Cutting control method and device for full-width transverse roller

Abstract

The invention relates to a cutting control method and device for a full-width transverse roller. The cutting control method comprises the steps that a simulation cutting database is obtained; training the cutting performance parameter prediction model according to the simulation database to obtain a trained target cutting performance parameter prediction model; obtaining a cutting database based on the target cutting performance parameter prediction model; different cutting working conditions of the full-width transverse roller are obtained, and optimal cutting parameters corresponding to the cutting working conditions are determined according to the different cutting working conditions and the cutting database; the method comprises the steps of obtaining the current actual cutting working condition of the full-width transverse roller, determining the current optimal cutting parameter of the full-width transverse roller from the optimal cutting parameters, and controlling the full-width transverse roller to execute the cutting task according to the optimal cutting parameters. Optimization of the cutting parameters of the full-width transverse roller is achieved, the full-width transverse roller is controlled to execute the cutting task according to the optimal cutting parameters, and continuous cutting control is achieved.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent control technology, and in particular to a cutting control method and device for a full-width transverse drum. Background Art

[0002] During the excavation of semi-coal-rock tunnels, the properties of coal and rock will suddenly change within the same tunnel section. The cutting parameters set for a single coal and rock property will cause problems such as motor burnout, reducer damage, and reduced tooth life. At the same time, the contradiction between the sudden change of cutting load and the slow response of the hydraulic system makes it difficult to achieve continuous cutting in semi-coal-rock tunnels, which cannot meet the use requirements.

[0003] The cutting process of tunneling equipment equipped with full-width horizontal drums mainly consists of two processes: grooving and upper and lower cutting. The two processes are closely related and influence each other. If continuous cutting is to be achieved, it is necessary to establish the relationship between the cutting parameters and cutting performance in the two processes. In related technologies, this can be achieved by building a full-scale experimental platform or conducting on-site experiments in mines, and by simulation models. However, the above methods cannot guarantee the accuracy of the simulation of the cutting process, and cannot optimize the cutting parameters to achieve continuous cutting control. Summary of the Invention

[0004] The present disclosure provides a cutting control method, device, electronic device and computer-readable storage medium for a full-width transverse drum.

[0005] The technical solutions disclosed in this disclosure are as follows:

[0006] According to the first aspect of the embodiment of the present disclosure, a cutting control method for a full-width transverse drum is provided, the method comprising: obtaining a simulation cutting database, wherein the cutting simulation database comprises simulation cutting parameters and corresponding simulation cutting performance parameters; training a cutting performance parameter prediction model according to the simulation database, and obtaining a trained target cutting performance parameter prediction model; obtaining a cutting database based on the target cutting performance parameter prediction model, wherein the cutting database comprises cutting parameters and corresponding cutting performance parameters; obtaining different cutting conditions of the full-width transverse drum, and determining the optimal cutting parameters corresponding to each cutting condition according to the different cutting conditions and the cutting database; obtaining the current actual cutting condition of the full-width transverse drum, determining the current optimal cutting parameters of the full-width transverse drum from the optimal cutting parameters, and controlling the full-width transverse drum to perform the cutting task according to the optimal cutting parameters.

[0007] According to the second aspect of the embodiment of the present disclosure, a cutting control device for a full-width transverse roller is provided, comprising: a first acquisition module for acquiring a simulation cutting database, wherein the cutting simulation database includes simulation cutting parameters and corresponding simulation cutting performance parameters; a second acquisition module for training a cutting performance parameter prediction model according to the simulation database, and acquiring a trained target cutting performance parameter prediction model; a third acquisition module for acquiring a cutting database based on the target cutting performance parameter prediction model, wherein the cutting database includes cutting parameters and corresponding cutting performance parameters; a determination module for acquiring different cutting conditions of the full-width transverse roller, and determining the optimal cutting parameters corresponding to each cutting condition according to the different cutting conditions and the cutting database; a cutting control module for acquiring the current actual cutting condition of the full-width transverse roller, determining the current optimal cutting parameters of the full-width transverse roller from the optimal cutting parameters, and controlling the full-width transverse roller to perform the cutting task according to the optimal cutting parameters.

[0008] According to the third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to execute the instructions to implement the cutting control method of the full-width transverse roller as described in the first aspect of the embodiment of the present disclosure.

[0009] According to the fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the cutting control method of the full-width transverse roller as described in the first aspect of the embodiment of the present disclosure.

