A rock mass blastability classification system

Abstract

The application discloses a rock mass blastability grading system and belongs to the field of mine exploitation, which comprises a data acquisition module, a model construction module, a model prediction module and a grading matching module, wherein the data acquisition module, the model construction module, the model prediction module and the grading matching module are sequentially connected; the data acquisition module is used for acquiring rock mass parameters and blasting parameters and establishing a rock mass blastability grading standard; the model construction module is used for constructing a rock mass blastability model; the model prediction module is used for inputting to-be-tested rock mass parameters and blasting parameters into the rock mass blastability model and outputting rock mass blasting parameters; and the grading matching module is used for matching the rock mass blasting parameters with the rock mass blastability grading standard to obtain the blastability grade of the to-be-tested rock mass. The application comprehensively considers factors such as rock physical and mechanical properties, rock mass geological structure, blasting materials, blasting parameters and process, establishes a scientific rock mass blastability grading system and can truly reflect the blasting property of rocks.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of mine exploitation, and particularly relates to a rock mass blastability grading system. BACKGROUND

[0002] Rock blastability refers to the performance of rock in resisting blasting fragmentation by explosives. It is the comprehensive performance of rock physical and mechanical properties, rock mass geological structure, explosives, blasting parameters and process in the blasting process, and influences the blasting effect. According to rock blastability, rock classification can be performed, explosive consumption can be estimated, quota can be formulated, and basis can be provided for blasting optimization. Due to the 'three high' (high explosion temperature, high explosion pressure, high explosion speed) characteristics of explosives, the complexity of rock structure and the limitations of testing and recording means, many difficulties are brought to the research work.

[0003] At present, in the prior art, various criteria and indexes are proposed for rock classification according to the main influencing factors of rock blastability. The main criteria include rock strength, unit explosive consumption, engineering geological parameters, rock elastic wave velocity, rock wave impedance, blasting rock particle displacement, critical velocity, blasting work index, rock elastic deformation energy coefficient and the like, which reflect the blastability of rock from different aspects. However, these indexes do not comprehensively consider the rock physical and mechanical properties, rock mass geological structure, explosives, blasting parameters and process, and a scientific classification system cannot be established. SUMMARY

[0004] The present application aims to provide a rock mass blastability grading system to solve the problems existing in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides a rock mass blastability grading system, comprising a data acquisition module, a model construction module, a model prediction module and a grading matching module, wherein the data acquisition module, the model construction module, the model prediction module and the grading matching module are connected in sequence.

[0006] The data acquisition module is used for acquiring rock mass parameters and blasting parameters, and establishing rock mass blastability grading standards.

[0007] The model construction module is used for constructing a rock mass blastability model.

[0008] The model prediction module is used for inputting the rock mass parameters and blasting parameters to be tested into the rock mass blastability model, and outputting rock mass blasting parameters.

[0009] The grading matching module is used for matching the rock mass blasting parameters with the rock mass blastability grading standards, and obtaining the blastability grade of the rock mass to be tested.

[0010] Preferably, the data acquisition module comprises a parameter acquisition unit and a hierarchical standard construction unit, wherein the parameter acquisition unit and the hierarchical standard construction unit are connected with the model construction module respectively;

[0011] The parameter acquisition unit is configured to acquire rock mass parameters and blasting parameters, wherein the rock mass parameters comprise rock types, rock firmness parameters and rock fissure parameters, and the blasting parameters comprise blasting materials, blasting specifications and the number of blasting materials.

[0012] The hierarchical standard construction unit is configured to establish a rock mass explosibility hierarchical standard according to the difficulty of rock resistance to blasting material blasting.

[0013] Preferably, the model construction module comprises a model construction unit and a model training unit, wherein the model construction unit and the model training unit are connected with the model prediction module respectively;

[0014] The model construction unit is configured to construct a rock mass explosibility model, wherein the rock mass explosibility model is a BP neural network model.

[0015] The model training unit is configured to train the BP neural network model based on the rock mass parameters and the blasting parameters until a set learning iteration number is reached to complete the training, thereby obtaining a trained rock mass explosibility model.

[0016] Preferably, the model prediction module comprises a model prediction unit and a parameter analysis unit, wherein the model prediction unit and the parameter analysis unit are connected with the hierarchical matching module respectively;

[0017] The model prediction unit is configured to input to-be-tested rock mass parameters and to-be-tested blasting parameters into the rock mass explosibility model, and output rock mass blasting parameters, wherein the rock mass blasting parameters comprise rock compressive strength, rock bulk density, rock mass integrity coefficient and explosive unit consumption.

