Dangerous rock mass stability analysis system, method and equipment and storage medium

Abstract

The invention discloses a dangerous rock mass stability analysis system, method and equipment and a storage medium. The system comprises a data acquisition module, a data fusion processing module and a modeling analysis module, the data acquisition module is used for acquiring target geological data of a target analysis object; the data fusion processing module is used for fusing the target geological data to generate a reconstruction data model of the target analysis object; and the modeling analysis module is used for obtaining a stability analysis result of the target analysis object according to the reconstructed data model in combination with a target machine learning algorithm. According to the dangerous rock mass stability analysis system disclosed by the invention, the collected geological data are fused, and the reconstructed data model obtained by fusion is analyzed by using the machine learning algorithm, so that the accuracy and the real-time performance of dangerous rock mass stability evaluation can be improved, and the stability analysis efficiency is improved. In addition, the system has the characteristics of intelligence and automation, the dependence on professionals can be reduced, and the labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a dangerous rock mass stability analysis system, method, device and storage medium. Background Art

[0002] With the continuous expansion of engineering construction and the continuous changes in the natural environment, the stability of dangerous rock masses has received more and more attention, and higher requirements have been put forward for their stability assessment and early warning.

[0003] In the traditional field of dangerous rock mass stability analysis and early warning, single-scale data and empirical judgment are usually used for evaluation and decision-making.

[0004] Existing dangerous rock mass monitoring methods, such as displacement monitoring and crack width monitoring, can provide certain data support, but are often limited to local or single type of data, and it is difficult to fully reflect the true state of dangerous rock mass. In addition, these methods rely on manual operation in data processing and analysis, which is inefficient and easily affected by subjective judgment, and cannot achieve real-time monitoring and rapid response. Summary of the invention

[0005] The present invention provides a dangerous rock mass stability analysis system, method, device, equipment and storage medium to realize intelligent analysis of the stability of dangerous rock masses.

[0006] According to one aspect of the present invention, a dangerous rock mass stability analysis system is provided, the system comprising: a data acquisition module, a data fusion processing module and a modeling analysis module;

[0007] The data acquisition module is used to acquire target geological data of the target analysis object;

[0008] The data fusion processing module is used to fuse the target geological data to generate a reconstructed data model of the target analysis object;

[0009] The modeling and analysis module is used to obtain the stability analysis result of the target analysis object based on the reconstructed data model and in combination with the target machine learning algorithm.

[0010] Optionally, the target geological data includes radar detection data, laser scanning data and aerial survey data.

[0011] Optionally, the data fusion processing module is specifically used for:

[0012] Processing the target geological data to obtain target standardized data;

[0013] Performing feature extraction on the target standardized data to obtain at least two target data features;

[0014] The weights of the target data features are determined respectively, and the target data features are weightedly fused according to the weights of the target data features to obtain fused features of the target analysis object, and the fused features are converted into the reconstructed data model.

[0015] Optionally, the modeling and analysis module is specifically used for:

[0016] Obtain a trained target machine learning algorithm, and use the reconstructed data model as input to the target machine learning algorithm;

[0017] Determine a stability analysis result of the target analysis object based on the output of the target machine learning algorithm.

[0018] Optionally, the modeling and analysis module is further used for:

[0019] generating a finite element network model according to the target geological data;

[0020] Acquire rock sample parameters corresponding to the target analysis object, match the rock sample parameters with the finite element network model, and obtain a target three-dimensional model;

[0021] The set boundary conditions and load conditions are obtained, and according to the target three-dimensional model, the boundary conditions and the load conditions, a stress distribution map and a displacement distribution map of the target analysis object are generated in combination with a finite element analysis method, and risk areas in the stress distribution map and the displacement distribution map are identified.

[0022] Optionally, the system further includes a monitoring and early warning module for acquiring sensor data and automatically sending an early warning message when the sensor data exceeds a preset threshold.

[0023] Optionally, the system further includes a user interaction module, which is used to provide a visual operation interface and receive a warning threshold value corresponding to the target analysis object set by a user.

