An artificial intelligence-based mine roadway on-demand support structure optimization method

Abstract

The application discloses a mine roadway on-demand support structure optimization method based on artificial intelligence, relates to the technical field of mine roadway support, and collects roadway measurement data of a roadway measurement point; captures and extracts the roadway measurement data to obtain fissure geological parameters, classifies and summarizes the fissure geological parameters to obtain a rock mass quality grade; constructs a roadway section model, collects parameters, obtains original rock mass mechanical parameters, simulates and constructs the roadway section model to obtain a same-grade simulation parameter library; compares and simulates the same-grade simulation parameter library to obtain a parameter simulation result, enhances defects of the roadway measurement point according to the parameter simulation result, and obtains an optimal support scheme; the support scheme design efficiency and accuracy are improved, the roadway stability and safety are improved, excessive support or insufficient support is avoided, and support economy is optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine roadway support, in particular to a mine roadway on-demand support structure optimization method based on artificial intelligence. BACKGROUND

[0002] The stability and safety of mine roadway is a key issue in mine engineering, and the stability and safety of the roadway directly affect the production efficiency, safety production and the safety of workers in the mine. Under the repeated disturbance of mining, the ground pressure near the roadway and stope is enhanced, the rock mass strength is reduced, and the quality grade is deteriorated, causing frequent ground pressure phenomena such as spalling, roof falling and collapse.

[0003] The emergence of the rock roadway on-demand support design idea provides a new and effective design approach for mine intelligent optimization design and rapid drawing, and avoids the design embarrassment caused by different people. In order to solve the problems existing in the current mine roadway support method, the present application provides a mine roadway on-demand support structure optimization method based on artificial intelligence, which uses artificial intelligence algorithm to process and analyze mine roadway data, establishes a roadway stability prediction model, and adjusts and optimizes the support structure in real time according to the prediction model and the use demand of the roadway, improves the intelligent level of mine roadway support, reduces the influence of human factors on the support effect, improves the safety and stability of the roadway, reduces the risk of roadway accidents, reduces the cost of mine roadway support, and improves the economic benefit of the mine. SUMMARY

[0004] The purpose of the present application can be achieved by the following technical solutions:

[0005] A mine roadway on-demand support structure optimization method based on artificial intelligence, comprising the following steps:

[0006] Step S1: collecting roadway measurement data of a roadway measurement point;

[0007] Step S2: capturing and extracting the roadway measurement data to obtain crack geological parameters, and classifying and summarizing the crack geological parameters to obtain a rock mass quality grade;

[0008] Step S3: constructing a roadway section model and collecting parameters to obtain original rock mass mechanical parameters, simulating and constructing the roadway section model to obtain a same-level simulation parameter library;

[0009] Step S4: simulating and comparing the same-level simulation parameter library to obtain a parameter simulation result, enhancing the defects of the roadway measurement point according to the parameter simulation result, and obtaining an optimal support scheme.

[0010] Preferably, the process of collecting roadway measurement data comprises:

[0011] The target mine roadway is laid to obtain a roadway survey point;

[0012] A target acquisition end is set according to the obtained roadway survey point, information acquisition is performed through the target acquisition end, and roadway survey data is obtained.

[0013] Preferably, the process of capturing and extracting the roadway survey data comprises:

[0014] The roadway survey data is obtained, close-range capture is performed on the roadway survey data to obtain close-range data of a fracture, sample testing is performed on the roadway survey data to obtain uniaxial compressive strength, and the like.

[0015] Information capture is performed on the obtained close-range data of the fracture to obtain a fracture geological parameter.

[0016] Preferably, the process of grading and summarizing based on the fracture geological parameter comprises:

[0017] Based on the fracture geological parameter, an index of incompleteness is obtained by summarizing the index of the rock mass.

[0018] Based on the obtained index of incompleteness, the quality of the rock mass is graded to obtain a quality index of the rock mass.

[0019] The obtained quality index of the rock mass is graded to obtain a quality grade of the rock mass.

[0020] Preferably, the process of constructing a roadway section model and acquiring parameters to obtain original rock mass mechanical parameters comprises:

[0021] The target mine roadway is expanded to obtain a roadway section model.

[0022] Based on the roadway survey point, the roadway section model is matched with a quality grade of the rock mass to obtain a quality grade of the survey point.

