Working condition adaptive control method under different working loads, working condition adaptive control system and electric engineering machinery for tunnel

Abstract

This application provides a working condition adaptive control method for an electric tunnel construction machine under different operating loads. The electric tunnel construction machine performs multiple operating modes in the working medium of the tunnel and includes a hydraulic pump and a motor. The motor provides power to the hydraulic pump, and the hydraulic pump drives the working parts in a predetermined operating mode. The working condition adaptive control method includes: obtaining full-condition operating parameters of the electric tunnel construction machine by performing multiple operating modes in each different working medium of the tunnel. The full-condition operating parameters include the hydraulic pump outlet pressure (P) and motor parameters. The motor parameters include at least one of the motor speed (n) and motor power (W); dividing the full-condition range of the outlet pressure (P) into n pressure intervals: (P... 1 P 2 ), (P 2 P 3 )…(P n P n+1 The motor speed (n) is divided into n speed ranges across its entire operating range: (n 1 n 2 ), (n 2 n 3 )…(n n n n+1 The motor power (W) is divided into n motor power ranges across the entire operating condition: (W) 1 W 2 ), (W 2 W 3 )…(W n W n+1 ); and optimize within each of the motor parameter ranges to obtain the optimal motor parameters for each range, and establish a full-condition operation database.

Description

Adaptive control methods for different operating loads, adaptive control systems for different operating loads, and electric engineering machinery for tunnels. Technical Field

[0001] This application relates to the field of tunnel machinery technology, and in particular to the working condition adaptive control method and working condition adaptive control system of electric engineering machinery used in plateau railway tunnels under different load modes. Background Technology

[0002] High-altitude railway tunnels are characterized by thin air and frigid temperatures, but also frequent adverse working conditions such as high temperature, high humidity, high dust, water inrush, and spraying. The complex geological conditions further exacerbate the problems faced by traditional fuel-powered construction equipment, including reduced power, increased energy consumption, and intensified emissions. These issues are particularly pronounced in long-distance tunnel construction, and the equipment also poses safety hazards such as competing with workers for oxygen and the accumulation of harmful gases. While a small number of electric equipment systems have been deployed in high-altitude railway tunnels, they still face industry challenges such as insufficient range and limited applicability. Therefore, the demand for electrified and green construction equipment is growing rapidly. Traditional fuel-powered construction equipment is limited by the narrow operating speed range of diesel engines, resulting in limited time spent operating within their energy-efficient range and hindering adaptive control. Therefore, electric-driven construction equipment, employing a combination of motor and hydraulic pump, better meets the requirements of adaptive hydraulic load control. Summary of the Invention

[0003] To address the aforementioned technical deficiencies, this application provides a working condition adaptive control method, a working condition adaptive control system, and electric engineering machinery for tunnels under different load modes.

[0004] This application provides an adaptive control method for tunnel electric engineering machinery under different operating loads. The tunnel electric engineering machinery performs multiple operating modes in the working medium of the tunnel and includes a hydraulic pump and a motor. The motor provides power to the hydraulic pump, and the hydraulic pump drives the working parts to perform predetermined operating modes. The adaptive control method includes:

[0005] By performing multiple operating modes on different working media in the tunnel using electric tunnel construction machinery, the full-condition operating parameters of the electric tunnel construction machinery are obtained. The full-condition operating parameters include hydraulic pump outlet pressure and motor parameters, and the motor parameters include at least one of motor speed and motor power.

[0006] The entire operating range of the outlet pressure is divided into n pressure intervals: (P1, P2), (P2, P3)...(P... n P n+1 The motor speed is divided into n speed ranges across the entire operating condition: (n1, n2), (n2, n3)...(n... n n n+1The motor power (corresponding full operating condition range) is divided into n motor power intervals: (W1, W2), (W2, W3)...(W n W n+1 );as well as

[0007] Optimize within each of the motor parameter ranges to obtain the optimal motor parameters for each range, and establish a full-condition operation database.

[0008] In some embodiments, the optimal motor parameters for each interval are obtained by using K-means clustering to find the optimal motor parameters for each interval.

[0009] K-means clustering includes:

[0010] The ideal parameters that have been obtained for each motor speed range are close to the motor's high-efficiency range, and these are used as the initial values ​​of the sample centers for the K-means clustering method.

[0011] Based on the initial values ​​of the sample centers, cluster the parameters within each motor parameter range, calculate the Euclidean distance between the group of parameters and the initial values ​​of the sample centers, and assign them to the closest group; and

[0012] The motor parameters are brought closer to the initial values ​​of the sample center, and then included in the sample database for that group. The sample center values ​​are then recalculated.

