An intelligent prediction system and method for hillside blasting semi-porosity based on deep learning

Abstract

The present invention provides a system and method for intelligent prediction of hillside blasting semi-porosity based on deep learning, including: collecting geological information of the blasted hillside and blasting information of the blasting material, constructing a number of field data of this blasting operation, generating a number of estimated blasting features of this blasting operation through training to establish a blasting decision plan, establishing a field model of the blasted hillside to analyze the blasting material distribution information and blasting risk information corresponding to the blasting decision plan, optimizing the blasting material distribution information according to the blasting risk information, obtaining the blasting semi-porosity corresponding to this blasting operation and displaying them separately, using deep learning to intelligently predict the semi-porosity during the hillside blasting process, accurately identifying the relationship between blasting conditions and semi-porosity, providing real-time prediction results for operators, significantly improving prediction accuracy and efficiency, helping to optimize blasting parameters, reducing safety risks, and providing strong technical support for hillside blasting operations.

Description

Technical Field

[0001] The present invention relates to the technical field of blasting construction, and in particular to a system and method for intelligently predicting hillside blasting semi-porosity based on deep learning. Background Art

[0002] Construction blasting is an engineering technique that uses the energy generated by explosives exploding under controlled conditions to complete specific engineering tasks. This construction technique is often used for tunnel excavation or rock fragmentation. Blasting plays a very important role in the excavation of key areas, among which the semi-porosity is an important indicator for evaluating the blasting effect. The semi-porosity is also called the blast hole trace rate, which is the percentage of the total length of the blast hole traces on the excavation wall to the blast hole length. Generally, in order to ensure that the blasting results are the same as expected, it is necessary to predict the semi-porosity before blasting. However, the prediction of the semi-porosity is often affected by many factors, such as:

[0003] Physical properties of rock: such as rock hardness, density, degree of crack development, etc. The structure of rock directly affects the effect of drilling and blasting performance.

[0004] Drilling parameters: hole diameter, hole spacing, hole depth, etc. will affect the effect after blasting. A larger hole diameter and appropriate hole spacing usually help increase the blasting effect, but may also affect the semi-hole rate.

[0005] Explosive selection and dosage: The choice of different types of explosives and their dosage has a direct impact on the blasting effect. Reasonable explosive selection and dosage configuration can improve the uniformity of blasting and affect the utilization rate of the hole.

[0006] Blasting design (such as hole diameter and hole spacing): The arrangement of drilling holes directly affects the blasting effect. Too large or too small hole spacing may lead to incomplete blasting and affect the utilization rate of the holes.

[0007] Geological conditions: Geological conditions such as cracks, joints, and rock bedding in the rock strata will affect the rock fragmentation after blasting, thereby affecting the prediction of semi-porosity.

[0008] Currently, most manufacturers make simple predictions based on historical data and engineering experience, which leads to large errors between blasting results and expected results. A small number of manufacturers are trying to perform scientific calculations, but the calculation errors are still large, the calculation process is long, and the investment cost is too high, which is not applicable to the current situation.

[0009] Therefore, the present invention provides a deep learning-based intelligent prediction system and method for hillside blasting semi-porosity. Summary of the Invention

[0010] The present invention provides a deep learning-based intelligent prediction system and method for hillside blasting semi-porosity. Deep learning is used to intelligently predict the semi-porosity during hillside blasting, accurately identifying the relationship between blasting conditions and semi-porosity, and providing real-time prediction results for operators. This significantly improves prediction accuracy and efficiency, helps optimize blasting parameters, reduces safety risks, and provides strong technical support for hillside blasting operations.

[0011] The present invention provides a deep learning-based intelligent prediction system for hillside blasting semi-porosity, comprising:

[0012] The data acquisition module is used to collect geological information of the blasted hillside and blasting information of the blasting materials, and to construct a number of on-site data of this blasting operation;

[0013] A deep analysis module is used to train the field data to generate a number of estimated blasting features of the current blasting operation and generate a blasting decision plan for the current blasting operation;

[0014] A prediction and analysis module, configured to establish a site model of the blasted hillside and use the site model to analyze blasting material distribution information and blasting risk information corresponding to the blasting decision plan;

[0015] The optimization and adjustment module is used to optimize the blasting material distribution information according to the blasting risk information, obtain the blasting half-hole rate corresponding to this blasting operation and display it separately.

[0016] In one practicable manner,

[0017] The data acquisition module includes:

[0018] a geological sampling unit configured to search the big data for the geographical location of the blasted hillside, determine basic information of the blasted hillside, determine high-probability geological information of the blasted hillside using the basic information, and perform on-site sampling on the blasted hillside using a first geological sampling method corresponding to the high-probability geological information to obtain a plurality of first sampling information;

[0019] a compensating sampling unit configured to obtain unknown information generated during the on-site sampling, establish known geological characteristics of the blasted hillside based on the sampling information, deduce a plurality of associated geological characteristics of the blasted hillside based on the known geological characteristics, search the big data for a second geological sampling method corresponding to each associated geological characteristic, perform compensating sampling on the blasted hillside using the second geological sampling method, obtain second sampling information corresponding to each unknown information, and establish geological information of the blasted hillside based on the first sampling information and the second sampling information;

[0020] a material collection unit, configured to obtain a plurality of blasting materials contained on the blasted hillside, collect material parameters corresponding to each blasting material, establish blasting information corresponding to each blasting material, and establish a corresponding blasting mark for each blasting information;

[0021] The data generation unit is used to establish the geological data of the blasted hillside based on the geological information, establish the equipment data of the blasted hillside based on the blasting information, determine the equipment range of the corresponding equipment data according to the blasting mark corresponding to the blasting information, and generate a plurality of field data of the blasting operation.

[0022] In one practicable manner,

[0023] The in-depth analysis module includes:

[0024] a data training unit, configured to input each of the field data into a convolution prediction model for convolution training, obtain a plurality of feature blocks presented by each of the field data in different convolution layers, cluster the corresponding feature blocks in each of the convolution layers, and generate a cluster set corresponding to each of the convolution layers;

[0025] a deep training unit for analyzing the plurality of cluster sets corresponding to each of the field data, generating data traceability information corresponding to each of the field data based on the convolution features corresponding to each of the convolution layers and the cluster features corresponding to each of the cluster sets, and performing error optimization on the data traceability information using an error function model to generate a plurality of data features;

[0026] a feature recombining unit for obtaining a blasting purpose of the current blasting operation, substituting each of the data features into the blasting purpose, obtaining operation-related information corresponding to each of the field data, performing semantic analysis on the operation-related information, and recombining the data features according to the semantic analysis results to generate a plurality of estimated blasting features of the current blasting operation;

[0027] A plan generation unit is used to divide the estimated blasting characteristics into estimated geological characteristics and estimated material characteristics, establish a blasting plan for the blasted slope based on the estimated geological characteristics, establish a blastable combination of the blasting materials based on the estimated material characteristics, and establish a blasting decision plan for the current blasting operation based on the blasting plan and the blastable combinations.