[0023] The roadway section model is marked according to the quality grade of the survey point to obtain a marked roadway model, and parameters of the marked roadway model are acquired to obtain original rock mass mechanical parameters.

[0024] Preferably, the process of simulating and constructing based on the roadway section model to obtain a same-grade simulation parameter library comprises:

[0025] Based on the original rock mass mechanical parameters, numerical simulation of support is performed on the marked roadway model to obtain support simulation data.

[0026] An initial parameter level library is constructed according to the quality grade of the survey point.

[0027] Based on the support simulation data, the initial parameter level library is summarized to obtain a same-grade simulation parameter library.

[0028] Preferably, the process of simulating and comparing according to the peer analog parameter library comprises:

[0029] According to the obtained peer analog parameter library, the marked roadway model is simulated and run, and the parameters of the marked roadway model during the simulation and running are collected to obtain running rock mass mechanical parameters;

[0030] Based on the marked roadway model, the effect of the running rock mass mechanical parameters is compared according to the original rock mass mechanical parameters to obtain parameter simulation results.

[0031] Preferably, the process of defect enhancement of the roadway measurement point according to the parameter simulation results to obtain the best support scheme comprises:

[0032] According to the parameter simulation results, an effect threshold is set, the parameter simulation results are optimally searched through the effect threshold, and non-significant simulation results are obtained, and the roadway measurement point corresponding to the non-significant simulation results is recorded as a to-be-optimized measurement point;

[0033] Based on the peer analog parameter library, the to-be-optimized measurement point is supported and optimized to obtain optimized support data, the marked roadway model is optimally simulated and run through the optimized support data, and the parameters of the marked roadway model during the optimal simulation and running are collected to obtain optimized rock mass mechanical parameters;

[0034] According to the obtained original rock mass mechanical parameters, the effect of the optimized rock mass mechanical parameters is compared to obtain optimized parameter simulation results, the obtained optimized parameter simulation results are sorted according to effects to obtain best simulation support data, and the peer analog parameter library is updated and screened according to the best simulation support data to obtain the best support scheme.

[0035] Compared with the prior art, the beneficial effects of the present application are:

[0036] 1. By collecting comprehensive data of measurement points of a mine roadway, extracting and analyzing the comprehensive data, obtaining fissure image information, and processing and analyzing the rock mass data information, the rock mass quality grade is obtained, so that a reasonable support scheme can be obtained, and the stability of the roadway during use is ensured, and the service life of the roadway is prolonged.

[0037] 2. The roadway cross-section model is constructed, targeted simulation design is carried out under different rock mass quality grades, and according to the simulation results of the rock mass quality grading, a support scheme suitable for different geological conditions can be designed, and the applicability and flexibility of the support scheme are improved.

[0038] 3. The weak measurement points in the roadway cross-section model are simulated again, and are updated and optimized, so that over-supporting or insufficient supporting is avoided, the design is more reasonable and efficient, unnecessary support materials and construction costs are reduced, and economic benefits are improved. BRIEF DESCRIPTION OF DRAWINGS

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0041] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, an artificial intelligence-based method for optimizing on-demand support structures in mine roadways includes the following steps:

[0043] Step S1: Collect tunnel measurement data from tunnel measurement points;

[0044] Step S2: Capture and extract tunnel measurement data to obtain fracture geological parameters, and classify and summarize the fracture geological parameters to obtain the rock mass quality grade;

[0045] Step S3: Construct a tunnel cross-section model and collect parameters to obtain the original rock mass mechanical parameters. Simulate the tunnel cross-section model to obtain a simulation parameter library of the same level.

[0046] Step S4: Compare simulation results with the same-level simulation parameter library, and enhance the defects of the roadway measurement points according to the simulation results to obtain the best support scheme.

[0047] It needs further explanation that, during the specific implementation process, the mining of the ore body disrupts the original stress balance, and the original stress is transferred to the surrounding pillars and other core safety protection positions of the mine, resulting in a new redistribution of stress. This leads to a continuous increase in stress concentrated in the pillars, causing the continuous manifestation of ground pressure phenomena such as spalling, roof falls, and collapses in the roadways. This is one of the root causes of the widespread instability of the surrounding rock in mines. The mining disturbance leads to the expansion of rock fissures and a continuous decrease in strength, which is another root cause of the widespread instability of the surrounding rock in mine roadways. Therefore, strengthening the timely and effective support of the surrounding rock mass of the frequently accessed passages for personnel and machinery, such as ore access roads, drilling roadways, and transport roadways, and eliminating safety hazards such as spalling and roof falls in roadways and chambers, is of great significance for safe production in mines. The process of collecting roadway measurement data includes:

[0048] laying a capture on the target mine roadway to obtain a roadway survey point;

[0049] The target mine roadway represents a mine roadway that needs to be supported, and the laying of the capture represents setting a survey point in the target mine roadway and collecting information at the survey point.