[0013] In some embodiments, the K-means clustering method includes: terminating clustering when the motor parameters reach the optimal or the maximum number of iterations, and taking the final cluster center value as the optimal motor speed corresponding to each hydraulic pump pressure range.

[0014] In some embodiments, an optimal parameter model of the motor is established with the optimal motor speed, and the outlet pressure of the hydraulic pump is used as the input value, while the motor speed and / or the electronic control power are used as the output values.

[0015] Using the optimal parameters of the motor as a theoretical reference, fuzzy neural adaptive control of the motor-hydraulic pump is adopted. The motor speed and / or motor power are adjusted in real time through commands to make the motor parameters close to the optimal parameters.

[0016] In some embodiments, a predetermined operating mode is selected;

[0017] If the motor is idling and there is no fault alarm, based on the selected operating mode, the corresponding motor parameters are retrieved from the full operating condition database and sent to the motor to execute the predetermined operating mode.

[0018] If the motor is not idling and / or there is a fault alarm, after reducing the motor speed to idle and / or resolving the fault, select the corresponding motor parameters from the full-condition operation database and input the selected motor parameters into the motor to execute the predetermined operation mode.

[0019] In some embodiments, if the predetermined work mode is a single work mode that includes only one work mode, the intelligent decision system is turned off and the single work mode is executed.

[0020] If the predetermined operating mode is a composite operating mode including at least two operating modes, the intelligent decision-making system is activated. The intelligent decision-making system is used to adjust the motor parameters of the motor working in different operating modes to adapt to the different operating modes.

[0021] In some embodiments, the full-condition operation database includes at least one of the following:

[0022] The experimental operating parameters were obtained by repeatedly operating the electric tunnel construction machinery at the test site based on a combination of each operating medium and each operating mode, to obtain the experimental operating parameters of the hydraulic pump outlet pressure and the motor.

[0023] The on-site operation parameters are obtained by using the electric tunnel construction machinery to perform operations on the construction site based on a combination of each working medium and each working mode, thereby obtaining the on-site operation parameters of the hydraulic pump outlet pressure and the motor.

[0024] In some embodiments, the operation mode includes at least one of the following: muck removal, invert arch excavation, tunnel hazard removal, crushing, and milling.

[0025] In some embodiments, based on a predetermined operating mode, the predetermined working trajectory of the working parts is stored in a full-condition operating database and sent to the electric engineering machinery for tunneling along with the corresponding optimal motor speed.

[0026] This application also provides a condition-adaptive control system for implementing a condition-adaptive control method, comprising:

[0027] The motor is powered by a battery.

[0028] A hydraulic pump, powered by the motor, drives the working elements of the electric tunnel construction machinery to perform a predetermined work mode;

[0029] A pressure sensor is used to detect the outlet pressure of the hydraulic pump; and

[0030] The controller module is used to adjust the motor speed of the motor according to the outlet pressure of the hydraulic pump, and is configured to perform optimization in each of the motor speed ranges to obtain the optimal motor speed in each range and establish a full-condition operation database.

[0031] In some embodiments, the adaptive operating condition control system includes:

[0032] A one-button start switch is used to enable the controller module to send a start command to the motor, driving the working parts to perform the relevant operating modes;

[0033] The one-button stop switch is used to send a stop command to the motor after being activated, stopping the working parts from performing the relevant operating modes.

[0034] In some embodiments, the adaptive operating condition control system includes:

[0035] A speed sensor is used to detect the speed of the motor;

[0036] The controller module is configured to execute:

[0037] If the motor is not idling and / or a fault alarm is detected, the working parts will not operate even if the one-button start switch is activated to avoid damage.

[0038] When the motor is idling and no fault alarm occurs, and the one-button start switch is activated, the appropriate motor speed is selected from the full-condition operation database according to the predetermined operation mode, and the motor is driven to rotate at the corresponding motor speed.

[0039] In some embodiments, the working condition adaptive control system includes an alarm speaker for sounding a warning horn to alert non-workers to stay away from the work site before the working component performs a predetermined working mode.

[0040] In some embodiments, the controller module is configured to perform:

[0041] When the motor speed is not the optimal motor speed, the motor speed is adjusted in real time through fuzzy neural adaptation so that the motor speed reaches the optimal motor speed.

[0042] In some embodiments, the adaptive control system includes a job selection module connected to the controller module for selecting a single or compound job mode to be executed. The single job mode includes only one job mode, and the compound job mode includes at least two job modes.

[0043] In some embodiments, the adaptive control system includes an intelligent decision-making system connected to the job selection module. When a single job mode is selected, the intelligent decision-making system is turned off; when a composite job mode is selected, the intelligent decision-making system is turned on to adjust the motor speed to the corresponding optimal motor speed between different job modes.

