Automatic generation method, device, equipment and medium for coal roadway support parameters

Abstract

This disclosure relates to the field of coal roadway support technology, and in particular to a method, apparatus, equipment, and medium for automatically generating coal roadway support parameters. The method involves receiving user-inputted roadway parameters to be predicted; determining the grid monitoring point positions corresponding to the target roadway model based on these parameters; obtaining the initial support parameters corresponding to the target roadway model based on the predicted roadway parameters and the target support parameter prediction model; performing simulation processing using Flac3D based on the predicted roadway parameters, initial support parameters, and grid monitoring point positions to obtain the target displacement corresponding to the target roadway model; determining whether the target displacement falls within a preset range; and, if the target displacement falls within the preset range, determining the initial support parameters as the target support parameters. This method can accurately and intelligently determine the most suitable roadway support scheme to meet the needs of on-site construction.

Description

Technical Field

[0001] This disclosure relates to the field of coal roadway support technology, and in particular to a method, apparatus, equipment and medium for automatically generating coal roadway support parameters. Background Technology

[0002] In the process of coal mining, a large number of roadways need to be excavated. In order to prevent the deformation and collapse of the surrounding rock in the roadways from posing a danger to the construction workers, roadway support is used to keep the roadways unobstructed and the surrounding rock stable, so as to avoid unsafe events such as deformation and collapse of the surrounding rock in the roadways.

[0003] In the existing technology, the selection of various support parameters in the design of roadway support projects usually depends on the experience of experts and the level of personal knowledge. Then, the support scheme for the roadway is determined manually based on FLAC3D simulation.

[0004] However, the use of existing technologies, which rely heavily on manual methods and experience, makes it impossible to accurately and intelligently determine the most suitable tunnel support scheme, thus failing to meet the needs of on-site construction. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, equipment, and medium for automatically generating coal roadway support parameters to address the aforementioned technical problems. The terminal equipment can determine the location of grid monitoring points based on the user-input roadway parameters to be predicted, and automatically obtain initial support parameters based on the target support parameter prediction model. This eliminates the need to rely on manual experience and individual knowledge to determine support parameters. Furthermore, it can automatically obtain the target displacement through Flac3D simulation processing based on the roadway parameters to be predicted, the initial support parameters, and the grid monitoring point locations. It also determines whether the current support parameters best match the target support parameters of the actual project, thereby accurately and intelligently determining the most suitable roadway support scheme to meet the needs of on-site construction.

[0006] A first aspect of this disclosure provides a method for automatically generating coal roadway support parameters, the method comprising: receiving roadway parameters to be predicted input by a user, and determining the location of grid monitoring points corresponding to a target roadway model based on the roadway parameters to be predicted;

[0007] Based on the prediction model of the roadway parameters to be predicted and the target support parameters, the initial support parameters corresponding to the target roadway model are obtained.

[0008] Based on the predicted tunnel parameters, the initial support parameters, and the location of the grid monitoring points, the target displacement corresponding to the target tunnel model is obtained by simulation processing using Flac3D.

[0009] Determine whether the target displacement falls within a preset range;

[0010] When it is determined that the target displacement falls within a preset range, the initial support parameters are determined as the target support parameters.

[0011] In one embodiment, obtaining the initial support parameters corresponding to the predicted roadway parameters based on the prediction model of the predicted roadway parameters and the target support parameters includes:

[0012] The roadway parameters to be predicted are input into the trained target support parameter prediction model;

[0013] Obtain the initial support parameters output by the target support parameter prediction model.

[0014] In one embodiment, before inputting the roadway parameters to be predicted into the trained target support parameter prediction model, the method further includes:

[0015] Based on historical tunnel parameters and historical support parameters, a target training set is determined, wherein the historical tunnel parameters correspond one-to-one with the tunnel models, the historical support parameters correspond one-to-one with the historical tunnel parameters, and the target training set includes tunnel parameters corresponding to multiple tunnel models and support parameters corresponding to the tunnel parameters corresponding to the tunnel models.

[0016] The target training set is input into the initial support parameter prediction model, and the initial support parameter prediction model is trained to obtain the target support parameter prediction model.

[0017] In one embodiment, before determining the target training set based on historical roadway parameters and historical support parameters, the method further includes:

[0018] Determine whether there is any abnormal data in the historical tunnel parameters and the historical support parameters;

[0019] If so, clean up any abnormal data in the historical tunnel parameters and historical support parameters;

[0020] After cleaning and processing the abnormal data in the historical roadway parameters and historical support parameters, the historical roadway parameters and historical support parameters are standardized.