[0050] A target collection end is set according to the obtained roadway survey point, and information is collected through the target collection end to obtain roadway survey data.

[0051] The roadway survey data includes digital close-range photographic images, field reconnaissance records, and rock sample data. The digital close-range photographic images represent photographs taken of various parts of the roadway rock mass, including the roof, walls, and floor, and the photographs can clearly reflect the structural characteristics of the rock mass, including fissures, joints, rock layers, etc. The field reconnaissance records represent the engineer's investigation of the rock mass of the mine roadway, recording macroscopic features such as fissure development, rock mass structure, and rock sample data representing the rock samples at the selected roadway survey points in the mine roadway.

[0052] The roadway survey data is captured and extracted to obtain fissure geological parameters, which are used to classify and summarize the rock mass quality grade. The specific process includes:

[0053] The roadway survey data is obtained, and close-range capture is performed on the obtained roadway survey data to obtain close-range fissure data.

[0054] The close-range capture represents using image extraction methods to collect fissure images from the digital close-range photographic images and field reconnaissance records in the mine roadway to obtain close-range fissure data, which represents the picture information of the fissures in the mine roadway. The image extraction method represents using three-dimensional image acquisition or two-dimensional image acquisition to obtain the three-dimensional coordinates or two-dimensional coordinates of the fissure images, i.e., the fissure images are extracted while the corresponding position coordinates are also marked.

[0055] A sample test is performed on the obtained roadway survey data to obtain uniaxial compressive strength.

[0056] The sample test represents performing a point load test on the rock sample data in the roadway survey data and making corrections to obtain the uniaxial compressive strength of the rock. The point load test represents clamping the rock sample between two spherical loading cone heads and applying a load until the rock sample is fractured. The obtained uniaxial compressive strength is marked as wherein represents the uncorrected uniaxial compressive strength of the rock, P is the peak load when the pressure is applied, and D is the distance between the two cone head ends, is the size effect correction coefficient, and m is a correction index, which can be 0.40-0.45, and in the embodiment, 0.42, It is shown that in the embodiment, the uniaxial compressive strength of a standard rock sample with a diameter of 50 mm is converted;

[0057] Information capturing is performed on the obtained fracture close-range data to obtain fracture geological parameters;

[0058] It needs to be further explained that in the specific implementation process, the information capturing represents calculating the fracture information in the fracture close-range data, wherein the fracture information includes but is not limited to fracture centroid coordinates, fracture length, fracture dip angle, and the specific process includes:

[0059] The ratio of the photo pixel to the length is obtained by framing the sheet-shaped reference in each fracture image, then the two endpoint coordinate values of the fracture are read from the current coordinate system by clicking the two endpoints of the fracture with the mouse, and the fracture centroid coordinates, fracture length, fracture dip angle and other parameters are obtained according to the two endpoint coordinates and the ratio, wherein the centroid represents the center point of the intersection of the fractures;

[0060] Based on the fracture geological parameters, the rock mass is subjected to index induction to obtain an incompleteness index;

[0061] The index induction represents that according to the various structural planes and soft interlayers with large quantity and large scale existing in the roadway rock mass, there is a significant difference between the rock mass strength and the rock strength, so it is necessary to grade the quality of the rock mass, and then select the bearing capacity and other parameters according to the quality grade, and the incompleteness index includes the volume joint number, the rock quality index and the rock mass integrity index;

[0062] The volume joint number is marked as wherein, K represents a conversion coefficient, n is the joint number in a single fracture image, and A is the size of the fracture image;

[0063] The rock quality index is marked as RQD, wherein, In the embodiment, the volume joint base estimation method is adopted, when RQD=100, when RQD=0;

[0064] The rock mass integrity index is marked as wherein, , is the longitudinal wave velocity of the rock mass, is the longitudinal wave velocity of the indoor rock (block);

[0065] According to the obtained incompleteness index, the quality of the rock mass is graded to obtain a rock mass quality index;

