CN116092269A - Tunnel engineering rock mass disaster early warning method and device and electronic equipment - Google Patents
Tunnel engineering rock mass disaster early warning method and device and electronic equipment Download PDFInfo
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Abstract
The invention relates to the technical field of tunnel engineering geological disaster early warning, in particular to a disaster early warning method for an abnormal structure in a rock mass, and specifically relates to a disaster early warning method, a device and electronic equipment for the tunnel engineering rock mass; the method comprises the following steps: configuring a plurality of monitoring points in the tunnel, wherein a geological sensor is arranged in each monitoring point to acquire monitoring information of the corresponding monitoring point; acquiring a core monitoring point and a reference monitoring point matched with the core monitoring point based on the real-time monitoring information of the plurality of monitoring points and a preset expert system; acquiring core monitoring information and reference monitoring information based on the core monitoring point and the reference monitoring point; fusing the core monitoring information and the reference monitoring information to obtain target monitoring information; and inputting the target monitoring information into a preset early warning recognition model to output early warning information.
Description
Technical Field
The invention relates to the technical field of tunnel engineering geological disaster early warning, in particular to a disaster early warning method for abnormal structures in a rock mass, and specifically relates to a disaster early warning method, a device and electronic equipment for the tunnel engineering rock mass.
Background
Geological disasters often start from erosion damage and stress unbalance of local rock-soil structures, especially weak structures or soil-rock interface areas under the action of water; therefore, geological investigation, monitoring and diagnosis of the rock-soil body weak structure area are important means for disaster prevention and reduction; the traditional geological radar or acoustic wave detector and other devices can only realize geological survey in a larger range at present, aiming at a weak interlayer or a weak structural surface of a specific area in geotechnical engineering, the traditional method is difficult to reach in the precision range, visual monitoring and accurate evaluation on potential geological disasters can not be realized, the stability and safety of geotechnical engineering or side slope engineering are seriously influenced, and a new survey means is urgently needed for carrying out visual monitoring and accurate early warning on the potential geological disasters.
Disclosure of Invention
In order to solve the technical problems, the application provides a tunnel engineering rock mass disaster early warning method, a tunnel engineering rock mass disaster early warning device and electronic equipment.
In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:
in a first aspect, a method for pre-warning a disaster in a rock mass in tunnel engineering, the method comprising: configuring a plurality of monitoring points in the tunnel, wherein a geological sensor is arranged in each monitoring point to acquire monitoring information of the corresponding monitoring point; acquiring a core monitoring point and a reference monitoring point matched with the core monitoring point based on the real-time monitoring information of the plurality of monitoring points and a preset expert system; acquiring core monitoring information and reference monitoring information based on the core monitoring point and the reference monitoring point; fusing the core monitoring information and the reference monitoring information to obtain target monitoring information; and inputting the target monitoring information into a preset early warning recognition model to output early warning information.
In a first implementation manner of the first aspect, the geological sensor includes a stress sensor, an osmotic pressure sensor, a displacement sensor and a temperature sensor, and the stress sensor, the osmotic pressure sensor, the displacement sensor and the temperature sensor are used for respectively acquiring stress data, osmotic pressure data, displacement data and temperature data of the tunnel.
In a second implementation manner of the first aspect, the expert system includes a knowledge graph constructed based on disaster monitoring information acquired by a derivative tunnel surrounding geological survey device, the tunnel surrounding geological survey device including a spot hole photography system, a sidewall photography system, a geological compass tape, and a mapping tool.
With reference to the second possible implementation manner of the first aspect, in a third possible implementation manner, fusing the core monitoring information with the reference monitoring information to obtain target monitoring information includes: and acquiring historical data of corresponding monitoring points based on the tunnel surrounding rock geological survey device, determining geology corresponding to the core monitoring points and acquired by the reference monitoring points based on the historical data and the monitoring information variation corresponding to the tunnel, assigning values to obtain weight values of the corresponding monitoring points, and fusing the core monitoring information and the reference monitoring information based on the weight values to obtain target monitoring information.
With reference to the third possible implementation manner of the first aspect, in a fourth possible implementation manner, the target monitoring information includes stress data, osmotic pressure data, displacement data and temperature data.