[0010] The technical solutions provided by the embodiments of the present disclosure bring at least the following beneficial effects:

[0011] In an embodiment of the present disclosure, a simulation cutting database is obtained, wherein the cutting simulation database includes simulation cutting parameters and corresponding simulation cutting performance parameters, a cutting performance parameter prediction model is trained according to the simulation database, and a trained target cutting performance parameter prediction model is obtained. Based on the target cutting performance parameter prediction model, a cutting database is obtained, wherein the cutting database includes cutting parameters and corresponding cutting performance parameters, different cutting conditions of the full-width transverse drum are obtained, and according to different cutting conditions and the cutting database, the optimal cutting parameters corresponding to each cutting condition are determined, and the current actual cutting condition of the full-width transverse drum is obtained. , determine the current optimal cutting parameters of the full-width transverse drum from the optimal cutting parameters, and control the full-width transverse drum to perform the cutting task according to the optimal cutting parameters. Therefore, the present disclosure can obtain the cutting database by combining the cutting simulation database with the target cutting performance parameter prediction model, and can determine the optimal cutting parameters corresponding to each cutting condition according to different cutting conditions and the cutting database, thereby realizing the optimization of the cutting parameters of the full-width transverse drum, controlling the full-width transverse drum to perform the cutting task according to the optimal cutting parameters, realizing continuous cutting control, improving the working efficiency of the tunneling equipment, and enhancing the adaptability of the tunneling equipment in semi-coal rock.

[0012] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0014] Figure 1 The figure is a flow chart of a cutting control method for a full-width transverse drum according to an exemplary embodiment.

[0015] Figure 2 It is a structural diagram of a full-width transverse roller according to another exemplary embodiment.

[0016] Figure 3 It is a flow chart of a cutting control method of a full-width transverse drum according to another exemplary embodiment.

[0017] Figure 4 It is a flow chart of a cutting control method of a full-width transverse drum according to another exemplary embodiment.

[0018] Figure 5 It is a schematic diagram showing the cutting of a full-width transverse roller according to another exemplary embodiment.

[0019] Figure 6It is a block diagram of a cutting control device for a full-width transverse drum according to another exemplary embodiment.

[0020] Figure 7 is a block diagram of an electronic device according to another exemplary embodiment. DETAILED DESCRIPTION

[0021] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0022] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.

[0023] The following embodiments describe in detail the cutting control method, device and electronic equipment for the full-width transverse roller proposed in the present disclosure.

[0024] Figure 1 A schematic flow chart of a cutting control method for a full-width transverse roller provided in an embodiment of the present disclosure.

[0025] like Figure 1 As shown, the cutting control method of the full-width transverse drum proposed in this embodiment includes the following steps:

[0026] S101, obtaining a simulation cutting database, wherein the cutting simulation database includes simulation cutting parameters and corresponding simulation cutting performance parameters.

[0027] It should be noted that, at present, manual control is still the main method of operation in underground coal mines. The underground coal mine environment is harsh and there are many equipments. Manual control operation is dangerous and the cutting efficiency is low. The intelligentization of tunneling equipment is an important part of realizing intelligent tunneling of coal mine tunnels. In the process of coal mine tunneling, the longitudinal axis cutting drum has the longest history and the widest range of use. It can cut out sections of different shapes and has strong adaptability to the stress conditions of surrounding rocks and the ups and downs of tunnels. However, the cutting trajectory is complex, including grooving, up and down swing cutting, left and right swing cutting, etc. The tunnel forming speed is slow. The structure of the full-width horizontal drum is as follows: Figure 2 As shown, the full-width transverse drum can simplify the cutting trajectory into grooving and up and down cutting, which greatly improves the cutting efficiency, and is comparable to the longitudinal axis cutting drum in terms of adaptability to the ups and downs and turns of the roadway.

[0028] It should be noted that in traditional cutting simulations, discrete element numerical simulation technology is often used to simulate the full-width horizontal drum cutting process. However, when multi-body motion is involved and multiple mechanical characteristics need to be paid attention to, the above method cannot achieve accurate analysis of complex systems, especially in mechatronic control systems. The complex dynamic interaction between the coal wall and the drum cannot be accurately simulated, and the parameter matching accuracy is insufficient. The present invention obtains a coal-rock simulation model and a cutting equipment simulation model of a full-width horizontal drum, couples the coal-rock simulation model and the cutting equipment simulation model, and obtains a cutting coupling model. Based on the cutting coupling model, the accuracy of the cutting simulation can be improved.

[0029] In the disclosed embodiment, a coal-rock simulation model and a cutting equipment simulation model of a full-width horizontal drum can be obtained, the coal-rock simulation model and the cutting equipment simulation model can be coupled to obtain a cutting coupling model, a simulation experiment can be performed on the cutting coupling model, and simulation cutting parameters and corresponding simulation cutting performance parameters can be obtained. Based on the simulation cutting parameters and the corresponding simulation cutting performance parameters, a simulation cutting database can be constructed.

[0030] Optionally, coal and rock samples can be taken from the mining area to measure the physical and mechanical parameters of the coal and rock to construct a coal and rock simulation model that conforms to reality, and a multi-body dynamic model of the full-width transverse roller (cutting equipment simulation model) can be constructed, and the coal and rock simulation model and the cutting equipment simulation model can be coupled to obtain a cutting coupling model.

[0031] For example, coal and rock samples can be sampled from the mining area, and standard samples can be prepared according to the physical and mechanical properties of coal and rock. The elastic modulus, Poisson's ratio, uniaxial compressive strength and tensile strength of the samples can be measured through uniaxial compression test and Brazilian splitting test, and the average value of the experimental results can be taken as the elastic modulus E, Poisson's ratio μ, and compressive strength σ of the coal and rock mass. c and tensile strength σ t According to the typical coal block shape, the discrete element particle size is set. The intrinsic parameters of the particles and the contact parameters between the particles can be set according to the actual situation. The discrete element coal and rock particles are calibrated according to the compressive and tensile strengths, and a coal-rock model library is established. According to the coal and rock conditions in the mining area, appropriate parameters are selected from the coal-rock model library to establish a coal-rock simulation model (layered coal-rock model) containing the hardness, thickness and confining pressure of coal and rock.