[0018] The parameter analysis unit is configured to analyze the rock mass blasting parameters to obtain a comprehensive weight of the rock mass blasting parameters.

[0019] Preferably, in the parameter analysis unit, the subjective weight of the rock mass blasting parameters is calculated by an analytic hierarchy process, the objective weight of the rock mass blasting parameters is calculated by an entropy weight method, and the subjective weight and the objective weight are optimized by a game theory comprehensive weighting method to obtain the comprehensive weight of the rock mass blasting parameters.

[0020] Preferably, the hierarchical matching module comprises a hierarchical determination unit and a hierarchical matching unit.

[0021] The hierarchical determination unit is configured to obtain a plurality of grade parameters in a rock mass blastability hierarchical standard according to the rock mass blasting parameters.

[0022] The hierarchical matching unit is configured to match the rock mass blasting parameters with the rock mass blastability hierarchical standard based on the grade parameters and the comprehensive weights to obtain the blastability grade of the rock mass to be tested.

[0023] Preferably, the hierarchical matching unit further comprises a feedforward neural network, the feedforward neural network is trained based on the grade parameters and the comprehensive weights to obtain a trained feedforward neural network, the test grade parameters and the test comprehensive weights are input into the trained feedforward neural network to obtain an output result, and the blastability grade corresponding to the maximum value in the output result is selected as the blastability grade of the rock mass to be tested.

[0024] Preferably, the rock mass blastability hierarchical standard is divided into extremely easy to blast, very easy to blast, easy to blast, medium, difficult to blast, very difficult to blast and extremely difficult to blast.

[0025] The technical effect of the present application is as follows.

[0026] The present application provides a rock mass blastability hierarchical system, which acquires rock mass parameters and blasting parameters through a data acquisition module, establishes a rock mass blastability hierarchical standard, constructs a rock mass blastability model through a model construction module, inputs the rock mass parameters and the blasting parameters to be tested into the rock mass blastability model through a model prediction module to output rock mass blasting parameters, and matches the rock mass blasting parameters with the rock mass blastability hierarchical standard through a hierarchical matching module to obtain the blastability grade of the rock mass to be tested. The present application can comprehensively consider the physical and mechanical properties of rock, the geological structure of rock mass, blasting materials, blasting parameters and process factors, establish a scientific rock mass blastability hierarchical system, and truly reflect the blasting property of rock. BRIEF DESCRIPTION OF DRAWINGS

[0027] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of this application and their explanations are used to explain this application, and do not constitute an improper limitation on this application. In the drawings:

[0028] Figure 1 The system schematic diagram in the embodiments of the present application. DETAILED DESCRIPTION

[0029] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0030] Embodiment one

[0031] As Figure 1As shown, the rock mass blastability grading system provided in the embodiment includes:

[0032] The data acquisition module, the model construction module, the model prediction module and the grading matching module are connected in sequence.

[0033] The data acquisition module is configured to acquire rock mass parameters and blasting parameters, and establish a rock mass blastability grading standard.

[0034] The model construction module is configured to construct a rock mass blastability model.

[0035] The model prediction module is configured to input the rock mass parameters and the blasting parameters to be tested into the rock mass blastability model, and output rock mass blasting parameters.

[0036] The grading matching module is configured to match the rock mass blasting parameters with the rock mass blastability grading standard, and obtain the blastability grade of the rock mass to be tested.

[0037] In some embodiments, the data acquisition module includes a parameter acquisition unit and a grading standard construction unit, wherein the parameter acquisition unit and the grading standard construction unit are connected with the model construction module.

[0038] The parameter acquisition unit is configured to acquire rock mass parameters and blasting parameters, wherein the rock mass parameters include rock type, rock firmness parameters and rock fissure parameters, and the blasting parameters include blasting material, blasting specification and blasting material quantity.

[0039] In the embodiment, the main factors affecting the rock blastability include internal factors of the physical and mechanical properties of the rock itself and external factors such as explosive properties and blasting process. The former is determined by the geological formation conditions, mineral composition, structure and later geological structure of the rock, which is characterized by physical and mechanical properties such as rock density or unit weight, porosity, dilatancy, elasticity, plasticity, brittleness and rock strength; the latter depends on the type of explosive, the form and weight of the explosive charge, the charging structure, the initiation method and the interval time, the size, number, direction of the minimum resistance line and free surface, and the relative position of the free surface and the explosive charge. Among them, the physical and mechanical properties of the rock itself are the most important influencing factors. In addition, it also includes the influence on the blasting effect such as blasting size, blasting pile form and throwing distance.