[0024] According to another aspect of the present invention, a method for analyzing the stability of a dangerous rock mass is provided, comprising:

[0025] Collect target geological data of the target analysis object through the data acquisition module;

[0026] The target geological data is fused through a data fusion processing module to generate a reconstructed data model of the target analysis object;

[0027] The modeling and analysis module obtains the stability analysis result of the target analysis object according to the reconstructed data model and in combination with the target machine learning algorithm.

[0028] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0029] at least one processor; and

[0030] a memory communicatively connected to the at least one processor; wherein,

[0031] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the dangerous rock mass stability analysis method described in any embodiment of the present invention.

[0032] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the dangerous rock mass stability analysis method described in any embodiment of the present invention when executed.

[0033] According to another aspect of the present invention, a computer program product is provided, which includes a computer program / instruction, and when the computer program / instruction is executed by a processor, the steps of the dangerous rock mass stability analysis method described in any embodiment of the present invention are implemented.

[0034] The present invention discloses a dangerous rock mass stability analysis system, including: a data acquisition module, a data fusion processing module and a modeling analysis module; the data acquisition module is used to collect target geological data of the target analysis object; the data fusion processing module is used to fuse the target geological data to generate a reconstructed data model of the target analysis object; the modeling analysis module is used to obtain the stability analysis result of the target analysis object according to the reconstructed data model and in combination with the target machine learning algorithm. The dangerous rock mass stability analysis system disclosed by the present invention can improve the accuracy and real-time performance of the dangerous rock mass stability assessment and improve the efficiency of the stability analysis by fusing the collected geological data and analyzing the fused reconstructed data model using a machine learning algorithm. In addition, the system has the characteristics of intelligence and automation, which can reduce the dependence on professionals and reduce labor costs.

[0035] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 is a structural schematic diagram of a dangerous rock mass stability analysis system provided according to Embodiment 1 of the present invention;

[0038] Figure 2 is a flow chart of a dangerous rock mass stability analysis method provided according to the second embodiment of the present invention;

[0039] Figure 3 It is a schematic diagram of the structure of an electronic device for implementing the dangerous rock mass stability analysis method of the third embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] Embodiment 1

[0043] Figure 1 The structural diagram of a dangerous rock mass stability analysis system provided in the first embodiment of the present invention is applicable to the situation of stability analysis of dangerous rock mass. The dangerous rock mass stability analysis system can be implemented in the form of hardware and / or software and can be used to execute the dangerous rock mass stability analysis method. The dangerous rock mass stability analysis system can be configured in an electronic device. Figure 1 As shown, the system includes: a data acquisition module 110, a data fusion processing module 120 and a modeling and analysis module 130.

[0044] The data acquisition module 110 is used to acquire target geological data of the target analysis object.

[0045] The target analysis object is a dangerous rock mass object for which stability analysis is performed by the dangerous rock mass stability analysis system provided in this embodiment, and the target geological data is geological related data obtained by surveying and detecting the target analysis object.

[0046] The data fusion processing module 120 is used to fuse the target geological data and generate a reconstructed data model of the target analysis object.

[0047] In this embodiment, the target geological data of the target analysis object collected by the data acquisition module 110 may include multiple source data, such as radar data, laser data, drone aerial survey data, etc. The data fusion processing module 120 may fuse the target geological data from different sources to obtain a reconstructed data model. The reconstructed data model is a unified standard data model obtained by fusing the target geological data from different sources.

[0048] The modeling and analysis module 130 is used to obtain the stability analysis results of the target analysis object based on the reconstructed data model and combined with the target machine learning algorithm.

[0049] Among them, the target machine learning algorithm can be algorithms such as support vector machine (SVM) or random forest (RF).

[0050] In this embodiment, by training the target machine learning algorithm and using the trained algorithm model, a stability analysis can be performed on the reconstructed data model of the target analysis object, and labels such as "stable", "unstable", and "critically stable" corresponding to the target analysis object can be identified, and the confidence results corresponding to the labels can be output.

[0051] Optionally, the target geological data includes radar detection data, laser scanning data and aerial survey data.