[0021] In one embodiment, the step of obtaining the target displacement corresponding to the target roadway model by performing simulation processing using Flac3D based on the roadway parameters to be predicted, the initial support parameters, and the grid monitoring point locations includes:

[0022] The target script is automatically generated based on the predicted tunnel parameters, the initial support parameters, and the locations of the grid monitoring points.

[0023] After generating the target script, the target script is imported into Flac3D so that Flac3D can perform simulation processing to obtain the target displacement corresponding to the target tunnel model.

[0024] In one embodiment, the method further includes: when it is determined that the target displacement does not belong to the preset range, adjusting the parameters corresponding to the target support parameter prediction model until the target displacement belongs to the preset range.

[0025] In one embodiment, the method further includes: displaying the tunnel parameters to be predicted on a first preset interface;

[0026] After obtaining the target displacement and target support parameters corresponding to the target tunnel model, the target displacement and target support parameters are displayed on the second preset page.

[0027] A second aspect of this disclosure provides an automatic coal roadway support parameter generation device, the device comprising:

[0028] The grid monitoring point location determination module is used to receive the roadway parameters to be predicted input by the user, and determine the grid monitoring point location corresponding to the target roadway model based on the roadway parameters to be predicted.

[0029] The initial support parameter acquisition module is used to acquire the initial support parameters corresponding to the target roadway model based on the roadway parameters to be predicted and the target support parameter prediction model.

[0030] The target displacement acquisition module is used to obtain the target displacement corresponding to the target roadway model by performing simulation processing using Flac3D based on the roadway parameters to be predicted, the initial support parameters, and the grid monitoring point positions.

[0031] The judgment module is used to determine whether the target displacement belongs to a preset range;

[0032] The target support parameter determination module is used to determine the initial support parameters as target support parameters when the target displacement is determined to be within a preset range.

[0033] A third aspect of this disclosure provides an electronic device, comprising:

[0034] One or more processors;

[0035] Storage device for storing one or more programs.

[0036] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any of the first aspects.

[0037] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the methods described in any of the first aspects.

[0038] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0039] This disclosure provides a method, apparatus, equipment, and medium for automatically generating coal roadway support parameters. The method involves receiving user-input roadway parameters to be predicted, determining the grid monitoring point locations corresponding to the target roadway model based on these parameters, obtaining initial support parameters corresponding to the target roadway model based on the predicted roadway parameters and the target support parameter prediction model, performing simulation processing using Flac3D based on the predicted roadway parameters, initial support parameters, and grid monitoring point locations, and obtaining the target displacement corresponding to the target roadway model, determining whether the target displacement falls within a preset range, and, if the target displacement falls within the preset range, determining the initial support parameters as the target support parameters. Through the above process, the terminal equipment can determine the location of the grid monitoring points based on the roadway parameters to be predicted input by the user, and automatically obtain the initial support parameters according to the target support parameter prediction model. It does not need to rely on human experience and individual knowledge to determine the support parameters. Furthermore, it can automatically obtain the target displacement through Flac3D simulation processing based on the roadway parameters to be predicted, the initial support parameters, and the location of the grid monitoring points, and determine whether the current support parameters are the most consistent with the target support parameters of the actual project. Thus, it can accurately and intelligently determine the most suitable roadway support scheme to meet the needs of on-site construction. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0041] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating an automatic generation method for coal roadway support parameters provided in this embodiment of the present disclosure;

[0043] Figure 2This is a schematic diagram of the structure of an automatic coal roadway support parameter generation device provided in an embodiment of the present disclosure;

[0044] Figure 3 This is an internal structural diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0045] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0046] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0047] The terms "first," "second," etc., used in this disclosure and in the claims are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this disclosure can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0048] Currently, coal mining primarily involves underground extraction, requiring the excavation of numerous roadways. To ensure safety during coal mining, maintaining roadway access and surrounding rock stability through roadway support is crucial for coal mine construction and production. With the increasing depth, scope, and intensity of coal mining, coupled with the increasing complexity and uncertainty of geological conditions, the conditions for roadway support are becoming increasingly complex. Consequently, the requirements and standards for roadway support are rising, along with the growing difficulty of roadway support operations.

[0049] In existing technologies, the design of roadway support engineering usually relies on the experience of experts and the knowledge level of individuals. Support parameters in roadway support engineering design are determined for different roadway models. After determining the support parameters, it is necessary to manually write scripts that include roadway parameters, various roadway models, and support parameters, and then manually import the scripts into FLAC3D. FLAC3D is used to simulate and analyze the influence of various factors on the deformation and stress of the roadway surrounding rock, and to calculate and analyze the stress of support bodies such as anchor bolts and anchor cables, so as to determine the most suitable support scheme.

