Load self-adaptive control method and system for heading machine

Abstract

The invention relates to a heading machine load self-adaptive control method and system, and the method comprises the steps: a data analysis module is disposed on a heading machine equipment layer, collects the heading data of the heading machine equipment layer in real time, and calculates a load coefficient for self-adaptive adjustment; and the control module is connected with the data analysis module and the heading machine equipment layer, and analyzes the accessed load coefficient into a control signal for adjusting rotation and lifting of a cutting arm in the heading machine equipment layer. The method comprises the following steps: determining a motion trail model of a cutting operation surface of a heading machine equipment layer by acquiring perception data of a manual cutting section, determining a load coefficient generated by the motion trail model according to the state change of the cutting operation surface in the cutting process, and adjusting rotation and lifting of a cutting arm in the heading machine equipment layer based on the load coefficient. The self-adaptive control of the equipment layer of the heading machine is realized, the accuracy and the stability of on-line identification of the heading working face are greatly improved, the equipment failure rate and the safety risk of operation are reduced to the greatest extent, and the method has wide applicability.

Description

Technical Field

[0001] The present application relates to the technical field of tunneling and mining, and in particular to a method and system for adaptively controlling a tunneling machine load. Background Art

[0002] Existing tunneling operations face the dilemma of poor online identification accuracy and stability. Because the geological environment in which the tunneling machine works is relatively complex, some of them are mixed with harder rocks such as gangue. Rocks of different textures will cause load impact and motor overload on the tunneling machine equipment layer, resulting in a sudden increase in cutting current, intensified vibration of the machine body, and severe wear of the cutting head, which seriously reduces the reliability of the tunneling machine system. The existing solution aims to monitor the motor current in the cutting process in real time through a current transformer. When the current rise is detected, the working state of the electro-hydraulic proportional valve is adjusted to adjust the flow rate of the tunneling machine hydraulic system, thereby reducing the swing speed of the cutting arm, reducing the load of the mechanical structure and electrical components under high-intensity working conditions, and protecting the equipment. However, the existing solution is too single in judging the working surface being cut, and can only roughly judge the load of the cutting arm, with poor accuracy and timeliness. At the same time, the existing solution only considers the damage of structural parts and electrical parts caused by excessive load on the cutting arm, but does not consider the problems of low cutting load, low cutting efficiency, and redundancy of tunneling machine drive.

[0003] It should be noted that the above introduction to the technical background is only for the convenience of providing a clear and complete description of the technical solutions of the present application and for the convenience of understanding by those skilled in the art. It cannot be considered that the above technical solutions are well known to those skilled in the art simply because they are described in the background technology section of the present application. Summary of the invention

[0004] The purpose of the present application is to provide a method and system for adaptive control of the load of a tunnel boring machine, so as to solve the problems in the prior art that the automatic cutting operation of the tunnel boring machine equipment layer has poor line recognition accuracy and stability, resulting in cutting only according to a preset cutting trajectory, and the tunnel boring machine equipment layer lacks adaptive function, which greatly increases the failure rate of the equipment and the safety risk of the operation.

[0005] In order to solve the above problems, a roadheader load adaptive control system involved in this application adopts the following technical solutions:

[0006] The data analysis module is arranged on the tunnel boring machine equipment layer, collects the tunnel boring data of the tunnel boring machine equipment layer in real time, and calculates the load factor for adaptive adjustment;

[0007] The control module is connected to the data analysis module and the tunnel boring machine equipment layer, and analyzes the connected load coefficient into a control signal for adjusting the rotation and lifting of the cutting arm in the tunnel boring machine equipment layer.

[0008] In order to solve the above problems, a roadheader load adaptive control method involved in the present application is applicable to the roadheader load adaptive control system involved in the present application. The roadheader load adaptive control method adopts the following technical solutions:

[0009] Obtaining sensory data of artificially cut sections;

[0010] Determine a motion trajectory model of a cutting working surface of a tunnel boring machine equipment layer according to the sensing data;

[0011] In response to the automatic cutting condition of the tunnel boring machine equipment layer, the load coefficient generated by the motion trajectory model is determined according to the state change of the cutting working surface during the cutting process, and the swing trajectory of the cutting arm in the tunnel boring machine equipment layer is adjusted according to the load coefficient.

[0012] In order to solve the above problems, a load adaptive control device for a roadheader involved in the present application adopts the following technical solutions:

[0013] A first acquisition module, the first acquisition module is used to acquire the perception data of the artificial cutting section;

[0014] A second acquisition module, the second acquisition module is used to determine a motion trajectory model of a cutting working surface of a tunnel boring machine equipment layer according to the perception data;

[0015] An adjustment module is used to respond to the automatic cutting condition of the tunnel boring machine equipment layer, determine the load coefficient generated by the motion trajectory model according to the state change of the cutting working surface during the cutting process, and adjust the swing trajectory of the cutting arm in the tunnel boring machine equipment layer according to the load coefficient.