[0066] It needs to be further explained that in the specific implementation process, the quality classification of rock mass is a comprehensive reflection of the good and bad of the engineering rock mass characteristics. In engineering practice, the rock mass is generally classified according to the specifications, and the bearing capacity and other parameters are selected according to the quality classification. In this embodiment, the RMR method is used to classify the quality of the surrounding rock mass of the target mine roadway, and the rock mass quality index is obtained. The rock mass quality index includes uniaxial compressive strength, rock quality index, structure surface spacing, structure surface condition, groundwater condition, and the relationship between structure surface occurrence and engineering trend. The obtained rock mass quality index is marked as RMR, wherein RMR = R1 + R2 + R3 + R4 + R5 + R6, R3 is the structure surface spacing, R4 is the structure surface condition, R5 is the groundwater condition, and R6 is the relationship between the structure surface occurrence and the engineering trend.

[0067] The obtained rock mass quality index is graded to obtain the rock mass quality grade, and the rock mass quality grade includes a first quality grade, a second quality grade, a third quality grade, a fourth quality grade, and a fifth quality grade.

[0068] The grading score represents that the index grade is divided according to the calculation result of the rock mass quality index, and the rock mass quality grade is obtained. In this embodiment, the rock mass quality index is divided into five grade intervals. When 100 ≥ RMR ≥ 81, it is recorded as a first quality grade, indicating very good rock mass. When 80 ≥ RMR ≥ 61, it is recorded as a second quality grade, indicating good rock mass. When 60 ≥ RMR ≥ 41, it is recorded as a third quality grade, indicating general rock mass. When 40 ≥ RMR ≥ 21, it is recorded as a fourth quality grade, indicating poor rock mass. When 21 > RMR, it is recorded as a fifth quality grade, indicating very poor rock mass.

[0069] A roadway cross-section model is constructed and parameters are collected to obtain original rock mass mechanical parameters. A same-level simulation parameter library is obtained by simulating and constructing the roadway cross-section model. The specific steps include:

[0070] A target mine roadway is obtained, and the obtained target mine roadway is expanded to obtain a roadway cross-section model.

[0071] The model expansion represents that the target mine roadway in reality is converted into a three-dimensional model, that is, a roadway cross-section model, which is used to provide a simulation space. The simulation is performed under different quality grades to obtain the best support scheme. The function and structure of the roadway cross-section model are completely the same as those of the target mine roadway in reality.

[0072] ​The grade area matching based on the roadway measurement point is matched with the rock mass quality grade of the roadway section model to obtain a measurement point quality grade, which includes a first-grade measurement point grade, a second-grade measurement point grade, a third-grade measurement point grade, a fourth-grade measurement point grade and a fifth-grade measurement point grade.

[0073] The grade area matching indicates that the rock mass quality grade at the roadway measurement point is obtained by matching the rock mass quality grade of the roadway measurement point in the roadway section model, which is recorded as a measurement point quality grade, indicating that each roadway measurement point obtains a corresponding rock mass quality grade, which is used to set a corresponding support scheme parameter according to the rock mass quality grade, to obtain the most suitable support parameter according to the requirement of the roadway measurement point, so as to effectively guarantee the stability of the support structure of the roadway and set the support scheme according to local conditions.

[0074] The obtained measurement point quality grade is used to distinguish the roadway section model to obtain a marked roadway model, the distinguishing indicates that the measurement point quality grade corresponding to the roadway measurement point is distinguished in the roadway section model, for example, distinguished by different colors, so that the user can quickly distinguish the rock mass quality grade of the roadway measurement point in the marked roadway model; for example, the first-grade quality grade of the roadway measurement point is marked as gray, the second-grade quality grade of the roadway measurement point is marked as green, the third-grade quality grade of the roadway measurement point is marked as blue, the fourth-grade quality grade of the roadway measurement point is marked as orange, and the fifth-grade quality grade of the roadway measurement point is marked as red.

[0075] The obtained marked roadway model is used to collect parameters to obtain original rock mass mechanical parameters, and the obtained original rock mass mechanical parameters are associated with corresponding research areas.

[0076] The parameter collection indicates that the mechanical parameters of the rock mass in the marked roadway model are collected, and the original rock mass mechanical parameters include but are not limited to rock strength, elastic modulus and Poisson's ratio of rock, and stress data.