[0032] For example, the cutting equipment can be three-dimensionally modeled based on equipment parameters such as the size of the full-width transverse drum and the arrangement of the cutting teeth, and the material properties and motion pair parameters can be set to construct a cutting equipment simulation model of the full-width transverse drum. At the same time, in order to improve the simulation efficiency, the cutting equipment simulation model can be simplified according to actual conditions. For example, the connection position and the material receiving position can be simplified, etc., especially when the geometric complexity is low, which can reduce computing resources and maintain accuracy.

[0033] In the embodiment of the present disclosure, after the coal-rock simulation model and the cutting equipment simulation model are obtained, the coal-rock simulation model and the cutting equipment simulation model can be coupled through a coupling interface to obtain a cutting coupling model.

[0034] Optionally, communication between multiple software can be achieved through the Application Program Interface (API) between software, the main simulation software can be set, the calculation domain of the cutting coupling model can be set, and when the exfoliated coal rock particles exceed the particle boundary, the particles are automatically deleted to improve the simulation efficiency and establish boundary conditions. If the structure and load are symmetrical, the symmetrical boundary conditions can be used to reduce the size of the cutting coupling model, thereby reducing the amount of calculation and realizing collaborative simulation of the cutting process.

[0035] It should be noted that in order to ensure the accuracy of the cut coupling model, the cut coupling model can be verified using experimental data to obtain the error rate δ between the experimental value and the simulation value. The expression of the error rate δ can be: δ is the error rate, y exp is the experimental value, y sim is the simulation value.

[0036] Optionally, in response to the error rate being less than an error threshold, a final cutting coupling model is obtained; if in response to the error rate being greater than or equal to the error threshold, parameters of the cutting coupling model are adjusted to obtain the final cutting coupling model.

[0037] In the embodiment of the present disclosure, after obtaining the cutting coupling model, a simulation experiment can be performed on the cutting coupling model to obtain simulation cutting parameters and corresponding simulation cutting performance parameters, and a simulation cutting database can be constructed based on the simulation cutting parameters and the corresponding simulation cutting performance parameters.

[0038] It should be noted that the main simulated cutting parameters in the grooving process include coal rock hardness, propulsion cylinder speed, and drum speed. Other auxiliary parameters include grooving position and cutting depth. The main simulated cutting parameters in the upper and lower cutting processes include coal rock hardness, swing arm speed, and drum speed. Other auxiliary parameters include cutting height and cutting trajectory. For two different processes, multiple groups of simulation experiments are set up, and simulation experiments are carried out on the cutting coupling model to simulate the cutting process of the full-width horizontal drum and obtain the simulated cutting parameters and the corresponding simulated cutting performance parameters.

[0039] Optionally, the load fluctuation coefficient in the simulated cutting performance parameters can be calculated using data from the simulation experiment: Where ξ is the load fluctuation coefficient, D(F) is the standard deviation of the cut load, and E(F) is the mean of the cut load.

[0040] Optionally, the cutting specific energy consumption in the simulated cutting performance parameter can be calculated based on the data during the simulation experiment: Among them, F is the cutting load, v is the cutting speed, ρ is the material density, and M is the mass of the cutting material.

[0041] Optionally, the tunneling efficiency in the simulated cutting performance parameter can be calculated based on the data during the simulation experiment: Among them, Q is the excavation efficiency, T is the cutting time, and M is the mass of the cut material.

[0042] It should be noted that the simulated cutting performance parameters may also include coal loading rate and cross-sectional smoothness, etc. The calculation process of the coal loading rate and cross-sectional smoothness will not be described in detail here.

[0043] S102: Training a cutting performance parameter prediction model according to a simulation database to obtain a trained target cutting performance parameter prediction model.

[0044] It should be noted that the current cutting parameter setting of underground tunneling equipment relies on manual experience and lacks a theoretical basis for setting cutting parameters. When using multivariate functions to fit the cutting parameters and cutting performance, due to the lack of large-scale high-quality data sets, the fitting accuracy is low and the error between the fitting results and the actual results is large, which affects the subsequent selection of the optimal cutting parameters.

[0045] It should be noted that the cutting performance parameter prediction model is trained using the joint simulation database as a training sample to obtain a trained target cutting performance parameter prediction model.

[0046] In an embodiment of the present disclosure, the simulated cutting parameters can be input into the cutting performance parameter prediction model to obtain the cutting performance prediction parameters corresponding to the simulated cutting parameters. The cutting performance parameter prediction model is trained based on the cutting performance prediction parameters corresponding to the simulated cutting parameters and the simulated cutting performance parameters to obtain a trained target cutting performance parameter prediction model.