[0016] In order to solve the above problems, the present application involves an electronic device, comprising: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to execute the instructions to implement the adaptive cutting control method of the tunnel boring machine involved in the present application.

[0017] In order to solve the above problems, the present application involves a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the adaptive cutting control method of the tunnel boring machine involved in the present application.

[0018] In order to solve the above-mentioned problem, the present application relates to a computer program product, including a computer program, which, when executed by a processor in a communication device, implements the adaptive cutting control method for a roadheader involved in the present application.

[0019] The beneficial effects of this application are as follows:

[0020] By acquiring the perception data of the artificial cutting section, the motion trajectory model of the cutting working surface of the tunnel boring machine equipment layer is determined, and according to the state changes of the cutting working surface during the cutting process, the load coefficient generated by the motion trajectory model is determined. Based on the load coefficient, the rotation and lifting of the cutting arm in the tunnel boring machine equipment layer are adjusted to achieve adaptive control of the tunnel boring machine equipment layer, which greatly improves the accuracy and stability of online identification of the tunnel boring working face, minimizes the equipment failure rate and safety risks of the operation, and has wide applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solution of the embodiment of the present application, the following is a brief introduction to the drawings required for use in the embodiment:

[0022] Figure 1 A schematic diagram of the structure of a load adaptive control system for a tunnel boring machine provided in an embodiment of the present application;

[0023] Figure 2 A schematic flow chart of a method for adaptive load control of a tunnel boring machine provided in an embodiment of the present application;

[0024] Figure 3 A schematic flow chart of a second method for adaptive cutting control of a roadheader provided in an embodiment of the present application;

[0025] Figure 4 A schematic flow chart of a third method for adaptive cutting control of a roadheader provided in an embodiment of the present application;

[0026] Figure 5 A schematic diagram of the structure of a load adaptive control device for a tunnel boring machine provided in an embodiment of the present application;

[0027] Figure 6 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application;

[0028] Figure 7 It is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the technical purpose, technical solution and beneficial effects of the present application clearer, the technical solution of the present application is further described below in conjunction with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application, that is, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0030] Figure 1 This is a schematic diagram of the structure of a load adaptive control system for a tunnel boring machine provided in an embodiment of the present application. Figure 1 As shown, the tunnel boring machine load adaptive control system includes but is not limited to a tunnel boring machine equipment layer, a data analysis module and a control module, wherein the data analysis module is arranged on the tunnel boring machine equipment layer, collects the tunneling data of the tunnel boring machine equipment layer in real time, and calculates the load coefficient X for adaptive adjustment; the control module is connected to the data analysis module and the tunnel boring machine equipment layer, and parses the connected load coefficient X into a control signal for controlling the rotation and lifting of the cutting arm in the tunnel boring machine equipment layer. Furthermore, based on the control signal, the tunnel boring machine equipment layer realizes the adaptive control function.

[0031] Alternatively, as an example, Figure 1 As shown, the data analysis module includes a driving current acquisition unit, a cutting vibration data acquisition unit, a hydraulic pressure acquisition unit and a calculation unit (the driving current acquisition unit, the cutting vibration data acquisition unit, the hydraulic pressure acquisition unit and the calculation unit are not shown in FIG. Figure 1), the driving current acquisition unit collects the motor current I of the tunnel boring machine equipment layer in real time, and the motor current I is usually proportional to the load of the cutting head in the tunnel boring machine equipment layer; the cutting vibration data acquisition unit collects the cutting vibration data V of the tunnel boring machine equipment layer in real time, which is used to detect the mechanical vibration generated in the cutting process (the mechanical vibration includes amplitude, frequency, phase and other data), and converts the mechanical vibration into a corresponding electrical signal for output, reflecting the difficulty of cutting the material on the cutting working surface; the hydraulic pressure acquisition unit collects the hydraulic pressure P of the tunnel boring machine equipment layer in real time, and the hydraulic pressure P is directly related to the actual load and thrust of the cutting head, wherein the driving current acquisition unit, the cutting vibration data acquisition unit and the hydraulic pressure acquisition unit can be set in the form of sensors. It should be noted that the cutting vibration data acquisition unit based on the sensor setting may include eddy current, inductive, capacitive, voltage and resistive strain types. The eddy current cutting vibration sensor uses the eddy current effect to measure the vibration and displacement of the tunnel boring machine equipment layer; the inductive cutting vibration sensor is based on the principle of electromagnetic induction to convert the mechanical vibration data into an electrical parameter signal; the capacitive cutting vibration sensor obtains variable capacitance by changing the gap or common area, and then measures the mechanical vibration data; the voltage cutting vibration sensor uses the voltage effect of the crystal to convert the pressure generated by the mechanical vibration into an electrical signal; the resistive strain cutting vibration sensor further measures the vibration amount of the tunnel boring machine equipment layer by measuring the change in resistance.

