solution, in an embodiment, the method of the present invention uses the rotating speed of the cutter head of the stable section and the propelling speed of the stable section as the set values of the control parameters of the stable section.
In the above technical solution, the tunneling load prediction model may be a BP neural network model or a convolutional neural network model.
In order to solve the technical problem, the invention also provides a TBM control parameter decision device based on tunneling load prediction, which comprises a sensor, an acquisition module and a prediction module;
the sensor is used for acquiring control parameter data and corresponding response parameter data;
the acquisition module is configured to acquire a control parameter set value related to a stable section in a current full-face tunnel boring machine circulation section, a control parameter of a set time length before the current time and corresponding response parameter historical data;
the prediction module is configured to obtain a response parameter prediction value corresponding to a control parameter set value according to a preset tunneling load prediction model based on the control parameter set value, the control parameter and response parameter historical data corresponding to the control parameter set value;
judging whether the current full-face tunnel boring machine reaches a stable boring stage or not based on the predicted value and the actual value of the response parameter;
if the stable tunneling stage is not reached, adjusting the currently set control parameters;
the preset tunneling load prediction model is a model which is trained by adopting a supervised machine learning method and is used for predicting response parameters.
In the technical scheme of the device, in order to further improve the prediction accuracy of the model, a data relationship between a control parameter and a response parameter is firstly obtained, namely a torque depth index (TPI) mean value, a torque depth index fitting value and a torque depth index fitting goodness of fit, a field depth index (FPI) mean value, a field depth index fitting value and a field depth index fitting goodness of fit are obtained by calculation based on the control parameter and the response parameter, output data are response parameter mean values, and the data are used as input data of the tunneling load prediction model;
the control parameters comprise the rotating speed and the propelling speed of the cutter head, and the response parameters comprise the torque and the propelling force of the cutter head;
the torque cut depth index (TPI) mean is calculated by:
in the formula: t is i For the cutter torque value of the ith second of the rise, p i Is the penetration value of the ith second of the ascending section, N1 is the number of seconds of the ascending section, v i For the advancing speed of the i-th second of the rising section, n i The cutter head rotating speed of the ith second of the ascending section;
the torque cutting depth index fitting value is a slope obtained by fitting the torque and the penetration of the cutter head of the ascending section;
the torque depth of cut index goodness of fit is the goodness of fit of the torque and penetration of the cutter head of the ascending section;
the field depth index (FPI) mean value calculation method comprises the following steps:
in the formula: f i The thrust value of the cutter head at the ith second of the ascending section;
the on-site cutting depth index fitting value is a slope obtained by fitting the thrust and penetration of the cutter head of the ascending section;
and the fitting goodness of the on-site cutting depth index is the fitting goodness of the thrust and the penetration of the cutter head of the ascending section.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a schematic diagram of a BP neural network structure in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a data cleaning process according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating calculation of a TPI fitting value, a TPI goodness of fit, a FPI fitting value and a FPI goodness of fit in an embodiment of the invention;
FIG. 4 is a schematic diagram of input and output of model features according to an embodiment of the present invention;
FIG. 5 is a diagram showing the thrust prediction effect of the cutter head in the embodiment of the invention;
FIG. 6 is a graph showing the predicted effect of cutter head torque in an embodiment of the present invention;
fig. 7 is a schematic flow chart of a TBM control parameter decision method based on tunneling load prediction according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a TBM control parameter decision-making device based on tunneling load prediction according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The abbreviations involved have the following meanings:
TBM, full-face tunnel boring machine;
TPI, torque cut depth index;
FPI, field cutting depth index.
In the embodiment of the invention, a complete TBM circulation section means that after a cutterhead is started, a hob starts to contact a tunnel face to break rocks, and after the TBM propelling distance reaches the maximum stroke of an oil cylinder, the tunneling is stopped, and the oil cylinder is started to contract to prepare for the tunneling of the next circulation section. A complete cycle section comprises four stages of an idle push section, an ascending section, a stable section and a descending section.