[0047] It should be noted that the present disclosure does not limit the type of the cutting performance parameter prediction model. Optionally, the cutting performance parameter prediction model can be a generative adversarial network (GAN) model, a gradient boosting tree (GBT) model, or a radial basis function network (RBFN) model.

[0048] Among them, the GAN model improves model accuracy by learning through the mutual game between the generative model and the discriminative model. It can generate high-quality samples and diversified data and is suitable for processing unknown or high-dimensional relationships. The GBT model iteratively trains multiple weak learners and combines their prediction results to gradually reduce the model error. It performs well in regression problems and can effectively handle complex nonlinear relationships. It is suitable for processing high-dimensional input data. The RBFN model maps the input space to a high-dimensional feature space through nonlinear radial basis functions, and then uses linear combinations to achieve predictions. It can better handle local nonlinear problems and is suitable for scenarios with small samples and the need for fast training.

[0049] It should be noted that the evaluation index value of the cutting performance parameter prediction model can be determined based on the cutting performance prediction parameters and the simulated cutting performance parameters corresponding to the simulated cutting parameters. For example: the mean square error, mean relative error and determination coefficient can be determined based on the cutting performance prediction parameters and the simulated cutting performance parameters corresponding to the simulated cutting parameters.

[0050] In the embodiment of the present disclosure, the evaluation index value of the cutting performance parameter prediction model can be determined based on the cutting performance prediction parameters corresponding to the simulation cutting parameters and the simulation cutting performance parameters. In response to the evaluation index value meeting the training end condition, the trained target cutting performance parameter prediction model is obtained.

[0051] S103: Acquire a cutting database based on the target cutting performance parameter prediction model, wherein the cutting database includes cutting parameters and corresponding cutting performance parameters.

[0052] In an embodiment of the present disclosure, after obtaining the trained target cutting performance parameter prediction model, the cutting parameters generated by the random function can be obtained, and the cutting parameters can be input into the target cutting performance parameter prediction model to obtain the cutting performance parameters corresponding to the cutting parameters. Based on the cutting parameters and the cutting performance parameters corresponding to the cutting parameters, a cutting database is constructed.

[0053] Furthermore, after the cutting database is acquired, the cutting database may be stored in a data recording device of the full-width transverse drum.

[0054] S104, obtaining different cutting conditions of the full-width transverse drum, and determining the optimal cutting parameters corresponding to each cutting condition based on the different cutting conditions and the cutting database.

[0055] It should be noted that the present disclosure does not limit the specific process of determining the optimal cutting parameters corresponding to each cutting condition based on different cutting conditions and cutting databases, and the parameters can be selected according to actual conditions.

[0056] Optionally, a cutting parameter optimization model is obtained, and based on a multi-objective optimization algorithm, the cutting parameter optimization model is solved in the cutting database to obtain a solution set. Based on different cutting conditions, the weight values ​​of the cutting performance parameters corresponding to each cutting condition are obtained. According to fuzzy theory, the membership function of each objective function is obtained. Based on the weight values ​​and membership functions of the cutting performance parameters, the membership value of each solution in the solution set is obtained. Based on the membership value of each solution, the optimal cutting parameters corresponding to each cutting condition are determined.

[0057] S105, obtaining the current actual cutting working condition of the full-width transverse drum, determining the current optimal cutting parameters of the full-width transverse drum from the optimal cutting parameters, and controlling the full-width transverse drum to perform the cutting task according to the optimal cutting parameters.

[0058] It should be noted that there are significant differences in the mechanical characteristics of coal and rock under different working conditions. In the excavation of semi-coal and rock tunnels, due to the sudden changes in the properties of coal and rock, traditional cutting control methods are difficult to adjust quickly to adapt to different working conditions. Semi-coal and rock excavation often adopts segmented adjustment, that is, non-continuous cutting, which cannot meet the excavation needs of efficient mines.

[0059] In the disclosed embodiment, the current actual cutting working condition of the full-width transverse drum can be obtained, the current optimal cutting parameters of the full-width transverse drum can be determined from the optimal cutting parameters, and the full-width transverse drum can be controlled to perform the cutting task according to the optimal cutting parameters to ensure the safety of the cutting equipment of the full-width transverse drum, thereby realizing continuous cutting and constant power operation of the cutting equipment during the working condition adjustment process.

[0060] In summary, the cutting control method for the full-width transverse roller provided by the embodiment of the present disclosure obtains a simulation cutting database, wherein the cutting simulation database includes simulation cutting parameters and corresponding simulation cutting performance parameters, and trains a cutting performance parameter prediction model according to the simulation database to obtain a trained target cutting performance parameter prediction model, and obtains a cutting database based on the target cutting performance parameter prediction model, wherein the cutting database includes cutting parameters and corresponding cutting performance parameters, obtains different cutting conditions of the full-width transverse roller, and determines the optimal cutting parameters corresponding to each cutting condition according to different cutting conditions and the cutting database, and obtains the full-width transverse roller. The current actual cutting conditions, the current optimal cutting parameters of the full-width transverse drum are determined from the optimal cutting parameters, and the full-width transverse drum is controlled to perform the cutting task according to the optimal cutting parameters. Therefore, the present disclosure can obtain the cutting database by combining the cutting simulation database with the target cutting performance parameter prediction model, and can determine the optimal cutting parameters corresponding to each cutting condition according to different cutting conditions and the cutting database, thereby realizing the optimization of the cutting parameters of the full-width transverse drum, controlling the full-width transverse drum to perform the cutting task according to the optimal cutting parameters, realizing continuous cutting control, improving the working efficiency of the tunneling equipment, and enhancing the adaptability of the tunneling equipment in semi-coal rock.