[0044] This application also provides an electric engineering machine for tunnels, including the aforementioned working condition adaptive control system.

[0045] In some embodiments, the electric tunneling machinery is configured as an electric tunneling excavator.

[0046] Through repeated testing of the operating device, test data were collected under different working media and operating conditions to obtain a database of operating parameters for electric excavators under all operating conditions. For each operating condition, the operating parameters were classified into interval levels, and an optimal parameter model was established. This optimal parameter model was then matched to different operating conditions using neural network fuzzy adaptive control. The optimal parameter model for each operating condition was stored in the operating system via a pre-storage mode, establishing a one-click operating system for a single operating condition to achieve automated operation. During multi-condition compound operation, the intelligent control system intervened in the operating mode, made intelligent decisions, and adjusted and matched the optimal parameters in real time to reduce energy consumption, improve operating efficiency, and enhance the overall machine's endurance. Attached Figure Description

[0047] By convention, the various features in the accompanying drawings described below are not necessarily drawn to scale. The dimensions of the various features and elements in the drawings may be enlarged or reduced to more clearly illustrate the embodiments of this application.

[0048] Figure 1 is a schematic diagram of the composition of an electric tunnel excavator according to an embodiment of this application;

[0049] Figure 2 is a schematic diagram of the modules of the working condition adaptive control system of the tunnel electric excavator according to an embodiment of this application;

[0050] Figure 3 is a schematic diagram of an electric tunnel excavator according to an embodiment of this application and various combinations of different working media and different operating modules;

[0051] Figure 4 is a schematic diagram of the power transmission of the power system of the tunnel electric excavator according to an embodiment of this application;

[0052] Figure 5 is a flowchart of the data acquisition process for the tunnel electric excavator under all working conditions according to an embodiment of this application;

[0053] Figure 6 is a flowchart illustrating the adaptive control method for the working conditions of a tunnel electric excavator according to an embodiment of this application; and

[0054] Figure 7 is a schematic diagram of the one-button operation mode of the tunnel electric excavator under different working loads according to an embodiment of this application. Detailed Implementation

[0055] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0056] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used in this application is for the purpose of describing particular embodiments only and is not intended to limit this application; the terms "comprising" and "having" and any variations thereof in the specification, claims and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0057] In the description of the embodiments of this application, the technical terms "first" and "second," etc., are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0058] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0059] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In addition, if the character " / " appears in this application, it generally indicates that the related objects before and after it are in an "or" relationship.

[0060] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two).

[0061] In the description of the embodiments of this application, the term "at least one" refers to one or more (including two), and the term "at least part" refers to part or all of them.

[0062] In the description of the embodiments of this application, unless otherwise expressly specified and limited, the technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0063] As shown in Figure 1, taking an electric excavator as an example, the electric engineering machinery equipment used in tunnels is explained. An electric excavator usually mainly includes a working device 1, a motor 2, a hydraulic pump 3, a multi-way valve 4, a hydraulic system 5, a control module 6, a detection module 7, a display module 8, a fault alarm module 9, an operation module 10, an intelligent decision-making system 11, a controller module 12, and a battery system 13.

[0064] As shown in Figure 1, the power system of the electric excavator includes a power battery system 13, a motor controller 61, a motor 2, a hydraulic pump 3, a multi-way valve 4, and actuators (slewing motor, travel motor, hydraulic cylinder, etc.). The working device 1 includes a boom, a boom cylinder, a stick, a stick cylinder, a bucket, a bucket cylinder, etc. The power battery 13 is connected to the motor 2 to provide power to the motor, and the motor 2 is connected to the hydraulic pump 3 to provide power to the hydraulic pump. The hydraulic pump 3 is connected to the multi-way valve 4, the hydraulic system 5, and the working device 1, providing power output for the working device 1 to operate, rotate, and travel.

[0065] As shown in Figure 2, the control module 6 includes a motor controller 61 and a vehicle controller 62; the detection module 7 includes a motor speed sensor 71 and a hydraulic pump pressure sensor 72; the display module 8 includes a display device; the fault and alarm module 9 includes a fault alarm light 91 and an alarm speaker 92; the operation module 10 includes a one-button stop switch 101, a one-button start switch 102, an automatic / manual switching switch 103, and an operation mode selection module 104; the intelligent decision system 11 is used to adjust the parameters of the motor working in different operation modes to adapt to different operation modes.

