Intelligent prediction method for supporting strength of underground mine roadway

Abstract

The invention provides an intelligent prediction method for underground mine roadway support strength, and relates to the technical field of support strength prediction.The method comprises the steps that predetermined support features are read, and target support feature information is obtained; analyzing the target support feature information to obtain a target system strength index; building a support anti-impact database based on big data, and extracting a first simulation anti-impact record in the support anti-impact database; introducing an anti-impact capability evaluation function to obtain a first simulation anti-impact index; recording as an anti-impact index of the target system; and taking the target system strength index and the target system anti-impact index as input information of a strength prediction model to obtain target support strength. According to the method, the problems that the supporting strength of the underground mine roadway cannot be scientifically, accurately and quantitatively predicted and evaluated in the prior art, roadway supporting failure cannot be found in time, and targeted maintenance cannot be carried out in time can be solved. Safer, more economical and more efficient roadway support is realized.

Description

Technical Field

[0001] The present application relates to the technical field of support strength prediction, and in particular to an intelligent prediction method for the support strength of underground mine tunnels. Background Art

[0002] With the increase in the depth of coal mining and the complexity of geological conditions, the problem of tunnel support has become increasingly prominent. Traditional tunnel support methods mainly rely on empirical formulas and simple calculations, which are difficult to meet the high requirements of modern coal mines for tunnel support. In order to improve the stability and safety of tunnel support, researchers have proposed many new support technologies and methods.

[0003] However, these technologies and methods still have some problems in practical application. On the one hand, due to the complexity and uncertainty of geological conditions, traditional support design methods often cannot accurately predict the actual support effect of the tunnel. On the other hand, traditional tunnel support design methods often ignore the mechanical properties and structural characteristics of the tunnel surrounding rock, resulting in unreasonable support design and frequent tunnel accidents.

[0004] In summary, there are technical problems in the existing technology that cannot scientifically, accurately and quantitatively predict and evaluate the support strength of underground mine tunnels, and thus cannot timely detect tunnel support failures and carry out timely and targeted maintenance, which ultimately leads to frequent tunnel support accidents. Summary of the invention

[0005] The purpose of this application is to provide an intelligent prediction method for the support strength of underground mine tunnels, so as to solve the technical problems in the prior art that the support strength of underground mine tunnels cannot be scientifically, accurately and quantitatively predicted and evaluated, and thus the failure of tunnel support cannot be discovered in time and timely targeted maintenance cannot be carried out, which ultimately leads to frequent tunnel support accidents.

[0006] In view of the above problems, the present application provides an intelligent prediction method for the support strength of underground mine tunnels.

[0007] In the first aspect, the present application provides an intelligent prediction method for the support strength of an underground mine tunnel, which is implemented by an intelligent prediction system for the support strength of an underground mine tunnel, wherein the intelligent prediction method for the support strength of an underground mine tunnel comprises: reading predetermined support characteristics, and collecting multi-dimensional characteristics of a target tunnel support based on the predetermined support characteristics to obtain target support characteristic information; analyzing the target support characteristic information to obtain a target system strength index; establishing a support impact resistance database based on big data, and extracting a first simulated impact resistance record from the support impact resistance database; introducing an impact resistance evaluation function to evaluate and analyze the first simulated impact resistance record to obtain a first simulated impact resistance index; recording the mean of the first simulated impact resistance index as the target system impact resistance index; using the target system strength index and the target system impact resistance index as input information of a strength prediction model to obtain output information, wherein the output information includes the target support strength of the target tunnel support.

[0008] In the second aspect, the present application also provides an intelligent prediction system for the support strength of underground mine tunnels, which is used to execute an intelligent prediction method for the support strength of underground mine tunnels as described in the first aspect, wherein the intelligent prediction system for the support strength of underground mine tunnels includes: a feature collection module, the feature collection module is used to read predetermined support features, and based on the predetermined support features, perform multi-dimensional feature collection on the target tunnel support to obtain target support feature information; a strength analysis module, the strength analysis module is used to analyze the target support feature information to obtain a target system strength index; a simulation acquisition module, the simulation acquisition module is used to build a simulation system based on big data A support impact resistance database, and extracting the first simulation impact resistance record in the support impact resistance database; a first impact resistance analysis module, the first impact resistance analysis module is used to introduce an impact resistance evaluation function to evaluate and analyze the first simulation impact resistance record to obtain a first simulation impact resistance index; a second impact resistance analysis module, the second impact resistance analysis module is used to record the mean value of the first simulation impact resistance index as the target system impact resistance index; a strength prediction module, the strength prediction module is used to use the target system strength index and the target system impact resistance index as input information of a strength prediction model to obtain output information, the output information includes the target support strength of the target tunnel support.