[0028] In one practicable manner,

[0029] The solution generating unit includes:

[0030] a feature classification subunit for performing regression analysis on the estimated blasting features to obtain correlation relationships between different estimated blasting features, thereby obtaining a plurality of correlation feature chains of the current blasting operation, extracting key information from each of the correlation feature chains, and, based on the extraction results, treating a plurality of estimated blasting features included in a first correlation feature chain having a geological description as estimated geological features, and treating a plurality of estimated blasting features included in a second correlation feature chain having a material description as estimated material features;

[0031] a plan construction subunit, configured to establish a geological model of the blasted hillside based on the estimated geological characteristics, determine a plurality of blasting locations of the blasting plan and the blasting intensity corresponding to each blasting location in combination with the corresponding blasting purpose, locate each blasting location in the geological model and mark the intensity accordingly, run the geological model to determine the blasting sequence of the blasting operation, and generate a blasting plan for the blasted hillside;

[0032] a combination construction subunit, configured to obtain the geological hardness corresponding to each blasting location in the geological model, determine the optional material combination corresponding to each blasting location in combination with the corresponding blasting intensity, determine the combination completeness and combination cost corresponding to each optional material combination based on the estimated material characteristics, and select the target optional material combination with the qualified combination completeness and the lowest combination cost corresponding to each blasting location as the blastable combination for the blasting location;

[0033] The plan generating subunit is used to improve the blasting plan by using the blastable combination corresponding to each blasting position, determine the operation preparation guide, blasting sequence guide and blasting safety guide of the blasting operation, and generate a blasting decision plan for the blasting operation.

[0034] In one practicable manner,

[0035] The prediction analysis module includes:

[0036] a model building unit, configured to build a field model of the blasted hillside according to the field data, determine a plurality of field blasting locations according to the blasting decision plan, and locate each of the field blasting locations in the field model as a key location of the model;

[0037] a material adjustment unit, configured to establish, according to the blasting decision plan, the screening of corresponding required blasting materials for each key location of the model, run the field model to obtain impact information of required blasting materials between key locations of different models, adjust parameters of corresponding required blasting materials according to the impact information of required blasting materials, and obtain blasting material distribution information of the blasted hillside;

[0038] a key monitoring unit, configured to reset the material of the on-site model according to the blasting material distribution information, control the on-site model after the material reset to execute the blasting decision plan, collect blasting information corresponding to each key position of the model, randomly sample the blasting information to obtain a plurality of training information, and establish a decision tree based on the relationship between each training information and different blasting information;

[0039] The risk identification unit is used to use random forest to perform risk prediction on each decision tree to generate corresponding risk results, and to count the risk level and risk range corresponding to each risk result to generate blasting risk information of the blasting decision plan.

[0040] In one practicable manner,

[0041] The optimization and adjustment module includes:

[0042] a risk interpretation unit, configured to perform semantic analysis on the blasting risk information, generate a plurality of blasting risk descriptions of the blasting decision plan, identify risk keywords corresponding to each of the blasting risk descriptions, and generate a keyword set for the blasting decision plan;

[0043] a method construction unit for establishing a risk elimination method set for the blasting decision plan based on searching for a plurality of risk elimination methods corresponding to each of the risk keywords and inputting the methods into corresponding word positions of the keyword set, and obtaining method exclusion features and method support features between the risk elimination methods corresponding to different word positions in the risk elimination method set;

[0044] a mode recombining unit, configured to generate a plurality of non-combination modes based on the mode exclusion characteristics, eliminate the non-combination modes in the risk elimination mode set, generate a plurality of target combination modes, and screen a plurality of optimal combination modes included in the target combination modes based on the mode support characteristics;

[0045] an optimization execution unit, configured to optimize the blasting material distribution information using the corresponding optimization combinations in descending order of the optimization degree corresponding to each optimization combination, to obtain corresponding preliminary optimization results; and when each of the preliminary optimization results is abnormal, to optimize the blasting material distribution information using each target combination to obtain a further optimization result, and to select an effective target combination with the lowest blasting risk to optimize the blasting material distribution information;

[0046] The continuous analysis unit is used to simulate the optimization results corresponding to the blasting material distribution information, obtain several safe blasting positions for the current blasting operation, identify several blasting forces corresponding to each of the safe blasting positions, obtain and display the blasting half-hole rate corresponding to each of the safe blasting positions.

[0047] In one practicable manner,

[0048] Also includes:

[0049] When the preliminary optimization result is normal, the blasting material distribution information is optimized using a corresponding effective optimal combination method.

[0050] In one practicable manner,

[0051] Also includes:

[0052] The blasting supervision module is used to estimate the regional blasting range corresponding to the safe blasting position and establish a blasting danger warning area of ​​the blasted hillside.

[0053] The present invention provides a deep learning-based intelligent prediction method for hillside blasting semi-porosity, comprising:

[0054] Step 1: Collect geological information of the blasted hillside and blasting information of the blasting materials to construct several field data of this blasting operation;

[0055] Step 2: training the field data to generate a number of estimated blasting features for the current blasting operation, and generating a blasting decision plan for the current blasting operation;

[0056] Step 3: Establish a site model of the blasted hillside, and use the site model to analyze the blasting material distribution information and blasting risk information corresponding to the blasting decision plan;

[0057] Step 4: Optimize the blasting material distribution information according to the blasting risk information, obtain the blasting half-hole rate corresponding to this blasting operation, and display it separately.

[0058] In one practicable manner,

[0059] The step 4 comprises:

[0060] Step 41: performing semantic analysis on the blasting risk information to generate a plurality of blasting risk descriptions of the blasting decision plan, identifying risk keywords corresponding to each blasting risk description, and generating a keyword set of the blasting decision plan;

[0061] Step 42: Based on searching for a plurality of risk elimination methods corresponding to each of the risk keywords and inputting them into the corresponding word positions of the keyword set, a risk elimination method set for the blasting decision plan is established, and method exclusion features and method support features between the risk elimination methods corresponding to different word positions are obtained in the risk elimination method set;

[0062] Step 43: generating a plurality of non-combination methods based on the method exclusion feature, eliminating the non-combination methods from the risk elimination method set, generating a plurality of target combination methods, and screening a plurality of optimal combination methods included in the target combination methods based on the method support feature;

[0063] Step 44: Optimizing the blasting material distribution information using the corresponding optimization combinations in descending order of the optimization degree corresponding to each optimization combination to obtain corresponding preliminary optimization results. When each preliminary optimization result is abnormal, optimizing the blasting material distribution information using each target combination to obtain a further optimization result, and selecting an effective target combination with the lowest blasting risk to optimize the blasting material distribution information.