[0050] However, in practical applications, the FLAC3D modeling process is relatively complex, making it difficult and costly for construction workers who have not learned and trained in the software to get started. Furthermore, due to the complexity and uncertainty of geological conditions, the selection of support parameters in the design of tunnel support projects relies too much on the experience of experts and the knowledge level of individuals, making it impossible to accurately and intelligently determine the most suitable tunnel support scheme and meet the needs of on-site construction.

[0051] To address the aforementioned issues, this disclosure provides a method, apparatus, equipment, and medium for automatically generating coal roadway support parameters. The method involves receiving user-inputted roadway parameters to be predicted; determining the grid monitoring point locations corresponding to the target roadway model based on these parameters; obtaining the initial support parameters corresponding to the target roadway model based on the predicted roadway parameters and the target support parameter prediction model; performing simulation processing using Flac3D based on the predicted roadway parameters, initial support parameters, and grid monitoring point locations to obtain the target displacement corresponding to the target roadway model; determining whether the target displacement falls within a preset range; and, if the target displacement falls within the preset range, determining the initial support parameters as the target support parameters. Through the above process, the terminal equipment can determine the location of the grid monitoring points based on the roadway parameters to be predicted input by the user, and automatically obtain the initial support parameters according to the target support parameter prediction model. It does not need to rely on human experience and individual knowledge to determine the support parameters. Furthermore, it can automatically obtain the target displacement through Flac3D simulation processing based on the roadway parameters to be predicted, the initial support parameters, and the location of the grid monitoring points, and determine whether the current support parameters are the most consistent with the target support parameters of the actual project. Thus, it can accurately and intelligently determine the most suitable roadway support scheme to meet the needs of on-site construction.

[0052] To illustrate this solution in more detail, the following will use examples to illustrate it. Figure 1 To explain, it is understandable that Figure 1 The steps involved may include more or fewer steps in actual implementation, and the order of these steps may also be different, depending on whether the automatic repair method provided in the embodiments of this application can be achieved.

[0053] Figure 1 This is a flowchart illustrating an automatic coal roadway support parameter generation method provided in an embodiment of this disclosure. The method in this embodiment is executed by an automatic coal roadway support parameter generation device applied to electronic equipment, which can be implemented in hardware / or software. Figure 1 As shown, the method for automatically generating coal roadway support parameters specifically includes the following steps:

[0054] S11: Receive the roadway parameters to be predicted input by the user, and determine the location of the grid monitoring point corresponding to the target roadway model based on the roadway parameters to be predicted.

[0055] Specifically, the parameters of the roadway to be predicted correspond one-to-one with the roadway model. Based on the parameters of the roadway to be predicted, the corresponding target roadway model can be generated by simulation software such as Flac3D. The target roadway model includes, but is not limited to, any one of the following: straight-walled circular arch roadway, rectangular roadway, and sloping-roof trapezoidal roadway. The parameters of the roadway to be predicted include, but are not limited to, roadway burial depth, coal seam thickness, coal seam number, coal seam strength, immediate roof thickness, immediate roof strength, basic roof thickness, basic roof strength, immediate floor thickness, immediate floor strength, old floor thickness, and old floor strength. This disclosure does not impose specific limitations, and those skilled in the art can set them according to the actual situation.

[0056] It should be noted that the aforementioned tunnel parameters to be predicted were determined by the construction personnel based on site conditions and historical manual experience. However, this is not a limitation, and this disclosure does not impose specific restrictions; those skilled in the art can set the parameters according to the actual situation.

[0057] The grid monitoring point location refers to the multiple grid monitoring points set for monitoring the surrounding rock conditions in the target roadway model during the modeling and simulation experiment using Flac3D simulation software. Different roadway models correspond to different grid monitoring point locations. This disclosure does not specifically limit the setting of grid monitoring points.

[0058] Specifically, after receiving the roadway parameters to be predicted input by the user, the terminal device, such as a computer, determines the location of the grid monitoring point corresponding to the target roadway model based on the roadway parameters.

[0059] Optionally, based on the above embodiments, in some embodiments of this disclosure, a target application can be installed on a terminal device such as a computer. The target application, upon receiving user-inputted roadway parameters, determines the location of the grid monitoring points corresponding to the target roadway model based on these parameters. However, this is not limited to this; this disclosure does not impose specific limitations, and those skilled in the art can set it according to actual circumstances.

[0060] S12: Based on the prediction model of the roadway parameters to be predicted and the target support parameters, obtain the initial support parameters corresponding to the target roadway model.