[0052] Specifically, radar detection data can be obtained using a geological radar system. Preferably, the geological radar system can be used to detect dangerous rock bodies at a frequency of 300MHz, and the detection depth can be set to 50 meters and the resolution can be 0.1 meters to obtain underground rock structure and crack distribution data and high-resolution images of the internal structure of dangerous rock bodies, wherein the detection depth is adjusted according to the thickness of the dangerous rock bodies. Laser scanning data can be obtained using a laser scanner. Preferably, the laser scanner can be used to scan the surface of the dangerous rock body at a rate of 100,000 points per second to obtain three-dimensional geometric data with an accuracy of 0.01 meters and high-precision three-dimensional point cloud data on the surface of the dangerous rock body. Aerial survey data can be obtained using unmanned aerial survey equipment. Preferably, the unmanned aerial survey equipment can be made to fly according to a pre-planned grid path, and the flight altitude of the unmanned aerial survey equipment can be set to 200 meters. At the same time, a high-resolution camera can be equipped to collect terrain and geological structure data at a ground resolution of 5 centimeters, and collect high-resolution image data of dangerous rock bodies and their surrounding areas.

[0053] Optionally, the data fusion processing module 120 is specifically used for:

[0054] The target geological data is processed to obtain target standardized data; features are extracted from the target standardized data to obtain at least two target data features; the weights of the target data features are determined respectively, and the target data features are weightedly fused according to the weights of the target data features to obtain fused features of the target analysis object, and the fused features are converted into a reconstructed data model.

[0055] Specifically, after the data acquisition module 110 sends the collected target geological data of the target analysis object to the data fusion processing module 120, the data fusion processing module 120 can first perform data processing operations such as denoising, data format unification and data standardization on the target geological data to obtain target standardized data to ensure data quality. Then, based on the data fusion algorithm of deep learning, the target standardized data obtained after processing the target geological data of different dimensions can be feature fused and reconstructed to form a unified reconstructed data model. Finally, the distributed storage system of the cloud computing platform can be used to store the reconstructed data model obtained after processing so that the data can be used for data analysis later.

[0056] Furthermore, the specific steps of data feature fusion reconstruction are as follows (taking the target geological data including three data sources of radar detection data, laser scanning data and aerial survey data as an example):

[0057] (1) Data input

[0058] The three types of data in the data input layer are:

[0059] Radar detection data input layer: two-dimensional matrix;

[0060] Laser scanning data input layer: 3D point cloud;

[0061] Aerial survey data input layer: two-dimensional image.

[0062] (2) Feature extraction

[0063] In the feature extraction layer, the feature extraction methods of the three types of data are:

[0064] The radar detection data uses a convolutional neural network (CNN) to extract spatial features;

[0065] The laser scanning data uses a three-dimensional convolutional neural network (3D CNN) to extract point cloud features;

[0066] Aerial survey data is used to extract image features using convolutional neural networks (CNN).

[0067] (3) Feature Fusion

[0068] In the feature fusion layer, the attention mechanism is used to calculate the importance weight of each feature, and then the extracted features are multiplied by the corresponding weights for weighted fusion to obtain the fused features of the target analysis object.

[0069] (4) Data output

[0070] In the data output layer, a fully connected layer is used to convert the fused features into a reconstructed data model to form a unified data set.

[0071] This deep learning-based data fusion algorithm, through a variety of advanced neural network structures and fusion strategies, can fully utilize the advantages of multimodal data to generate high-quality reconstructed data models and outputs required by other applications.

[0072] Optionally, the modeling and analysis module 130 is specifically used for:

[0073] Obtain a trained target machine learning algorithm and use the reconstructed data model as the input of the target machine learning algorithm; determine the stability analysis result of the target analysis object according to the output of the target machine learning algorithm.

[0074] Specifically, the modeling and analysis module 130 can use a trained target machine learning algorithm, such as support vector machine (SVM) and random forest (RF), and use the reconstructed data model as algorithm input to perform pattern recognition and stability analysis on the data.