A hollow pushing section in a TBM circulation section: the cutter head starts to rotate, the rotating speed and the propelling speed of the cutter head start to slowly increase, the cutter head propels towards the direction of the tunnel face, at the moment, the torque needs to overcome the internal resistance of the cutter head, the friction force between the cutter head and the tunnel wall and the angular acceleration generated by the rotation of the cutter head and is slowly increased along with the internal resistance, the friction force between the cutter head and the tunnel wall, and the propelling force of the cutter head needs to overcome the shield friction and the dragging force of a rear matching system is slowly increased along with the internal resistance; the rotating speed of the cutter head tends to be stable, and the angular acceleration generated by starting and rotating the cutter head does not need to be overcome, so that the torque of the cutter head tends to be slightly reduced at the stage; and after the rotating speed of the cutter head is stable, starting to enter the ascending section.
Ascending section in TBM cycle section: in the interaction stage of the rock machine, the cutter head is in contact with the face, and compared with the cutter head in the idle pushing section, the resistance of the face is required to be overcome, and the thrust of the cutter head, the torque of the cutter head, the propelling speed, the rotating speed of the cutter head and the like are increased sharply; the phase is similar to a large rock torque test and can basically reflect geological conditions and surrounding rock properties, the propelling stroke of the phase is 100-200mm, the duration is about 220s, and then the phase enters a stable section.
Stabilization in TBM cycle: in the stage, the TBM starts to stably tunnel, each index starts to tend to enter a stable state and fluctuates around a certain numerical value, and in the stage, if no surrounding rock property is mutated, stable tunneling is finished until a circulation section;
descending section in TBM circulation section: when the circulation tunneling reaches the stroke of the propulsion oil cylinder, the machine needs to be stopped and replaced, the thrust of the cutterhead, the torque of the cutterhead, the propulsion speed, the rotating speed of the cutterhead and the like at the stage begin to drop sharply until the thrust, the torque of the cutterhead, the propulsion speed, the rotating speed of the cutterhead and the like reach zero, and the machine begins to replace steps and enters the tunneling of the next circulation section.
The judgment basis of the TBM ascending section is as follows: according to the fact that the cutter thrust F and the cutter torque T are larger than the friction resistance F f And idling torque T f The threshold is used as a preliminary criterion for the starting point of the ascending segment, and is shown as the following formula:
in the formula: f f 、T f A frictional resistance threshold and an idle torque threshold, respectively, may be obtained from the TBM device.
The basis for judging the characteristics of the stable section is as follows: in the real-time data processing process, the fluctuation of a set value of the propelling speed v of the ascending section is large, and the standard deviation sigma is also large; whereas the propulsion speed v at the stationary section is set to a value with low volatility, so that the standard deviation sigma remains substantially constant. The set value of the propelling speed v is an adjusting parameter of an operatorBy adjusting the set value of the propulsion speed v, the propulsion speed v can be set. Counting the standard deviation range of the set value of the propulsion speed v of the circulation section in the stable maintaining process, and using the standard deviation range as the standard sigma of the real-time data division of the starting point of the stable section of the ascending section entering the stable tunneling process s Is shown as the formula:
σ≤σ s
wherein:
in the formula: v. of i In order to set the propulsion speed,
the mean value is set for the propulsion speed and N is the number of samples.
From the above, it can be seen that some parameters have phase change characteristics, which can be used to determine the phase of the TBM. By further analyzing the parameters of each stage, it can be known that these parameters with the characteristics of stage change vary with other parameters.
In one embodiment, the cutter head rotation speed and the propulsion speed are selected as control parameters, and the cutter head torque and the cutter head thrust are selected as response parameters. The response parameters, the control parameters and the penetration degree obtained by calculating the control parameters are used as key tunneling parameters of the invention, and the meanings of the parameters are shown in table 1.
TABLE 1
The method has the idea that the value of the response parameter is predicted based on the key tunneling parameter, so that the TBM stage or whether the TBM stage reaches a stable stage is judged by comparing the predicted value and the actual value of the response parameter. If the predicted value and the actual value of the response parameter are approximately the same for the set control parameter, the TBM is indicated to have reached the expected stage.
In the following, the method according to the invention is described by way of example using the kinson engineering data. The total length of a four-section TBM3 (Yongji number) total length 19771m (starting mileage K71+476 and ending mileage K51+ 705) of a water supply engineering main line is guided by a middle city of Jilin province, 199 series of historical data including pile numbers, cutter torque, cutter thrust, cutter rotating speed, propelling speed, penetration degree and the like are stored at a frequency of 1Hz by means of a national 973 project, construction data of different working conditions in nearly eight hundred days are completely recorded, more than 200 hundred million pieces of big data are formed after tunneling is finished, and convenience is brought to machine learning. And selecting a BP neural network model as a tunneling load prediction model.