[0040] In the embodiment, the blasting effect of explosive explosion on rock mainly has two aspects, one is to overcome the cohesion between rock particles, break the internal structure of rock and produce fresh fracture surface; the other is to expand and destroy the original and secondary fissures of rock. The former depends on the firmness of the rock itself; the latter is controlled by the fissure of the rock. Therefore, the firmness of the rock and the fissure of the rock are the most fundamental factors affecting the blastability of the rock.

[0041] The grading standard construction unit is used to establish the rock mass blastability grading standard according to the difficulty of rock resistance to blasting material blasting breaking.

[0042] In the embodiment, the rock mass blastability grading standard is divided into seven grades: extremely easy to blast, very easy to blast, easy to blast, medium, difficult to blast, very difficult to blast and extremely difficult to blast.

[0043] In some embodiments, the model construction module comprises a model construction unit and a model training unit, wherein the model construction unit and the model training unit are connected with the model prediction module respectively.

[0044] The model construction unit is used to construct a rock mass blastability model, wherein the rock mass blastability model is a BP neural network model.

[0045] In the embodiment, the number of intermediate layers and the number of neurons of each layer of the BP neural network model can be arbitrarily set according to specific conditions.

[0046] The model training unit trains the BP neural network model based on the rock mass parameters and the blasting parameters until the training is completed when the set learning iteration number is reached, and obtains the trained rock mass blastability model.

[0047] In some embodiments, the model prediction module comprises a model prediction unit and a parameter analysis unit, wherein the model prediction unit and the parameter analysis unit are connected with the grading matching module respectively.

[0048] The model prediction unit is used to input the to-be-tested rock mass parameters and the to-be-tested blasting parameters into the rock mass blastability model, and output rock mass blasting parameters, wherein the rock mass blasting parameters include rock compressive strength, rock bulk density, rock mass integrity coefficient and explosive unit consumption.

[0049] In the embodiment, the rock compressive strength reflects the blasting strength, the rock bulk density reflects the rock properties, the rock mass integrity coefficient reflects the geological structure properties of the rock mass, and the amount of explosive unit consumption is a comprehensive reflection of the properties of the rock mass and the properties of the rock.

[0050] In the embodiment, the rock strength is the ability of the rock to resist compression, shear and tensile stresses, thereby causing the rock to be damaged. Rock strength is a constant used in material mechanics to represent the material's resistance to the above three simple stresses, and is often measured under uniaxial static load. When blasting, the rock is subjected to instantaneous impact load, so the rock strength should be given new content, and the dynamic strength index under triaxial action should be emphasized. Only by considering the rock strength during blasting can the blasting properties of the rock be truly reflected.

[0051] The parameter analysis unit is used to analyze the rock mass blasting parameters to obtain the comprehensive weight of the rock mass blasting parameters.

[0052] In some embodiments, in the parameter analysis unit, the subjective weight of the rock mass blasting parameter is calculated by the analytic hierarchy process, the objective weight of the rock mass blasting parameter is calculated by the entropy weight method, and the subjective weight and the objective weight are optimized by the game theory comprehensive weighting method to obtain the comprehensive weight of the rock mass blasting parameter.

[0053] In some embodiments, the hierarchical matching module comprises a hierarchical determination unit and a hierarchical matching unit.

[0054] The hierarchical determination unit is configured to obtain a plurality of grade parameters in the rock mass blastability hierarchical standard according to the rock mass blasting parameter.

[0055] The hierarchical matching unit is configured to match the rock mass blasting parameter with the rock mass blastability hierarchical standard based on the grade parameters and the comprehensive weight to obtain the blastability grade of the rock mass to be tested.

[0056] In some embodiments, the hierarchical matching unit further comprises constructing a feedforward neural network, training the feedforward neural network based on the grade parameters and the comprehensive weight to obtain a trained feedforward neural network, inputting the to-be-tested grade parameters and the to-be-tested comprehensive weight into the trained feedforward neural network to obtain an output result, and selecting the blastability grade corresponding to the maximum value in the output result as the blastability grade of the rock mass to be tested.

[0057] In some embodiments, the rock mass blastability hierarchical standard is divided into extremely easy to blast, very easy to blast, easy to blast, medium, difficult to blast, very difficult to blast, and extremely difficult to blast.