[0064] Step 45: Simulate the optimization result corresponding to the blasting material distribution information to obtain several safe blasting positions for the current blasting operation, identify several blasting forces corresponding to each of the safe blasting positions, obtain and display the blasting half-hole rate corresponding to each of the safe blasting positions.

[0065] The achievable beneficial effects of the above technical solution are: in order to help operators optimize blasting operations and reduce operational risks, geological information of the blasted hillside and blasting information of the blasting materials are first collected before the blasting operation, thereby constructing the field data of this blasting operation, and further determining several estimated blasting characteristics of this report operation by training the field data, and further constructing the corresponding blasting decision plan based on the relationship between each estimated blasting characteristic, and then determining the blasting material distribution information and blasting decision plan of the blasting decision plan by constructing a field model and using the model to simulate the plan. At this time, a blasting decision plan that can complete this blasting has been obtained. However, in order to avoid spontaneous combustion or blasting interruption caused by uneven distribution of blasting materials, the blasting material distribution information is optimized using blasting risk information. After the optimization is completed, the blasting half-hole rate of this blasting operation is determined. In this way, not only can the blasting operation of the blasted hillside be guided by decision-making, an effective decision-making plan can be generated, thereby determining the blasting half-hole rate suitable for this blasting operation. This not only provides technical reference for operators, but also helps operators to blast according to the blasting decision plan, greatly reducing the original risk, and providing operators with accurate and effective half-hole rate data, which is convenient for operators to carry out the next step of work.

[0066] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0067] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0069] Figure 1 Schematic diagram of the composition of a deep learning-based intelligent prediction system for hillside blasting semi-porosity in an embodiment of the present invention;

[0070] Figure 2 The figure is a schematic diagram of the workflow of an intelligent prediction method for hillside blasting semi-porosity based on deep learning in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0072] Example 1

[0073] This embodiment provides a deep learning-based intelligent prediction system for hillside blasting semi-porosity. Figure 1 Shown, including:

[0074] The data acquisition module is used to collect geological information of the blasted hillside and blasting information of the blasting materials, and to construct a number of on-site data of this blasting operation;

[0075] A deep analysis module is used to train the field data to generate a number of estimated blasting features of the current blasting operation and generate a blasting decision plan for the current blasting operation;

[0076] A prediction and analysis module, configured to establish a site model of the blasted hillside and use the site model to analyze blasting material distribution information and blasting risk information corresponding to the blasting decision plan;

[0077] The optimization and adjustment module is used to optimize the blasting material distribution information according to the blasting risk information, obtain the blasting half-hole rate corresponding to this blasting operation and display it separately.

[0078] In this example, the geological information represents the internal structure and material composition of the blasted hillside;

[0079] In this example, blasting materials refer to materials used in blasting operations, such as explosives;

[0080] In this example, the blasting information represents the quantity and distribution of a type of blasting material;

[0081] In this example, the field data represents the data present on the blasted hillside;

[0082] In this example, the estimated blasting characteristics represent the characteristics of the blasted hillside before the blasting operation;

[0083] In this example, the blasting decision plan represents the plan used to carry out this blasting operation;

[0084] In this example, the scene model refers to a model showing the structure of each area of ​​the scene in a virtual space;

[0085] In this example, the blasting material distribution information represents the distribution information formed after various blasting materials are arranged in advance when executing the blasting decision plan;

[0086] In this example, the blasting risk information represents information about potential dangers generated when performing blasting operations;

[0087] In this example, the purpose of optimizing the blasting material distribution information using the blasting risk information is to reasonably arrange the positions of the blasting materials to avoid continuous blasting or blasting interruption.

[0088] The working principle and beneficial effects of the above technical solution: In order to help operators optimize blasting operations and reduce operational risks, geological information of the blasted hillside and blasting information of the blasting materials are collected before the blasting operation, so as to construct the field data of this blasting operation, and further train the field data to determine several estimated blasting characteristics of this report operation, and further construct the corresponding blasting decision plan based on the relationship between each estimated blasting characteristic, and then determine the blasting material distribution information and blasting decision plan of the blasting decision plan by constructing a field model and using the model to simulate the plan. At this time, a plan that can complete this blasting has been obtained. However, in order to avoid spontaneous combustion or blasting interruption caused by uneven distribution of blasting materials, the blasting material distribution information is optimized using blasting risk information. After the optimization is completed, the blasting half-hole rate of this blasting operation is determined. In this way, not only can the blasting operation of the blasted hillside be guided by decision-making, an effective decision-making plan can be generated, thereby determining the blasting half-hole rate suitable for this blasting operation. This not only provides technical reference for operators, but also helps operators to blast according to the blasting decision plan, greatly reducing the original risk, and providing operators with accurate and effective half-hole rate data, which is convenient for operators to carry out the next step of work.

[0089] Example 2

[0090] On the basis of Example 1, the data acquisition module of the deep learning-based intelligent prediction system for hillside blasting semi-porosity includes:

[0091] a geological sampling unit configured to search the big data for the geographical location of the blasted hillside, determine basic information of the blasted hillside, determine high-probability geological information of the blasted hillside using the basic information, and perform on-site sampling on the blasted hillside using a first geological sampling method corresponding to the high-probability geological information to obtain a plurality of first sampling information;

[0092] a compensating sampling unit configured to obtain unknown information generated during the on-site sampling, establish known geological characteristics of the blasted hillside based on the sampling information, deduce a plurality of associated geological characteristics of the blasted hillside based on the known geological characteristics, search the big data for a second geological sampling method corresponding to each associated geological characteristic, perform compensating sampling on the blasted hillside using the second geological sampling method, obtain second sampling information corresponding to each unknown information, and establish geological information of the blasted hillside based on the first sampling information and the second sampling information;

[0093] a material collection unit, configured to obtain a plurality of blasting materials contained on the blasted hillside, collect material parameters corresponding to each blasting material, establish blasting information corresponding to each blasting material, and establish a corresponding blasting mark for each blasting information;

[0094] The data generation unit is used to establish the geological data of the blasted hillside based on the geological information, establish the equipment data of the blasted hillside based on the blasting information, determine the equipment range of the corresponding equipment data according to the blasting mark corresponding to the blasting information, and generate a plurality of field data of the blasting operation.