[0032] Optionally, as an example, the calculation unit in the data analysis module dynamically estimates the connected motor current I, cutting vibration data V and hydraulic pressure P to generate a load coefficient Z. Further, a model can be designed, which can use a weighted summation method to fuse the motor current I, cutting vibration data V and hydraulic pressure P to generate a load coefficient Z, which is used to estimate the load of the tunnel boring machine equipment layer, wherein the expression of the load coefficient Z is as follows:

[0033] Z K =ω I *I K +ω V *V K +ω P *P K

[0034] Among them, Z K is the load factor at the current moment; I K is the data value of the motor current of the tunnel boring machine equipment layer; V K is the cutting vibration data of the tunnel boring machine equipment layer; K is the hydraulic pressure data value of the tunnel boring machine equipment layer; ω I ,ω V ,ωP They are the weight coefficient of motor current, the weight coefficient of cutting vibration data and the weight coefficient of hydraulic pressure respectively.

[0035] In order to further improve the accuracy of the load factor Z, the calculation unit of this embodiment can be set by a Kalman filter subunit, which dynamically estimates the connected motor current I, cutting vibration data V and hydraulic pressure P to generate the load factor Z; the control module in the roadheader load adaptive control system can be set by a PID control algorithm unit, which is connected to the Kalman filter subunit, and the output of the Kalman filter subunit is feedback-adjusted to generate a control signal. Specifically, the state update equation of the Kalman filter is as follows:

[0036] x k =Ax k-1 +Bu k +w k

[0037] Among them, X K is the state vector at the current moment, that is, the load estimation value at the current moment; A is the state transfer matrix; B is the control input matrix; u K is the control input; X K-1 is the state vector at the previous moment, that is, the load estimation value at the previous moment; w K is process noise, which usually satisfies Gaussian distribution.

[0038] Furthermore, the measurement update equation of the Kalman filter is:

[0039] z k =Hx k +v k

[0040] Among them, Z K is the dynamic estimate of the Kalman filter, i.e., the load factor at the current moment, which includes a weighted combination of the motor current I, the cut vibration data V, and the hydraulic pressure P; H is the measurement matrix, which usually represents a measure of mapping the state vector to the dynamic estimate; μ K To measure noise, a Gaussian distribution is usually used.

[0041] Furthermore, the dynamic estimate Z output by the Kalman filter subunit is K Transmitted to the PID control algorithm unit. First, set a target value X set , the target value X set is the load value of the tunnel boring machine equipment layer under ideal conditions, which can be obtained by consulting the geological conditions of the tunneling working face. The specific acquisition process is not repeated here. In the PID control algorithm (i.e., proportional-integral-differential control algorithm), the dynamic estimation value ZK It will be used to calculate the load error e k , where the load error e k The expression is:

[0042] e k =X set -Z K

[0043] The PID control algorithm is used to dynamically adjust the cutting arm of the tunnel boring machine equipment layer. The control signal Y output by the PID control algorithm unit can be adjusted according to the load error e k Characterization:

[0044]

[0045] Among them, Y K is the control signal at the current moment; K P is the proportional gain, which amplifies the error; K i is the integral gain, eliminating long-term errors; K d is the differential gain, which predicts the future error; Δt is the time interval.

[0046] Control signal Y K Applied to the cutting arm control device of the tunnel boring machine equipment layer to adjust the speed and movement state of the cutting arm. K >0, indicating that the load of the tunnel boring machine equipment layer at the current moment is lower than the target value X set , it is necessary to increase the speed of the cutting arm; if Y K <0, indicating that the load of the tunnel boring machine equipment layer at the current moment is higher than the target value X set , the speed of the cutting arm needs to be reduced.

[0047] It should be further explained that the control signal Y output by the PID control algorithm unit can be associated with the actual motion command of the roadheader equipment layer, and the motion command can be characterized as:

[0048] Motion command = proportional coefficient * control signal Y

[0049] The motion instruction is represented as a speed instruction of the cutting arm; the proportional coefficient is used to convert the control signal Y output by the PID control algorithm unit into a corresponding speed.

[0050] To sum up, in the entire automatic cutting condition, the data analysis module collects the motor current I, cutting vibration data V and hydraulic pressure P of the tunnel boring machine equipment layer in real time; obtains the dynamic estimation value (i.e. the load coefficient at the current moment) based on the Kalman filter subunit; calculates the load error based on the PID control algorithm unit, and calculates the control signal according to the load error; the speed and motion state of the cutting arm are adjusted by the control signal, thereby realizing the automatic avoidance function of the tunnel boring machine equipment layer and greatly improving the accuracy and stability of online identification of the tunnel boring working face.

[0051] Figure 2 A schematic diagram of a method for adaptively controlling a load of a tunnel boring machine provided in an embodiment of the present application. Figure 2 The method includes but is not limited to the following steps:

[0052] S201, obtaining perception data of the manually cut section.