[0061] The initial support parameters are used to determine how to support the roadway in the roadway support project. The initial support parameters include, but are not limited to, the diameter, length, spacing and row spacing of the roof anchor bolts and the side anchor bolts, the diameter, length, spacing, row spacing and arrangement of the roof anchor cables, but are not limited to these. This disclosure does not specifically limit the parameters, and those skilled in the art can set them according to the actual situation.

[0062] The aforementioned target support parameter prediction model refers to a model used to predict the initial support parameters corresponding to the target roadway model based on the roadway parameters to be predicted. For example, the target support parameter prediction model can be a BP neural network, but it is not limited thereto. This disclosure does not impose specific limitations, and those skilled in the art can set it according to the actual situation.

[0063] Specifically, after receiving the roadway parameters to be predicted input by the user, the terminal device, such as a computer, responds to the roadway parameters to be predicted by calling the trained target support parameter prediction model to obtain the initial support parameters corresponding to the target roadway model.

[0064] S13: Based on the roadway parameters to be predicted, the initial support parameters, and the location of the grid monitoring points, the target displacement corresponding to the target roadway model is obtained by simulation processing using Flac3D.

[0065] Flac3D is a digital simulation software that is often used to simulate and analyze the influence of various factors on the deformation and stress of the surrounding rock of the roadway based on roadway parameters and support parameters, as well as to calculate and analyze the stress of support bodies such as anchor bolts and anchor cables, so as to optimize the support scheme and obtain a more suitable support scheme.

[0066] The aforementioned target displacement refers to the displacement of the surrounding rock in the roadway. It is usually used to characterize the displacement of the surrounding rock in the entire roadway by the displacement corresponding to the grid monitoring point position, but it is not limited to this. This disclosure does not specifically limit it, and those skilled in the art can set it according to the actual situation.

[0067] Specifically, the terminal equipment can automatically perform simulation processing using Flac3D based on the parameters of the roadway to be predicted, the initial support parameters, and the location of the grid monitoring points, thereby obtaining the target displacement corresponding to the target roadway model.

[0068] Optionally, based on the above embodiments, in some embodiments of this disclosure, one implementation of S13 may be:

[0069] S131: Automatically generate the target script based on the roadway parameters to be predicted, the initial support parameters, and the location of grid monitoring points.

[0070] The target script is used to obtain information such as the predicted tunnel parameters, initial support parameters, and grid monitoring point locations during the simulation process using Flac3D. The target script can be, for example, a TXT script. Generating a TXT script involves writing the predicted tunnel parameters, initial support parameters, and grid monitoring point locations using Flac3D command streams and the Fish language. However, this is not a limitation, and this disclosure does not impose specific restrictions; those skilled in the art can set it according to actual circumstances.

[0071] S132: After generating the target script, import the target script into Flac3D so that Flac3D can perform simulation processing to obtain the target displacement corresponding to the target tunnel model.

[0072] Specifically, the terminal device automatically generates a target script based on the target roadway model's corresponding roadway parameters to be predicted, initial support parameters, and grid monitoring point locations. After generating the target script, it is imported into Flac3D, enabling Flac3D to perform simulation processing based on the information in the target script to obtain the target displacement corresponding to the target roadway model.

[0073] Optionally, importing the target script into Flac3D can be achieved by calling a preset program interface, but it is not limited thereto. This disclosure does not impose specific limitations, and those skilled in the art can set it according to the actual situation.

[0074] Thus, the method for automatically generating coal roadway support parameters provided in this embodiment, through the above process, can automatically generate a target script based on the roadway parameters to be predicted, the initial support parameters, and the grid monitoring point positions, and automatically import the target script into Flac3D for simulation processing, without the need for manual methods, and can intelligently obtain the target displacement.

[0075] S14: Determine whether the target displacement falls within the preset range.

[0076] The preset range is a range set to determine whether the current initial support parameters are the most suitable and most consistent with the target support parameters in the actual roadway support project. This disclosure does not specifically limit the specific value of the preset range, and those skilled in the art can set it according to the actual situation.

[0077] S15: When it is determined that the target displacement is within the preset range, the initial support parameters are determined as the target support parameters.

[0078] Specifically, after obtaining the target displacement, the terminal device determines whether the target displacement falls within a preset range. If the target displacement is determined to fall within the preset range, the current initial support parameters can be determined as the target support parameters.