[0009] In a third aspect, a computer-readable storage medium stores a computer program, and when executed, the computer program implements the steps of an intelligent prediction method for the support strength of an underground mine tunnel as described in any one of the first aspects above.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] By reading the predetermined support features, and collecting multi-dimensional features of the target tunnel support based on the predetermined support features, the target support feature information is obtained; the target support feature information is analyzed to obtain the target system strength index; a support impact resistance database is established based on big data, and the first simulation impact resistance record in the support impact resistance database is extracted; the impact resistance evaluation function is introduced to evaluate and analyze the first simulation impact resistance record to obtain the first simulation impact resistance index; the mean of the first simulation impact resistance index is recorded as the target system impact resistance index; the target system strength index and the target system impact resistance index are used as input information of the strength prediction model to obtain output information, and the output information includes the target support strength of the target tunnel support, that is, first, the predetermined support features are read, and multi-dimensional features of the target tunnel support are collected based on these features to obtain the target support feature information. Then, this information is analyzed to obtain the target system strength index, which reflects the strength level of the tunnel support. Then, based on big data technology, a support impact resistance database is established, and the first simulation impact resistance record is extracted. The impact resistance evaluation function is introduced to evaluate and analyze these records to obtain the first simulation impact resistance index. The mean value of this index is recorded as the target system impact resistance index, which represents the ability of the support system to resist impact. Finally, the target system strength index and the target system impact resistance index are used as input information and sent to the strength prediction model. After model processing, the output information includes the target support strength of the target tunnel support, which is a prediction of the expected performance of the tunnel support in actual work. Through this series of data collection, analysis and modeling processes, a scientific and systematic method is provided for the strength evaluation of tunnel support, which helps to predict and prevent the risk of support failure in advance in actual engineering.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0014] Figure 1A schematic diagram of a process flow of an intelligent prediction method for underground mine tunnel support strength for this application;

[0015] Figure 2 The present invention is a schematic diagram of the structure of an intelligent prediction system for underground mine tunnel support strength.

[0016] Description of reference numerals:

[0017] Feature collection module 11, strength analysis module 12, simulation acquisition module 13, first impact resistance analysis module 14, second impact resistance analysis module 15, strength prediction module 16. DETAILED DESCRIPTION

[0018] This application provides an intelligent prediction method for the support strength of underground mine tunnels, which solves the technical problem that the existing technology cannot scientifically, accurately and quantitatively predict and evaluate the support strength of underground mine tunnels, and thus cannot timely discover tunnel support failures and timely targeted maintenance, which ultimately leads to frequent tunnel support accidents. It achieves the technical effect of accurately predicting the strength of tunnel support, providing a scientific basis for tunnel support design and application guidance, and improving the stability and safety of tunnel support.

[0019] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0020] For example, please refer to the attached Figure 1 The present application provides an intelligent prediction method for underground mine tunnel support strength, wherein the intelligent prediction method for underground mine tunnel support strength is applied to an intelligent prediction system for underground mine tunnel support strength, and the intelligent prediction method for underground mine tunnel support strength specifically includes the following steps:

[0021] Step 1: Read the predetermined support features, and collect multi-dimensional features of the target tunnel support based on the predetermined support features to obtain target support feature information.

[0022] Step 2: Analyze the target support characteristic information to obtain the target system strength index.

[0023] Step three: Establish a support and impact resistance database based on big data, and extract the first simulation impact resistance record in the support and impact resistance database.

[0024] Step 4: Introduce an impact resistance evaluation function to evaluate and analyze the first simulation impact resistance record to obtain a first simulation impact resistance index.

[0025] Step 5: Record the average value of the first simulation impact resistance index as the target system impact resistance index.

[0026] Step six: Use the target system strength index and the target system impact resistance index as input information of the strength prediction model to obtain output information, wherein the output information includes the target support strength of the target tunnel support.