[0053] In a feasible implementation, the sensory data of the artificially cut section mainly involves the real-time measurement and recording of various physical quantities and geometric data in the cutting process. These data are of great significance for evaluating the cutting quality, optimizing the cutting process and improving production safety. Among them, the physical quantity includes directly measuring the temperature, pressure, vibration and other data in the cutting process through a sensor network (the sensor network is usually set by a sensor), and the sensor network can be installed on the device of the tunnel boring machine equipment layer for real-time data recording, including obtaining the cutting current through a current transformer, obtaining the vibration signal through the cutting head vibration sensor, obtaining the pressure data through the cylinder pressure sensor, and obtaining the displacement information of the cylinder through the cylinder displacement sensor. The measurement of geometric data can use laser rangefinders, three-dimensional scanners and other equipment to accurately measure the section after cutting and obtain the geometric data of the artificially cut section, such as shape, size, flatness, etc. On the other hand, the manual cutting process can also be monitored in real time through devices such as cameras. Although the video data is indirect, useful information can be extracted through relevant image processing technology, such as the cutting arm rotation data and lifting data. Then, combined with the operation data of the tunnel boring machine and the historical cutting records, the perception data of the manual cutting section can be obtained through data analysis methods.

[0054] S202: Determine a motion trajectory model of the tunnel boring machine equipment layer cutting working surface based on the perception data.

[0055] In a feasible implementation, the sensory data of the artificial cutting section is first preprocessed to remove duplicate data, process missing values, outliers and abnormal dates, etc., to ensure the accuracy and reliability of the data. The processed data is then organized into a format suitable for analysis, such as time series data, three-dimensional spatial data, etc. Then, the environment of the cutting operation surface is modeled according to the sensory data of the artificial cutting section, including the shape, size, geological conditions, etc. of the tunnel. Then, based on the environmental model and the sensory data, a trajectory planning algorithm (such as a genetic algorithm, a particle swarm optimization algorithm, etc.) can be used to plan the motion trajectory of the cutting operation surface of the tunnel boring machine. The motion trajectory can usually be represented as a data curve (there is a mapping relationship between the data curve and the sensory data), and the performance data of the tunnel boring machine equipment layer (such as maximum propulsion speed, maximum cutting depth, etc.), geological conditions (such as rock formation hardness, rock formation inclination, etc.) and operation requirements (such as section shape, dimensional accuracy, etc.) should be considered. Then, based on the trajectory planning results, a motion trajectory model of the cutting working surface of the tunnel boring machine equipment layer is constructed. The motion trajectory model includes the change patterns of the position, posture movement speed and other data of the tunnel boring machine equipment layer over time, as well as the motion trajectory and cutting data of the cutting arm (such as cutting depth, swing speed of the cutting arm, etc.).

[0056] Furthermore, constructing the motion trajectory model also includes simulation verification (the constructed motion trajectory model can be simulated and verified using relevant simulation software to simulate the actual cutting process of the tunnel boring machine equipment layer, and the accuracy and reliability of the model can be evaluated through simulation results, potential problems can be discovered and optimized), field tests (in actual tunneling operations, the constructed motion trajectory model is applied to the tunnel boring machine equipment layer control system, and field tests are conducted to verify the practicality and effectiveness of the model by real-time monitoring of the motion state and cutting effect of the tunnel boring machine equipment layer), and feedback and optimization operations (according to the feedback results of the field tests, the motion trajectory model is further optimized and adjusted; the processing algorithm of the perception data and the trajectory planning algorithm are continuously optimized to improve the accuracy and robustness of the model).

[0057] S203, in response to the automatic cutting working condition of the tunnel boring machine equipment layer, according to the state change of the cutting working surface during the cutting process, determine the load coefficient generated by the motion trajectory model, and adjust the swing trajectory of the cutting arm in the tunnel boring machine equipment layer according to the load coefficient.

[0058] In a feasible implementation, in the automatic cutting condition of the tunnel boring machine, the state change of the cutting working surface during the cutting process is mainly obtained by real-time collection of various sensor data from the tunnel boring machine equipment layer, including position, posture, speed, acceleration, hydraulic pressure, vibration, etc.; at the same time, high-definition cameras or laser radars and other equipment can also be used to obtain images or point cloud data of the cutting working surface. Then, the collected data is processed and analyzed to identify the current state of the cutting working surface, such as rock type, hardness, crack conditions, flatness, etc.

[0059] Furthermore, the applicability of the current motion trajectory model is evaluated according to the real-time status of the cutting operation surface. If there is a large deviation between the load coefficient of the motion trajectory model (which can usually be represented as a motion trajectory curve) and the actual cutting condition, or the cutting effect is not ideal (such as excessive cutting force, severe vibration, uneven cutting surface, etc.), the motion trajectory model needs to be adjusted. If the load coefficient of the adjusted motion trajectory model still deviates greatly from the actual cutting condition, it is necessary to interrupt the automatic cutting condition of the tunnel boring machine equipment layer, change to manual cutting, and report the abnormality of the motion trajectory model. If there is no deviation or a small deviation between the load coefficient of the motion trajectory model and the actual cutting condition, the cutting trajectory of the tunnel boring machine is adjusted based on the load coefficient, and the automatic cutting condition continues.