[0079] Thus, the method for automatically generating coal roadway support parameters provided in this embodiment receives the roadway parameters to be predicted input by the user, determines the location of the grid monitoring points corresponding to the target roadway model based on the roadway parameters to be predicted, obtains the initial support parameters corresponding to the target roadway model based on the roadway parameters to be predicted and the target support parameter prediction model, performs simulation processing using Flac3D based on the roadway parameters to be predicted, the initial support parameters, and the grid monitoring point locations, and obtains the target displacement corresponding to the target roadway model, determines whether the target displacement belongs to a preset range, and determines the initial support parameters as the target support parameters when it is determined that the target displacement belongs to the preset range. Through the above process, the terminal equipment can determine the location of the grid monitoring points based on the roadway parameters to be predicted input by the user, and automatically obtain the initial support parameters according to the target support parameter prediction model. It does not need to rely on human experience and individual knowledge to determine the support parameters. Furthermore, it can automatically obtain the target displacement through Flac3D simulation processing based on the roadway parameters to be predicted, the initial support parameters, and the location of the grid monitoring points, and determine whether the current support parameters are the most consistent with the target support parameters of the actual project. Thus, it can accurately and intelligently determine the most suitable roadway support scheme to meet the needs of on-site construction.

[0080] Optionally, based on the above embodiments, in some embodiments of this disclosure, one implementation of S12 may be:

[0081] S121: Input the roadway parameters to be predicted into the trained target support parameter prediction model.

[0082] Specifically, after receiving the roadway parameters to be predicted input by the user, the terminal device, such as a computer, responds to the roadway parameters to be predicted by calling the trained target support parameter prediction model and inputting the roadway parameters to be predicted into the trained target support parameter prediction model.

[0083] Optionally, based on the above embodiments, in some embodiments of this disclosure, the following steps are further included before performing S121:

[0084] S1211: Determine the target training set based on historical tunnel parameters and historical support parameters.

[0085] Among them, the historical tunnel parameters correspond one-to-one with the tunnel model, the historical support parameters correspond one-to-one with the historical tunnel parameters, and the target training set includes the tunnel parameters corresponding to multiple tunnel models, as well as the support parameters corresponding to the tunnel parameters of the tunnel models.

[0086] Specifically, the terminal equipment collects historical roadway parameters corresponding to different roadway models, as well as historical support parameters corresponding to each historical roadway parameter. Based on the collected historical roadway parameters and corresponding historical support parameters of different roadway models, a target training set is formulated for training the initial support parameter prediction model.

[0087] Optionally, based on the above embodiments, since the accuracy of the historical roadway parameters and historical support parameters determines the accuracy of the target training set when determining the target training set, and the target training set is used to train the initial support parameter prediction model, in order to ensure that the initial support parameter prediction model can be trained more accurately, in some embodiments of this disclosure, the following steps are further included before executing S1211:

[0088] Step A: Determine if there are any abnormal data in the historical tunnel parameters and historical support parameters.

[0089] Outlier data refers to values ​​that deviate from the overall data distribution. In order to ensure that a more accurate target training set is obtained, outlier data that deviates from the overall data distribution should be cleaned.

[0090] Step B: If so, clean up any abnormal data in the historical roadway parameters and historical support parameters.

[0091] The cleaning process may include, but is not limited to, deleting or replacing abnormal data. This disclosure does not impose specific limitations, and those skilled in the art can set it according to the actual situation.

[0092] Specifically, when it is determined that there are abnormal data in the historical roadway parameters and historical support parameters, the abnormal data in the historical roadway parameters and historical support parameters are cleaned.

[0093] For example, the tunnel model includes straight-walled arched tunnels, rectangular tunnels, and sloping-top trapezoidal tunnels. However, when collecting historical tunnel parameters, tunnel parameters corresponding to other tunnel models may be collected, such as trapezoidal tunnels. That is, the base angle of a trapezoidal tunnel is close to 90 degrees, which makes the entire trapezoidal tunnel close to a rectangular tunnel. In this case, it is approximated as a rectangular tunnel and the abnormal data is deleted. However, it is not limited to this. This disclosure does not specifically limit it. Those skilled in the art can set it according to the actual situation.

[0094] Step C: After cleaning and processing the abnormal data in the historical roadway parameters and historical support parameters, the historical roadway parameters and historical support parameters are standardized.

[0095] The standardization process refers to selecting and classifying historical tunnel parameters and historical support parameters in a preset table to facilitate the subsequent construction of the target training set. For example, straight-walled arched tunnels included in the tunnel model can be represented by numbers, such as 1, and rectangular tunnels can be represented by 2. However, this is not a limitation and this disclosure is not specific. Those skilled in the art can set it according to the actual situation.

[0096] Specifically, after confirming that the cleaning of abnormal data in the historical roadway parameters and historical support parameters has been completed, the cleaned historical roadway parameters and historical support parameters are further standardized.

[0097] Thus, the method for automatically generating coal roadway support parameters provided in this embodiment, through the above process, can clean and standardize historical roadway parameters and historical support parameters before using them, thereby obtaining a highly accurate target training set, which ensures that the initial support parameter prediction model can be trained using a more accurate target training set.