[0058] The technical effects of the present embodiment are as follows:

[0059] The present embodiment provides a rock mass blastability hierarchical system, which acquires rock mass parameters and blasting parameters through a data acquisition module, establishes a rock mass blastability hierarchical standard, constructs a rock mass blastability model through a model construction module, inputs the rock mass parameters and the blasting parameters to be tested into the rock mass blastability model through a model prediction module to output rock mass blasting parameters, and matches the rock mass blasting parameters with the rock mass blastability hierarchical standard through a hierarchical matching module to obtain the blastability grade of the rock mass to be tested. The present application can comprehensively consider the physical and mechanical properties of rocks, the geological structure of rock mass, blasting materials, blasting parameters and process factors, establish a scientific rock mass blastability hierarchical system, and truly reflect the blasting property of rocks.

[0060] The above description is only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any changes or replacements within the technical range disclosed by the present application can be easily thought of by those skilled in the art, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A rock mass blastability classification system, characterized in that, include: The system comprises a data acquisition module, a model building module, a model prediction module, and a hierarchical matching module, wherein the data acquisition module, the model building module, the model prediction module, and the hierarchical matching module are connected in sequence. The data acquisition module is used to acquire rock mass parameters and blasting parameters, and to establish a rock mass blastability classification standard. The rock mass parameters include: rock type, rock firmness parameters and rock fracture parameters, and the blasting parameters include: blasting materials, blasting specifications and quantity of blasting materials. The model building module is used to build a rock mass blastability model; The model building module includes a model building unit and a model training unit, wherein the model building unit and the model training unit are respectively connected to the model prediction module; The model building unit is used to build a rock mass blastability model, wherein the rock mass blastability model is a BP neural network model; The model training unit trains the BP neural network model based on the rock mass parameters and blasting parameters until the set number of learning iterations is reached, thus completing the training and obtaining a trained rock mass blastability model. The model prediction module is used to input the rock mass parameters to be measured and the blasting parameters to be measured into the rock mass blastability model and output the rock mass blasting parameters. The model prediction module includes a model prediction unit and a parameter analysis unit, wherein the model prediction unit and the parameter analysis unit are respectively connected to the hierarchical matching module; The model prediction unit is used to input the rock mass parameters to be measured and the blasting parameters to be measured into the rock mass blastability model and output the rock mass blasting parameters, wherein the rock mass blasting parameters include: rock compressive strength, rock unit weight, rock mass integrity coefficient and explosive consumption per unit. The parameter analysis unit is used to analyze the rock mass blasting parameters and obtain the comprehensive weight of the rock mass blasting parameters; the subjective weight of the rock mass blasting parameters is calculated by the analytic hierarchy process, the objective weight of the rock mass blasting parameters is calculated by the entropy weight method, and the subjective weight and objective weight are optimized by the game theory comprehensive weighting method to obtain the comprehensive weight of the rock mass blasting parameters. The hierarchical matching module includes: a hierarchical determination unit and a hierarchical matching unit; The grading determination unit is used to obtain several grade parameters in the rock mass blastability grading standard based on the rock mass blasting parameters. The grading matching unit is used to match the rock mass blasting parameters with the rock mass blastability grading standard based on the grading parameters and the comprehensive weight, so as to obtain the blastability grade of the rock mass to be tested. The graded matching unit further includes constructing a feedforward neural network, training the feedforward neural network based on the grade parameters and the comprehensive weights to obtain a trained feedforward neural network; inputting the grade parameters to be tested and the comprehensive weights to be tested into the trained feedforward neural network to obtain the output results, and selecting the explosiveness grade corresponding to the maximum value in the output results as the explosiveness grade of the rock mass to be tested; The grading and matching module is used to match the rock mass blasting parameters with the rock mass blastability grading standard to obtain the blastability level of the rock mass to be tested.

2. The rock mass blastability classification system according to claim 1, characterized in that, The data acquisition module includes a parameter acquisition unit and a hierarchical standard construction unit, wherein the parameter acquisition unit and the hierarchical standard construction unit are respectively connected to the model construction module; The parameter acquisition unit is used to acquire rock mass parameters and blasting parameters; The grading standard construction unit is used to establish a rock mass blastability grading standard based on the difficulty of rock resisting blasting and fragmentation by blasting materials.

3. The rock mass blastability classification system according to claim 1, characterized in that, The rock mass blastability classification standard is divided into: extremely blastable, very blastable, blastable, moderate, difficult to blast, very difficult to blast, and extremely difficult to blast.

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