Soil pressure dynamic characteristic modeling method, shield tunneling machine control system and shield tunneling machine
1. A soil pressure dynamic characteristic modeling method is characterized by comprising the following steps:
the method comprises the following steps: collecting historical input and output data of a shield machine control system as identification data;
step two: determining an ARX model structure of the earth pressure dynamic characteristic of an earth cabin of an earth pressure balance shield machine; the dynamic ARX model of the soil pressure of the shield machine takes the actual value of the soil pressure in the soil cabin as an output signal, takes a screw machine rotating speed signal as a control input, and takes the propelling speed and the total propelling force of the shield machine as intermediate variables;
step three: and constructing an optimization model of soil pressure ARX model parameters.
2. The modeling method for the dynamic characteristics of the earth pressure according to claim 1, wherein the ARX model of the earth pressure system of the earth pressure balance shield machine has the structure that:
in the formula: y is0To be offset, a1～any、b1～bnu、c1～cndIs a regression coefficient matrix; ny, nu, nd represent the model order; y (k), y (k-1) and y (k-ny) are respectively the kth sampling point, the kth-1 sampling point and the kth-nThe soil pressure measured values of y sampling points, and k is the kth sampling point; u is a screw machine rotating speed signal, f is more than or equal to 1 and is the pure lag time of u, and g is more than or equal to 1 and is the pure lag time of d; d (k-g), d (k-g-1) and d (k-g-nd +1) are intermediate variables of the k-g sampling moment, the k-g-1 sampling moment and the k-g-nd +1 sampling moment of the propulsion process respectively, and epsilon (k) is a modeling error.
3. The modeling method for dynamic characteristics of soil pressure according to claim 2, wherein the identification data in the first step comprises soil pressure of a soil cabin, speed of a screw machine, propulsion speed and total propulsion force, and a data sampling period is set to be 1 second; and selecting a plurality of data of I continuous working sections covering various earth pressure change modes from historical data of the shield machine as earth pressure ARX modeling data of the shield machine.
4. The earth pressure dynamic characteristic modeling method according to claim 3, characterized in that the earth pressure ARX modeling data of the shield tunneling machine is yi(1)～yi(Ni)、ui(1)～ui(Ni)、di(1)～di(Ni) I is 1,2, …, I, where I is the number of the ith continuous push operation data segment in the data set, I is the number of data segments included in the data set, NiIs the length of the ith data, yi(1)、yi(Ni) Respectively representing the first and Nth of the ith data in the windowiIndividual data of soil pressure measurement data of soil chamber ui(1)、ui(Ni) Respectively representing the first and Nth of the ith data in the windowiSpeed signal of the screw machine of individual data, di(1)、di(Ni) Respectively representing the first and Nth of the ith data in the windowiThe soil pressure intermediate variable of the soil chamber of each datum.
5. The modeling method of dynamic characteristics of soil pressure according to claim 4, wherein θ (y) is set0,a1,a2,...,any,b1,b2,...,bnu,c1,c2,...,cnd),cl＝(c1l,c2l) 1,2, …, nd is the parameter set to be identified, and l is the current order of the intermediate variable of the soil pressure of the soil cabin; calculating the q of the soil pressure by taking the m-th data corresponding time of the i-th section as a starting point according to the formula (1) and the ARX modeling dataiForward step prediction output
Wherein: theta is the transpose of the parameter set to be recognized, Xi,mRepresents a set of variables corresponding to the parameter set θ to be recognized in equation (1).
6. The method according to claim 5, wherein X isi,mThe expression of (a) is formula (3); the expression of theta is formula (4);
Θ＝θT (4)
wherein: n is max ([ ny, f + nu, g + nd)]),n＜Ni (5)
qi＝min(Np,Ni-m) (6)
In the formula (3), the reaction mixture is,
7. the modeling method of dynamic characteristics of soil pressure according to claim 6,
the third step is specifically as follows: estimating a parameter theta of the soil pressure model by minimizing the mean square error of the soil pressure and the actual soil pressure value Y by forward prediction in multiple steps, and adding a stability condition of the soil pressure model; adding step response mode constraint and model static amplification coefficient constraint, and constructing an ARX model parameter optimization model as a formula (7):
s.t.