In order to improve the prediction accuracy of the tunneling load prediction model and better reflect the internal association and time sequence relation of historical data, 8 input indexes are obtained by further calculating the historical data, and response parameters are used as labels for learning. The 8 input indexes are a TPI mean value, a TPI fitting value, TPI goodness of fit, an FPI mean value, an FPI fitting value, an FPI goodness of fit, a cutter head rotating speed mean value and a propulsion speed mean value; the label is the cutter torque mean value and the cutter thrust mean value. During training, the average value of the rotation speed of the cutter head and the average value of the propulsion speed are calculated and obtained based on historical data; and during prediction, the average value of the rotating speed of the cutter head and the average value of the propelling speed are set values.
(1) And establishing a BP neural network model as shown in figure 1. The model comprises: 1 input layer, 1 hidden layer and 1 output layer. Input layer node { x 1 ,x 2 ,…,x 8 8, 17 hidden layer nodes and 2 output layer nodes. A training rate of 0.8 was set, i.e., 80% of the data to be obtained was used as training data, and the remaining 20% was used as test data.
And setting a training termination condition as the iteration number or a target threshold value. When a complete training set passes through the neural network once and back once, the process is called an iteration. In the present embodiment, the number of iterations 2000 is set. Mean Square Error (MSE) is used to evaluate the difference between the target output and the actual output of the BP neural network. In order to reduce the mean square error below the target threshold, a gradient descent method may be used, that is, the weight of each sample is changed to the negative gradient direction, that is, the mean square error is graded for the weights of the neurons. In this embodiment, the target threshold is 5%. In other embodiments, the target threshold is 10%, 9%, 8%, 7%, 6%, 4%, 3%, 2%, or 1%.
The activation function is set to: the relu function.
The weights and biases of the input layer to the hidden layer and the weights and biases of the hidden layer to the output layer are initialized to random values. When the BP neural network model is trained by different network parameters to be converged and the mean square error is smaller than the target threshold, the BP neural network model under the parameters is credible. In other words, the current configuration (e.g., weights, bias) of the trained BP neural network model can truly and effectively reflect the influence of the input (e.g., rock breaking control parameter data) on the output (e.g., tunneling load). Based on the configuration of the BP neural network model at this time, a derivation value (e.g., a slope) of the output to the input of the network is obtained, so that a quantitative conclusion of the influence of the input to the output can be obtained.
(2) And dividing the data to obtain 12884 cycle sections in total, deleting the ascending section or the cycle section with the stable section data less than 30s, regarding the cycle section as an incomplete cycle tunneling process, and adjusting the size of 30 s. Then, the abnormal values and the vacancy values were subjected to data cleaning processing, and 12501 loop segments were left.
The data cleaning process is shown in fig. 2. Abnormal value processing refers to that a negative value caused by contraction of a propulsion oil cylinder and an ultrahigh value caused by sensor failure are replaced by an uplink and downlink data mean value when the data volume is 1; and if the data volume is larger than or equal to 1, deleting the rows. Outliers include negative and ultrahigh values. In one embodiment, the determination is made by the propulsion speed, and when the propulsion speed is greater than 150mm/min, the value is considered to be the ultra-high value. The vacancy value processing refers to data which are failed to be acquired due to sensor failure, and the data are filled by the mean value of uplink data and downlink data when the data volume is 1; and if the data volume is larger than or equal to 1, deleting the rows. In one embodiment, the data acquisition frequency of the TBM device is 1Hz, i.e. 1s of data is acquired once. In the data acquired at one time, the data volume of the missing data can be judged through the time stamp.
(3) And calculating the cycle segment data to obtain partial input X1, X1= (TPI mean value, TPI fitting value, TPI goodness of fit, FPI mean value, FPI fitting value and FPI goodness of fit) for training the BP neural network model. The TPI mean value calculation method comprises the following steps:
wherein, T i For the cutter torque value of the ith second of the rise, p i Is the penetration value of the ith second of the ascending section, N1 is the ith data of the ascending section, v i For the advancing speed of the ascending section in the ith second, n i The rotating speed of the cutter head in the ith second of the ascending section.