[0095] In this example, the basic information represents the location, height, slope, and hardness of the blasted hillside;

[0096] In this example, high-probability geological information refers to geological information that may be present on the blasted hillside, that is, geological information with a high probability of appearing on the blasted hillside under the influence of its specific basic information;

[0097] In this example, the first geological sampling method refers to the sampling method used when the blasted hillside is first sampled. That is, data verifying high-probability geological information can be collected through this sampling method, and different high-probability geological information may not necessarily correspond to the same first geological sampling method.

[0098] In this example, the first sampling information represents sampling information that may correspond to high-probability geological information;

[0099] In this example, unknown information refers to information whose specific geological information has not been determined after sampling and verification using the first geological sampling method;

[0100] In this example, the known geological features represent geological features of the blasted hillside constructed using the first sampling information, and the known geological features correspond to the high-probability geological information;

[0101] In this example, the associated geological feature refers to a geological feature that is associated with a known geological feature;

[0102] In this example, the second geological sampling method refers to the method used for the second sampling of the blasted hillside. That is, through this sampling method, data for verifying the associated geological features can be collected, and different associated geological features may not necessarily correspond to the same second geological sampling method.

[0103] In this example, compensatory sampling refers to the process of performing a second sampling according to a second geological sampling method;

[0104] In this example, material parameters represent the parameters presented by the blasting material, including: functional parameters, appearance parameters, position parameters, etc.;

[0105] In this example, the equipment range represents the range of blasting materials involved in the work.

[0106] The working principle and beneficial effects of the above technical solution are as follows: before carrying out the blasting operation, the geographical location of the blasted hillside is first determined, and then sampling is carried out to determine the geological information of the blasted hillside, and the blasting materials on the blasted hillside are counted to determine the equipment information on the blasted hillside. Based on the two types of information obtained, the on-site data around the blasting are established. In the process of analyzing the geological information, deep learning technology is used to analyze the high-frequency geological information of the blasted hillside and deduce its related geological information. In this way, not only can the geological information of the blasted hillside be quickly determined, but also the efficiency and accuracy of the information analysis are guaranteed, providing strong technical support for subsequent blasting work.

[0107] Example 3

[0108] On the basis of Example 1, the deep learning-based intelligent prediction system for hillside blasting semi-porosity, the deep analysis module includes:

[0109] a data training unit, configured to input each of the field data into a convolution prediction model for convolution training, obtain a plurality of feature blocks presented by each of the field data in different convolution layers, cluster the corresponding feature blocks in each of the convolution layers, and generate a cluster set corresponding to each of the convolution layers;

[0110] a deep training unit for analyzing the plurality of cluster sets corresponding to each of the field data, generating data traceability information corresponding to each of the field data based on the convolution features corresponding to each of the convolution layers and the cluster features corresponding to each of the cluster sets, and performing error optimization on the data traceability information using an error function model to generate a plurality of data features;

[0111] a feature recombining unit for obtaining a blasting purpose of the current blasting operation, substituting each of the data features into the blasting purpose, obtaining operation-related information corresponding to each of the field data, performing semantic analysis on the operation-related information, and recombining the data features according to the semantic analysis results to generate a plurality of estimated blasting features of the current blasting operation;

[0112] A plan generation unit is used to divide the estimated blasting characteristics into estimated geological characteristics and estimated material characteristics, establish a blasting plan for the blasted slope based on the estimated geological characteristics, establish a blastable combination of the blasting materials based on the estimated material characteristics, and establish a blasting decision plan for the current blasting operation based on the blasting plan and the blastable combinations.

[0113] In this example, the convolutional prediction model refers to a model that performs prediction tasks using a convolutional neural network;

[0114] In this example, the number of convolutional layers is 3;

[0115] In this example, the feature block represents the features presented by a field data in a convolutional layer;

[0116] In this example, the cluster set represents the result of grouping feature blocks with the same characteristics in the same convolutional layer into one category;

[0117] In this example, a convolutional layer corresponds to a convolutional feature, which indicates the degree of convolution of a convolutional layer.

[0118] In this example, a cluster set corresponds to a set feature, and the set feature represents the common features of all feature blocks in a cluster set;

[0119] In this instance, data traceability information indicates the data source of a field data;

[0120] In this example, the error function model represents a model used to measure the gap between the predicted value and the actual value of the data source information;

[0121] In this example, the operation association information indicates the relationship between a field data and the current blasting operation when the current blasting operation is performed;

[0122] In this example, the estimated blasting characteristics represent the characteristics generated by reorganizing the data characteristics during the blasting operation. The estimated blasting characteristics can be divided into estimated geological characteristics and estimated material characteristics.

[0123] In this example, the estimated geological characteristics represent the characteristics of the geological changes on the blasted hillside during the blasting operation, and the estimated material characteristics represent the characteristics of the changes in the blasting materials on the blasted hillside during the blasting operation.

[0124] In this example, the blasted plan represents the plan that needs to be executed in order to achieve the blasting purpose;

[0125] In this example, the blastable combination refers to a combination of materials that need to be detonated in order to achieve the purpose of blasting.

[0126] The working principle and beneficial effects of the above technical solution are as follows: In order to achieve the blasting purpose, an effective blasting decision-making plan needs to be established. First, the field data is input into the convolutional prediction model for training to obtain its feature blocks in different convolutional layers. Then, the feature blocks in the same convolutional layer are clustered to construct a cluster set. The convolutional features of the convolutional layer and the set features of the cluster set are further used to trace the field data. Then, the error function model is used to optimize the error of the data tracing information, and the data characteristics of the field data are determined. The operation association information between each field data and the blasting operation is further analyzed in combination with the blasting purpose. The data features are further reorganized through semantic analysis to determine the estimated blasting features of this blasting operation. The estimated blasting features are then classified and the corresponding blasting plan and blastable combination are constructed. Finally, the two are fused to obtain the blasting decision plan for this blasting operation. Through the above means, not only can a blasting decision plan for this blasting operation be constructed, but also the most effective blasting decision plan can be built based on the characteristics of the blasted hillside, thereby improving the intelligence of the system.