max{|s1|,|s2|,…,|snymodel stability condition of | } < 1
yustep(k)≤0,k＝1,2,…,NstepStep response mode constraints
yd1step(k)≥0,k＝1,2,…,Nstep
yd2step(k)≥0,k＝1,2,…,Nstep
Static magnification factor constraint
Wherein:is the total number of predicted outputs; s1,s2,…,snyIs a characteristic root of the soil compaction ARX model; yu (yu of China)step、yd1step、yd2stepRespectively, the unit step response between the screw speed, the propelling speed and the total propelling force calculated based on the ARX model, NstepIs unit step response step length;k 1, k 2, k 3,are respectively earth pressure phaseConstraining the minimum value and the maximum value of the static amplification coefficients of the rotating speed, the propelling speed and the total propelling force of the screw machine; c. C1l,c2lThe regression coefficients of the propulsion speed and the total propulsion variable in the model (1) are respectively;
Yi,m＝(Yi(m+1),Yi(m+2),...,Yi(m+qi))T (8)
for the m +1 th to m + q th data in the ith sectioniAnd the vector is formed by actual measurement data of the soil pressure of each soil chamber.
8. The modeling method for dynamic characteristics of soil pressure according to claim 7, wherein the order (ny, nu, nd) of the earth pressure ARX model of the soil cabin is estimated by minimizing the mean square error of the earth pressure and the actual value Y of the earth pressure predicted forward in multiple steps:
s.t. constraint homomorphic (7)
9. A shield machine control system is characterized by comprising a data acquisition module, a controller, a model training module and a prediction control module;
the data acquisition module is used for acquiring real-time shield tunneling machine propulsion data;
the controller is used for processing the data acquired by the data acquisition module to obtain an actual value of soil pressure in the soil cabin, a rotating speed signal of the screw machine, the propelling speed of the shield machine and the total propelling force;
the model training module is used for learning the earth pressure dynamic characteristic modeling method according to any one of claims 1-8;
and the prediction control module is used for storing the dynamic soil pressure characteristic ARX model after the model training module finishes training, outputting a predicted soil pressure value of the soil cabin and feeding back the predicted soil pressure value to the controller.
10. A shield machine comprises a shield machine body, a propelling mechanism and a screw machine, wherein the propelling mechanism and the screw machine are arranged on the shield machine body; the spiral machine is connected with the soil cabin of the shield machine body and used for outputting the muck in the soil cabin; wherein the shield machine further comprises the control system of claim 9;
a soil cabin pressure sensor connected with the controller is arranged in the soil cabin and used for acquiring an actual value of soil pressure in the soil cabin;
a proximity switch connected with the controller is arranged in the screw machine and used for acquiring a rotating speed signal of the screw machine;
and the pressure sensor and the oil cylinder stroke sensor which are connected with the controller in the propelling mechanism are used for acquiring the propelling speed and the total propelling force of the shield tunneling machine.
Background
The sealed cabin soil pressure control system is an important subsystem in a soil pressure balance shield machine control system, is a dynamic working system in the whole transmission space from the shield excavation surface, the sealed cabin to the outlet of the screw conveyer, is an important component of the shield machine, and has the double functions of maintaining the stability of the excavation surface and conveying muck. When the shield machine is pushed forward by the pushing hydraulic cylinder, rock soil cut by the cutter head is modified to fill all spaces in the sealed cabin and the spiral conveyor shell, and meanwhile, the water and soil pressure of the stratum of the excavation surface is balanced by means of the filled modified soil. The soil discharge amount can be controlled by adjusting the rotating speed of the screw conveyor or the soil feeding amount can be controlled by adjusting the propelling speed of the shield machine propelling hydraulic cylinder, so that the soil discharge amount and the soil feeding amount of the shield machine are kept or close to balance, and the stability of the stratum of the excavation surface is maintained and the deformation of the earth surface is prevented. Therefore, in the shield construction process, the sealed cabin plays an important role in maintaining the stability of the excavation surface and conveying the modified soil. The construction control parameters of the system comprise the propelling speed, the rotating speed of the screw conveyer and the like, and the setting of the parameters directly determines the soil pressure in the sealed cabin.