The TPI fitting value is the slope obtained by fitting the torque and the penetration of the cutter head of the ascending section; the fitting goodness of the TPI is the fitting goodness of the torque and the penetration of the cutter head of the ascending section.
The FPI mean value calculation method comprises the following steps:
wherein, F l The cutter disc thrust F value of the ith second of the ascending section is shown.
The FPI fitting value is a slope obtained by fitting the thrust and penetration of the cutter head of the ascending section; the fitting goodness of the FPI is the fitting goodness of the thrust and the penetration of the cutter head of the ascending section.
The first 30X 1 input values from the first 30 cycle data are shown in table 2.
TABLE 2
(4) And deleting the cycle sections with the TPI goodness-of-fit or FPI goodness-of-fit less than or equal to 0.5. As shown in FIG. 3, the goodness of fit R of the middle cutter torque 2 Is 0.5, so the loop segment needs to be deleted.
(5) And (4) calculating and processing the stable segment data of the circular segment to obtain part of input X2, X2= (cutter head rotating speed mean value, propulsion speed mean value) for training the BP neural network model. The first 30X 2 input values from the first 30 cycle data are shown in table 3.
TABLE 3
(6) And calculating the output Y = [ Y1, Y2] of the BP neural network model, wherein Y1 is the cutter torque mean value, Y2 is the cutter thrust mean value, and Y is used as a training label. The first 30 sets [ Y1, Y2] for the first 30 loop segment data are shown in Table 4.
TABLE 4
(7) X = [ X1, X2] as input of the BP neural network model, and Y = [ Y1, Y2] as input thereof, as shown in fig. 4. 80% of the data of the loop segment was used for training and 20% for testing, and the test results are shown in fig. 5-6. As can be seen from fig. 5 and 6, in the test, the BP neural network model has a good prediction effect, the predicted value is close to the actual value, and the predicted value can be used for helping the TBM operator to judge whether the current full-face tunnel boring machine reaches the stable boring stage, and whether to adjust the control parameter is determined according to the judgment result, so as to achieve the purpose of assisting the driving of the TBM.
The above is the training process for the BP neural network model. The input of the BP neural network model in the training process is data collected in real time. The output is a recorded set value or a real-time acquisition value, and then the average value is calculated. The trained BP neural network model can be used as a preset tunneling load prediction model.
When the BP neural network model is used, the input of the BP neural network model is data collected in real time, and the input data related to the stable section is a set value. The predicted tunnelling load index value is obtained as shown in figure 7 using the following steps:
acquiring a control parameter set value related to a stable section in a circulation section of a current full-face Tunnel Boring Machine (TBM);
acquiring a control parameter of a set time length before the current time and corresponding response parameter historical data;
acquiring a response parameter predicted value corresponding to a control parameter set value according to a preset tunneling load prediction model based on the control parameter set value and the historical data;
meanwhile, collecting the actual value of the response parameter at the current moment;
judging whether the current full-face tunnel boring machine reaches a stable boring stage or not based on the predicted value and the actual value of the response parameter; and if the stable tunneling stage is not reached, adjusting the set value of the current control parameter. By accurately predicting the response parameters of the TBM and assisting in adjusting the control parameters of TBM tunneling, the ascending section consumption time is shortened, and an efficient stable section rock breaking stage is entered as soon as possible, so that the effect of assisting in TBM tunneling is achieved. The prediction result of the response parameters can be further used for distinguishing the properties of the surrounding rocks, warning collapse and the like. The BP neural network model may be replaced with other machine models, such as a convolutional neural network model.
In the method, the predicted values of the cutterhead torque and the cutterhead thrust do not exceed rated values of TBM equipment about the cutterhead torque and the cutterhead thrust to the maximum extent, and when the errors of the predicted values and the actual measured values of the cutterhead torque and the cutterhead thrust do not exceed 10% and reach more than 20s, the current circulation section is considered to have reached a stable tunneling stage.