[0098] S1212: Input the target training set into the initial support parameter prediction model, train the initial support parameter prediction model, and obtain the target support parameter prediction model.

[0099] Specifically, the terminal device inputs the target training set into the initial support parameter prediction model to train the initial support parameter prediction model. During the training process, network parameters, training times, learning rate, and the minimum training error are set. When the loss function of the initial support parameter prediction model converges, the current initial support parameter prediction model is determined as the target support parameter prediction model.

[0100] S122: Obtain the initial support parameters output by the target support parameter prediction model.

[0101] Specifically, the terminal device inputs the roadway parameters to be predicted into the trained target support parameter prediction model, so that the target support parameter prediction model can output the initial support parameters corresponding to the target roadway model based on the roadway parameters to be predicted.

[0102] Thus, the method for automatically generating coal roadway support parameters provided in this embodiment, through the above process, can automatically predict the initial support parameters corresponding to the target roadway model based on the parameters of the roadway to be predicted, without relying on manual experience and individual knowledge to determine the initial support parameters, thereby improving the accuracy of the initial support parameters.

[0103] Optionally, based on the above embodiments, in some embodiments of this disclosure, when it is determined that the target displacement does not belong to the preset range, the reference is continued. Figure 1 As shown, it also includes:

[0104] S16: Adjust the parameters corresponding to the target support parameter prediction model until the target displacement falls within the preset range.

[0105] Specifically, when the terminal device determines that the target displacement is not within the preset range, it indicates that the current initial support parameters are not the most suitable support parameters. In this case, it is necessary to adjust the parameters corresponding to the target support parameter prediction model, and use the adjusted target support parameter prediction model to re-obtain the support parameters until the target displacement is within the preset range. Then, the current initial support parameters can be determined as the target support parameters.

[0106] Thus, the method for automatically generating coal roadway support parameters provided in this embodiment, through the above process, can automatically adjust the parameters corresponding to the target support parameter prediction model when it is determined that the current initial support parameters are not the most suitable support parameters, until the most suitable target support parameters are obtained, thereby accurately and intelligently determining the most suitable roadway support scheme to meet the needs of on-site construction.

[0107] Optionally, based on the above embodiments, in some embodiments of this disclosure, in order to enable users to intuitively obtain the roadway parameters to be predicted, as well as the target displacement and target support parameters, the method further includes:

[0108] The parameters of the roadway to be predicted are displayed on the first preset interface.

[0109] After obtaining the target displacement and target support parameters corresponding to the target tunnel model, the target displacement and target support parameters are displayed on the second preset page.

[0110] Specifically, when the target script is imported into Flac3D for simulation processing, the predicted roadway parameters in the target script can be visualized on the first preset interface, and the target displacement and target support parameters can be visualized on the second preset interface.

[0111] Thus, the method for automatically generating coal roadway support parameters provided in this embodiment, through the above process, can visually display the roadway parameters to be predicted, the target displacement, and the target support parameters to the user on a preset interface, thereby improving the user experience.

[0112] This disclosure also provides an automatic coal roadway support parameter generation device, used to execute any of the automatic coal roadway support parameter generation methods provided in the above embodiments, and possessing the corresponding beneficial effects of the automatic coal roadway support parameter generation method.

[0113] Figure 2 The present invention discloses an automatic coal roadway support parameter generation device, which includes: a grid monitoring point location determination module 11, an initial support parameter acquisition module 12, a target displacement acquisition module 13, a judgment module 14, and a target support parameter determination module 15.

[0114] Among them, the grid monitoring point location determination module 11 is used to receive the roadway parameters to be predicted input by the user, and determine the grid monitoring point location corresponding to the target roadway model based on the roadway parameters to be predicted.

[0115] The initial support parameter acquisition module 12 is used to acquire the initial support parameters corresponding to the target roadway model based on the roadway parameters to be predicted and the target support parameter prediction model.

[0116] The target displacement acquisition module 13 is used to obtain the target displacement corresponding to the target roadway model by performing simulation processing through Flac3D based on the roadway parameters to be predicted, the initial support parameters, and the grid monitoring point positions.

[0117] The judgment module 14 is used to determine whether the target displacement belongs to a preset range;

[0118] The target support parameter determination module 15 is used to determine the initial support parameter as the target support parameter when it is determined that the target displacement belongs to a preset range.

[0119] In the above embodiment, the initial support parameter acquisition module 12 is specifically used to input the roadway parameters to be predicted into the trained target support parameter prediction model; and to acquire the initial support parameters output by the target support parameter prediction model.