Many researchers have carried out experimental research and theoretical analysis on the earth pressure balance principle of the earth pressure balance shield machine and the earth pressure modeling method, and the modeling method can be mainly divided into modeling based on a mechanism model and an intelligent means. The research based on the mechanism model comprises a mechanical analysis method and a numerical analysis method, wherein the mechanical analysis method is mainly used for calculating the soil pressure distribution based on mechanical balance. Numerical analysis is carried out on the relation between the soil pressure and the shield machine operation parameters (such as the speed of a spiral conveyor, the speed of a cutter head, the propelling speed of the shield machine and the like) by adopting numerical simulation methods such as finite elements and the like. The research on the relation between the pressure of the sealed cabin and the tunneling parameter in the prior literature is based on qualitative analysis and theoretical derivation. The research results are generally obtained under certain assumed and ideal conditions, and provide a good theoretical basis for the research of the stability of the heading face. However, due to the random characteristics of the stratum, the pressure control of the sealed cabin lacks a complete quantitative relation model between the soil pressure and the control variable, the stability of the pressure control of the sealed cabin is poor, and a sufficient theoretical basis cannot be provided for setting the relevant operation parameters of the shield machine required for controlling the pressure of the sealed cabin, so that the partition plate observation pressure setting, the adjustment of the rotating speed of the screw machine and the adjustment of the propelling speed under different stratum conditions are mainly operated by the experience of a shield construction unit. The numerical difference adopted by different construction units is large, and the construction quality is difficult to guarantee.
In view of the important role of the soil pressure on keeping the stability of the tunneling surface and controlling the deformation of the ground, the invention considers a dynamic property modeling method of the soil pressure balance shield machine with dynamic mode constraint, establishes a dynamic model for the dynamic change relation between the pressure of the sealed cabin and the tunneling parameters, and can guide the adjustment of the tunneling parameters by using the model or design an automatic soil pressure control algorithm based on the model to lay a technical foundation for effectively controlling the pressure balance of the sealed cabin of the shield machine in the tunneling process.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method aims at the problems that a shield machine earth pressure control system model established by using a mechanics and numerical analysis method is mainly based on assumptions which are possibly different from actual construction process conditions, and the performance of the model is highly dependent on the precision of physical parameters and is easily influenced by external environment.
In order to solve the technical problem, the invention provides a soil pressure dynamic characteristic modeling method, which comprises the following steps:
the method comprises the following steps: collecting historical input and output data of the control system as identification data;
step two: determining an ARX model structure of the earth pressure dynamic characteristic of an earth cabin of an earth pressure balance shield machine; the dynamic ARX model of the soil pressure of the shield machine takes the actual value of the soil pressure in the soil cabin as an output signal, takes a screw machine rotating speed signal as a control input, and takes the propelling speed and the total propelling force of the shield machine as intermediate variables;
step three: and constructing an optimization model of soil pressure ARX model parameters.
Further, the structure of the ARX model of the earth pressure balance shield machine earth pressure system is as follows:
in the formula: y is0To be offset, a1～any、b1～bnu、c1～cndIs a regression coefficient matrix; ny, nu, nd represent the model order; y (k), y (k-1) and y (k-ny) are respectively the soil pressure measurement values of the kth sampling point, the kth sampling point and the kth sampling point, and k is the kth sampling point; u is a screw machine rotating speed signal, f is more than or equal to 1 and is the pure lag time of u, and g is more than or equal to 1 and is the pure lag time of d; d (k-g), d (k-g-1) and d (k-g-nd +1) are intermediate variables of the k-g sampling moment, the k-g-1 sampling moment and the k-g-nd +1 sampling moment of the propulsion process respectively, and epsilon (k) is a modeling error.
Further, the identification data in the first step comprise soil pressure of the soil cabin, speed of the screw machine, propelling speed and total propelling force, and a data sampling period is set to be 1 second; and selecting a plurality of data of I continuous working sections covering various earth pressure change modes from historical data of the shield machine as earth pressure ARX modeling data of the shield machine.