[0127] Example 4

[0128] On the basis of Example 3, the intelligent prediction system for hillside blasting semi-porosity based on deep learning, the solution generation unit includes:

[0129] a feature classification subunit for performing regression analysis on the estimated blasting features to obtain correlation relationships between different estimated blasting features, thereby obtaining a plurality of correlation feature chains of the current blasting operation, extracting key information from each of the correlation feature chains, and, based on the extraction results, treating a plurality of estimated blasting features included in a first correlation feature chain having a geological description as estimated geological features, and treating a plurality of estimated blasting features included in a second correlation feature chain having a material description as estimated material features;

[0130] a plan construction subunit, configured to establish a geological model of the blasted hillside based on the estimated geological characteristics, determine a plurality of blasting locations of the blasting plan and the blasting intensity corresponding to each blasting location in combination with the corresponding blasting purpose, locate each blasting location in the geological model and mark the intensity accordingly, run the geological model to determine the blasting sequence of the blasting operation, and generate a blasting plan for the blasted hillside;

[0131] a combination construction subunit, configured to obtain the geological hardness corresponding to each blasting location in the geological model, determine the optional material combination corresponding to each blasting location in combination with the corresponding blasting intensity, determine the combination completeness and combination cost corresponding to each optional material combination based on the estimated material characteristics, and select the target optional material combination with the qualified combination completeness and the lowest combination cost corresponding to each blasting location as the blastable combination for the blasting location;

[0132] The plan generating subunit is used to improve the blasting plan by using the blastable combination corresponding to each blasting position, determine the operation preparation guide, blasting sequence guide and blasting safety guide of the blasting operation, and generate a blasting decision plan for the blasting operation.

[0133] In this example, regression analysis represents the process of analyzing the relationships between different estimated burst characteristics;

[0134] In this example, the association relationship represents the relationship between different estimated burst features, including logical relationship, position relationship, embedding relationship and joint relationship;

[0135] In this example, the associated feature chain represents the result of connecting the estimated blasting features with associated relationships according to logical conditions;

[0136] In this example, the first associated feature chain represents a feature chain belonging to geological information, and the second associated feature chain represents a feature chain belonging to blasting material information;

[0137] In this example, the geological model refers to a model that displays geological information of a blasted hillside in a virtual space;

[0138] In this example, the optional material combination represents the blasting materials required to blast a blasting location;

[0139] In this example, the combination completeness indicates the completeness of the optional material combination composed of the existing materials on the blasted hillside, that is, analyzing whether the existing blasting materials on the blasted hillside can meet the corresponding optional material combination;

[0140] In this example, the combination cost represents the total cost of an optional material combination;

[0141] In this example, the operation preparation guide indicates what needs to be prepared before performing this blasting operation, the blasting sequence guide indicates the blasting sequence during this blasting operation, and the blasting safety guide indicates the safety operations that need to be followed when blasting at each blasting location.

[0142] The working principle and beneficial effects of the above technical solution: In order to further improve the blasting decision-making plan and provide an effective work reference for the operator, the estimated blasting characteristics are subjected to regression analysis to determine the correlation between them, thereby generating a corresponding correlation feature chain, and the characteristics corresponding to each key feature chain are determined through semantic description, and then the blasting intensity of each blasting location is analyzed in combination with the corresponding blasting purpose, and the blasting sequence of this blasting operation is determined by running the geological model, and the corresponding blasting plan is generated, and further optional material combinations are constructed according to the geological hardness and blasting strength, and the appropriate blastable combination is selected through the total completeness and total cost, and then a number of guidelines related to this blasting operation are generated, and a blasting decision plan is obtained. In this way, not only the estimated blasting characteristics can be analyzed and combined in detail, but also a blasting decision plan can be constructed by conducting a detailed analysis of each blasting location, thereby improving the effectiveness and practicality of the blasting decision plan.

[0143] Example 5

[0144] Based on Example 1, the deep learning-based intelligent prediction system for hillside blasting semi-porosity rate, the prediction and analysis module includes:

[0145] a model building unit, configured to build a field model of the blasted hillside according to the field data, determine a plurality of field blasting locations according to the blasting decision plan, and locate each of the field blasting locations in the field model as a key location of the model;

[0146] a material adjustment unit, configured to establish, according to the blasting decision plan, the screening of corresponding required blasting materials for each key location of the model, run the field model to obtain impact information of required blasting materials between key locations of different models, adjust parameters of corresponding required blasting materials according to the impact information of required blasting materials, and obtain blasting material distribution information of the blasted hillside;

[0147] a key monitoring unit, configured to reset the material of the on-site model according to the blasting material distribution information, control the on-site model after the material reset to execute the blasting decision plan, collect blasting information corresponding to each key position of the model, randomly sample the blasting information to obtain a plurality of training information, and establish a decision tree based on the relationship between each training information and different blasting information;

[0148] The risk identification unit is used to use random forest to perform risk prediction on each decision tree to generate corresponding risk results, and to count the risk level and risk range corresponding to each risk result to generate blasting risk information of the blasting decision plan.

[0149] In this example, the key locations of the model correspond one-to-one with the on-site blasting locations;

[0150] In this example, the required blasting material indicates the material required for blasting the key locations of the model;

[0151] In this example, the blasting material impact information indicates that when an blasting material performs an operation, the operations that its related blasting materials will perform under its influence;

[0152] In this example, parameter adjustment includes: position adjustment, material quantity adjustment, material replacement and other operations related to adjusting the required blasting materials;

[0153] In this example, the decision tree represents a cross tree used to present the relationship between training information and different burst information;

[0154] In this example, the risk level indicates the level of risk that would be present if a hazard were to occur;

[0155] In this example, the risk range refers to the area affected when a hazard occurs.

[0156] The working principle and beneficial effects of the above technical solution: Since blasting operations are dangerous, in order to reduce the probability of accidents, a field model is first built before the blasting operation, in which the key positions of the model are located, and the required blasting materials corresponding to each key position of the model are determined. Then, the blasting material impact information is used to adjust the parameters of the required blasting materials, and the distribution information of the blasting materials on the blasted hillside is determined, and then the field model is synchronously reset. Finally, the blasting decision plan is simulated by running the field model, and the blasting information corresponding to each key position of the model is collected. The blasting information is then sampled and trained, and random forest is used for risk estimation to determine the blasting risk information of the blasting decision plan. By using random forest for risk estimation, each blasting position can be taken into account, thereby improving the authenticity of the blasting risk information.