[0077] The obtained marked roadway model is used to support numerical simulation based on the original rock mass mechanical parameters to obtain support simulation data.

[0078] It needs to be further explained that in the specific implementation process, the numerical simulation represents the numerical value of different support methods in the marked roadway model under different rock mass quality levels, that is, the support simulation data, according to the change of the original rock mass mechanical parameters corresponding to the different support method data, whether the support method numerical value is effective is analyzed, and the most suitable support scheme is finally generated, in the embodiment, the simple spraying support and the anchor net support are used for roadway support, the advantages of the simple spraying support and the anchor net support are used to find the most suitable support application mode in the marked roadway model, that is, different thicknesses of sprayed concrete, different specifications of anchor rods and anchor nets are designed, the sprayed concrete thickness, the anchor rod specification and the anchor net specification are collected, that is, all possible support simulation data are obtained, and then the support simulation data are screened to obtain the optimal result, that is, the best support mode;

[0079] The process of the numerical simulation includes:

[0080] According to the research area level corresponding to the obtained research area, different sprayed concrete data and different anchor net specifications are set according to the research area level, all possible support numerical values applied to the marked roadway model are represented, and the set data is recorded, that is, the support simulation data; for example, the rock mass quality level of the roadway measurement point c1 of the marked roadway model is the second measurement point level, then based on the second measurement point level, taking the simple spraying support for simulation as an example, different support simulation data such as the strength of the concrete, the thickness of the concrete and the spraying parameters are set in different combinations, and different combinations are recorded to obtain the support simulation data, when the concrete strength is q1, the concrete thickness is h1 and the spraying parameter is p1, it is a group of support simulation data, which can be changed to the concrete strength q2, the concrete thickness h2 and the spraying parameter p2, or the concrete strength q1, the concrete thickness h2 and the spraying parameter p3, that is, all possible support simulation data within the set range under the condition of meeting the second measurement point level, wherein, "under the condition of meeting the second measurement point level" means that the support simulation data meets the second rock mass quality of the measurement point, that is, the maximum strength of the simple spraying that the second rock mass quality level can withstand, and the rock mass quality will not be damaged, and only the rock mass quality is protected;

[0081] According to the obtained measurement point quality level, an initial parameter level library is constructed, wherein the constructed initial parameter level library is used to store the original rock mass mechanical parameters, the measurement point quality level and the support simulation data, and the initial parameter level library includes a first original level library, a second original level library, a third original level library, a fourth original level library and a fifth original level library;

[0082] obtaining a same-level simulation parameter library according to the obtained support simulation data of the same rock mass quality level based on the quality level of the measurement point, the same-level simulation parameter library comprising a first parameter level library, a second parameter level library, a third parameter level library, a fourth parameter level library, and a fifth parameter level library;

[0083] It should be further explained that, in the specific implementation process, the same-level induction means collecting support simulation data of the same rock mass quality level according to the quality level of the measurement point, and then uploading the corresponding level of roadway measurement point, original rock mass mechanical parameters, and support simulation data obtained by simulating all supportable data of the roadway measurement point to the corresponding initial parameter level library to obtain a same-level simulation parameter library, which is used to intuitively obtain different support simulation data that can be set in the marked roadway model under different rock mass quality levels; for example, the second quality level corresponding to the roadway measurement point, the original rock mass mechanical parameters of the roadway measurement point, and the support simulation data obtained by simulating all supportable data of the roadway measurement point are uploaded to the second original level library to obtain the second parameter level library.

[0084] According to the same-level simulation parameter library, a parameter simulation result is obtained, and the parameter simulation result is used to enhance the defects of the roadway measurement point to obtain an optimal support scheme, and the specific steps include:

[0085] According to the obtained same-level simulation parameter library, the marked roadway model is simulated and run, and the parameters of the marked roadway model during the simulation and running are collected to obtain running rock mass mechanical parameters.

[0086] It should be further explained that, in the specific implementation process, the simulation and running means applying corresponding support simulation data to each roadway measurement point in the marked roadway model according to the obtained same-level simulation parameter library, and recording the rock mass mechanical parameters once for each application of support simulation data, and recording the rock mass mechanical parameters as running rock mass mechanical parameters; for example, when the roadway measurement point is supported by using anchor net support, the rock mass mechanical parameters of the roadway measurement point are recorded once for each application of anchor net support parameters, such as anchor rod parameters and steel mesh parameters; when the anchor rod parameter is m1 and the steel mesh parameter is j1, the rock mass mechanical parameters at this time are recorded; when the anchor rod parameter is m1 and the steel mesh parameter is j2, the rock mass mechanical parameters at this time are recorded; when the anchor rod parameter is m1 and the steel mesh parameter is j3, the rock mass mechanical parameters at this time are recorded; and so on.