With reference to the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner, the preset early warning recognition model includes a neural network model.
With reference to the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner, the preset early warning recognition model is obtained based on a network training method, and the training method includes: and acquiring historical data of corresponding monitoring points based on the tunnel surrounding rock geological survey device, determining geology corresponding to the core monitoring points and acquired by the reference monitoring points based on the historical data and the monitoring information variation corresponding to the tunnel, assigning values to obtain weight values of the corresponding monitoring points, and fusing the core monitoring information and the reference monitoring information based on the weight values to obtain target monitoring information.
With reference to the sixth possible implementation manner of the first aspect, in a seventh possible implementation manner, the training method specifically includes: the stress data set, the osmotic pressure data set, the displacement data set and the temperature data set are respectively used as sub-data sets in a training set and are used as four inputs of an early warning recognition model; and training the early warning recognition model by using four sub-data sets in the training set until probability distribution output by the early warning recognition model reaches target probability distribution, obtaining a corresponding weight value, and adjusting the weight value of the initial model to finish training the early warning recognition model.
In a second aspect, an embodiment of the present application further provides a tunnel engineering rock mass disaster early warning device, including: the monitoring information acquisition module is used for acquiring monitoring information of a plurality of corresponding monitoring points in the tunnel; the monitoring point classification module is used for acquiring a core monitoring point and a reference monitoring point matched with the core monitoring point based on the real-time monitoring information of the plurality of monitoring points and a preset expert system; the monitoring information classification module is used for acquiring core monitoring information and reference monitoring information based on the core monitoring points and the reference monitoring points; the monitoring information fusion module is used for fusing the core monitoring information and the reference monitoring information to obtain target monitoring information; and the early warning module is used for inputting the target monitoring information into a preset early warning recognition model and outputting early warning information.
In a third aspect, an embodiment of the present application provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the tunnel engineering rock mass disaster early warning method according to any one of the above claims when running the executable instructions stored in the memory.
In the technical scheme provided by the embodiment of the application, the optimal monitoring points are obtained by obtaining the monitoring points in the surrounding rock of the tunnel based on the monitoring points and the expert system, and the target monitoring information is obtained by fusing the monitoring information obtained based on the optimal monitoring points and the monitoring information of the reference monitoring points. And processing the target monitoring information based on the early warning recognition model to obtain final early warning information.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
The methods, systems, and/or programs in the accompanying drawings will be described further in terms of exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein the exemplary numbers represent like mechanisms throughout the various views of the drawings.
Fig. 1 is a schematic block diagram of an apparatus provided in an embodiment of the present application.
Fig. 2 is a flow chart of a method of pre-warning of a tunnel engineering rock disaster, according to some embodiments of the present application.
Detailed Description
In order to better understand the technical solutions described above, the following detailed description of the technical solutions of the present application is provided through the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limit the technical solutions of the present application, and the technical features of the embodiments and embodiments of the present application may be combined with each other without conflict.
In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant teachings. However, it will be apparent to one skilled in the art that the present application may be practiced without these details. In other instances, well-known methods, procedures, systems, components, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.
The flowcharts are used in this application to describe implementations performed by systems according to embodiments of the present application. It should be clearly understood that the execution of the flowcharts may be performed out of order. Rather, these implementations may be performed in reverse order or concurrently. Additionally, at least one other execution may be added to the flowchart. One or more of the executions may be deleted from the flowchart.
According to the technical scheme provided by the embodiment of the application, the early warning identification model is mainly constructed to process the real-time data in the tunnel to early warning information, and the early warning of the abnormal information in the tunnel is realized through the computer processing technology.
The embodiment of the application provides a terminal device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes a tunnel engineering rock disaster early warning method to identify and judge forecast information in an abnormal structure in a tunnel rock body.
In this embodiment, the terminal may be a server, and includes a memory, a processor, and a communication unit for the physical structure of the server. The memory, the processor and the communication unit are electrically connected with each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory is used for storing specific information and programs, and the communication unit is used for sending the processed information to the corresponding user side.