[0157] Example 6

[0158] On the basis of Example 1, the intelligent prediction system for hillside blasting semi-porosity based on deep learning, the optimization and adjustment module includes:

[0159] a risk interpretation unit, configured to perform semantic analysis on the blasting risk information, generate a plurality of blasting risk descriptions of the blasting decision plan, identify risk keywords corresponding to each of the blasting risk descriptions, and generate a keyword set for the blasting decision plan;

[0160] a method construction unit for establishing a risk elimination method set for the blasting decision plan based on searching for a plurality of risk elimination methods corresponding to each of the risk keywords and inputting the methods into corresponding word positions of the keyword set, and obtaining method exclusion features and method support features between the risk elimination methods corresponding to different word positions in the risk elimination method set;

[0161] a mode recombining unit, configured to generate a plurality of non-combination modes based on the mode exclusion characteristics, eliminate the non-combination modes in the risk elimination mode set, generate a plurality of target combination modes, and screen a plurality of optimal combination modes included in the target combination modes based on the mode support characteristics;

[0162] an optimization execution unit, configured to optimize the blasting material distribution information using the corresponding optimization combinations in descending order of the optimization degree corresponding to each optimization combination, to obtain corresponding preliminary optimization results; and when each of the preliminary optimization results is abnormal, to optimize the blasting material distribution information using each target combination to obtain a further optimization result, and to select an effective target combination with the lowest blasting risk to optimize the blasting material distribution information;

[0163] The continuous analysis unit is used to simulate the optimization results corresponding to the blasting material distribution information, obtain several safe blasting positions for the current blasting operation, identify several blasting forces corresponding to each of the safe blasting positions, obtain and display the blasting half-hole rate corresponding to each of the safe blasting positions.

[0164] In this example, the method exclusion feature represents the contradictory content between the two risk elimination methods, and the method expenditure feature represents the identical content between the two risk elimination methods;

[0165] In this example, non-combination means two or more risk elimination methods that cannot occur at the same time;

[0166] In this example, the target combination method represents the result of arranging the risk elimination methods in the risk elimination method set;

[0167] In this instance, the optimal combination mode represents the target combination mode with mode support characteristics;

[0168] In this example, the degree of optimization is related to the support feature of the method. The larger the support feature, the higher the degree of optimization;

[0169] In this example, a safety blasting location is subjected to at least one blasting force.

[0170] The working principle and beneficial effects of the above technical solution: In order to reduce the risk of this blasting work, the blasting risk information is semantically analyzed to determine the risk keywords, and then the risk elimination method corresponding to each risk keyword is searched. According to the method exclusion characteristics and method support characteristics between the risk elimination methods, a suitable combination method is selected to optimize the blasting material distribution information, determine the blasting force corresponding to each safe blasting position, and determine its corresponding blasting half-hole rate. In this way, the half-hole rate corresponding to each safe blasting position can be calculated, providing technical references to operators to facilitate the next step of work.

[0171] Example 7

[0172] On the basis of Example 6, the deep learning-based intelligent prediction system for hillside blasting semi-porosity further includes:

[0173] When the preliminary optimization result is normal, the blasting material distribution information is optimized using a corresponding effective optimal combination method.

[0174] The working principle and beneficial effects of the above technical solution are as follows: when the preliminary optimization result is normal, the blasting material distribution information is directly optimized, thereby improving the efficiency and speed of the optimization.

[0175] Example 8

[0176] Based on Example 6, the deep learning-based intelligent prediction system for hillside blasting semi-porosity further includes:

[0177] The blasting supervision module is used to estimate the regional blasting range corresponding to the safe blasting position and establish a blasting danger warning area of ​​the blasted hillside.

[0178] The working principle and beneficial effects of the above technical solution: In order to further ensure the safety of blasting work, the blasting range of each safe blasting position is analyzed and corresponding warnings are taken.

[0179] Example 9

[0180] This example provides an intelligent prediction method for hillside blasting semi-porosity based on deep learning. Figure 2 Shown, including:

[0181] Step 1: Collect geological information of the blasted hillside and blasting information of the blasting materials to construct several field data of this blasting operation;

[0182] Step 2: training the field data to generate a number of estimated blasting features for the current blasting operation, and generating a blasting decision plan for the current blasting operation;

[0183] Step 3: Establish a site model of the blasted hillside, and use the site model to analyze the blasting material distribution information and blasting risk information corresponding to the blasting decision plan;

[0184] Step 4: Optimize the blasting material distribution information according to the blasting risk information, obtain the blasting half-hole rate corresponding to this blasting operation, and display it separately.

[0185] In this example, the geological information represents the internal structure and material composition of the blasted hillside;

[0186] In this example, blasting materials refer to materials used in blasting operations, such as explosives;

[0187] In this example, the blasting information represents the quantity and distribution of a type of blasting material;

[0188] In this example, the field data represents the data present on the blasted hillside;

[0189] In this example, the estimated blasting characteristics represent the characteristics of the blasted hillside before the blasting operation;

[0190] In this example, the blasting decision plan represents the plan used to carry out this blasting operation;

[0191] In this example, the scene model refers to a model showing the structure of each area of ​​the scene in a virtual space;

[0192] In this example, the blasting material distribution information represents the distribution information formed after various blasting materials are arranged in advance when executing the blasting decision plan;

[0193] In this example, the blasting risk information represents information about potential dangers generated when performing blasting operations;

[0194] In this example, the purpose of optimizing the blasting material distribution information using the blasting risk information is to reasonably arrange the positions of the blasting materials to avoid continuous blasting or blasting interruption.

[0195] The working principle and beneficial effects of the above technical solution: In order to help operators optimize blasting operations and reduce operational risks, geological information of the blasted hillside and blasting information of the blasting materials are collected before the blasting operation, so as to construct the field data of this blasting operation, and further train the field data to determine several estimated blasting characteristics of this report operation, and further construct the corresponding blasting decision plan based on the relationship between each estimated blasting characteristic, and then determine the blasting material distribution information and blasting decision plan of the blasting decision plan by constructing a field model and using the model to simulate the plan. At this time, a plan that can complete this blasting has been obtained. However, in order to avoid spontaneous combustion or blasting interruption caused by uneven distribution of blasting materials, the blasting material distribution information is optimized using blasting risk information. After the optimization is completed, the blasting half-hole rate of this blasting operation is determined. In this way, not only can the blasting operation of the blasted hillside be guided by decision-making, an effective decision-making plan can be generated, thereby determining the blasting half-hole rate suitable for this blasting operation. This not only provides technical reference for operators, but also helps operators to blast according to the blasting decision plan, greatly reducing the original risk, and providing operators with accurate and effective half-hole rate data, which is convenient for operators to carry out the next step of work.