[0066] The motor controller 61 of the control module 6 is connected to the motor 2, the motor speed sensor 71 in the detection module 7, and the vehicle controller 62. It is used to acquire the rotational speed of the motor speed sensor, control the motor speed and its start and stop, and receive motor speed control commands and vehicle status output by the vehicle controller 62. The vehicle controller 62 in the control module 6 is connected to the motor controller 61, the power battery 5, the hydraulic pump pressure sensor 72 in the detection module 7, the multi-way valve 4, and the hydraulic system 5. It is used to acquire the pump outlet pressure P collected by the pressure sensor 81 and the load provided by the hydraulic system 5 to control the motor controller. The hydraulic pump pressure sensor 82 is located at the outlet of the hydraulic pump 3.

[0067] The intelligent decision-making system 11 is connected to the operation mode selection module 104. When the operation mode is a single working condition, the intelligent decision-making system is in the off state; when the operation mode is a compound working condition, the intelligent decision-making system is in the on state.

[0068] Figure 3 shows a schematic diagram of various combinations of electric excavators for tunnels, different working media, and different working modes. It shows the selection of various working modes of electric excavators for tunnels under different working media. The working media can be divided into clay, frozen soil, rock, gravel, soil, mud and rock mixture, etc. The working modes of electric excavators can include muck removal, invert arch excavation, tunnel hazard removal, crushing, milling and other operations.

[0069] As shown in Figure 5, this application provides a data acquisition process for a tunnel electric excavator under different working conditions and working modes: the working device 1 performs repeated test operations for each working medium under different working modes to obtain experimental working parameters, including hydraulic pump outlet pressure P, motor speed n, motor power W, etc. In addition, the on-site working parameters of the tunnel electric excavator under the on-site environment are collected during construction. The experimental working parameters and the on-site working parameters together constitute a database of full-condition working parameters of the tunnel electric excavator.

[0070] As shown in Figure 6, this application provides a method for optimal parameter matching of an electric tunnel excavator for different working media under different working modes, including: dividing the working parameters obtained by repeating the same working media multiple times (e.g., more than 100 times) under the same working mode into intervals, and dividing the outlet pressure of the hydraulic pump into n pressure intervals (P1, P2), (P2, P3)...(P... n P n+1 The motor speed corresponding to the pump's outlet pressure is divided into (n1, n2), (n2, n3)...(n...). n n n+1The motor power corresponding to the motor speed is divided into (W1, W2), (W2, W3)...(W...). n W n+1 Using K-means clustering, for each operating cycle, the optimal motor speed and corresponding motor power values ​​within the hydraulic pump outlet pressure range are searched within the corresponding motor parameter range. This yields the optimal parameters within the range, establishing an optimal parameter model. This optimal model serves as the optimal reference model for the motor. The hydraulic pump outlet pressure is used as the input value, and the motor speed and power are used as the output values. Fuzzy neural adaptive control of the motor and hydraulic pump is employed. The motor parameters are adjusted in real-time through adaptive motor control commands, ensuring that the actual motor output approaches the optimal parameters, minimizing power consumption. Similar methods are used to obtain the optimal parameters for other operating modes. The adaptive optimal parameters for all operating modes are stored in an optimal parameter database, providing data for operating systems operating under single and combined conditions.

[0071] As shown in Figure 7, the automatic operation of the electric excavator for tunnels according to this application includes: during tunnel construction, selecting an operation mode based on the actual working conditions at the construction site, and the operator can confirm the selected operation mode on the display device. After confirming the operation mode, the automatic operation mode is activated, and the display device shows whether the motor is in an idling state and whether there is a fault signal; if the motor is not in an idling state, the controller adjusts the motor to an idling state; if the display device displays a fault alarm, the fault is investigated and resolved according to the fault alarm prompt; if the motor is in an idling state and there is no fault signal, the operator can activate the one-button start switch to start the operation according to the pre-stored operation mode input into the working condition adaptive parameter database; after the operation is completed, the one-button stop switch is activated to end the operation.

[0072] This application provides an adaptive control method, control system, and electric excavator under different load modes. The single-operation mode of muck removal and the combined operation mode of invert arch excavation and muck removal of the electric excavator are selected as examples. Other operation modes and procedures are similar to the above examples and will not be described in detail.