Further, the earth pressure ARX modeling data of the shield tunneling machine is yi(1)～yi(Ni)、ui(1)～ui(Ni)、di(1)～di(Ni) I is 1,2, …, I, where I is the number of the ith continuous push operation data segment in the data set, I is the number of data segments included in the data set, NiIs the length of the ith data, yi(1)、yi(Ni) Respectively representing the first and Nth of the ith data in the windowiIndividual data of soil pressure measurement data of soil chamber ui(1)、ui(Ni) Respectively representing the first and Nth of the ith data in the windowiSpeed signal of the screw machine of individual data, di(1)、di(Ni) Respectively representing the first and Nth of the ith data in the windowiThe soil pressure intermediate variable of the soil chamber of each datum.
Further, let θ ═ y0,a1,a2,...,any,b1,b2,...,bnu,c1,c2,...,cnd),cl＝(c1l,c2l) 1,2, …, nd is the parameter set to be identified, and l is the current order of the intermediate variable of the soil pressure of the soil cabin; calculating the q of the soil pressure by taking the m-th data corresponding time of the i-th section as a starting point according to the formula (1) and the ARX modeling dataiForward step prediction output
Wherein: theta is the transpose of the parameter set to be recognized, Xi,mRepresents a set of variables corresponding to the parameter set θ to be recognized in equation (1).
Further, Xi,mThe expression of (a) is formula (3); the expression of theta is formula (4);
Θ＝θT (4)
wherein: n is max ([ ny, f + nu, g + nd)]),n＜Ni (5)
qi＝min(Np,Ni-m) (6)
In the formula (3), the reaction mixture is,j＝m-n+1,m-n+2,…,m。
further, the third step is specifically: estimating a parameter theta of the soil pressure model by minimizing the mean square error of the soil pressure and the actual soil pressure value Y by forward prediction in multiple steps, and adding a stability condition of the soil pressure model; adding step response mode constraint and model static amplification coefficient constraint, and constructing an ARX model parameter optimization model as a formula (7):
s.t.
max{|s1|,|s2||,…,|snymodel stability condition of | } < 1
yustep(k)≤0,k＝1,2,…,NstepStep response mode constraints
yd1step(k)≥0,k＝1,2,…,Nstep
yd2step(k)≥0,k＝1,2,…,Nstep
Static magnification factor constraint
Wherein:to output for predictionThe total number of (2); s1,s2,…,snyIs a characteristic root of the soil compaction ARX model (1); yu (yu of China)step、yd1step、yd2stepRespectively, the unit step response between the screw speed, the propelling speed and the total propelling force calculated based on the ARX model (1), NstepIs unit step response step length;k 1， k 2， k 3，respectively constraining the minimum value and the maximum value of the static amplification coefficient of the soil pressure relative to the rotating speed, the propelling speed and the total propelling force of the screw machine; c. C1l,c2lThe regression coefficients of the propulsion speed and the total propulsion variable in the model (1) are respectively;
Yi,m＝(Yi(m+1),Yi(m+2),...,Yi(m+qi))T (8)
for the m +1 th to m + q th data in the ith sectioniAnd the vector is formed by actual measurement data of the soil pressure of each soil chamber.
Further, the order (ny, nu, nd) of the earth pressure ARX model is estimated by minimizing the mean square error of the multi-step forward predicted earth pressure with the actual earth pressure Y:
the invention also provides a shield machine control system, which comprises a data acquisition module, a controller, a model training module and a prediction control module;
the data acquisition module is used for acquiring real-time shield tunneling machine propulsion data;
the controller is used for processing the data acquired by the data acquisition module to obtain an actual value of soil pressure in the soil cabin, a rotating speed signal of the screw machine, the propelling speed of the shield machine and the total propelling force;
the model training module is used for learning the soil pressure dynamic characteristic modeling method;
the prediction control module is used for storing the dynamic soil pressure characteristic ARX (autoregressive with exogenous variables) model after the model training module finishes training and outputting a predicted soil pressure value of the soil cabin.