[0060] In summary, by acquiring the perception data of the artificial cutting section, the motion trajectory model of the cutting working face of the tunnel boring machine equipment layer is determined, and according to the state change of the cutting working face during the cutting process, the load coefficient generated by the motion trajectory model is determined. Based on the load coefficient, the rotation and lifting of the cutting arm in the tunnel boring machine equipment layer are adjusted to realize adaptive control of the tunnel boring machine equipment layer, which greatly improves the accuracy and stability of online identification of the tunnel boring working face, minimizes the equipment failure rate and safety risks of the operation, and has wide applicability.

[0061] Figure 3 This is a flow chart of the second method for adaptive cutting control of a roadheader provided in the embodiment of the present application. Figure 3 As shown, the method includes but is not limited to the following steps:

[0062] S301, performing a panoramic detection of the excavation working face according to a preset first sensing network to obtain environmental data.

[0063] In a feasible implementation, the geological structure of the excavation working face is obtained, wherein the geological structure includes the thickness of the rock layer, the inclination of the rock layer and the direction of the rock layer; based on the preset first sensing network, the geological structure is panoramically detected to obtain the first detection data; based on the preprocessing of the first detection data, the environmental data is obtained. The first sensing network is used to detect different positions of the excavation working face, including real-time video images of the working face; sensors for measuring gas content, wind speed, temperature and humidity; and devices such as GPS, gyroscopes, accelerometers, etc. for measuring the position and posture of the equipment layer of the excavator.

[0064] For further details of step S301, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0065] S302, acquiring perception data of the artificially cut section according to the environmental data.

[0066] In a feasible implementation method, the first excavation data in the layer cutting process of the tunnel boring machine equipment is configured according to the environmental data. First, it is necessary to analyze the environmental data of the excavation working face, including but not limited to rock hardness (such as hardness, bedding structure, water content, mineral inclusions, etc.), geological conditions (such as ground pressure, faults, cracks, etc.), tunnel size and shape (such as cross-sectional size, shape requirements, etc.), and working environment (such as temperature, humidity, dust concentration, etc.). Based on the above environmental data, the execution data of the tunnel boring machine during the cutting process is configured, mainly including: the swing speed of the cutting arm (for softer rocks, a smaller swing speed is used to reduce energy consumption and pick wear; for hard rocks, the swing speed is appropriately increased, but care must be taken to prevent equipment overload and pick damage), the lifting and rotating speed of the cutting arm (the lifting and rotating speed of the cutting arm is adjusted according to the rock hardness and geological conditions to ensure cutting efficiency while protecting the equipment), the cutting depth (according to the cross-sectional size and shape requirements of the tunnel, the appropriate cutting depth is set; for thin rock layers or narrow working spaces, special low-profile cutting equipment may be required or the cutting depth may be adjusted), the arrangement and angle of the pick (according to the rock properties and bedding structure, the picks are reasonably arranged and their angles are adjusted to improve cutting efficiency and reduce wear), and lubrication and cooling (according to the working environment temperature and equipment operating conditions, the lubrication and cooling systems are adjusted in time to ensure normal operation of the equipment). After configuring the first excavation data, manually control the tunnel boring machine to perform cutting operations, including: inputting the configured first excavation data to the tunnel boring machine equipment layer through the human-computer interaction interface; during the cutting process, pay close attention to the operating status and cutting effect of the tunnel boring machine, and adjust the cutting speed, hydraulic pressure, depth and other data in time according to the actual situation; in case of abnormal conditions (such as equipment failure, severe wear of the cutting teeth, etc.), immediately stop the cutting operation and take corresponding measures; check the quality of the tunnel section after cutting to ensure that it meets the design requirements, and adjust the cutting strategy and the first excavation data according to the section quality.

[0067] In the process of manually controlling the cutting action of the equipment layer of the tunnel boring machine, the perception data of the artificial cutting section is obtained according to the interaction between the preset second perception network and the cutting action. Furthermore, according to the changes in physical quantities during the cutting process, the mapping relationship between the artificial cutting section and the preset second perception network is obtained; according to the mapping relationship, the cutting action is perceived to obtain the perception data of the artificial cutting section. It should be noted that the process of obtaining the perception data of the artificial cutting section is through the second perception network installed on the equipment layer of the tunnel boring machine, such as laser scanners, cameras, current transformers, cutting head vibration sensors, cylinder pressure sensors, cylinder displacement sensors, etc., to fully perceive various physical quantities in the cutting process. The second perception network needs to be accurately deployed at key parts of the tunnel boring machine, such as the cutting head, fuselage, cutting arm, etc., to ensure that various data in the cutting process can be accurately captured.

[0068] The interaction between the second perception network and the cutting action includes: sending action instructions, real-time collection of second perception network data, and data interaction and processing. Specifically, as an example, the operator sends cutting action instructions to the tunnel boring machine equipment layer through the human-computer interaction interface, including the swing speed, depth, direction, etc. of the cutting arm; these instructions are converted into control signals to control the rotation and lifting of the cutting arm in the tunnel boring machine equipment layer; while the tunnel boring machine equipment layer performs the cutting action, the second perception network collects perception data in the cutting process in real time, such as the shape, size, rock properties, etc. of the cutting surface; the collected second perception network data is transmitted to the data processing center in real time; the data processing center performs preprocessing, feature extraction, pattern recognition and other operations on the received data to extract useful information about the shape and size of the cutting section.