[0120] In the above embodiments, the device further includes: a training module, configured to determine a target training set based on historical roadway parameters and historical support parameters, wherein the historical roadway parameters correspond one-to-one with roadway models, the historical support parameters correspond one-to-one with the historical roadway parameters, and the target training set includes roadway parameters corresponding to multiple roadway models and support parameters corresponding to the roadway parameters corresponding to the roadway models; the target training set is input into an initial support parameter prediction model to train the initial support parameter prediction model and obtain the target support parameter prediction model.

[0121] In the above embodiments, the device further includes: a parameter processing module, used to determine whether there is abnormal data in the historical roadway parameters and the historical support parameters; if so, to clean the abnormal data in the historical roadway parameters and the historical support parameters; after completing the cleaning process of the abnormal data in the historical roadway parameters and the historical support parameters, to perform standardization processing on the historical roadway parameters and the historical support parameters.

[0122] In the above embodiment, the target displacement obtaining module 13 is specifically used to automatically generate a target script based on the roadway parameters to be predicted, the initial support parameters, and the grid monitoring point positions; after generating the target script, the target script is imported into Flac3D so that Flac3D can perform simulation processing to obtain the target displacement corresponding to the target roadway model.

[0123] In the above embodiments, the target support parameter determination module 15 is further configured to adjust the parameters corresponding to the target support parameter prediction model when it is determined that the target displacement does not belong to the preset range, until the target displacement belongs to the preset range.

[0124] In the above embodiments, the device further includes: a parameter display module, used to display the roadway parameters to be predicted on a first preset interface; and after obtaining the target displacement and target support parameters corresponding to the target roadway model, to display the target displacement and the target support parameters on a second preset page.

[0125] Thus, in this embodiment, the grid monitoring point location determination module receives the roadway parameters to be predicted input by the user and determines the grid monitoring point location corresponding to the target roadway model based on the roadway parameters to be predicted; the initial support parameter acquisition module obtains the initial support parameters corresponding to the target roadway model based on the roadway parameters to be predicted and the target support parameter prediction model; the target displacement acquisition module obtains the target displacement corresponding to the target roadway model by performing simulation processing through Flac3D based on the roadway parameters to be predicted, the initial support parameters, and the grid monitoring point locations; the judgment module determines whether the target displacement belongs to a preset range; and the target support parameter determination module determines the initial support parameters as the target support parameters when it is determined that the target displacement belongs to the preset range. Through the above process, the terminal equipment can determine the location of the grid monitoring points based on the roadway parameters to be predicted input by the user, and automatically obtain the initial support parameters according to the target support parameter prediction model. It does not need to rely on human experience and individual knowledge to determine the support parameters. Furthermore, it can automatically obtain the target displacement through Flac3D simulation processing based on the roadway parameters to be predicted, the initial support parameters, and the location of the grid monitoring points, and determine whether the current support parameters are the most consistent with the target support parameters of the actual project. Thus, it can accurately and intelligently determine the most suitable roadway support scheme to meet the needs of on-site construction.

[0126] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure, such as... Figure 3 As shown, the electronic device includes a processor 310, a memory 320, an input device 330, and an output device 340; the number of processors 310 in the computer device can be one or more. Figure 3 Taking a processor 310 as an example; the processor 310, memory 320, input device 330, and output device 340 in the electronic device can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0127] The memory 320, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of the present invention. The processor 310 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 320, thereby implementing the methods provided in the embodiments of the present invention.

[0128] The memory 320 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 320 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 320 may further include memory remotely located relative to the processor 310, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] Input device 330 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of electronic devices, and may include a keyboard, mouse, etc. Output device 340 may include display devices such as a display screen.

[0130] This disclosure also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to implement the method provided in this embodiment of the invention. The method includes:

[0131] Receive the roadway parameters to be predicted input by the user, and determine the location of the grid monitoring point corresponding to the target roadway model based on the roadway parameters to be predicted;

[0132] Based on the prediction model of the roadway parameters to be predicted and the target support parameters, the initial support parameters corresponding to the target roadway model are obtained.

[0133] Based on the predicted tunnel parameters, the initial support parameters, and the location of the grid monitoring points, the target displacement corresponding to the target tunnel model is obtained by simulation processing using Flac3D.

[0134] Determine whether the target displacement falls within a preset range;

[0135] When it is determined that the target displacement falls within a preset range, the initial support parameters are determined as the target support parameters.

[0136] Of course, the computer-executable instructions provided in the embodiments of the present invention are not limited to the method operations described above, but can also perform related operations in the methods provided in any embodiment of the present invention.