[0027] Specifically, the predetermined support features are first read, and based on these features, multi-dimensional features of the target tunnel support are collected to obtain the target support feature information. This step is intended to establish the basis for analysis and ensure the accuracy and comprehensiveness of subsequent analysis data. Traditional tunnel support design relies on simplified assumptions and empirical formulas, which may lead to insufficient or excessive support design. By reading the predetermined support features and collecting multi-dimensional features, more accurate support feature information can be obtained, thereby improving the accuracy of support design. Then, the target support feature information is analyzed to obtain the target system strength index. This provides a preliminary assessment of the strength of the support system. Due to the complexity and uncertainty of geological conditions, tunnel support accidents occur from time to time. By analyzing the target support feature information and obtaining the target system strength index, the safety of the support structure can be better evaluated and the occurrence of support accidents can be reduced. Next, a support impact resistance database is established based on big data, and the first simulation impact resistance record in the database is extracted. This step involves the application of big data, and the extraction of simulation records prepares for subsequent evaluation and analysis. In addition, the impact resistance evaluation function is introduced to evaluate and analyze the first simulation impact resistance record to obtain the first simulation impact resistance index. This index is a quantitative evaluation of the impact resistance of the support system. The mean value of the first simulation impact resistance index is recorded as the target system impact resistance index. Finally, the target system strength index and the target system impact resistance index are used as input information of the strength prediction model. Through the processing of the model, the output information is obtained, including the target support strength of the target tunnel support. This series of actions are connected in series to achieve an accurate evaluation and prediction of the target tunnel support strength. Through this analysis method, not only the accuracy of the evaluation is improved, but also strong data support is provided for the design and optimization of tunnel support.

[0028] Furthermore, the intelligent prediction method for the support strength of underground mine tunnels also includes: the predetermined support characteristics include at least the top anchor cable length, the top anchor cable spacing, the top anchor cable spacing and the anchoring length, and the anchoring length includes the top anchoring length and the side anchoring length.

[0029] Specifically, the predetermined support features are first analyzed as a whole, including the top anchor length, top anchor spacing, top anchor spacing and anchor length. These parameters are key elements in the design of tunnel support and directly affect the stability and safety of the support structure. By accurately measuring and setting these parameters, it can be ensured that the support system can effectively withstand underground pressure and impact, thereby ensuring the stability and safety of the tunnel. Specifically, first, the length of the top anchor is determined, which is the distance that the anchor extends to the deep part of the surrounding rock, affecting the anchoring force and support effect. Then, the spacing of the top anchor is set, that is, the lateral distance between the anchors, which determines the uniformity and overall stability of the support. Next, the spacing of the top anchor is considered, that is, the longitudinal distance between the anchors, which affects the bearing capacity and deformation resistance of the anchoring structure. In addition, the anchor length is also an important parameter, including the top anchor length and the side anchor length. The top anchor length refers to the anchor depth of the anchor in the roof, while the side anchor length refers to the anchor depth of the anchor in the side of the tunnel. The setting of these two parameters needs to comprehensively consider the properties of the surrounding rock, stress state and support requirements to ensure the anchoring effect and support efficiency. Finally, by accurately measuring and setting these predetermined support characteristics, a scientific design basis can be provided for tunnel support. The reasonable configuration of these parameters helps to improve the stability of the support structure and enhance its ability to resist underground pressure and impact, thereby ensuring the safety and lasting stability of the tunnel.

[0030] Furthermore, the intelligent prediction method for the support strength of underground mine tunnels also includes: reading a predetermined exposed length value; reading the anchoring length value of the anchoring length, wherein the anchoring length value includes a top anchoring length value and a side anchoring length value; the predetermined exposed length value and the top anchoring length value and the side anchoring length value together constitute a predetermined limit value; reading a predetermined reinforcement arch strategy, and analyzing and calculating the predetermined limit value based on the predetermined reinforcement arch strategy to obtain a parameter value of the top anchor cable length, recorded as a top anchor cable length value; reading a predetermined spacing strategy, and analyzing and calculating the parameter values ​​of the top anchor cable spacing and the top anchor cable row spacing based on the predetermined spacing strategy, recorded as a top anchor cable spacing value and a top anchor cable row spacing value, respectively; the predetermined limit value, the top anchor cable length value, the top anchor cable spacing value and the top anchor cable row spacing value together constitute the target support characteristic information.