[0069] More specifically, laser scanning and image processing technology can be used to reconstruct the three-dimensional shape and size of the cutting section based on the collected second perception network data; and the quality of the cutting section can be evaluated to check whether it meets the design requirements. If quality problems are found in the cutting section, such as over-excavation, under-excavation, etc., it is necessary to adjust the cutting data in time or take other remedial measures; the shape and size of the cutting section and the quality assessment results are presented to the operator in a visual manner, and feedback information is provided to the operator through the human-computer interaction interface to help them better understand the various situations in the cutting process and make corresponding adjustments.

[0070] For further details of step S302, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0071] S303, determining a motion trajectory model of the tunnel boring machine equipment layer cutting working surface according to the perception data.

[0072] In a feasible implementation, first data related to the motion state of the equipment layer of the tunnel boring machine is obtained from the sensed data, wherein the first data includes position, angle (pitch angle, yaw angle, roll angle, etc.), and speed (linear speed, angular speed, etc.). Based on preprocessing the first data, the second data is obtained. Based on rigid body kinematics, the second data is processed in time series to obtain a sequence of trajectory points of the tunnel boring machine cutting working surface in the tunnel that changes with time. Further, based on rigid body kinematics, a real-time position related to time is obtained from the second data; based on time series processing of the real-time position, a sequence of trajectory points of the tunnel boring machine cutting working surface in the tunnel that changes with time is obtained. It should be noted that the coordinates of each time point are calculated based on the real-time position data, which include longitude and latitude, X / Y / Z coordinates, etc. These coordinates constitute the trajectory of the cutting arm in the tunnel boring machine equipment layer; the calculated trajectory points are visualized in chronological order to intuitively observe the movement trajectory of the tunnel boring machine equipment layer in the tunnel, and at the same time, the position, speed, direction and other information of the cutting arm in the tunnel boring machine equipment layer are displayed in real time so that the operator can adjust the tunneling strategy in time.

[0073] After obtaining the trajectory point sequence of the tunnel boring machine equipment layer cutting working surface changing with time in the tunnel, the motion trajectory model of the tunnel boring machine equipment layer cutting working surface in the tunnel is obtained based on the trajectory fitting of the trajectory point sequence, wherein the trajectory fitting operation includes polynomial fitting, least squares fitting, etc. The trajectory fitting method should be selected according to actual needs, which will not be elaborated here.

[0074] For further details of step S303, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0075] S304, in response to the automatic cutting condition of the tunnel boring machine equipment layer, according to the state change of the cutting working surface during the cutting process, determine the load coefficient generated by the motion trajectory model, and adjust the swing trajectory of the cutting arm in the tunnel boring machine equipment layer according to the load coefficient.

[0076] In a feasible implementation, the second excavation data of the tunnel boring machine equipment layer is collected in real time according to the state change of the cutting working surface during the cutting process, wherein the second excavation data of the motion trajectory model can be obtained by constructing an error function and minimizing the error function; or numerical optimization methods, such as gradient descent method, Newton method, etc., can be used to obtain the second excavation data of the motion trajectory model. The appropriate second excavation data acquisition method should be selected according to actual needs, which will not be repeated here. Next, based on Kalman filtering of the second excavation data, the dynamic estimation value of the automatic cutting operation is determined; then, based on PID control of the dynamic estimation value, the load coefficient generated by the motion trajectory model is determined.

[0077] In this embodiment, after obtaining the load coefficient, the motor current adjustment value, cutting vibration data adjustment value and hydraulic pressure adjustment value of the tunnel boring machine equipment layer are obtained based on the analysis of the load coefficient by the PID control algorithm unit; according to the motor current adjustment value, the cutting vibration data adjustment value and the hydraulic pressure adjustment value, the rotation and lifting of the cutting arm in the tunnel boring machine equipment layer are adjusted. Furthermore, the cutting arm of the tunnel boring machine equipment layer is dynamically adjusted by the control signal output by the PID control algorithm unit, that is, the control signal is applied to the cutting arm control device of the tunnel boring machine equipment layer to adjust the speed and motion state of the cutting arm. If the scalar value of the control signal is greater than 0, it indicates that the load of the tunnel boring machine equipment layer is relatively low at the current moment, and the speed of the cutting arm needs to be increased; if the scalar value of the control signal is less than 0, it indicates that the load of the tunnel boring machine equipment layer is relatively high at the current moment, and the speed of the cutting arm needs to be reduced.

[0078] For further details of step S304, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0079] In summary, by acquiring the perception data of the artificial cutting section, the motion trajectory model of the cutting working face of the tunnel boring machine equipment layer is determined, and according to the state change of the cutting working face during the cutting process, the load coefficient generated by the motion trajectory model is determined. Based on the load coefficient, the rotation and lifting of the cutting arm in the tunnel boring machine equipment layer are adjusted to realize adaptive control of the tunnel boring machine equipment layer, which greatly improves the accuracy and stability of online identification of the tunnel boring working face, minimizes the equipment failure rate and safety risks of the operation, and has wide applicability.