[0061] As a possible implementation, Figure 3 As shown, based on the above embodiment, according to different cutting conditions and the cutting database, the specific process of determining the optimal cutting parameters corresponding to each cutting condition includes the following steps:

[0062] S301, obtaining a cutting parameter optimization model.

[0063] In an embodiment of the present disclosure, multiple sub-objective functions and constraints are obtained, wherein the constraints include at least a decision space constructed by cutting parameters, inequality constraints, and equality constraints, and a cutting parameter optimization model is constructed based on the multiple sub-objective functions and constraints.

[0064] Optionally, the expression for cutting the parameterized model can be:

[0065] min y d =f d (x d )

[0066] f d (x d )=(f d1 (x d ),f d2 (x d )…f dn (x d ))

[0067]

[0068] Among them, x d is the cutting parameter variable, y d is a multi-objective optimization function, f d1 (x d ),f d2 (x d )…f dn (x d ) is each sub-objective function, X d is the decision space composed of cutting parameter variables, g i (x d ) is the i-th inequality constraint, h j (x d ) is the jth equality constraint, and the power constraint of the full-width transverse roller and the maximum output torque constraint can be selected as inequality constraints.

[0069] S302 , based on a multi-objective optimization algorithm, solving a cutting parameter optimization model in a cutting database to obtain a solution set.

[0070] Optionally, based on a fast elite multi-objective genetic algorithm, the cutting parameter optimization model can be solved in the cutting database to obtain a Pareto solution set.

[0071] S303: Based on different cutting working conditions, obtain the weight value of the cutting performance parameter corresponding to each cutting working condition.

[0072] Optionally, different cutting conditions may be determined based on the distribution of coal and rock, and according to the different cutting conditions, weight values ​​of cutting performance parameters corresponding to each cutting condition may be determined.

[0073] For example, for cutting condition 1, the weight of cutting specific energy consumption is set to 0.7, the load fluctuation coefficient is set to 0.1, and the excavation efficiency is set to 0.2.

[0074] S304: Obtain the membership function of each objective function according to fuzzy theory, and obtain the membership value of each solution in the solution set according to the weight value of the cut performance parameter and the membership function.

[0075] Among them, the size of the membership value can reflect the degree of optimization of the objective function.

[0076] In the embodiment of the present disclosure, the membership function of each objective function can be obtained according to fuzzy theory, and the membership value of each solution in the solution set can be obtained according to the weight value of the cut performance parameter and the membership function.

[0077] S305 : Determine the optimal cutting parameters corresponding to each cutting condition based on the membership value of each solution.

[0078] Optionally, for each cutting condition, the solution corresponding to the maximum membership value may be selected as the optimal cutting parameter for the cutting condition.

[0079] In summary, the cutting control method for the full-width transverse roller provided in the embodiment of the present disclosure obtains a cutting parameter optimization model, solves the cutting parameter optimization model in the cutting database based on a multi-objective optimization algorithm, obtains a solution set, obtains the weight value of the cutting performance parameters corresponding to each cutting condition based on different cutting conditions, obtains the membership function of each objective function according to fuzzy theory, obtains the membership value of each solution in the solution set based on the weight value and membership function of the cutting performance parameter, and determines the optimal cutting parameters corresponding to each cutting condition based on the membership value of each solution. Therefore, the present disclosure can determine the optimal cutting parameters corresponding to each cutting condition through a multi-objective optimization algorithm and the membership function of each objective function, thereby improving the accuracy and reliability of obtaining the optimal cutting parameters, providing data support for subsequent entry into the continuous cutting control mode and execution of the grooving process and the upper and lower cutting process, which is conducive to further optimizing the cutting process.

[0080] The specific process of the cutting control method of the full-width transverse roller proposed in the present disclosure is explained below.