[0073] In some embodiments, experimental parameter collection is first repeated to obtain experimental operation data of the tunnel electric excavator's working device 1 performing muck removal and rock clearing operations at the tunnel face. The operation data includes parameters such as the hydraulic pump outlet pressure P, the corresponding motor speed n, and the corresponding motor power W, forming a database of muck removal conditions. Additionally, on-site operation data obtained by the tunnel electric excavator under construction site conditions is also included in the database, forming a complete database of muck removal conditions. The hydraulic pump outlet pressure is divided into n pressure intervals (P1, P2), (P2, P3)...(P...).n P n+1 The corresponding motor speeds are divided into (n1, n2), (n2, n3)...(n... n n n+1 The corresponding motor power is divided into (W1, W2), (W2, W3)... (W n W n+1 By employing K-means clustering, for each operating cycle hydraulic pump outlet pressure range, the optimal motor speed value and the corresponding motor power value within the corresponding motor speed range are found within the hydraulic pump pressure range. This yields the optimal parameters within the range, and an optimal parameter model is established. The K-means clustering method may include the following steps:

[0074] S1: The ideal parameters that have been obtained for each motor speed range are close to the motor's high-efficiency range, which are used as the initial values ​​of the samples and the initial values ​​of the sample centers for the K-means clustering method.

[0075] S2: Cluster the parameters of the samples in each motor interval based on the initial values ​​of the sample centers, calculate the Euclidean distance between the group of parameters and the sample centers, and assign them to the class with the closest distance.

[0076] S3: The motor interval parameters are brought closer to the initial value of the sample center, and the data is added to the sample database for this group. The sample center value is then recalculated.

[0077] S4: Obtain the speed of the next set of motor intervals and repeat steps S1 to S4.

[0078] When the target parameters reach their optimum or the maximum number of iterations is reached, clustering is terminated, and the final cluster center value is taken as the optimal speed value corresponding to each hydraulic pump pressure range, which is n. 1* n 2* …n n* The optimal power values ​​corresponding to the optimal speed values ​​are P. 1* P 2* …P n* An optimal parameter model for the motor is established. Using this optimal model as a reference model, the pump outlet pressure is used as the input value, and the motor speed and power are used as the output values. Fuzzy neural adaptive control of the motor and hydraulic pump is employed, using adaptive motor control commands to adjust motor parameters in real time, ensuring the actual motor output approaches the optimal parameters, thus minimizing power consumption. The optimal parameters for the slag discharge condition are stored in an optimal parameter database to provide data input for the automatic operation mode.

[0079] The operation process of the automatic operation mode for slag removal includes: as shown in Figure 2, first select the electric excavator for construction operation, start the one-button start switch 102, operate on the display device module 8 to select the slag removal mode in the operation mode. At this time, it is a single working condition operation. The intelligent decision system 11 is turned off. Enter the working medium interface, select crushed stone and confirm to complete the selection of the slag removal and crushed stone operation mode.

[0080] When the automatic / manual switching switch 103 of the operation module 10 is switched to automatic mode, the controller module 12 will detect the automatic mode signal. After detecting the automatic mode signal, the controller module 12 will communicate with the motor controller 61 via the CAN bus to detect whether the motor speed is in an idling state and automatically receive the detection signal sent by the vehicle controller 62. The controller module 12 will automatically receive and identify whether there is a fault alarm. If the motor is not in an idling state or there is a fault alarm signal, the fault alarm module 9 will display the cause of the fault alarm on the display device via the CAN bus and sound an alarm through the fault alarm light 91. The controller module 12 will communicate with the motor controller 61 via the CAN bus to adjust the motor speed to idle speed. Even if the one-button start switch 102 of the operation module 10 is activated, the system will not work at this time to prevent damage to the power actuator. If the motor speed is in an idling state and there is no fault alarm signal, activating the one-button start switch 102 of the operation module 10 will send a command to the motor controller 61 and send a signal to the warning speaker 92 via the CAN bus to automatically sound the horn to indicate that the operation is about to begin and remind non-workers to stay away from the work site. Motor 2 will run automatically according to the pre-stored mode. Pump outlet pressure sensor 72 and motor speed sensor 71 will send detection signals to controller module 12. At this time, display device 8 will display the hydraulic pump pressure and motor speed in real time through CAN bus communication, reducing power consumption.

[0081] Slag discharge pre-storage operation mode: When the automatic manual switch 103 is activated, the operator operates the boom, stick, bucket, slewing and other actions in the working device 1 to perform slag discharge operation. At the same time, a predetermined working trajectory is formed for pre-storage. The motor speed automatically reaches the optimal parameter value through the parameter input of the working condition adaptive parameter database. At this time, the slag discharge operation realizes the working condition adaptive operation mode according to the predetermined working trajectory.