In this embodiment, the storage module is divided into two storage areas, where one storage area is a program storage unit and the other storage area is a data storage unit. The program storage unit is equivalent to a firmware area, the read-write authority of the area is set to be in a read-only mode, and the data stored in the area can not be erased and changed. And the data in the data storage unit can be erased or read and written, and when the capacity of the data storage area is full, the newly written data can cover the earliest historical data.
The Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Ele ultrasound ric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also Digital Signal Processors (DSPs)), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Referring to fig. 2, in this embodiment, the method for pre-warning the disaster of the running tunnel engineering rock mass includes the following specific methods:
and S210, configuring a plurality of monitoring points in the tunnel surrounding rock, wherein a geological sensor is arranged in each monitoring point to acquire monitoring information of the corresponding monitoring point.
In this embodiment, a plurality of monitoring points configured in the tunnel surrounding rock are used to obtain monitoring information of the tunnel surrounding rock, where the plurality of monitoring points may be set based on a monitoring result target. In addition, in the embodiment, the geological sensor comprises a stress sensor, an osmotic pressure sensor, a displacement sensor and a temperature sensor, wherein the stress sensor, the osmotic pressure sensor, the displacement sensor and the temperature sensor are used for respectively acquiring stress data, osmotic pressure data, displacement data and temperature data of the tunnel.
Step S220, acquiring a core monitoring point and a reference monitoring point matched with the core monitoring point based on the real-time monitoring information of the plurality of monitoring points and a preset expert system.
In this embodiment, the expert system includes a knowledge graph constructed based on disaster monitoring information obtained from a derivative tunnel surrounding geological survey device including a spot hole photography system, a sidewall photography system, a geological compass tape, and a mapping tool.
And the devices and the equipment are connected based on the configuration mode of the corresponding line to obtain a final connection line, so that the acquisition of each target data in the surrounding rock of the tunnel is realized. And constructing a knowledge graph based on the obtained target data, wherein the construction of the knowledge graph is based on big data, so that the target data is based on the target data corresponding to the derivative tunnel.
And step S230, acquiring core monitoring information and reference monitoring information based on the core monitoring point and the reference monitoring point.
In this embodiment, the monitoring information includes stress data, osmotic pressure data, displacement data, and temperature data.
And step 240, fusing the core monitoring information and the reference monitoring information to obtain target monitoring information.
In this embodiment, the processing method of this procedure includes: and acquiring historical data of corresponding monitoring points based on the tunnel surrounding rock geological survey device, determining geology corresponding to the core monitoring points and acquired by the reference monitoring points based on the historical data and the monitoring information variation corresponding to the tunnel, assigning values to obtain weight values of the corresponding monitoring points, and fusing the core monitoring information and the reference monitoring information based on the weight values to obtain target monitoring information.
And S250, inputting the target monitoring information into a preset early warning recognition model to output early warning information.
In this embodiment, the early warning recognition model is a neural network, and the preset early warning recognition model is obtained based on a network training method, and the training method includes: extracting features in the target monitoring information, inputting the target monitoring information into an initial model, obtaining feature vectors of the target monitoring information, obtaining probability distribution based on the feature vectors and a probability density function, and adjusting the weight value of the initial model to target probability distribution based on the probability distribution.
In this embodiment, for probability distribution in which the probability distribution is of an abnormal structure, information belonging to the abnormal geological structure in the monitored information can be obtained in advance through the trained model, so that a prediction result of the corresponding information is achieved. And sends the information to the corresponding user end for the engineering personnel to grasp.
The training process for the application scenario in this embodiment mainly includes:
the stress data set, the osmotic pressure data set, the displacement data set and the temperature data set are respectively used as sub-data sets in a training set and are used as four inputs of an early warning recognition model; and training the early warning recognition model by using four sub-data sets in the training set until probability distribution output by the early warning recognition model reaches target probability distribution, obtaining a corresponding weight value, and adjusting the weight value of the initial model to finish training the early warning recognition model.