[0075] Among them, pattern recognition can assign a specific category to each data point. For example, in geological data analysis, different areas may be classified as "stable", "potentially unstable" or "unstable". In addition, each category label has a corresponding probability value, which indicates the possibility that the data point belongs to a certain category. Stability analysis can predict the stability category of each data point, such as "stable", "unstable" or "critically stable". These labels help identify potential unstable areas. And the predicted probability of each category can provide a confidence assessment of the prediction results. For example, the probability that an area is predicted to be unstable is 85%.

[0076] Furthermore, assuming that there is a set of preprocessed 3D geological data, including features such as formation thickness, fracture density, and terrain slope, these data are used to train and evaluate support vector machine (SVM) and random forest (RF) models.

[0077] The training method of support vector machine (SVM) is:

[0078] (1) Data preparation: Divide the geological data into training set and test set.

[0079] (2) Model training: Use the training set to train the SVM model and select a suitable kernel function (such as RBF kernel) and penalty parameter C.

[0080] (3) Model evaluation: Model performance is evaluated through cross-validation, and indicators such as accuracy, precision, and recall are calculated.

[0081] (4) Prediction analysis: The test set is input into the trained SVM model to predict its stability category and compare it with the actual situation to evaluate the model accuracy.

[0082] The training method of random forest (RF) is:

[0083] (1) Data preparation: Similarly, divide the data into training set and test set.

[0084] (2) Model training: Use the training set to train the random forest model and set hyperparameters such as the number of trees and maximum depth.

[0085] (3) Model evaluation: The performance of the random forest model is evaluated through cross-validation and various performance indicators are calculated.

[0086] (4) Prediction analysis: The test set is input into the trained random forest model to predict its stability category and compare it with the actual situation to evaluate the model accuracy.

[0087] Through the above steps, we can realize the training of machine learning algorithms, and then use the trained algorithms to perform pattern recognition and stability prediction analysis of geological data, providing a scientific basis for geological disaster warning and prevention.

[0088] Optionally, the modeling and analysis module 130 is further used for:

[0089] Generate a finite element network model based on the target geological data; obtain the rock sample parameters corresponding to the target analysis object, match the rock sample parameters with the finite element network model to obtain the target three-dimensional model; obtain the set boundary conditions and load conditions, and generate the stress distribution map and displacement distribution map of the target analysis object based on the target three-dimensional model, boundary conditions and load conditions, combined with the finite element analysis method, and identify the risk areas in the stress distribution map and displacement distribution map.

[0090] Specifically, rock samples are collected for the target analysis object, and then the collected rock samples can be tested for microscopic rock mechanical properties in the laboratory. The test parameters include compressive strength (100-200MPa), tensile strength (5-20MPa) and elastic modulus (20-50GPa), etc., so as to obtain the rock sample parameters corresponding to the target analysis object. Then, unified geometric meshing is performed according to radar detection data, laser scanning data and aerial survey data to generate an initial finite element network model, including macro and micro data, so as to realize multi-scale data. Among them, a high-resolution local model can be constructed for important areas, and a lower-resolution model can be used for other areas. The local fine model and the overall rough model are merged, and the constructed multi-scale three-dimensional model is used as a finite element network model. Then, the rock sample parameters such as compressive strength, tensile strength and elastic modulus of the measured rock samples are distributed to different areas of the finite element network model, and the target three-dimensional model is obtained after matching. Then, the set boundary conditions and load conditions are obtained, wherein the boundary conditions include boundary conditions such as fixed boundaries and sliding boundaries, and the load conditions include loads set by conditions such as earthquakes and rainfall. According to the target three-dimensional model, boundary conditions and load conditions, the finite element analysis method can be used to: perform static analysis to calculate the stress, strain and displacement distribution under normal conditions; perform dynamic analysis to simulate the response under seismic loads; perform seepage analysis to calculate water seepage and pore water pressure under rainfall conditions. Furthermore, finite element analysis software (such as ANSYS, ABAQUS) can be used for numerical solution, and iterative algorithms (such as Newton-Raphson method) can be used to ensure the accuracy of the calculation results. Finally, the stress distribution map and displacement distribution map of the target analysis object are generated, and the risk areas in the stress distribution map and displacement distribution map are identified.