[0081] For example, if Figure 4 As shown, the coal rock distribution can be determined, and the coal rock hardness, compressive tensile strength and surrounding rock stress, etc. can be determined based on the coal rock distribution, and the experimental cutting parameters and corresponding experimental cutting performance parameters can be obtained, and the coal rock simulation model and the cutting equipment simulation model of the full-width transverse roller can be obtained. The coal rock simulation model and the cutting equipment simulation model are coupled to obtain a cutting coupling model, and a simulation experiment is performed on the cutting coupling model to obtain the simulation cutting parameters and the corresponding simulation cutting performance parameters. Based on the simulation cutting parameters and the corresponding simulation cutting performance parameters, a simulation cutting database is constructed, and the cutting performance parameter prediction model is trained according to the simulation database to obtain the trained target cutting performance parameter prediction model. Based on the target cutting performance parameter prediction model, a cutting database is obtained, and different cutting conditions of the full-width transverse roller are obtained. According to different cutting conditions and the cutting database, the optimal cutting parameters corresponding to each cutting condition are determined, and the current actual cutting condition of the full-width transverse roller is obtained. The current optimal cutting parameters of the full-width transverse roller are determined from the optimal cutting parameters, as shown in FIG. Figure 5As shown, the full-width horizontal drum can be controlled to perform continuous cutting according to the optimal cutting parameters, that is, the full-width horizontal drum is controlled to perform the groove excavation process first, and the optimal cutting parameters including cutting height, cutting depth, drum speed, propulsion cylinder speed and other parameters are automatically matched according to the distribution of coal and rock. The cutting performance parameters corresponding to the optimal cutting parameters can be determined, such as cutting performance indicators such as maximum cutting load and cutting energy consumption ratio. After completing the groove excavation process, the upper and lower cutting processes are entered to plan the cutting path and match the optimal cutting parameters including lifting cylinder speed, drum speed and cutting trajectory. When cutting to the coal-rock interface, that is, when there is a sudden change in the coal-rock properties, the working condition is switched and the continuous cutting control program is entered. A continuous iterative control strategy can be used to issue a coordinated control strategy for the cutting flow control valve and the cutting inverter, and the optimal cutting parameters corresponding to each cutting condition are used to adjust the cutting parameters in real time. Continuous cutting is performed according to the cutting path. After completing a cutting process, the excavation crawler moves to the next groove excavation starting point to start a new cutting cycle.

[0082] In the embodiment of the present disclosure, the optimal cutting parameters for performing the cutting task can be obtained from the data recording device of the full-width transverse drum, and the target cutting performance parameters corresponding to the optimal cutting parameters can be recalculated based on the optimal cutting parameters. The cutting database can be updated based on the optimal cutting parameters and the target cutting performance parameters, and the updated cutting database can be fed back to the control system of the tunneling equipment, and the optimal cutting parameters can be re-determined so that the equipment can work according to the new optimal parameters in subsequent cutting operations.

[0083] In summary, the cutting control method of the full-width transverse drum proposed in the present invention expands the cutting simulation database by combining the cutting simulation database with the target cutting performance parameter prediction model, obtains the cutting database, and optimizes the cutting parameters of the full-width transverse drum based on the multi-objective optimization algorithm and the weight value of the cutting performance parameters corresponding to each cutting condition. During the excavation process, the optimal cutting parameters corresponding to different cutting conditions are used to form a continuous cutting control scheme library for different coal and rock stratification conditions and mine geological conditions. The full-width transverse drum is controlled to perform the cutting task according to the optimal cutting parameters, thereby realizing continuous cutting control. The database can also be continuously updated according to data feedback during the actual excavation process, and the optimal cutting parameters are iterated, which is conducive to further optimizing and improving the cutting process, improving the working efficiency of the excavation equipment, and enhancing the adaptability of the excavation equipment in semi-coal and rock.

[0084] Figure 6 FIG. 1 is a block diagram of a cutting control device for a full-width transverse drum according to an exemplary embodiment. Figure 6 As shown, the cutting control device 600 of the full-width transverse drum of the embodiment of the present disclosure may specifically include: a first acquisition module 601 , a second acquisition module 602 , a third acquisition module 603 , a determination module 604 and a cutting control module 605 .

[0085] A first acquisition module 601 is configured to acquire a simulation cutting database, wherein the cutting simulation database includes simulation cutting parameters and corresponding simulation cutting performance parameters;

[0086] A second acquisition module 602 is configured to train a cutting performance parameter prediction model according to the simulation database to obtain a trained target cutting performance parameter prediction model;

[0087] A third acquisition module 603 is configured to acquire a cutting database based on the target cutting performance parameter prediction model, wherein the cutting database includes cutting parameters and corresponding cutting performance parameters;

[0088] A determination module 604 is configured to obtain different cutting conditions of the full-width transverse drum and determine optimal cutting parameters corresponding to each cutting condition based on the different cutting conditions and the cutting database;

[0089] The cutting control module 605 is used to obtain the current actual cutting working condition of the full-width transverse roller, determine the current optimal cutting parameters of the full-width transverse roller from the optimal cutting parameters, and control the full-width transverse roller to perform the cutting task according to the optimal cutting parameters.

[0090] In one embodiment of the present disclosure, the first acquisition module 601 is also used to: obtain a coal-rock simulation model and a cutting equipment simulation model of a full-width transverse drum, couple the coal-rock simulation model and the cutting equipment simulation model to obtain a cutting coupling model; perform simulation experiments on the cutting coupling model to obtain simulation cutting parameters and corresponding simulation cutting performance parameters; and construct a simulation cutting database based on the simulation cutting parameters and the corresponding simulation cutting performance parameters.