In addition, referring to fig. 1, in this embodiment, a tunnel advanced geological prediction apparatus 100 is configured, and is configured to perform the above method, and a monitoring information obtaining module 110 is configured to obtain monitoring information of a plurality of corresponding monitoring points in the tunnel. The monitoring point classification module 120 is configured to obtain a core monitoring point and a reference monitoring point matched with the core monitoring point based on real-time monitoring information of the plurality of monitoring points and a preset expert system. The monitoring information classification module 130 is configured to obtain core monitoring information and reference monitoring information based on the core monitoring point and the reference monitoring point. And the monitoring information fusion module 140 is configured to fuse the core monitoring information with the reference monitoring information to obtain target monitoring information. And the forecasting module 150 is used for inputting the target monitoring information into a preset early warning recognition model and outputting early warning information.
It should be understood that, for the technical terms that do not have noun interpretation in the foregoing, those skilled in the art can clearly determine the meaning of the terms according to the foregoing disclosure, for example, for some terms such as threshold values and coefficients, those skilled in the art can derive and determine the terms according to the logical relationship between the foregoing and the following terms, and the value ranges of these values may be selected according to practical situations, for example, 0.1 to 1, for example, 1 to 10, for example, 50 to 100, which are not limited herein.
The person skilled in the art can undoubtedly determine technical features/terms of some preset, reference, predetermined, set and preference labels, such as threshold values, threshold value intervals, threshold value ranges, etc., from the above disclosure. For some technical feature terms which are not explained, a person skilled in the art can reasonably and unambiguously derive based on the logical relation of the context, so that the technical scheme can be clearly and completely implemented. The prefixes of technical feature terms, such as "first", "second", "example", "target", etc., which are not explained, can be unambiguously deduced and determined from the context. Suffixes of technical feature terms, such as "set", "list", etc., which are not explained, can also be deduced and determined unambiguously from the context.
The foregoing of the disclosure of the embodiments of the present application will be apparent to and complete with respect to those skilled in the art. It should be appreciated that the process of deriving and analyzing technical terms not explained based on the above disclosure by those skilled in the art is based on what is described in the present application, and thus the above is not an inventive judgment of the overall scheme.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this application, and are therefore within the spirit and scope of the exemplary embodiments of this application.
Meanwhile, the present application uses specific terminology to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, certain features, structures, or characteristics of at least one embodiment of the present application may be combined as suitable.
In addition, those of ordinary skill in the art will understand that the various aspects of the present application may be illustrated and described in terms of several patentable categories or cases, including any novel and useful processes, machines, products, or combinations of materials, or any novel and useful improvements thereto. Accordingly, aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "unit," component, "or" system. Furthermore, aspects of the present application may be embodied as a computer product in at least one computer-readable medium, the product comprising computer-readable program code.
The computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer readable signal medium may be propagated through any suitable medium including radio, electrical, fiber optic, RF, or the like, or any combination of the foregoing.
Computer program code required for execution of aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python, etc., or similar conventional programming languages such as the "C" programming language, visual Basic, fortran 2003,Perl,COBOL 2002,PHP,ABAP, dynamic programming languages such as Python, ruby and Groovy or other programming languages. The programming code may execute entirely on the user's computer, or as a stand-alone software package, or partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as software as a service (SaaS).
Furthermore, the order in which the processing elements and sequences are described, the use of numerical letters, or other designations are used is not intended to limit the order in which the processes and methods of the present application are performed, unless specifically indicated in the claims. While in the foregoing disclosure there has been discussed, by way of various examples, some embodiments of the invention which are presently considered to be useful, it is to be understood that this detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments of this application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
It should also be appreciated that in the foregoing description of the embodiments of the present application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one of the embodiments of the invention. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the subject application. Indeed, less than all of the features of a single embodiment disclosed above.
Claims (10)
1. The method for pre-warning the disaster of the rock mass in the tunnel engineering is characterized by comprising the following steps:
configuring a plurality of monitoring points in the tunnel, wherein a geological sensor is arranged in each monitoring point to acquire monitoring information of the corresponding monitoring point;
acquiring a core monitoring point and a reference monitoring point matched with the core monitoring point based on the real-time monitoring information of the plurality of monitoring points and a preset expert system;
acquiring core monitoring information and reference monitoring information based on the core monitoring point and the reference monitoring point;
fusing the core monitoring information and the reference monitoring information to obtain target monitoring information;
and inputting the target monitoring information into a preset early warning recognition model to output early warning information.