[0091] Optionally, the system further includes a monitoring and early warning module 140 for acquiring sensor data and automatically sending an early warning message when the sensor data exceeds a preset threshold.

[0092] Specifically, displacement sensors, stress-strain sensors, environmental monitoring sensors and other sensors can be deployed at key locations of the dangerous rock mass that is the target analysis object to collect displacement data, rock stress state and environmental data of the target analysis object in real time, and the sensor data can be transmitted in real time to the monitoring and early warning module 140 of the dangerous rock mass stability analysis system through a wireless sensor network (WSN). When the sensor data is detected to exceed a preset safety threshold, the automated early warning system is immediately activated, and early warning information is sent to relevant personnel through text messages, emails and mobile application push.

[0093] Preferably, the warning information can be sent through an automated system in a variety of ways, including sound and light alarms, text messages, mobile push notifications, and emails, to ensure that the information is delivered to relevant personnel in a timely manner.

[0094] Optionally, the system further includes a user interaction module 150 for providing a visual operation interface and receiving a warning threshold value corresponding to a target analysis object set by a user.

[0095] Specifically, the user interaction module 150 can provide a visual operation interface for the user, through which the analysis results can be presented in the form of graphics and animations, which is convenient for the user to understand and make decisions. The user can view the monitoring data, three-dimensional model and warning information in real time through the intuitive and easy-to-use visual operation interface, and can adjust the monitoring strategy and warning threshold through the interface according to the real-time data and analysis results to adapt to the dynamic changes of the dangerous rock mass. The user interaction module 150 can also receive the warning threshold corresponding to the target analysis object set by the user, so that the user can adjust the warning function.

[0096] Furthermore, this dangerous rock mass stability analysis system also provides historical data query and recording functions, allowing users to review and analyze historical events and optimize future prevention and control measures. The system includes a decision support system that can provide prevention and control suggestions and emergency response plans based on data analysis to help users develop effective risk management measures.

[0097] The present invention discloses a dangerous rock mass stability analysis system, including: a data acquisition module, a data fusion processing module and a modeling analysis module; the data acquisition module is used to collect target geological data of the target analysis object; the data fusion processing module is used to fuse the target geological data to generate a reconstructed data model of the target analysis object; the modeling analysis module is used to obtain the stability analysis result of the target analysis object according to the reconstructed data model and in combination with the target machine learning algorithm. The dangerous rock mass stability analysis system disclosed by the present invention can improve the accuracy and real-time performance of the dangerous rock mass stability assessment and improve the efficiency of the stability analysis by fusing the collected geological data and analyzing the fused reconstructed data model using a machine learning algorithm. In addition, the system has the characteristics of intelligence and automation, which can reduce the dependence on professionals and reduce labor costs.

[0098] Embodiment 2

[0099] Figure 2 The flowchart of a dangerous rock mass stability analysis method provided in the second embodiment of the present invention can be executed by a dangerous rock mass stability analysis system, such as Figure 2 As shown, the method includes:

[0100] S110, collecting target geological data of the target analysis object through a data collection module.

[0101] Optionally, the target geological data includes radar detection data, laser scanning data and aerial survey data.

[0102] S120, fusing the target geological data through a data fusion processing module to generate a reconstructed data model of the target analysis object.

[0103] In this embodiment, the method of fusing the target geological data through the data fusion processing module to generate the reconstructed data model of the target analysis object may be:

[0104] The target geological data is processed to obtain target standardized data; features are extracted from the target standardized data to obtain at least two target data features; the weights of the target data features are determined respectively, and the target data features are weightedly fused according to the weights of the target data features to obtain fused features of the target analysis object, and the fused features are converted into a reconstructed data model.

[0105] S130. Obtain the stability analysis result of the target analysis object through the modeling and analysis module according to the reconstructed data model and in combination with the target machine learning algorithm.

[0106] In this embodiment, the method of obtaining the stability analysis result of the target analysis object by reconstructing the data model through the modeling analysis module and combining the target machine learning algorithm may be:

[0107] Obtain a trained target machine learning algorithm and use the reconstructed data model as the input of the target machine learning algorithm; determine the stability analysis result of the target analysis object according to the output of the target machine learning algorithm.