[0196] Example 10

[0197] On the basis of Example 9, the method for intelligently predicting half-porosity of hillside blasting based on deep learning, step 4 includes:

[0198] Step 41: performing semantic analysis on the blasting risk information to generate a plurality of blasting risk descriptions of the blasting decision plan, identifying risk keywords corresponding to each blasting risk description, and generating a keyword set of the blasting decision plan;

[0199] Step 42: Based on searching for a plurality of risk elimination methods corresponding to each of the risk keywords and inputting them into the corresponding word positions of the keyword set, a risk elimination method set for the blasting decision plan is established, and method exclusion features and method support features between the risk elimination methods corresponding to different word positions are obtained in the risk elimination method set;

[0200] Step 43: generating a plurality of non-combination methods based on the method exclusion feature, eliminating the non-combination methods from the risk elimination method set, generating a plurality of target combination methods, and screening a plurality of optimal combination methods included in the target combination methods based on the method support feature;

[0201] Step 44: Optimizing the blasting material distribution information using the corresponding optimization combinations in descending order of the optimization degree corresponding to each optimization combination to obtain corresponding preliminary optimization results. When each preliminary optimization result is abnormal, optimizing the blasting material distribution information using each target combination to obtain a further optimization result, and selecting an effective target combination with the lowest blasting risk to optimize the blasting material distribution information.

[0202] Step 45: Simulate the optimization result corresponding to the blasting material distribution information to obtain several safe blasting positions for the current blasting operation, identify several blasting forces corresponding to each of the safe blasting positions, obtain and display the blasting half-hole rate corresponding to each of the safe blasting positions.

[0203] In this example, the method exclusion feature represents the contradictory content between the two risk elimination methods, and the method expenditure feature represents the identical content between the two risk elimination methods;

[0204] In this example, non-combination means two or more risk elimination methods that cannot occur at the same time;

[0205] In this example, the target combination method represents the result of arranging the risk elimination methods in the risk elimination method set;

[0206] In this instance, the optimal combination mode represents the target combination mode with mode support characteristics;

[0207] In this example, the degree of optimization is related to the support feature of the method. The larger the support feature, the higher the degree of optimization;

[0208] In this example, a safety blasting location is subjected to at least one blasting force.

[0209] The working principle and beneficial effects of the above technical solution: In order to reduce the risk of this blasting work, the blasting risk information is semantically analyzed to determine the risk keywords, and then the risk elimination method corresponding to each risk keyword is searched. According to the method exclusion characteristics and method support characteristics between the risk elimination methods, a suitable combination method is selected to optimize the blasting material distribution information, determine the blasting force corresponding to each safe blasting position, and determine its corresponding blasting half-hole rate. In this way, the half-hole rate corresponding to each safe blasting position can be calculated, providing technical references to operators to facilitate the next step of work.

[0210] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A deep learning-based intelligent prediction system for hillside blasting semi-hole rate, characterized by: include: The data acquisition module is used to collect geological information of the blasted hillside and blasting information of the blasting materials, and to construct a number of on-site data of this blasting operation; A deep analysis module is used to train the field data to generate a number of estimated blasting features of the current blasting operation and generate a blasting decision plan for the current blasting operation; A prediction and analysis module, configured to establish a site model of the blasted hillside and use the site model to analyze blasting material distribution information and blasting risk information corresponding to the blasting decision plan; An optimization and adjustment module is used to optimize the blasting material distribution information according to the blasting risk information, obtain the blasting half-hole rate corresponding to the current blasting operation, and display it separately; The in-depth analysis module includes: a data training unit, configured to input each of the field data into a convolution prediction model for convolution training, obtain a plurality of feature blocks presented by each of the field data in different convolution layers, cluster the corresponding feature blocks in each of the convolution layers, and generate a cluster set corresponding to each of the convolution layers; a deep training unit for analyzing the plurality of cluster sets corresponding to each of the field data, generating data traceability information corresponding to each of the field data based on the convolution features corresponding to each of the convolution layers and the cluster features corresponding to each of the cluster sets, and performing error optimization on the data traceability information using an error function model to generate a plurality of data features; a feature recombining unit for obtaining a blasting purpose of the current blasting operation, substituting each of the data features into the blasting purpose, obtaining operation-related information corresponding to each of the field data, performing semantic analysis on the operation-related information, and recombining the data features according to the semantic analysis results to generate a plurality of estimated blasting features of the current blasting operation; a plan generating unit, configured to divide the estimated blasting characteristics into estimated geological characteristics and estimated material characteristics, establish a blasting plan for the blasted slope according to the estimated geological characteristics, establish a blastable combination of the blasting materials according to the estimated material characteristics, and establish a blasting decision plan for the current blasting operation according to the blasting plan and the blastable combinations; The solution generating unit includes: a feature classification subunit for performing regression analysis on the estimated blasting features to obtain correlation relationships between different estimated blasting features, thereby obtaining a plurality of correlation feature chains of the current blasting operation, extracting key information from each of the correlation feature chains, and, based on the extraction results, treating a plurality of estimated blasting features included in a first correlation feature chain having a geological description as estimated geological features, and treating a plurality of estimated blasting features included in a second correlation feature chain having a material description as estimated material features; a plan construction subunit, configured to establish a geological model of the blasted hillside based on the estimated geological characteristics, determine a plurality of blasting locations of the blasting plan and the blasting intensity corresponding to each blasting location in combination with the corresponding blasting purpose, locate each blasting location in the geological model and mark the intensity accordingly, run the geological model to determine the blasting sequence of the blasting operation, and generate a blasting plan for the blasted hillside; a combination construction subunit, configured to obtain the geological hardness corresponding to each blasting location in the geological model, determine the optional material combination corresponding to each blasting location in combination with the corresponding blasting intensity, determine the combination completeness and combination cost corresponding to each optional material combination based on the estimated material characteristics, and select the target optional material combination with the qualified combination completeness and the lowest combination cost corresponding to each blasting location as the blastable combination for the blasting location; The plan generating subunit is used to improve the blasting plan by using the blastable combination corresponding to each blasting position, determine the operation preparation guide, blasting sequence guide and blasting safety guide of the blasting operation, and generate a blasting decision plan for the blasting operation.