[0031] Specifically, first of all, this process involves reading and calculating multiple key parameters to determine the target support characteristic information. It mainly includes the predetermined exposed length value, anchor length value (divided into top anchor length value and side anchor length value), and the top anchor length value, top anchor spacing value and top anchor row spacing value calculated based on the predetermined reinforcement arch strategy and the predetermined spacing strategy. These parameters together constitute a detailed description of the tunnel support design, which is crucial to ensuring the stability and safety of the tunnel.

[0032] Specifically, first read the value of the predetermined exposed length, which is the length of the anchor cable exposed outside the surrounding rock, which has a direct impact on the overall performance of the anchor cable. Then, read the anchor length value, including the top anchor length value and the side anchor length value, which determine the bond strength and anchoring effect between the anchor cable and the surrounding rock. Next, combine the predetermined exposed length value with the top anchor length value and the side anchor length value to form a predetermined limit value. This limit value is a basic requirement for support design and affects the overall performance of the support structure. Based on the predetermined reinforcement arch strategy, the predetermined limit value is analyzed and calculated to obtain the parameter value of the top anchor cable length, that is, the top anchor cable length value. This step is an important part of determining the length of the anchor cable, which requires comprehensive consideration of the surrounding rock conditions, support requirements and the effect of the reinforcement arch. In addition, read the predetermined spacing strategy, and analyze and calculate the top anchor cable spacing value and the top anchor cable row spacing value based on this strategy. These values ​​have a significant impact on the layout of the anchor cable and the support effect, and need to be determined according to specific engineering conditions and design requirements. Finally, the predetermined limit, top anchor cable length, top anchor cable spacing, and top anchor cable row spacing together constitute the target support characteristic information. This information provides a detailed parameter basis for the tunnel support design, helps to ensure the stability and safety of the support structure, and improves the overall support effect of the tunnel.

[0033] Furthermore, the intelligent prediction method for the support strength of underground mine tunnels also includes: standardizing the target support feature information to obtain target standardized support feature information; performing variation weighted calculation on the target standardized support feature information using the coefficient of variation principle to obtain a target variation weighted result; and recording the target variation weighted result as the target system strength index.

[0034] Specifically, the target support characteristic information is first standardized. The purpose of this step is to eliminate the dimensional effects between different parameters so that the parameters can be compared and analyzed at the same scale. Through standardization, the target standardized support characteristic information can be obtained, which is the basis for subsequent analysis and calculation. Specifically, first, the target support characteristic information is standardized. This usually involves subtracting the value of each parameter from its mean value and then dividing it by the standard deviation. In this way, each parameter is converted into unitless data, its mean value becomes 0, and its standard deviation becomes 1. This processing method helps to eliminate the influence of dimensions so that different parameters can be directly compared. Then, the target standardized support characteristic information is subjected to variation weighted calculation using the coefficient of variation principle. The coefficient of variation is an indicator of the relative discreteness of data, which takes into account the mean and standard deviation of the data. In the variation weighted calculation, the weight of each parameter is determined by its coefficient of variation, that is, the greater the data fluctuation, the higher its weight. This weighting method can highlight those parameters that have a greater impact on the support effect, making the analysis results more accurate and reliable. Finally, the target variation weighted result is recorded as the target system strength index. This index is a comprehensive evaluation of the strength of the target support system, taking into account the influence of multiple parameters and weighting them according to the coefficient of variation principle. The target system strength index can be used as an important indicator to evaluate and compare the strength and stability of different support schemes. Through this analysis and calculation method, a scientific and accurate evaluation basis can be provided for tunnel support design.

[0035] Furthermore, the intelligent prediction method for the support strength of underground mine tunnels also includes: the first simulated anti-impact record refers to a record of an anti-impact simulation test of a first vibration event on the target tunnel support; wherein the first simulated anti-impact record includes the first vibration kinetic energy, the first vibration potential energy and the first simulated energy absorption of the target tunnel support of the first vibration event, and the first simulated energy absorption includes the first top anchor cable simulated energy absorption and the first side anchor rod simulated energy absorption; the first epicenter distance of the first vibration event is obtained, and the anti-impact capacity evaluation function is called to analyze the first simulated anti-impact record to obtain the first simulated anti-impact index.