In the TBM operation process, if surrounding rocks become hard or broken, the torque thrust is generally changed in the same direction, namely the rotating speed of the cutter head is in positive correlation with the torque of the cutter head, and the propelling speed is in positive correlation with the thrust of the cutter head. If the predicted values are all larger than the actual values, the stable section is not reached, and the accelerator needs to be continuously increased to reach the stable section as soon as possible. Otherwise, it indicates that the operation is too fast, which affects the equipment life cycle and the tool life. However, the situation that the prediction of the thrust of the cutter head is larger than the actual measurement, and the prediction of the torque of the cutter head is smaller than the actual measurement, or the prediction of the thrust of the cutter head is smaller than the actual measurement, and the prediction of the torque of the cutter head is larger than the actual measurement exists. For the former, the actual measurement of torque is too large and the actual measurement of thrust is relatively small due to the fact that the cutter head rotating speed is independently adjusted to be too large, and a driver is reminded to slow down the rotating speed of the cutter head at this time; for the latter, it may be that the actual measurement of the thrust is too large and the actual measurement of the torque is relatively small only by adjusting the propulsion speed knob too large alone, and at this time, the driver should be reminded to slow down the propulsion speed; and in the process of crushing the surrounding rock, the thrust and the torque of the cutterhead are not very large. In addition, the starting time of the stable section is uncertain, a driver can reach the stable value as soon as possible by adjusting the set values of the rotating speed and the propelling speed of the cutter head of the ascending section, and in the process, the driver operates according to experience, so that the driver is a test for a novice driver. Therefore, one implementation of the method of the invention is to help a novice driver to judge whether the current stable section is reached through the predicted value, assist the TBM in tunneling, reduce equipment damage and the like.
Correspondingly, the method can be implemented as a TBM control parameter decision device, the structure of which is shown in fig. 8, and the device includes a sensor, an acquisition module and a prediction module. And the sensor is used for acquiring control parameter data and corresponding response parameter data. The acquisition module is configured to acquire a control parameter set value related to a stable section in the current full-face tunnel boring machine circulation section, a control parameter of a set time period before the current time and corresponding response parameter historical data. The prediction module is configured to obtain a response parameter prediction value corresponding to a control parameter set value according to a preset tunneling load prediction model based on the control parameter set value, the control parameter and the corresponding response parameter historical data. And judging whether the current full-face tunnel boring machine reaches a stable boring stage or not based on the predicted value and the actual value of the response parameter, and if not, adjusting the currently set control parameter. The tunneling load prediction model is a model for predicting the tunneling load, which is trained by adopting a supervised machine learning method, input data of the model are a torque depth index (TPI) mean value, a torque depth index fitting value and a torque depth index goodness of fit, a field depth index (FPI) mean value, a field depth index fitting value and a field depth index goodness of fit, which are obtained by calculation based on control parameters and response parameters, and output data of the model are response parameter mean values. The control parameters comprise the rotating speed and the propelling speed of the cutter head, and the response parameters comprise the torque and the thrust of the cutter head. The calculation of the input data is the same as that of the above method solution. The units involved in the device can be realized by a processor, an FPGA or other special hardware.
In summary, the present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as a punch card or an in-groove protruding structure with instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + +, python, or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Claims (7)
1. A TBM control parameter decision method based on tunneling load prediction is characterized by comprising the following steps:
acquiring a control parameter set value related to a stable section in a circulation section of a current full-face Tunnel Boring Machine (TBM);
acquiring a control parameter of a set time length before the current time and historical data of a corresponding response parameter;
based on a control parameter set value and the historical data, acquiring a response parameter predicted value corresponding to the control parameter set value according to a preset tunneling load prediction model;
meanwhile, collecting the actual value of the response parameter at the current moment;
judging whether the current full-face tunnel boring machine reaches a stable boring stage or not based on the predicted value and the actual value of the response parameter;
if the stable tunneling stage is not reached, adjusting the set value of the current control parameter;
the preset tunneling load prediction model is a model which is trained by adopting a supervised machine learning method and is used for predicting response parameters;
the control parameters comprise the rotating speed and the propelling speed of the cutter head; the response parameters comprise cutter torque and cutter thrust;
the input data of the tunneling load prediction model comprises:
torque cut depth index (TPI) mean, torque cut depth index fitting value, torque cut depth index goodness of fit, field cut depth index (FPI) mean, field cut depth index fitting value, field cut depth index goodness of fit; wherein:
the torque cut depth index (TPI) mean is calculated by:
in the formula: t is i For the cutter torque value of the ith second of the rise, p i Is the penetration value of the ith second of the ascending section, N1 is the number of seconds of the ascending section, v i For the advancing speed of the ascending section in the ith second, n i The cutter head rotating speed of the ith second of the ascending section;
the torque cutting depth index fitting value is a slope obtained by fitting the torque and the penetration of the cutter head of the ascending section;
the torque depth of cut index goodness of fit is the goodness of fit of the torque and penetration of the cutter head of the ascending section;
the field depth index (FPI) mean value calculation method comprises the following steps:
in the formula: f i The thrust value of the cutter head at the ith second of the ascending section;
the on-site cutting depth index fitting value is a slope obtained by fitting the thrust and penetration of the cutter head of the ascending section;
and the fitting goodness of the on-site cutting depth index is the fitting goodness of the thrust and the penetration of the cutter head of the ascending section.