[0082] In other embodiments, for example, the adaptive control method and composite working mode for excavator invert excavation and muck removal, the working sequence is to first perform invert excavation and then muck removal. First, adaptive parameters for the invert excavation working condition are collected. Experiments are repeated to obtain data on the invert excavation operation performed by the electric excavator simulation device, including parameters such as the hydraulic pump outlet pressure P, the motor speed n corresponding to the pump outlet pressure, and the motor power W corresponding to the motor speed, forming a simulated excavation working condition database. Simultaneously, the actual on-site operating parameters measured by the excavator under construction site conditions are also included in the database, forming a parameter database for the invert excavation working condition. The hydraulic pump outlet pressure is divided into n different pressure ranges (P1, P2), (P2, P3)...(P...). n P n+1 The motor speed corresponding to the pump pressure is divided into (n1, n2), (n2, n3)...(n... n n n+1 The motor power corresponding to the motor speed is divided into (W1, W2), (W2, W3)... (W n W n+1 The optimal parameters for each hydraulic pump's outlet pressure range in a given operating cycle are obtained by using K-means clustering to find the optimal motor speed within the corresponding motor speed range. The optimal motor speed and power value at that speed are then used to establish the optimal parameter model. The K-means clustering method may include the following steps:

[0083] S1: Take the ideal parameters that have been obtained for each motor speed range and approach the motor's high-efficiency range as the initial values ​​of the samples, and use them as the initial values ​​of the sample centers for the K-means clustering method.

[0084] S2: Cluster the parameters of the samples in each motor interval based on the initial values ​​of the sample centers, calculate the Euclidean distance between the group of parameters and the sample centers, and assign them to the class with the closest distance.

[0085] S3: The motor interval parameters are brought closer to the initial value of the sample center, and the data is added to the sample database for this group. The sample center value is then recalculated.

[0086] S4: Obtain the speed of the next set of motor intervals and repeat steps S1 to S4.

[0087] When the target parameters reach their optimum or the maximum number of iterations is reached, clustering is terminated, and the final cluster center values ​​are taken as the optimal speed values ​​corresponding to each hydraulic pump pressure range, denoted as n1, n2, ... n. n The optimal power values ​​corresponding to the optimal speed values ​​are W1, W2, ... W. nAn optimal parameter model for the motor is established. Using this optimal model as a reference model, the pump outlet pressure is used as the input value, and the motor speed and power are used as the output values. Fuzzy neural adaptive control of the motor and hydraulic pump is employed, using the optimal motor model as the theoretical reference model. Motor parameters are adjusted in real time through adaptive motor control commands, ensuring that the actual motor output approaches the optimal parameters, minimizing power consumption. The adaptive optimal parameters for the invert excavation condition are stored in an optimal parameter database to provide data for composite working conditions. Similarly, adaptive parameters for the muck removal condition are obtained, and these two sets of parameters are stored in the optimal parameter database in the order of invert excavation followed by muck removal.

[0088] The operation sequence of the composite working condition mode is to first excavate the invert arch and then remove the slag. The specific process is as follows: As shown in Figure 2, first select the electric excavator to carry out the operation, start the one-button start switch 102, and operate on the display device module 8 to select the working mode as the composite working condition mode. First, excavate the invert arch and then remove the slag. At this time, the intelligent decision system 11 is turned on, and the working medium selection interface is entered. Select crushed stone and confirm to complete the selection of the composite working condition mode.

[0089] As shown in Figure 2, by operating the automatic / manual switch 103 via the operation module 10 to switch to automatic mode, the controller module 12 will detect the automatic mode signal. Upon detecting the automatic mode signal, the controller module 12 will communicate with the motor controller 61 via the CAN bus to detect whether the motor is in an idling state and automatically receive the detection signal sent by the vehicle controller 62. The controller module 12 will automatically receive and identify whether there is a fault alarm. If the motor is not in an idling state or if a fault alarm is detected, the fault alarm module 9 will display the cause of the fault alarm on the display device via the CAN bus, and the fault alarm light 91 will sound. The controller module 12 will communicate with the motor controller 61 via the CAN bus to automatically reduce the speed of motor 2 to idle and activate the one-button start operation button 102 of the operation module 10. At this time, the system will not work to prevent damage to the power actuator. If the motor is in an idling state and there is no fault alarm signal, activating the one-button start switch 102 of the operation module 10 will send a command to the motor controller 61 and send a signal via the CAN bus to the warning speaker 92 to automatically sound the horn to indicate that work is about to begin. Non-operators should stay away from the work area. Motor 2 will operate automatically according to the pre-stored mode. Pump outlet pressure sensor 72 and motor speed sensor 71 will send detection signals to controller module 12. At this time, display device 8 will display the hydraulic pump pressure and motor speed in real time via CAN bus communication for invert arch excavation. After invert arch excavation for a period of time, the one-button start switch 102 will be activated again. Intelligent decision system 11 will communicate with vehicle controller 62 via CAN bus to control motor controller to output automatic motor adjustment commands, adjusting the motor and hydraulic pump speeds to the optimal parameters for slag discharge mode. At this time, display device 8 will display the hydraulic pump pressure and motor speed in real time via CAN bus communication, and then assist in the discharge of crushed stone materials. This achieves optimal operation under complex working conditions and reduces power consumption.