[0091] In one embodiment of the present disclosure, the second acquisition module 602 is further used to: input the simulated cutting parameters into the cutting performance parameter prediction model to obtain the cutting performance prediction parameters corresponding to the simulated cutting parameters; train the cutting performance parameter prediction model according to the cutting performance prediction parameters corresponding to the simulated cutting parameters and the simulated cutting performance parameters to obtain a trained target cutting performance parameter prediction model.

[0092] In one embodiment of the present disclosure, the third acquisition module 603 is also used to: obtain the cutting parameters generated by the random function, input the cutting parameters into the target cutting performance parameter prediction model to obtain the cutting performance parameters corresponding to the cutting parameters; and construct a cutting database based on the cutting parameters and the cutting performance parameters corresponding to the cutting parameters.

[0093] In one embodiment of the present disclosure, the determination module 604 is also used to: obtain a cutting parameter optimization model; based on a multi-objective optimization algorithm, solve the cutting parameter optimization model in the cutting database to obtain a solution set; based on the different cutting conditions, obtain the weight value of the cutting performance parameter corresponding to each cutting condition; according to fuzzy theory, obtain the membership function of each objective function, and according to the weight value and membership function of the cutting performance parameter, obtain the membership value of each solution in the solution set; based on the membership value of each solution, determine the optimal cutting parameter corresponding to each cutting condition.

[0094] In one embodiment of the present disclosure, the determination module 604 is also used to: obtain multiple sub-objective functions and constraints, wherein the constraints include at least a decision space, inequality constraints and equality constraints constructed by cutting parameters; and construct a cutting parameter optimization model based on the multiple sub-objective functions and constraints.

[0095] In one embodiment of the present disclosure, after controlling the full-width transverse drum to perform the cutting task according to the optimal cutting parameters, the device 600 is further used to: obtain the optimal cutting parameters for performing the cutting task from the data recording device of the full-width transverse drum; obtain the target cutting performance parameters corresponding to the optimal cutting parameters; and update the cutting database based on the optimal cutting parameters and the target cutting performance parameters.

[0096] In the embodiment of the present disclosure, the specific manner in which each module in the cutting control device of the full-width transverse roller of the above embodiment performs operations has been described in detail in the embodiment of the cutting control method of the full-width transverse roller and will not be repeated here.

[0097] In summary, the cutting control device for the full-width transverse roller provided by the embodiment of the present disclosure obtains a simulation cutting database, wherein the cutting simulation database includes simulation cutting parameters and corresponding simulation cutting performance parameters, trains a cutting performance parameter prediction model according to the simulation database, obtains the trained target cutting performance parameter prediction model, obtains the cutting database based on the target cutting performance parameter prediction model, wherein the cutting database includes cutting parameters and corresponding cutting performance parameters, obtains different cutting conditions of the full-width transverse roller, determines the optimal cutting parameters corresponding to each cutting condition according to the different cutting conditions and the cutting database, and obtains the full-width transverse roller. The current actual cutting conditions, the current optimal cutting parameters of the full-width transverse drum are determined from the optimal cutting parameters, and the full-width transverse drum is controlled to perform the cutting task according to the optimal cutting parameters. Therefore, the present disclosure can obtain the cutting database by combining the cutting simulation database with the target cutting performance parameter prediction model, and can determine the optimal cutting parameters corresponding to each cutting condition according to different cutting conditions and the cutting database, thereby realizing the optimization of the cutting parameters of the full-width transverse drum, controlling the full-width transverse drum to perform the cutting task according to the optimal cutting parameters, realizing continuous cutting control, improving the working efficiency of the tunneling equipment, and enhancing the adaptability of the tunneling equipment in semi-coal rock.

[0098] Figure 7 is a block diagram of an electronic device 1000 according to an exemplary embodiment.

[0099] like Figure 7 As shown, the electronic device 1000 includes:

[0100] The memory 1001 and the processor 1002, a bus 1003 connecting different components (including the memory 1001 and the processor 1002), the memory 1001 stores a computer program, and when the processor 1002 executes the program, the cutting control method of the full-width transverse roller of the embodiment of the present disclosure is implemented.

[0101] Bus 1003 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0102] The electronic device 1000 typically includes a variety of electronic device-readable media, which can be any available media that can be accessed by the electronic device 1000, including volatile and non-volatile media, removable and non-removable media.

[0103] The memory 1001 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 1004 and / or cache memory 1005. The electronic device 1000 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 1006 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 Not shown, often called a "hard drive"). Although Figure 7 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 1003 via one or more data medium interfaces. Memory 1001 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present disclosure.

[0104] A program / utility 1008 having a set (at least one) of program modules 1007 may be stored, for example, in memory 1001. Such program modules 1007 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 1007 generally implement the functions and / or methods of the embodiments described herein.

[0105] The electronic device 1000 may also communicate with one or more external devices 1009 (e.g., a keyboard, a pointing device, a display 1011, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 1000, and / or any device that enables the electronic device 1000 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 1012. Furthermore, the electronic device 1000 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 1013. Figure 7 As shown, the network adapter 1013 communicates with other modules of the electronic device 1000 via the bus 1003. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0106] The processor 1002 executes various functional applications and data processing by running programs stored in the memory 1001 .