[0036] Specifically, the first simulation impact resistance record is first analyzed as a whole. These records come from the impact resistance simulation test of the first vibration event on the target tunnel support. It contains key information, such as the first vibration kinetic energy and the first vibration potential energy of the first vibration event, as well as the first simulated energy absorption of the target tunnel support, which includes the first top anchor cable simulated energy absorption and the first side anchor rod simulated energy absorption. These data are crucial for evaluating the impact resistance and stability of the support structure. Specifically, first, the first source distance of the first vibration event is obtained. This refers to the distance from the vibration source to the tunnel support, which plays an important role in evaluating the vibration impact and support response. Then, the impact resistance evaluation function is called, which is a key tool for analyzing and evaluating the impact resistance of the support structure. Next, the first simulation impact resistance record is analyzed using the impact resistance evaluation function. This analysis involves a comprehensive consideration of the first vibration kinetic energy, the first vibration potential energy, and the first simulated energy absorption. The first simulated energy absorption, especially the simulated energy absorption of the top anchor cable and the side anchor rod, is an important indicator for evaluating the energy absorption capacity of the support structure. Finally, the first simulation impact resistance index is obtained through analysis. This index is a quantitative assessment of the impact resistance of the support structure in the first vibration event. It comprehensively considers the vibration energy and the energy absorption characteristics of the support structure, providing an important basis for evaluating the impact resistance of the support structure.

[0037] Furthermore, the intelligent prediction method for underground mine tunnel support strength also includes: the expression of the impact resistance evaluation function is as follows: Wherein, E(x) refers to the first simulation impact resistance index of the target tunnel support under the first vibration event x, A cable and A bolt They respectively refer to the simulated energy absorption of the first top anchor cable and the simulated energy absorption of the first side anchor rod, R1(x) and R2(x) respectively refer to the first vibration kinetic energy and the first vibration potential energy of the first vibration event x, e refers to the attenuation coefficient, and r refers to the first earthquake source distance.

[0038] Specifically, the expression of the impact resistance evaluation function is as follows: Wherein, E(x) refers to the first simulation impact resistance index of the target tunnel support under the first vibration event x, A cable and A bolt They respectively refer to the simulated energy absorption of the first top anchor cable and the simulated energy absorption of the first side anchor rod, R1(x) and R2(x) respectively refer to the first vibration kinetic energy and the first vibration potential energy of the first vibration event x, e refers to the attenuation coefficient, and r refers to the first earthquake source distance.

[0039] Furthermore, the intelligent prediction method for the support strength of underground mine tunnels also includes: obtaining the first vibration energy of the first vibration event; when the first vibration energy meets the energy limit, performing an impact simulation on the first vibration event.

[0040] Specifically, the first vibration energy of the first vibration event is analyzed first. This is a quantification of the energy size of the vibration event, which is crucial for evaluating the impact of vibration on tunnel support. The size of the vibration energy is directly related to the impact force that the support structure needs to withstand, so it is an important input parameter for the impact simulation test. When the first vibration energy meets the energy limit, that is, the vibration energy is within the predetermined range, an impact simulation is performed on the first vibration event. This step ensures the pertinence and practicality of the simulation test. It is only meaningful to conduct an impact simulation when the vibration energy reaches a certain level and may have a significant impact on the support structure. Finally, in this way, the vibration events that need to be simulated can be effectively screened out to ensure the efficiency and accuracy of the simulation test.

[0041] Furthermore, the intelligent prediction method for the support strength of underground mine tunnels also includes: forming a prediction training data set, the prediction training data set including a prediction training intensity index, a prediction training impact resistance index and a prediction training support strength; performing supervised learning and testing on the prediction training data set to obtain the strength prediction model.

[0042] Specifically, first, a prediction training data set is formed. This data set is the basis for building a strength prediction model, and contains key data such as the prediction training strength index, the prediction training impact resistance index, and the prediction training support strength. These data reflect the strength and impact resistance of the roadway under different support conditions, and are essential for training an accurate and reliable prediction model. Specifically, historical support data are first collected and sorted, including the strength index, impact resistance index, and support strength under various support conditions. Then, these data are integrated into a prediction training data set. This data set needs to be large and representative enough to ensure that the trained model has good generalization ability. Next, supervised learning is performed on the prediction training data set. This means using known support strengths as labels and training the model through a machine learning algorithm so that it can predict support strength based on strength index and impact resistance index. In this process, the model learns the relationship between different parameters and how they affect support strength together. Finally, the trained model is tested. This is to evaluate the prediction accuracy of the model and ensure that it can provide reliable results in practical applications. In this way, a verified strength prediction model can be obtained, which can predict the strength of the target roadway support based on the input strength index and impact resistance index.