The invention also provides a shield machine, which comprises a shield machine body, and a propelling mechanism and a screw machine which are arranged on the shield machine body; the spiral machine is connected with the soil cabin of the shield machine body and used for outputting the muck in the soil cabin; the shield machine also comprises a shield machine control system;
a soil cabin pressure sensor connected with the controller is arranged in the soil cabin and used for acquiring an actual value of soil pressure in the soil cabin;
a proximity switch connected with the controller is arranged in the screw machine and used for acquiring a rotating speed signal of the screw machine;
and the pressure sensor and the oil cylinder stroke sensor which are connected with the controller in the propelling mechanism are used for acquiring the propelling speed and the total propelling force of the shield tunneling machine.
The technical scheme of the invention has the following beneficial effects:
(1) the dynamic earth pressure characteristic modeling method of the earth pressure balance shield machine considering the dynamic mode constraint not only has higher model precision and better long-term prediction capability, but also can ensure that the model has stronger capability of adapting to the change of the external environment, can well describe the dynamic change characteristic of the earth pressure of the earth cabin of the shield machine, and has higher utilization value.
(2) The method is based on the idea of data-driven modeling, does not need to accurately know the accurate physical parameters and the operation mechanism in the earth pressure control system of the shield machine, and utilizes the sampling data of the earth pressure control system of the actual shield machine to construct the model.
(3) The method provided by the invention considers the dynamic mode constraint of the earth pressure system of the earth pressure balance shield machine, can ensure that the estimated model and the actual system have consistent stability, and adds constraint conditions according to the actual step response mode and the static amplification factor of the modeled earth pressure system to obtain the earth pressure ARX model of the earth hold with the step response mode and the static amplification factor consistent with the actual earth pressure system.
(4) The method improves the description capability and the long-term prediction capability of the model on the dynamic characteristics of the system by minimizing the multi-step forward prediction error of the model. A good foundation is laid for the design of a control system of the earth pressure of the earth cabin of the subsequent shield machine.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail with reference to specific examples.
Detailed Description
The following is a detailed description of embodiments of the invention, but the invention can be implemented in many different ways, as defined and covered by the claims.
Example 1:
a soil pressure dynamic characteristic modeling method comprises the following steps:
the method comprises the following steps: in order to establish an earth pressure dynamic characteristic ARX model of the earth pressure balance shield machine, 17 groups of historical input and output data of a shield machine pressure control subsystem are collected to be used as identification data, wherein the identification data comprises earth pressure of an earth cabin, speed of a screw machine, propulsion speed, total propulsion force and the like, and a data sampling period is set to be 1 second. Considering the characteristic of intermittent work of the shield machine, namely the characteristic that the continuous working time of one-time propelling operation of the shield machine is not long enough, and the number of sampling data in one continuous working section is small, some representative data of 17 continuous working sections covering various soil pressure change modes are selected from historical data of the shield machine to be used as a training set to establish an ARX model of the shield machine, and 6529 groups of input/output data are collected.
Step two: and determining an earth pressure dynamic characteristic ARX (autoregressive with exogenous variables) model structure of an earth pressure balance shield machine earth cabin. The dynamic characteristic ARX model of the earth pressure of the shield machine takes the actual value (bar) of the earth pressure in an earth cabin as an output signal, takes a rotating speed signal of a screw machine as a control input, takes the propelling speed (mm/min) and the total propelling force (KN) of the shield machine as intermediate variables, constructs an ARX model of an earth pressure balance shield machine earth pressure system, and has the following structure:
wherein: y is0To be offset, a1～any、b1～bnu、c1～cndIs a regression coefficient matrix; ny, nu, nd represent the model order; y (k), y (k-1) and y (k-ny) are respectively the kth sampling point, the kth sampling point and the kth-1 sampling point, namely the soil pressure measurement value of the kth sampling point, namely the soil pressure control system output signal, and k is the kth sampling point; u is a screw rotation speed signal, namely a soil pressure control signal, f is 5 and is the pure lag time of u, and g is 1 and is the pure lag time of d; d (k-g), d (k-g-1) and d (k-g-nd +1) are intermediate variables of the k-g sampling moment, the k-g-1 sampling moment and the k-g-nd +1 sampling moment of the propulsion process respectively, are model input signals and comprise two signals of the propulsion speed and the total propulsion force; ε (k) is the modeling error.