[0087] Based on the marked roadway model, the effect of the running rock mass mechanical parameters is compared with the obtained original rock mass mechanical parameters to obtain a parameter simulation result, the parameter simulation result representing the effect improvement ratio of the original rock mass mechanical parameters compared with the running rock mass mechanical parameters after applying the support simulation data; for example, if the stress data in the running rock mass mechanical parameters is improved by 5% compared with the stress data in the original rock mass mechanical parameters, then +5% represents the parameter simulation result under this kind of support simulation data.

[0088] Further, the effect comparison represents the stability and safety of the roadway measurement point after applying the support parameters and the effect before applying, obtaining the parameter simulation result, which represents whether the support simulation data applied at the roadway measurement point is effective, that is, whether it can improve the stability and support of the measurement point to protect the safety of the roadway, then, the rock mass mechanical parameters before and after applying the support simulation data need to be compared, and the effect of the applied support simulation data is divided according to the comparison result, obtaining the parameter simulation result, the division effect represents the percentage increase of the operating rock mass mechanical parameters after applying the support simulation data compared with the original rock mass mechanical parameters, that is, the support effect degree, for example, analyzing the stress data change of the roadway measurement point before and after applying the support simulation data, it can be concluded whether the applied support simulation data is effective, that is, according to the stress after applying the support simulation data, the stress is improved by e% compared with the original stress data, this improved e% is the simulation effect, that is, the parameter simulation result, which represents that the bearing capacity of the roadway measurement point is effectively improved on the basis of the original to improve the overall stability, if after applying a support simulation data, the stress is improved by f% compared with the original stress data, e

[0089] According to the obtained parameter simulation result, set the effect threshold, which represents the threshold set for judging whether the support simulation data is effective according to the improvement effect of each rock mass mechanical parameter;

[0090] The effect threshold is used to perform excellent screening on the parameter simulation result, and the non-significant simulation result is obtained, and the roadway measurement point corresponding to the non-significant simulation result is recorded as a measurement point to be optimized;

[0091] It needs to be further explained that in the specific implementation process, the excellent screening represents that the effect of the support simulation data in the parameter simulation result is not qualified according to the effect threshold, and the support simulation data at this point is optimized, which is not a single simple spraying or anchor net support, but a double support mode of simple spraying combined with anchor net support, wherein, the "effect of the support simulation data not qualified" represents the support simulation data corresponding to the parameter simulation result less than the effect threshold, and is recorded as the non-significant simulation result, which represents that the effect of the support parameters in this case is not significant, or not good enough, then optimization is needed, and better support parameters are selected to improve the stability of the roadway, then the roadway measurement point corresponding to this support simulation data is the measurement point to be optimized, that is, the measurement point to be optimized;

[0092] Based on the same level analog parameter library, the support optimization is performed on the to-be-optimized measuring point, and the optimized support data is obtained.

[0093] According to the obtained original rock mass mechanical parameters, the effect comparison is performed on the optimized rock mass mechanical parameters, the optimized parameter simulation result is obtained, the effect sorting is performed on the obtained optimized parameter simulation result, the best simulation support data is obtained, and the same level analog parameter library is updated and screened according to the best simulation support data, and the best support scheme is obtained.

[0094] It should be further explained that in the specific implementation process, the support optimization represents that all possible support simulation data are contained in the same level analog parameter library, corresponding support simulation data exist in the to-be-optimized measuring point, after the insignificant simulation results are removed, the combination of the simple shotcrete support and the anchor net support is performed, that is, the simple shotcrete support is performed first, and then the anchor net support is performed, all possible support simulation data are generated and applied to the marked roadway model for secondary simulation operation, the rock mass mechanical parameters are collected and recorded as the optimized rock mass mechanical parameters, and the effect comparison process before and after the application of the rock mass mechanical parameters to the support simulation data is repeated, the optimized parameter simulation result is obtained, the best support simulation data is selected from all the optimized parameter simulation results, and recorded as the best simulation support data. For the remaining roadway measuring points in the marked roadway model, the best support simulation data with the best parameter simulation result is also selected from the same level analog parameter library, and the best support scheme of the marked roadway model, that is, the best support scheme of the target mine roadway, is formed together with the best simulation support data of the to-be-optimized measuring point. Under the premise that a single support scheme can meet the stability, the double support structure is implemented for the weak measuring points, unnecessary over-supporting is avoided, material and construction costs are saved, economic benefits are improved, and the best support scheme is generated according to the actual situation in the construction process, the flexibility and adaptability of the construction are improved.