[0080] Figure 4 The third method for controlling the adaptive cutting of a roadheader provided in the present application is a flow chart of the method. Figure 4 As shown, the method includes but is not limited to the following steps:

[0081] S401, obtaining perception data of the artificially cut section.

[0082] For further details of step S401, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0083] S402: Generate a perception network curve according to the perception data.

[0084] For further details of step S402, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0085] S403, obtaining an automatic cutting data curve according to the automatic cutting working condition of the roadheader.

[0086] It should be noted that if the cutting is completed, the excavation action of the tunnel boring machine is terminated; if the cutting is not completed, it is necessary to compare the perception network curve with the automatic cutting data curve.

[0087] For further details of step S403, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0088] S404, obtaining a comparison result according to a comparison operation between the perception network curve and the automatic clipping data curve.

[0089] It should be noted that if there is no abnormality in the comparison operation result, the process returns to step S403 to continue to obtain the automatic cutting data curve. If there is an abnormality in the comparison operation result, the process proceeds to step S405.

[0090] S405, adjusting the swing trajectory of the cutting arm in the tunnel boring machine equipment layer according to the load coefficient output by the obtained motion trajectory model of the cutting working surface of the tunnel boring machine equipment layer.

[0091] It should be noted that if there is an abnormality in the comparison operation result, it means that there is a large deviation between the automatic cutting data curve and the perception network curve, and the cutting effect is not ideal (such as the cutting arm swings too much, vibrates violently, the cutting surface is uneven, etc.). By adjusting the motion trajectory of the cutting working surface of the tunnel boring machine, the automatic cutting data curve is obtained again. If there is no deviation or the deviation is small between the adjusted automatic cutting data curve and the perception network curve, it means that the adjustment has improved, and then return to step S403 to continue to obtain the automatic cutting data curve; if there is still a large deviation between the adjusted automatic cutting data curve and the perception network curve, it means that there is no improvement after adjustment, and it is necessary to report the abnormality and perform manual cutting.

[0092] In summary, by acquiring the perception data of the artificial cutting section, the motion trajectory model of the cutting working face of the tunnel boring machine equipment layer is determined, and according to the state change of the cutting working face during the cutting process, the load coefficient generated by the motion trajectory model is determined. Based on the load coefficient, the rotation and lifting of the cutting arm in the tunnel boring machine equipment layer are adjusted to realize adaptive control of the tunnel boring machine equipment layer, which greatly improves the accuracy and stability of online identification of the tunnel boring working face, minimizes the equipment failure rate and safety risks of the operation, and has wide applicability.

[0093] Figure 5 This is a schematic diagram of the structure of a load adaptive control device for a tunnel boring machine provided in an embodiment of the present application. Figure 5 As shown, the roadheader load adaptive control device 500 comprises:

[0094] A first acquisition module 501, the first acquisition module 501 is used to acquire the perception data of the artificial cutting section;

[0095] The second acquisition module 502 is used to determine the motion trajectory model of the cutting working surface of the tunnel boring machine equipment layer according to the perception data;

[0096] Adjustment module 503, adjustment module 403 is used to respond to the automatic cutting condition of the tunnel boring machine equipment layer, determine the load coefficient generated by the motion trajectory model according to the state change of the cutting working surface during the cutting process, and adjust the swing trajectory of the cutting arm in the tunnel boring machine equipment layer according to the load coefficient.

[0097] Figure 6 The present invention is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. Figure 6 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0098] like Figure 6 As shown, the electronic device 600 includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a memory 606 to a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0099] The following components are connected to the I / O interface 605: a memory 606 including a hard disk, etc.; and a communication part 607 including a network interface card such as a LAN (Local Area Network) card, a modem, etc., and the communication part 607 performs communication processing via a network such as the Internet; a drive 608 is also connected to the I / O interface 605 as needed.

[0100] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 607. When the computer program is executed by the processor 601, the above-mentioned functions defined in the method of the present application are executed.

[0101] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, and the instructions can be executed by the processor 601 of the electronic device 600 to complete the above method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0102] In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0103] Figure 7 It is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. Figure 7 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present application. Figure 7 As shown, the electronic device 700 includes a processor 701 and a memory 702. The memory 702 is used to store program codes, and the processor 701 is connected to the memory 702 to read the program codes from the memory 702 to implement the end cleaning control method of the fully mechanized working face in the above embodiment.

[0104] Optionally, the number of processors 701 may be one or more.

[0105] Optionally, the electronic device may further include an interface 703, and the number of the interface 703 may be multiple. The interface 703 may be connected to an application program and may receive data from an external device such as a sensor.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate but not limit the technical solutions of the present application. Any equivalent substitutions of the present application and any modifications or partial substitutions that do not depart from the spirit and scope of the present application shall be included in the scope of protection of the claims of the present application.