2. The tunnel engineering rock disaster warning method according to claim 1, wherein the geological sensor comprises a stress sensor, an osmotic pressure sensor, a displacement sensor and a temperature sensor, and the stress sensor, the osmotic pressure sensor, the displacement sensor and the temperature sensor are used for respectively acquiring stress data, osmotic pressure data, displacement data and temperature data of the tunnel.
3. The method of claim 1, wherein the expert system comprises a knowledge graph constructed based on disaster monitoring information obtained from a derivative tunnel surrounding geological survey device, the tunnel surrounding geological survey device comprising a spot hole photography system, a sidewall photography system, a geological compass tape, and a mapping tool.
4. The method of claim 3, wherein fusing the core monitoring information with the reference monitoring information to obtain target monitoring information, comprises:
and acquiring historical data of corresponding monitoring points based on the tunnel surrounding rock geological survey device, determining geology corresponding to the core monitoring points and acquired by the reference monitoring points based on the historical data and the monitoring information variation corresponding to the tunnel, assigning values to obtain weight values of the corresponding monitoring points, and fusing the core monitoring information and the reference monitoring information based on the weight values to obtain target monitoring information.
5. The tunneling rock disaster warning method according to claim 4, wherein the target monitoring information includes spot hole image data, stress data, osmotic pressure data, displacement data and temperature data.
6. The method of claim 5, wherein the pre-set pre-alarm recognition model comprises a neural network model.
7. The tunnel engineering rock disaster early warning method according to claim 6, wherein the preset early warning recognition model is obtained based on a network training method, and the training method comprises the following steps:
extracting features in the target monitoring information, inputting the target monitoring information into an initial model, obtaining feature vectors of the target monitoring information, obtaining probability distribution based on the feature vectors and a probability density function, and adjusting the weight value of the initial model to target probability distribution based on the probability distribution.
8. The tunnel engineering rock disaster warning method according to claim 7, wherein the training method specifically comprises:
the stress data set, the osmotic pressure data set, the displacement data set and the temperature data set are respectively used as sub-data sets in a training set and are used as four inputs of an early warning recognition model; and training the early warning recognition model by using four sub-data sets in the training set until probability distribution output by the early warning recognition model reaches target probability distribution, obtaining a corresponding weight value, and adjusting the weight value of the initial model to finish training the early warning recognition model.
9. Tunnel engineering rock mass calamity early warning device, characterized by, include:
the monitoring information acquisition module is used for acquiring monitoring information of a plurality of corresponding monitoring points in the tunnel;
the monitoring point classification module is used for acquiring a core monitoring point and a reference monitoring point matched with the core monitoring point based on the real-time monitoring information of the plurality of monitoring points and a preset expert system;
the monitoring information classification module is used for acquiring core monitoring information and reference monitoring information based on the core monitoring points and the reference monitoring points;
the monitoring information fusion module is used for fusing the core monitoring information and the reference monitoring information to obtain target monitoring information;
and the early warning module is used for inputting the target monitoring information into a preset early warning recognition model and outputting early warning information.
10. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
and the processor is used for realizing the tunnel engineering rock disaster early warning method according to any one of claims 1 to 8 when the executable instructions stored in the memory are operated.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116665422A (en) * | 2023-05-29 | 2023-08-29 | 广西交通设计集团有限公司 | Highway side slope falling stone risk monitoring and early warning system |
| CN117365658B (en) * | 2023-12-05 | 2024-03-12 | 中国科学院武汉岩土力学研究所 | Abnormal early warning system for multi-source heterogeneous information fusion of tunnel surrounding rock |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116665422A (en) * | 2023-05-29 | 2023-08-29 | 广西交通设计集团有限公司 | Highway side slope falling stone risk monitoring and early warning system |
| CN116665422B (en) * | 2023-05-29 | 2024-03-29 | 广西交通设计集团有限公司 | Highway side slope falling stone risk monitoring and early warning system |
| CN117365658B (en) * | 2023-12-05 | 2024-03-12 | 中国科学院武汉岩土力学研究所 | Abnormal early warning system for multi-source heterogeneous information fusion of tunnel surrounding rock |
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