[0095] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details, and the application is not limited to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the specification. The specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited by the claims and their entire scope and equivalents.

Claims

1. A method for optimizing a mine roadway support structure on demand based on artificial intelligence, characterized in that, The method comprises the following steps: Step S1: Collecting roadway survey data of a roadway survey point, the process comprising: Carrying out roadway laying capture on a target mine roadway to obtain a roadway survey point; Setting a target collection end according to the obtained roadway survey point, collecting information through the target collection end to obtain roadway survey data; Step S2: Capturing and extracting the roadway survey data to obtain a fissure geological parameter, classifying the fissure geological parameter to obtain a rock mass quality grade; Step S3: Constructing a roadway section model and collecting parameters to obtain original rock mass mechanical parameters, the process comprising: Model expansion on the target mine roadway to obtain a roadway section model; Grade area matching of the roadway section model based on the roadway survey point and the rock mass quality grade to obtain a survey point quality grade; Distinguishing and marking the roadway section model according to the survey point quality grade to obtain a marked roadway model, and collecting parameters of the marked roadway model to obtain the original rock mass mechanical parameters; Simulating and constructing through the roadway section model to obtain a same-grade simulation parameter library; Step S4: Simulation comparison according to the same-grade simulation parameter library to obtain a parameter simulation result, the process comprising: Simulating the marked roadway model according to the obtained same-grade simulation parameter library, collecting parameters of the marked roadway model during the simulation to obtain running rock mass mechanical parameters; Comparing the running rock mass mechanical parameters with the original rock mass mechanical parameters based on the marked roadway model to obtain the parameter simulation result; Defect enhancement of the roadway survey point according to the parameter simulation result to obtain an optimal support scheme, the process comprising: Setting an effect threshold according to the parameter simulation result, searching for excellent results through the effect threshold to obtain insignificant simulation results, and marking the roadway survey point corresponding to the insignificant simulation results as to-be-optimized survey points; Support optimization of the to-be-optimized survey points based on the same-grade simulation parameter library to obtain optimized support data, optimizing and simulating the marked roadway model through the optimized support data, collecting parameters of the marked roadway model during the optimized simulation to obtain optimized rock mass mechanical parameters; Comparing the optimized rock mass mechanical parameters with the original rock mass mechanical parameters according to the obtained original rock mass mechanical parameters to obtain an optimized parameter simulation result, sorting the obtained optimized parameter simulation result according to effects to obtain optimal simulation support data, and updating and screening the same-grade simulation parameter library according to the optimal simulation support data to obtain the optimal support scheme.

2. The method of claim 1, wherein the method is based on artificial intelligence. The process of capturing and extracting the roadway survey data comprises: Obtaining the roadway survey data, capturing a near view of the roadway survey data to obtain near view data of a fissure, and carrying out sample tests on the roadway survey data to obtain uniaxial compressive strength; Capturing information of the obtained near view data of the fissure to obtain a fissure geological parameter.

3. The method of claim 1, wherein the method is based on artificial intelligence. The process of classifying and summarizing the fissure geological parameter comprises: Index summarizing of a rock mass based on the fissure geological parameter to obtain an incompleteness index; Quality classification of the rock mass according to the obtained incompleteness index to obtain a rock mass quality index; Grading and scoring of the obtained rock mass quality index to obtain a rock mass quality grade.

4. The method of claim 1, wherein the method is based on artificial intelligence. The process of obtaining the same level simulation parameter library by simulating and constructing the roadway section model includes: Based on the original rock mass mechanical parameters, the numerical simulation of the support of the marked roadway model is carried out to obtain the support simulation data; According to the quality grade of the measured points, the initial parameter level library is constructed; According to the support simulation data, the initial parameter level library is inducted to the same level to obtain the same level simulation parameter library.

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