[0090] Pre-storage mode for invert excavation: When the automatic / manual switching switch 103 is activated, the operator operates the boom, stick, bucket, and slewing of the working device 1 to perform invert excavation operations. At the same time, a predetermined working trajectory is formed and stored. The motor speed automatically reaches the optimal parameter value through the parameter input of the working condition adaptive parameter database. At this time, the invert excavation will perform the working condition adaptive operation mode according to the predetermined working trajectory.

[0091] This application provides a working condition adaptive control method, control system, and electric excavator under different load modes. Test data for different working media under different working conditions is obtained through repeated operation of the working device 1, resulting in a database of electric excavator working parameters under all working conditions. For each working condition, the working parameters are divided into intervals and an optimal parameter model is established. Through neural network fuzzy adaptive control, the optimal parameters for different working conditions are matched. The optimal parameter model for each working condition is pre-stored in the working system to establish a single working condition working mode, achieving automated operation. For multi-working-condition composite operations, an intelligent control system intervenes in the working mode, making intelligent decisions and adjusting the optimal parameters in real time to improve working efficiency and enhance the overall machine's endurance.

[0092] The foregoing description of this application illustrates and describes some exemplary embodiments. Various additions, modifications, alterations, etc., can be made to these exemplary embodiments without departing from the spirit and scope of this application. All content included in the foregoing description or shown in the accompanying drawings is intended to be illustrative and not restrictive. Furthermore, this application only shows and describes selected embodiments of this application; however, within the scope of the inventive concept expressed herein, in accordance with the foregoing teachings, and / or within the skill or knowledge of those skilled in the art, this application can be used and modified in various other combinations, modifications, and environments. Moreover, certain features and characteristics of each embodiment may be selectively interchanged and applied to other illustrated and non-illustrated embodiments of this application.

Claims

1. A working condition adaptive control method for electric tunnel construction machinery under different operating loads, the electric tunnel construction machinery performing multiple operating modes in the working medium of a tunnel and including a hydraulic pump and a motor, the motor providing power to the hydraulic pump, the hydraulic pump driving working parts to perform predetermined operating modes, the working condition adaptive control method comprising: By executing multiple operating modes on different working media using electric tunnel boring machines, the full-condition operating parameters of the electric tunnel boring machines are obtained. These full-condition operating parameters include the hydraulic pump outlet pressure (P) and motor parameters, where the motor parameters include at least one of the motor speed (n) and motor power (W). The full-condition range of the outlet pressure (P) is divided into n pressure intervals: (P1, P2), (P2, P3)...(P... n P n+1 The motor speed (n) is divided into n speed ranges across the entire operating condition: (n1, n2), (n2, n3)...(n... n n n+1 The corresponding full operating range of the motor power (W) is divided into n motor power intervals: (W1, W2), (W2, W3)...(W n W n+1 ); and optimize within each of the motor parameter ranges to obtain the optimal motor parameters for each range, establish a full-condition operation database, and store the predetermined working trajectory of the working parts in the full-condition operation database based on the predetermined operation mode, and send it to the tunnel electric engineering machinery along with the corresponding optimal motor speed.

2. The adaptive control method according to claim 1, wherein the optimal motor parameters for each interval are obtained by optimizing within each interval using the K-means clustering method; K-means clustering includes: The ideal parameters that have been obtained for each motor speed range are close to the motor's high-efficiency range, and these are used as the initial values ​​of the sample centers for the K-means clustering method. Based on the initial values ​​of the sample centers, the parameters within each range of motor parameters are clustered, the Euclidean distance between the group of parameters and the initial values ​​of the sample centers is calculated, and the parameters are assigned to the closest group; and the motor parameters that tend to the initial values ​​of the sample centers are added to the sample database of that group, and the sample center values ​​are recalculated.

3. The adaptive control method for operating conditions according to claim 2, wherein the K-means clustering method includes: When the motor parameters reach the optimal level or the maximum number of iterations, the clustering is terminated, and the final cluster center value is taken as the optimal motor speed corresponding to each hydraulic pump pressure range.

4. The adaptive control method for operating conditions according to claim 3, wherein, The optimal parameter model of the motor is established based on the optimal motor speed, with the outlet pressure of the hydraulic pump as the input value and the motor speed and / or electronic control power as the output value. Using the optimal parameters of the motor as a theoretical reference, fuzzy neural adaptive control of the motor-hydraulic pump is adopted. The motor speed and / or motor power are adjusted in real time through commands to make the motor parameters close to the optimal parameters.