2. The method according to claim 1, wherein the obtaining of the control parameter of a set duration before the current time and the corresponding historical data of the response parameter comprises data cleaning;
the data cleaning comprises the steps of processing abnormal values, processing empty values and deleting cycle data with short duration;
processing the abnormal value, and if the data volume is 1, replacing the abnormal value by an uplink and downlink data mean value; if the data volume is larger than 1, deleting the rows;
processing the empty values, and if the data volume is 1, filling the empty values with the mean value of the uplink data and the downlink data; if the data volume is larger than 1, deleting the rows;
the short-duration cycle segment data is data of which the data length of an ascending segment or a stable segment in the cycle segment of the full-face tunnel boring machine is smaller than a set time length.
3. The method as claimed in claim 1, wherein the tunneling load prediction model is trained and learned by using a response parameter mean value as a label.
4. The method according to claim 1, wherein the control parameters and the corresponding response parameter historical data further comprise the following data processing before inputting the preset tunneling load prediction model:
and deleting the circulating section of the full-section tunnel boring machine, wherein the torque cutting depth index goodness of fit in the circulating section of the full-section tunnel boring machine or the field cutting depth index goodness of fit is less than or equal to a set value.
5. The method of claim 1, wherein the control parameter and response parameter are obtained by a sensor.
6. The method of claim 1, wherein the tunneling load prediction model is a BP neural network model or a convolutional neural network model.
7. A TBM control parameter decision-making device based on tunneling load prediction comprises a sensor, and is characterized by further comprising an acquisition module and a prediction module;
the sensor is used for acquiring control parameter data and corresponding response parameter data;
the acquisition module is configured to acquire a control parameter set value related to a stable section in a current full-section tunnel boring machine circulating section, a control parameter of a set time length before the current time and corresponding response parameter historical data;
the prediction module is configured to obtain a response parameter prediction value corresponding to a control parameter set value according to a preset tunneling load prediction model based on the control parameter set value and the historical data;
judging whether the current full-face tunnel boring machine reaches a stable boring stage or not based on the predicted value and the actual value of the response parameter;
if the stable tunneling stage is not reached, adjusting the set value of the currently set control parameter;
the preset tunneling load prediction model is a model which is trained by adopting a supervised machine learning method and is used for predicting response parameters;
the input data of the tunneling load prediction model are a torque depth index (TPI) mean value, a torque depth index fitting value and a torque depth index goodness of fit, a field depth index (FPI) mean value, a field depth index fitting value and a field depth index goodness of fit, which are calculated based on control parameters and response parameters, and the output data are response parameter mean values;
the control parameters comprise the rotating speed and the propelling speed of the cutter head, and the response parameters comprise the torque and the propelling force of the cutter head;
the torque cut depth index (TPI) mean is calculated by:
in the formula: t is i For the cutter torque value of the ith second of the rise, p i Is the penetration value of the ith second of the ascending section, N1 is the number of seconds of the ascending section, v i For the advancing speed of the ascending section in the ith second, n i The cutter head rotating speed of the ith second of the ascending section;
the torque cutting depth index fitting value is a slope obtained by fitting the torque and the penetration of the cutter head of the ascending section;
the torque depth of cut index goodness of fit is the goodness of fit of the torque and penetration of the cutter head of the ascending section;
the field depth index (FPI) mean value calculation method comprises the following steps:
in the formula: f i The cutter thrust value of the ith second of the ascending section is obtained;
the on-site cutting depth index fitting value is a slope obtained by fitting the thrust and penetration of the cutter head of the ascending section;
and the fitting goodness of the on-site cutting depth index is the fitting goodness of the thrust and the penetration of the cutter head of the ascending section.
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