Claims

1. A roadheader load adaptive control system, characterized in that: include: Tunnel boring machine equipment layer, data analysis module and control module, including: The data analysis module is arranged on the tunnel boring machine equipment layer, collects tunnel boring data of the tunnel boring machine equipment layer in real time, and calculates the load factor for adaptive adjustment; The control module is connected to the data analysis module and the tunnel boring machine equipment layer, and analyzes the connected load coefficient into a control signal for adjusting the rotation and lifting of the cutting arm in the tunnel boring machine equipment layer.

2. The roadheader load adaptive control system according to claim 1, characterized in that: The data analysis module includes a driving current acquisition unit, a cutting vibration data acquisition unit, a hydraulic pressure acquisition unit and a calculation unit, wherein the driving current acquisition unit collects the motor current of the tunnel boring machine equipment layer in real time; the cutting vibration data acquisition unit collects the cutting vibration data of the tunnel boring machine equipment layer in real time; the hydraulic pressure acquisition unit collects the hydraulic pressure of the tunnel boring machine equipment layer in real time; the calculation unit dynamically estimates the connected motor current, the cutting vibration data and the hydraulic pressure to generate the load factor.

3. The roadheader load adaptive control system according to claim 2, characterized in that: The calculation unit includes a Kalman filter subunit; the control module includes a PID control algorithm unit, wherein the Kalman filter subunit dynamically estimates the connected motor current, the cutting vibration data and the hydraulic pressure to generate the load coefficient; the PID control algorithm unit is connected to the Kalman filter subunit to perform feedback adjustment on the generated load coefficient to generate a control signal.

4. A method for adaptive load control of a tunnel boring machine, characterized in that: A load adaptive control system for a tunnel boring machine according to any one of claims 1 to 3, comprising: Obtaining sensory data of artificially cut sections; Determine a motion trajectory model of a cutting working surface of a tunnel boring machine equipment layer according to the sensing data; In response to the automatic cutting condition of the tunnel boring machine equipment layer, the load coefficient generated by the motion trajectory model is determined according to the state change of the cutting working surface during the cutting process, and the swing trajectory of the cutting arm in the tunnel boring machine equipment layer is adjusted according to the load coefficient.

5. The method according to claim 4, characterized in that The step of obtaining the perception data of the artificially cut section includes: According to the preset first perception network, a panoramic detection of the excavation working face is carried out to obtain environmental data; According to the environmental data, first excavation data in the process of cutting the equipment layer of the tunnel boring machine is configured, and according to the first excavation data, the cutting action of the tunnel boring machine is manually controlled; According to the interaction between the preset second perception network and the cutting action, perception data of the artificial cutting section is obtained.

6. The method according to claim 5, characterized in that The method of performing a panoramic detection of the excavation working face according to the preset first sensing network to obtain environmental data includes: Obtain the geological structure of the excavation face; Based on a preset first sensing network, a panoramic detection is performed on the geological structure to obtain first detection data; Environmental data is acquired based on preprocessing the first detection data.

7. The method according to claim 5, characterized in that The acquiring of the perception data of the artificial cutting section according to the interaction between the preset second perception network and the cutting action includes: According to the change of physical quantity in the cutting process, a mapping relationship between the artificial cutting section and the preset second perception network is obtained; According to the mapping relationship, the cutting action is sensed to obtain the sensed data of the artificial cutting section.

8. The method according to any one of claims 4 to 7, characterized in that: Determining the motion trajectory model of the cutting working surface of the tunnel boring machine equipment layer according to the sensing data includes: Acquire first data related to the motion state of the equipment layer of the tunnel boring machine from the sensed data; Based on preprocessing the first data, obtaining second data; Based on rigid body kinematics, the second data is processed in time series to obtain a trajectory point sequence of the cutting working surface of the roadheader equipment layer that changes with time; Based on the trajectory fitting of the trajectory point sequence, a motion trajectory model of the roadheader equipment layer cutting working surface is obtained.

9. The method according to claim 4, characterized in that The step of determining the load coefficient generated by the motion trajectory model according to the state change of the cutting operation surface during the cutting process includes: According to the state changes of the cutting working surface during the cutting process, the second excavation data of the tunnel boring machine equipment layer is collected in real time; Determining a dynamic estimate of the automatic cutting operation based on Kalman filtering of the second excavation data; Based on performing PID control on the dynamic estimation value, a load factor generated by the motion trajectory model is determined.

10. The method according to claim 1, characterized in that The step of adjusting the swing trajectory of the cutting arm in the equipment layer of the tunnel boring machine according to the load coefficient comprises: Based on the analysis of the load factor, the motor current adjustment value, the cutting vibration data adjustment value and the hydraulic pressure adjustment value of the equipment layer of the tunnel boring machine are obtained; According to the motor current adjustment value, the cutting vibration data adjustment value and the hydraulic pressure adjustment value, the rotation and lifting of the cutting arm in the equipment layer of the tunnel boring machine are adjusted.

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