[0043] In summary, the intelligent prediction method for underground mine tunnel support strength provided by the present application has the following technical effects:

[0044] By reading the predetermined support features, and collecting multi-dimensional features of the target tunnel support based on the predetermined support features, the target support feature information is obtained; the target support feature information is analyzed to obtain the target system strength index; a support impact resistance database is established based on big data, and the first simulation impact resistance record in the support impact resistance database is extracted; the impact resistance evaluation function is introduced to evaluate and analyze the first simulation impact resistance record to obtain the first simulation impact resistance index; the mean of the first simulation impact resistance index is recorded as the target system impact resistance index; the target system strength index and the target system impact resistance index are used as input information of the strength prediction model to obtain output information, and the output information includes the target support strength of the target tunnel support, that is, first, the predetermined support features are read, and multi-dimensional features of the target tunnel support are collected based on these features to obtain the target support feature information. Then, this information is analyzed to obtain the target system strength index, which reflects the strength level of the tunnel support. Then, based on big data technology, a support impact resistance database is established, and the first simulation impact resistance record is extracted. The impact resistance evaluation function is introduced to evaluate and analyze these records to obtain the first simulation impact resistance index. The mean value of this index is recorded as the target system impact resistance index, which represents the ability of the support system to resist impact. Finally, the target system strength index and the target system impact resistance index are used as input information and sent to the strength prediction model. After model processing, the output information includes the target support strength of the target tunnel support, which is a prediction of the expected performance of the tunnel support in actual work. Through this series of data collection, analysis and modeling processes, a scientific and systematic method is provided for the strength evaluation of tunnel support, which helps to predict and prevent the risk of support failure in advance in actual engineering.

[0045] Embodiment 2: Based on the same inventive concept as the intelligent prediction method for the support strength of underground mine tunnels in the aforementioned embodiment, the present application also provides an intelligent prediction system for the support strength of underground mine tunnels, please refer to the attached Figure 2 , the intelligent prediction system for underground mine tunnel support strength comprises:

[0046] A feature collection module 11, the feature collection module 11 is used to read the predetermined support features, and based on the predetermined support features, perform multi-dimensional feature collection on the target tunnel support to obtain target support feature information; a strength analysis module 12, the strength analysis module 12 is used to analyze the target support feature information to obtain a target system strength index; a simulation acquisition module 13, the simulation acquisition module 13 is used to establish a support impact resistance database based on big data, and extract a first simulation impact resistance record in the support impact resistance database; a first impact resistance analysis module 14, the first impact resistance analysis module 14 is used to introduce an impact resistance capacity evaluation function to evaluate and analyze the first simulation impact resistance record to obtain a first simulation impact resistance index; a second impact resistance analysis module 15, the second impact resistance analysis module 15 is used to record the mean value of the first simulation impact resistance index as the target system impact resistance index; a strength prediction module 16, the strength prediction module 16 is used to use the target system strength index and the target system impact resistance index as input information of a strength prediction model to obtain output information, the output information including the target support strength of the target tunnel support.

[0047] Furthermore, the feature collection module 11 in the intelligent prediction system for the support strength of underground mine tunnels is also used for: the predetermined support features include at least the top anchor cable length, the top anchor cable spacing, the top anchor cable spacing and the anchoring length, and the anchoring length includes the top anchoring length and the side anchoring length.

[0048] Furthermore, the feature collection module 11 in the intelligent prediction system for the support strength of underground mine tunnels is also used to: read a predetermined exposed length value; read the anchoring length value of the anchoring length, wherein the anchoring length value includes a top anchoring length value and a side anchoring length value; the predetermined exposed length value and the top anchoring length value and the side anchoring length value together constitute a predetermined limit value; read a predetermined reinforcement arch strategy, and analyze and calculate the predetermined limit value based on the predetermined reinforcement arch strategy to obtain a parameter value of the top anchor cable length, recorded as a top anchor cable length value; read a predetermined spacing strategy, and analyze and calculate the parameter values ​​of the top anchor cable spacing and the top anchor cable row spacing based on the predetermined spacing strategy, recorded as a top anchor cable spacing value and a top anchor cable row spacing value, respectively; the predetermined limit value, the top anchor cable length value, the top anchor cable spacing value and the top anchor cable row spacing value together constitute the target support feature information.