5. The adaptive control method for operating conditions according to claim 1, wherein, Select a predetermined operating mode; if the motor is idling and there is no fault alarm, based on the selected operating mode, select the corresponding motor parameters and the predetermined working trajectory of the working parts from the full operating condition database, and input the selected motor parameters and the predetermined working trajectory of the working parts into the motor to execute the predetermined operating mode; if the motor is not idling and / or there is a fault alarm, after reducing the speed of the motor (2) to idle and / or resolving the fault, select the corresponding motor parameters and the predetermined working trajectory of the working parts from the full operating condition database, and input the selected motor parameters and the predetermined working trajectory of the working parts into the motor to execute the predetermined operating mode.

6. The adaptive control method for operating conditions according to claim 1, wherein, If the predetermined work mode is a single work mode that includes only one work mode, the intelligent decision system (11) is turned off and the single work mode is executed; if the predetermined work mode is a composite work mode that includes at least two work modes, the intelligent decision system (11) is turned on and the composite work mode is executed. The intelligent decision system (11) is used to adjust the motor parameters of the motor working in different work modes to adapt to different work modes.

7. The adaptive control method for operating conditions according to claim 1, wherein the full-condition operation database includes at least one of the following: experimental operation parameters, obtained by repeatedly performing operations on the tunnel electric engineering machinery based on a combination of each operating medium and each operating mode in a test field to obtain information about the hydraulic pump outlet pressure (P) and motor parameters; and field operation parameters, obtained by performing operations on the construction site on the tunnel electric engineering machinery based on a combination of each operating medium and each operating mode to obtain information about the hydraulic pump outlet pressure (P) and motor parameters.

8. The adaptive control method for operating conditions according to claim 1, wherein the operating mode includes at least one of the following: slag removal, invert arch excavation, tunnel hazard removal, crushing, and milling.

9. A condition-adaptive control system for implementing the condition-adaptive control method according to any one of claims 1-8, comprising: The motor (2) is powered by a power battery; A hydraulic pump (3), powered by the motor (2), drives the working elements of the tunnel electric engineering machinery to perform a predetermined working mode; a pressure sensor (72) is used to detect the outlet pressure of the hydraulic pump (3); and a controller module (12) is used to adjust the motor speed of the motor (2) according to the outlet pressure of the hydraulic pump (3), and is configured to optimize within each motor speed range to obtain the optimal motor speed in each range, establish a full-condition working database, and store the predetermined working trajectory of the working parts in the full-condition working database based on the predetermined working mode, and send it to the tunnel electric engineering machinery along with the corresponding optimal motor speed.

10. The adaptive control system for operating conditions according to claim 9, comprising: A one-button start switch (102) is used to enable the controller module (12) to send a start command to the motor and drive the working parts to perform the relevant operation mode; a one-button stop switch (101) is used to enable the controller module (12) to send a stop command to the motor after being started, and stop the working parts from performing the relevant operation mode.

11. The adaptive control system for operating conditions according to claim 10, comprising: A speed sensor (71) is used to detect the speed of the motor (2); The controller module (12) is configured to: when the motor (2) is not in an idling state and / or a fault alarm is detected, even if the one-button start switch (102) is activated, the working component will not work to avoid damage; when the motor (2) is in an idling state and no fault alarm is detected, and the one-button start switch (102) is activated, the corresponding motor speed is selected from the full-condition operation database according to the predetermined operation mode, and the motor (2) is driven to rotate at the corresponding motor speed.

12. The adaptive control system according to claim 9 includes an alarm speaker (92) connected to the controller module (12) for sounding an alarm to remind non-workers to stay away from the work site before the working component performs a predetermined work mode.

13. The adaptive control system according to claim 9, wherein the controller module (12) is configured to perform: when the motor speed of the motor (2) is not the optimal motor speed, to adjust the motor speed in real time through fuzzy neural adaptation so that the motor speed reaches the optimal motor speed.

14. The adaptive control system according to claim 9 includes a job selection module (104) connected to the controller module (12) by signal, for selecting a single job mode and a compound job mode to be executed, wherein the single job mode includes only one job mode and the compound job mode includes at least two job modes.

15. The adaptive control system according to claim 14 includes an intelligent decision system (11) connected to the operation selection module (104) by signal. When a single operation mode is selected, the intelligent decision system (11) is turned off; when a compound operation mode is selected, the intelligent decision system (11) is turned on to adjust the motor speed to the corresponding optimal motor speed between different operation modes.

16. An electric engineering machine for tunnels, comprising the working condition adaptive control system as described in any one of claims 9-15.

17. The electric tunnel construction machinery according to claim 16, configured as an electric tunnel excavator.

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