[0137] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0138] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0139] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for automatically generating coal roadway support parameters, characterized in that, The method includes: Receive the roadway parameters to be predicted input by the user, and determine the location of the grid monitoring point corresponding to the target roadway model based on the roadway parameters to be predicted; Based on the prediction model of the roadway parameters to be predicted and the target support parameters, the initial support parameters corresponding to the target roadway model are obtained. Based on the predicted tunnel parameters, the initial support parameters, and the location of the grid monitoring points, the target displacement corresponding to the target tunnel model is obtained by simulation processing using Flac3D. Determine whether the target displacement falls within a preset range; When it is determined that the target displacement belongs to a preset range, the initial support parameters are determined as the target support parameters; The method for obtaining the initial support parameters corresponding to the predicted roadway parameters based on the prediction model of the roadway parameters to be predicted includes: The roadway parameters to be predicted are input into the trained target support parameter prediction model; Obtain the initial support parameters output by the target support parameter prediction model; Before inputting the roadway parameters to be predicted into the trained target support parameter prediction model, the method further includes: Based on historical tunnel parameters and historical support parameters, a target training set is determined, wherein the historical tunnel parameters correspond one-to-one with the tunnel models, the historical support parameters correspond one-to-one with the historical tunnel parameters, and the target training set includes tunnel parameters corresponding to multiple tunnel models and support parameters corresponding to the tunnel parameters corresponding to the tunnel models. The target training set is input into the initial support parameter prediction model, and the initial support parameter prediction model is trained to obtain the target support parameter prediction model. The method further includes: The predicted tunnel parameters are displayed on a first preset interface. After obtaining the target displacement and target support parameters corresponding to the target tunnel model, the target displacement and target support parameters are displayed on the second preset page.

2. The method according to claim 1, characterized in that, Before determining the target training set based on historical roadway parameters and historical support parameters, the process also includes: Determine whether there is any abnormal data in the historical tunnel parameters and the historical support parameters; If so, clean up any abnormal data in the historical tunnel parameters and historical support parameters; After cleaning and processing the abnormal data in the historical roadway parameters and historical support parameters, the historical roadway parameters and historical support parameters are standardized.

3. The method according to claim 1, characterized in that, The target displacement corresponding to the target roadway model is obtained by simulation processing using Flac3D based on the roadway parameters to be predicted, the initial support parameters, and the grid monitoring point positions, including: The target script is automatically generated based on the predicted tunnel parameters, the initial support parameters, and the locations of the grid monitoring points. After generating the target script, the target script is imported into Flac3D so that Flac3D can perform simulation processing to obtain the target displacement corresponding to the target tunnel model.

4. The method according to claim 1, characterized in that, The method further includes: When it is determined that the target displacement does not belong to the preset range, the parameters corresponding to the target support parameter prediction model are adjusted until the target displacement belongs to the preset range.

5. An automatic coal roadway support parameter generation device, used to execute the automatic coal roadway support parameter generation method as described in any one of claims 1 to 4, characterized in that, The device includes: The grid monitoring point location determination module is used to receive the roadway parameters to be predicted input by the user, and determine the grid monitoring point location corresponding to the target roadway model based on the roadway parameters to be predicted. The initial support parameter acquisition module is used to acquire the initial support parameters corresponding to the target roadway model based on the roadway parameters to be predicted and the target support parameter prediction model. The target displacement acquisition module is used to obtain the target displacement corresponding to the target roadway model by performing simulation processing using Flac3D based on the roadway parameters to be predicted, the initial support parameters, and the grid monitoring point positions. The judgment module is used to determine whether the target displacement belongs to a preset range; The target support parameter determination module is used to determine the initial support parameters as target support parameters when the target displacement is determined to be within a preset range. The initial support parameter acquisition module is further configured to input the roadway parameters to be predicted into the trained target support parameter prediction model; and acquire the initial support parameters output by the target support parameter prediction model. The device further includes: The training module is used to determine a target training set based on historical roadway parameters and historical support parameters. The historical roadway parameters correspond one-to-one with roadway models, and the historical support parameters correspond one-to-one with the historical roadway parameters. The target training set includes roadway parameters corresponding to multiple roadway models, and support parameters corresponding to the roadway parameters of the roadway models. The target training set is input into an initial support parameter prediction model, and the initial support parameter prediction model is trained to obtain the target support parameter prediction model. The parameter display module is used to display the roadway parameters to be predicted on a first preset interface; and after obtaining the target displacement and target support parameters corresponding to the target roadway model, to display the target displacement and target support parameters on a second preset page.

6. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the automatic generation method for coal roadway support parameters as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the automatic generation method for coal roadway support parameters as described in any one of claims 1 to 4.

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