[0049] Furthermore, the strength analysis module 12 in the intelligent prediction system for underground mine tunnel support strength is also used to: standardize the target support feature information to obtain target standardized support feature information; perform variation weighted calculation on the target standardized support feature information using the coefficient of variation principle to obtain a target variation weighted result; and record the target variation weighted result as the target system strength index.

[0050] Furthermore, the first anti-impact analysis module 14 in the intelligent prediction system for the support strength of underground mine tunnels is also used for: the first simulated anti-impact record refers to a record of an anti-impact simulation test of a first vibration event on the target tunnel support; wherein the first simulated anti-impact record includes the first vibration kinetic energy, the first vibration potential energy and the first simulated energy absorption of the target tunnel support of the first vibration event, and the first simulated energy absorption includes the first top anchor cable simulated energy absorption and the first side anchor rod simulated energy absorption; obtaining the first epicenter distance of the first vibration event, and calling the anti-impact capacity evaluation function to analyze the first simulated anti-impact record to obtain the first simulated anti-impact index.

[0051] Furthermore, the first impact resistance analysis module 14 in the intelligent prediction system for underground mine tunnel support strength is also used for: the expression of the impact resistance evaluation function is as follows: Wherein, E(x) refers to the first simulation impact resistance index of the target tunnel support under the first vibration event x, A cable and A bolt They respectively refer to the simulated energy absorption of the first top anchor cable and the simulated energy absorption of the first side anchor rod, R1(x) and R2(x) respectively refer to the first vibration kinetic energy and the first vibration potential energy of the first vibration event x, e refers to the attenuation coefficient, and r refers to the first earthquake source distance.

[0052] Furthermore, the simulation acquisition module 13 in the intelligent prediction system for underground mine tunnel support strength is also used to: obtain the first vibration energy of the first vibration event; when the first vibration energy meets the energy limit, perform impact simulation on the first vibration event.

[0053] Furthermore, the strength prediction module 16 in the intelligent prediction system for underground mine tunnel support strength is also used to: form a prediction training data set, the prediction training data set includes a prediction training intensity index, a prediction training impact resistance index and a prediction training support strength; perform supervised learning and verification on the prediction training data set to obtain the strength prediction model.

[0054] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The intelligent prediction method and specific examples of the support strength of an underground mine tunnel in Example 1 are also applicable to the intelligent prediction system of the support strength of an underground mine tunnel in this embodiment. Through the detailed description of the intelligent prediction method of the support strength of an underground mine tunnel, those skilled in the art can clearly know the intelligent prediction system of the support strength of an underground mine tunnel in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0055] Embodiment three, based on the same inventive concept as the method for intelligent prediction of the support strength of an underground mine tunnel in the aforementioned embodiment, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the steps of the method for intelligent prediction of the support strength of an underground mine tunnel described in any one of the aforementioned embodiments one are implemented.

[0056] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be 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 the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0057] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. An intelligent prediction method for underground mine tunnel support strength, characterized in that: include: Reading predetermined support features, and collecting multi-dimensional features of target tunnel support based on the predetermined support features to obtain target support feature information; Analyzing the target support characteristic information to obtain a target system strength index; Establishing a support and impact resistance database based on big data, and extracting the first simulation impact resistance record in the support and impact resistance database; Introducing an impact resistance evaluation function to evaluate and analyze the first simulation impact resistance record to obtain a first simulation impact resistance index; Recording the average value of the first simulation impact resistance index as the target system impact resistance index; The target system strength index and the target system impact resistance index are used as input information of a strength prediction model to obtain output information, wherein the output information includes the target support strength of the target tunnel support.

2. The intelligent prediction method for underground mine tunnel support strength according to claim 1, characterized in that: The predetermined support features include at least the top anchor cable length, the top anchor cable spacing, the top anchor cable row spacing and the anchoring length, and the anchoring length includes the top anchoring length and the side anchoring length.