[0108] Furthermore, the modeling and analysis module can also:

[0109] Generate a finite element network model based on the target geological data; obtain the rock sample parameters corresponding to the target analysis object, match the rock sample parameters with the finite element network model to obtain the target three-dimensional model; obtain the set boundary conditions and load conditions, and generate the stress distribution map and displacement distribution map of the target analysis object based on the target three-dimensional model, boundary conditions and load conditions, combined with the finite element analysis method, and identify the risk areas in the stress distribution map and displacement distribution map.

[0110] Furthermore, the dangerous rock mass stability analysis method provided in this embodiment also includes: acquiring sensor data through a monitoring and early warning module, and automatically sending an early warning message when the sensor data exceeds a preset threshold.

[0111] Furthermore, the dangerous rock mass stability analysis method provided in this embodiment also includes: providing a visual operation interface through a user interaction module, and receiving a warning threshold value corresponding to a target analysis object set by a user.

[0112] The dangerous rock mass stability analysis method disclosed in the present invention can be executed by the dangerous rock mass stability analysis system provided by any embodiment of the present invention, and has corresponding modules and beneficial effects.

[0113] Embodiment 3

[0114] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0115] like Figure 3As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0116] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0117] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a dangerous rock mass stability analysis method.

[0118] In some embodiments, the dangerous rock mass stability analysis method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the dangerous rock mass stability analysis described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the dangerous rock mass stability analysis method in any other appropriate manner (for example, by means of firmware).

[0119] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0120] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0121] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0122] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0123] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0124] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0125] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0126] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A dangerous rock mass stability analysis system, characterized in that: The system includes: a data acquisition module, a data fusion processing module and a modeling and analysis module; The data acquisition module is used to acquire target geological data of the target analysis object; The data fusion processing module is used to fuse the target geological data to generate a reconstructed data model of the target analysis object; The modeling and analysis module is used to obtain the stability analysis result of the target analysis object based on the reconstructed data model and in combination with the target machine learning algorithm.

2. The system according to claim 1, characterized in that The target geological data includes radar detection data, laser scanning data and aerial survey data.

3. The system according to claim 1, characterized in that The data fusion processing module is specifically used for: Processing the target geological data to obtain target standardized data; Performing feature extraction on the target standardized data to obtain at least two target data features; The weights of the target data features are determined respectively, and the target data features are weightedly fused according to the weights of the target data features to obtain fused features of the target analysis object, and the fused features are converted into the reconstructed data model.

4. The system according to claim 1, characterized in that The modeling and analysis module is specifically used for: Obtain a trained target machine learning algorithm, and use the reconstructed data model as input to the target machine learning algorithm; Determine a stability analysis result of the target analysis object based on the output of the target machine learning algorithm.

5. The system according to claim 4, characterized in that The modeling and analysis module is also used for: generating a finite element network model according to the target geological data; Acquire rock sample parameters corresponding to the target analysis object, match the rock sample parameters with the finite element network model, and obtain a target three-dimensional model; The set boundary conditions and load conditions are obtained, and according to the target three-dimensional model, the boundary conditions and the load conditions, a stress distribution map and a displacement distribution map of the target analysis object are generated in combination with a finite element analysis method, and risk areas in the stress distribution map and the displacement distribution map are identified.

6. The system according to claim 1, characterized in that The system also includes a monitoring and early warning module, which is used to obtain sensor data and automatically send early warning information when the sensor data exceeds a preset threshold.

7. The system according to claim 1, characterized in that The system also includes a user interaction module, which is used to provide a visual operation interface and receive a warning threshold value corresponding to the target analysis object set by the user.

8. A method for analyzing the stability of a dangerous rock mass, characterized in that: include: Collect target geological data of the target analysis object through the data acquisition module; The target geological data is fused through a data fusion processing module to generate a reconstructed data model of the target analysis object; The modeling and analysis module obtains the stability analysis result of the target analysis object according to the reconstructed data model and in combination with the target machine learning algorithm.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the dangerous rock mass stability analysis method described in claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the dangerous rock mass stability analysis method described in claim 8 when executed by a processor.

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