[0107] It should be noted that the implementation process and technical principles of the electronic equipment of this embodiment can be found in the aforementioned explanation of the cutting control method of the full-width transverse roller of the embodiment of the present disclosure, and will not be repeated here.

[0108] In order to implement the above embodiments, the present disclosure also proposes a computer-readable storage medium.

[0109] When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is enabled to perform the above-mentioned full-width cross-drum cutting control method. Optionally, the 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.

[0110] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0111] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A cutting control method for a full-width horizontal drum, characterized in that: The method comprises: Acquiring a simulation cutting database, wherein the cutting simulation database includes simulation cutting parameters and corresponding simulation cutting performance parameters; Training the cutting performance parameter prediction model according to the simulation database to obtain a trained target cutting performance parameter prediction model; Based on the target cutting performance parameter prediction model, obtaining a cutting database, wherein the cutting database includes cutting parameters and corresponding cutting performance parameters; Obtaining different cutting conditions of the full-width transverse drum, and determining optimal cutting parameters corresponding to each cutting condition based on the different cutting conditions and the cutting database; The current actual cutting working condition of the full-width transverse drum is obtained, the current optimal cutting parameters of the full-width transverse drum are determined from the optimal cutting parameters, and the full-width transverse drum is controlled to perform the cutting task according to the optimal cutting parameters.

2. The method according to claim 1, characterized in that The obtaining of the simulation cutting database includes: Acquire a coal-rock simulation model and a full-width horizontal drum cutting equipment simulation model, couple the coal-rock simulation model and the cutting equipment simulation model to obtain a cutting coupling model; Performing a simulation experiment on the cutting coupling model to obtain simulation cutting parameters and corresponding simulation cutting performance parameters; A simulation cutting database is constructed based on the simulation cutting parameters and corresponding simulation cutting performance parameters.

3. The method according to claim 1, characterized in that The step of training the cutting performance parameter prediction model according to the simulation database to obtain the trained target cutting performance parameter prediction model includes: Inputting the simulated cutting parameters into a cutting performance parameter prediction model to obtain cutting performance prediction parameters corresponding to the simulated cutting parameters; The cutting performance parameter prediction model is trained according to the cutting performance prediction parameters corresponding to the simulation cutting parameters and the simulation cutting performance parameters to obtain a trained target cutting performance parameter prediction model.

4. The method according to claim 3, characterized in that The obtaining of the cutting database based on the target cutting performance parameter prediction model includes: Obtaining a cutting parameter generated by a random function, inputting the cutting parameter into a target cutting performance parameter prediction model, and obtaining a cutting performance parameter corresponding to the cutting parameter; A cutting database is constructed based on the cutting parameters and the cutting performance parameters corresponding to the cutting parameters.

5. The method according to claim 1, wherein Determining the optimal cutting parameters corresponding to each cutting condition according to different cutting conditions and the cutting database includes: Obtaining a cutting parameter optimization model; Solving the cutting parameter optimization model in the cutting database based on a multi-objective optimization algorithm to obtain a solution set; Based on the different cutting working conditions, obtaining a weight value of a cutting performance parameter corresponding to each cutting working condition; According to fuzzy theory, a membership function of each objective function is obtained, and according to the weight value of the cutting performance parameter and the membership function, a membership value of each solution in the solution set is obtained; Based on the membership value of each solution, the optimal cutting parameters corresponding to each cutting working condition are determined.

6. The method according to claim 5, characterized in that The obtaining of the cutting parameter optimization model includes: Acquire multiple sub-objective functions and constraints, wherein the constraints include at least a decision space constructed by a cut parameter, an inequality constraint, and an equality constraint; A cutting parameter optimization model is constructed based on the multiple sub-objective functions and constraints.

7. The method according to any one of claims 1 to 6, characterized in that After controlling the full-width transverse drum to perform the cutting task according to the optimal cutting parameters, the method further includes: Obtaining optimal cutting parameters for executing a cutting task from a data recording device of the full-width transverse drum; Obtaining target cutting performance parameters corresponding to the optimal cutting parameters; The cutting database is updated based on the optimal cutting parameters and the target cutting performance parameters.

8. A cutting control device for a full-width horizontal drum, characterized in that: The device comprises: A first acquisition module is configured to acquire a simulation cutting database, wherein the cutting simulation database includes simulation cutting parameters and corresponding simulation cutting performance parameters; A second acquisition module is used to train the cutting performance parameter prediction model according to the simulation database to obtain the trained target cutting performance parameter prediction model; a third acquisition module, configured to acquire a cutting database based on a target cutting performance parameter prediction model, wherein the cutting database includes cutting parameters and corresponding cutting performance parameters; A determination module is used to obtain different cutting conditions of the full-width transverse drum and determine the optimal cutting parameters corresponding to each cutting condition based on the different cutting conditions and the cutting database; The cutting control module is used to obtain the current actual cutting conditions of the full-width transverse drum, determine the current optimal cutting parameters of the full-width transverse drum from the optimal cutting parameters, and control the full-width transverse drum to perform the cutting task according to the optimal cutting parameters.

9. An electronic device, characterized in that: including processor and memory; The processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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