3. The intelligent prediction method for underground mine tunnel support strength according to claim 2 is characterized in that: include: Read the predetermined exposed length value; Read the anchoring length value of the anchoring length, wherein the anchoring length value includes the top anchoring length value and the side anchoring length value; The predetermined exposed length value, the top anchoring length value, and the side anchoring length value together constitute a predetermined limit value; Reading a predetermined reinforcement arch strategy, and analyzing and calculating the predetermined limit value based on the predetermined reinforcement arch strategy to obtain a parameter value of the top anchor cable length, which is recorded as the top anchor cable length value; Reading a predetermined spacing strategy, and analyzing and calculating the parameter values ​​of the top anchor cable spacing and the top anchor cable row spacing based on the predetermined spacing strategy, which are recorded as the top anchor cable spacing value and the top anchor cable row spacing value respectively; The predetermined limit value, the top anchor cable length value, the top anchor cable spacing value and the top anchor cable row spacing value together constitute the target support characteristic information.

4. The intelligent prediction method for underground mine tunnel support strength according to claim 1 is characterized in that: include: Standardizing the target support characteristic information to obtain target standardized support characteristic information; Using the coefficient of variation principle, a weighted variation calculation is performed on the target standardized support characteristic information to obtain a target variation weighted result; The target variation weighted result is recorded as the target system strength index.

5. The intelligent prediction method for underground mine tunnel support strength according to claim 1, characterized in that: include: The first simulation anti-impact record refers to a record of an anti-impact simulation test of a first vibration event on the target tunnel support; The first simulated impact resistance record includes the first vibration kinetic energy, the first vibration potential energy and the first simulated energy absorption of the target tunnel support of the first vibration event, and the first simulated energy absorption includes the first top anchor cable simulated energy absorption and the first side anchor rod simulated energy absorption; The first earthquake source distance of the first vibration event is obtained, and the impact resistance evaluation function is called to analyze the first simulation impact resistance record to obtain the first simulation impact resistance index.

6. The intelligent prediction method for underground mine tunnel support strength according to claim 5, characterized in that: The expression of the impact resistance evaluation function is as follows: Wherein, E(x) refers to the first simulation impact resistance index of the target tunnel support under the first vibration event x, A cable and A bolt They respectively refer to the simulated energy absorption of the first top anchor cable and the simulated energy absorption of the first side anchor rod, R1(x) and R2(x) respectively refer to the first vibration kinetic energy and the first vibration potential energy of the first vibration event x, e refers to the attenuation coefficient, and r refers to the first earthquake source distance.

7. The intelligent prediction method for underground mine tunnel support strength according to claim 5, characterized in that: Also includes: Acquiring a first vibration energy of the first vibration event; When the first vibration energy meets the energy limit, an impact simulation is performed on the first vibration event.

8. The intelligent prediction method for underground mine tunnel support strength according to claim 1, characterized in that: include: Establishing a prediction training data set, wherein the prediction training data set includes a prediction training intensity index, a prediction training impact resistance index, and a prediction training support strength; The intensity prediction model is obtained by performing supervised learning and testing on the prediction training data set.

9. An intelligent prediction system for underground mine tunnel support strength, characterized in that: The steps for implementing the intelligent prediction method of underground mine tunnel support strength according to any one of claims 1 to 8 include: A feature collection module, the feature collection module is used to read the predetermined support features, and based on the predetermined support features, perform multi-dimensional feature collection on the target tunnel support to obtain target support feature information; A strength analysis module, the strength analysis module is used to analyze the target support characteristic information to obtain a target system strength index; A simulation acquisition module, the simulation acquisition module is used to establish a support and impact resistance database based on big data, and extract the first simulation impact resistance record in the support and impact resistance database; A first impact resistance analysis module, the first impact resistance analysis module is used to introduce an impact resistance capability evaluation function to evaluate and analyze the first simulation impact resistance record to obtain a first simulation impact resistance index; A second impact analysis module, wherein the second impact analysis module is used to record the average value of the first simulation impact index as the target system impact index; A strength prediction module is used to use the target system strength index and the target system impact resistance index as input information of a strength prediction model to obtain output information, wherein the output information includes the target support strength of the target tunnel support.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed, implements the steps of an intelligent prediction method for the support strength of underground mine tunnels as described in any one of claims 1 to 8.

Images