2. The deep learning-based intelligent prediction system for hillside blasting semi-porosity as claimed in claim 1, characterized in that: The data acquisition module includes: a geological sampling unit configured to search the big data for the geographical location of the blasted hillside, determine basic information of the blasted hillside, determine high-probability geological information of the blasted hillside using the basic information, and perform on-site sampling on the blasted hillside using a first geological sampling method corresponding to the high-probability geological information to obtain a plurality of first sampling information; a compensating sampling unit configured to obtain unknown information generated during the on-site sampling, establish known geological characteristics of the blasted hillside based on the first sampling information, deduce a plurality of associated geological characteristics of the blasted hillside based on the known geological characteristics, search the big data for a second geological sampling method corresponding to each associated geological characteristic, perform compensating sampling on the blasted hillside using the second geological sampling method, obtain second sampling information corresponding to each unknown information, and establish geological information of the blasted hillside based on the first sampling information and the second sampling information; a material collection unit, configured to obtain a plurality of blasting materials contained on the blasted hillside, collect material parameters corresponding to each blasting material, establish blasting information corresponding to each blasting material, and establish a corresponding blasting mark for each blasting information; The data generation unit is used to establish the geological data of the blasted hillside based on the geological information, establish the equipment data of the blasted hillside based on the blasting information, determine the equipment range of the corresponding equipment data according to the blasting mark corresponding to the blasting information, and generate a plurality of field data of the blasting operation.

3. The deep learning-based intelligent prediction system for hillside blasting semi-porosity as claimed in claim 1, characterized in that: The prediction analysis module includes: a model building unit, configured to build a field model of the blasted hillside according to the field data, determine a plurality of field blasting locations according to the blasting decision plan, and locate each of the field blasting locations in the field model as a key location of the model; a material adjustment unit, configured to establish, according to the blasting decision plan, the screening of corresponding required blasting materials for each key location of the model, run the field model to obtain impact information of required blasting materials between key locations of different models, adjust parameters of corresponding required blasting materials according to the impact information of required blasting materials, and obtain blasting material distribution information of the blasted hillside; a key monitoring unit, configured to reset the material of the on-site model according to the blasting material distribution information, control the on-site model after the material reset to execute the blasting decision plan, collect blasting information corresponding to each key position of the model, randomly sample the blasting information to obtain a plurality of training information, and establish a decision tree based on the relationship between each training information and different blasting information; The risk identification unit is used to use random forest to perform risk prediction on each decision tree to generate corresponding risk results, and to count the risk level and risk range corresponding to each risk result to generate blasting risk information of the blasting decision plan.

4. The deep learning-based intelligent prediction system for hillside blasting semi-porosity as claimed in claim 1, characterized in that: The optimization and adjustment module includes: a risk interpretation unit, configured to perform semantic analysis on the blasting risk information, generate a plurality of blasting risk descriptions of the blasting decision plan, identify risk keywords corresponding to each of the blasting risk descriptions, and generate a keyword set for the blasting decision plan; a method construction unit for establishing a risk elimination method set for the blasting decision plan based on searching for a plurality of risk elimination methods corresponding to each of the risk keywords and inputting the methods into corresponding word positions of the keyword set, and obtaining method exclusion features and method support features between the risk elimination methods corresponding to different word positions in the risk elimination method set; a mode recombining unit, configured to generate a plurality of non-combination modes based on the mode exclusion characteristics, eliminate the non-combination modes in the risk elimination mode set, generate a plurality of target combination modes, and screen a plurality of optimal combination modes included in the target combination modes based on the mode support characteristics; an optimization execution unit, configured to optimize the blasting material distribution information using the corresponding optimization combinations in descending order of the optimization degree corresponding to each optimization combination, to obtain corresponding preliminary optimization results; and when each of the preliminary optimization results is abnormal, to optimize the blasting material distribution information using each target combination to obtain a further optimization result, and to select an effective target combination with the lowest blasting risk to optimize the blasting material distribution information; The continuous analysis unit is used to simulate the optimization results corresponding to the blasting material distribution information, obtain several safe blasting positions for the current blasting operation, identify several blasting forces corresponding to each of the safe blasting positions, obtain and display the blasting half-hole rate corresponding to each of the safe blasting positions.

5. The deep learning-based intelligent prediction system for hillside blasting semi-porosity as claimed in claim 4, characterized in that: Also includes: When the preliminary optimization result is normal, the blasting material distribution information is optimized using a corresponding effective optimal combination method.

6. The deep learning-based intelligent prediction system for hillside blasting semi-porosity as claimed in claim 4, characterized in that: Also includes: The blasting supervision module is used to estimate the regional blasting range corresponding to the safe blasting position and establish a blasting danger warning area of ​​the blasted hillside.

7. A method for intelligently predicting half-porosity of hillside blasting based on deep learning, applied to the intelligent prediction system for half-porosity of hillside blasting based on deep learning according to claim 1, characterized in that: include: Step 1: Collect geological information of the blasted hillside and blasting information of the blasting materials to construct several field data of this blasting operation; Step 2: training the field data to generate a number of estimated blasting features for the current blasting operation, and generating a blasting decision plan for the current blasting operation; Step 3: Establish a site model of the blasted hillside, and use the site model to analyze the blasting material distribution information and blasting risk information corresponding to the blasting decision plan; Step 4: Optimize the blasting material distribution information according to the blasting risk information, obtain the blasting half-hole rate corresponding to this blasting operation, and display it separately.

8. The method for intelligently predicting half-porosity of hillside blasting based on deep learning according to claim 7, characterized in that: The step 4 comprises: Step 41: performing semantic analysis on the blasting risk information to generate a plurality of blasting risk descriptions of the blasting decision plan, identifying risk keywords corresponding to each blasting risk description, and generating a keyword set of the blasting decision plan; Step 42: Based on searching for a plurality of risk elimination methods corresponding to each of the risk keywords and inputting them into the corresponding word positions of the keyword set, a risk elimination method set for the blasting decision plan is established, and method exclusion features and method support features between the risk elimination methods corresponding to different word positions are obtained in the risk elimination method set; Step 43: generating a plurality of non-combination methods based on the method exclusion feature, eliminating the non-combination methods from the risk elimination method set, generating a plurality of target combination methods, and screening a plurality of optimal combination methods included in the target combination methods based on the method support feature; Step 44: Optimizing the blasting material distribution information using the corresponding optimization combinations in descending order of the optimization degree corresponding to each optimization combination to obtain corresponding preliminary optimization results. When each preliminary optimization result is abnormal, optimizing the blasting material distribution information using each target combination to obtain a further optimization result, and selecting an effective target combination with the lowest blasting risk to optimize the blasting material distribution information. Step 45: Simulate the optimization result corresponding to the blasting material distribution information to obtain several safe blasting positions for the current blasting operation, identify several blasting forces corresponding to each of the safe blasting positions, obtain and display the blasting half-hole rate corresponding to each of the safe blasting positions.

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