Let θ be (y)0,a1,a2,...,any,b1,b2,...,bnu,c1,c2,...,cnd),cl＝(c1l,c2l) And l is 1,2, …, nd is the coefficient of the parameter set to be identified, namely the ARX model (1), and l is the current order of the soil pressure intermediate variable of the soil cabin.
Step three: and constructing an optimization model of the soil pressure ARX model parameter theta, estimating the parameter theta by adopting an interior point method, and taking the corresponding model order, which has the minimum mean square value of the multistep forward prediction error of the ARX model and meets the dynamic mode constraint of the soil pressure system, of the estimated model as the order of the soil pressure ARX model. Setting a data set of a multi-segment shield tunneling machine earth pressure system which is selected from historical data and covers various earth pressure change modes as yi(1)～yi(Ni)、ui(1)～ui(Ni)、di(1)～di(Ni) I is 1,2, …,17, wherein i isThe number of the ith continuous push operation data segment in the data set, wherein I is the number of data segments contained in the data set, NiIs the length of the ith data, yi(1)、yi(Ni) Respectively representing the first and Nth of the ith data in the windowiIndividual data of soil pressure measurement data of soil chamber ui(1)、ui(Ni) Respectively representing the first and Nth of the ith data in the windowiData of screw machine speed signal, i.e. soil pressure control signal, di(1)、di(Ni) Respectively representing the first and Nth of the ith data in the windowiThe soil pressure intermediate variable of the soil chamber of each datum. Based on the ARX model (1) and the sampled data, the m (if N) of the i-th section is calculatedpPredicting step number N forward of 100 stepsp≥NiN, otherwise, N +100, …, N +100 × fix ((N)i-n)/100), fix representing the rounding operation) of the data corresponding to the soil pressure with the time as the starting point qiForward step prediction output
Wherein: theta is the transpose of the parameter set theta to be identified, as shown in formula (4), Xi,mRepresents a set of variables corresponding to the parameter set θ to be recognized in equation (1), as shown in equation (3).
Θ＝θT (4)
n＝max([ny,5+nu,1+nd]),n＜Ni (5)
qi＝min(100,Ni-m) (6)
In the formula (3), the reaction mixture is,j＝m-n+1,m-n+2,…,m。
the parameter theta of the soil pressure model is estimated by minimizing the mean square error between the multistep forward predicted soil pressure (2) calculated based on the soil pressure ARX model (1) and the soil pressure actual value Y, and the stability constraint condition of the soil pressure model is added in the parameter estimation process to ensure that the estimated model has the stability consistent with the actual soil pressure system. In addition, in order to ensure that the estimated model has a step response mode consistent with the actual soil pressure system and an output/input static amplification coefficient close to the actual soil pressure system, in the model parameter estimation process, a model step response mode constraint and a model static amplification coefficient constraint are added, and an ARX model (1) parameter optimization model is constructed as follows:
s.t.
max{|s1|,|s2|,…,|snymodel stability condition of | } < 1
yustep(k) 0, k-1, 2, …,300 step response mode constraint
yd1step(k)≥0,k＝1,2,…,300
yd2step(k)≥0,k＝1,2,…,300
Wherein: s1,s2,…,snyIs a characteristic root of the soil compaction ARX model (1); yu (yu of China)step、yd1step、yd2stepRespectively the unit step response between the soil pressure/screw speed, the soil pressure/propulsion speed and the soil pressure/total propulsion force calculated based on the ARX model (1), c1l,c2lAre the regression coefficients of the propulsion speed and the total propulsion force variable in the model (1), respectively.
Yi,m＝(Yi(m+1),Yi(m+2),...,Yi(m+qi))T (8)
For the m +1 th to m + q th data in the ith sectioniAnd the vector is formed by actual measurement data of the soil pressure of each soil chamber.
The order (ny, nu, nd) of the soil pressure ARX model (1) is estimated by minimizing the mean square error of the following multi-step forward predicted soil pressure (2) calculated based on the soil pressure ARX model (1) and the actual soil pressure Y:
optimization of ARX model parameters with dynamic mode constraints is performed off-line based on a multi-step prediction error minimization strategy, and modeling is performed by utilizing multiple groups of data sets so as to improve the dynamic characteristic description capability of the model.
According to the data collected in the first step, the modeling method and the parameter optimization method in the second step and the third step, an ARX model with dynamic characteristics of soil pressure and constraint of a dynamic mode in consideration can be obtained, and the 100-step forward model prediction error mean square error, the model stability (pole distribution), the step response mode, the static amplification factor and the optimized ARX model (1) order of the model are shown in the table 1.
TABLE 1 optimized dynamic ARX model parameters of soil pressure
The embodiment also provides a shield machine control system, which comprises a data acquisition module, a controller, a model training module and a prediction control module;
the data acquisition module is used for acquiring real-time shield tunneling machine propulsion data; the collected data are used for prediction of a subsequent control module, and the collected data parameters comprise the rotating speed of the screw machine, the soil pressure of the soil cabin, the propelling speed, the total propelling force and the like. In the working process of the shield machine, the industrial control machine stores all data transmitted back by the PLC, and the data acquisition module collects data of all parameters needed by the propulsion speed prediction controller.
The controller is used for processing the data acquired by the data acquisition module to obtain an actual value of soil pressure in the soil cabin, a rotating speed signal of the screw machine, the propelling speed of the shield machine and the total propelling force; the controller adopts a PLC controller. The PLC acquires the signal of the soil cabin pressure sensor, and the actual value of the soil pressure in the soil cabin is obtained through processing. The PLC acquires a proximity switch signal installed in the screw machine system, and calculates to obtain the rotating speed of the screw machine. The PLC outputs current signals to an overflow valve and a flow valve of a partition valve group of the propulsion system to control the pressure and the extension speed of the propulsion oil cylinder. The PLC acquires a pressure sensor signal and an oil cylinder stroke sensor signal of a partition valve group of the propulsion system, and obtains total thrust and propulsion speed after data processing.
In the working process of the shield machine, the data to be collected comprise soil pressure of an earth cabin (bar), speed of a screw machine (rpm), propulsion speed (mm/min), total propulsion force (kN) and the like. The measured value signal of the rotating speed signal of the screw machine is used as a control input signal of the soil pressure prediction control module of the soil cabin, the soil pressure value of the soil cabin is used as an output signal, and the influence of total propelling force (kN), propelling speed (mm/min) and soil pressure (bar) of the soil cabin is considered and input into the soil pressure prediction control module of the soil cabin. The soil pressure prediction is realized by utilizing a soil pressure prediction control module, data of a plurality of past moments are input by the control module, and the specific input data of the moments are determined by the order ny, nu and nd and the lag time of the model.
The model training module is used for learning the soil pressure dynamic characteristic modeling method.
The prediction control module stores an ARX model with trained soil pressure dynamic characteristics and the best test effect, shield machine propulsion data with corresponding parameters are input during prediction, and the predicted soil pressure value of the soil cabin is output after the processing of the ARX model.
The embodiment also provides a shield tunneling machine, which comprises a shield tunneling machine body, and a propelling mechanism and a screw machine which are arranged on the shield tunneling machine body; the spiral machine is connected with the soil cabin of the shield machine body and used for outputting muck in the soil cabin; the shield machine also comprises the shield machine control system; a soil cabin pressure sensor connected with the controller is arranged in the soil cabin and used for acquiring an actual value of soil pressure in the soil cabin; a proximity switch connected with the controller is arranged in the screw machine and used for acquiring a rotating speed signal of the screw machine; and the pressure sensor and the oil cylinder stroke sensor are connected with the controller in the propelling mechanism and are used for acquiring the propelling speed and the total propelling force of the shield tunneling machine.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
