A tunnel engineering rock mass disaster early warning method, device and electronic equipment

Abstract

The present invention relates to the technical field of early warning of geological disasters in tunnel engineering, and in particular to an early warning method for abnormal structural disasters inside a rock mass, specifically a method, device and electronic equipment for early warning of rock mass disasters in tunnel engineering; the method comprises: configuring a plurality of monitoring points in the tunnel, arranging a geological sensor in the monitoring point to obtain monitoring information of the corresponding monitoring point; obtaining a core monitoring point and a reference monitoring point coordinated with the core monitoring point based on the real-time monitoring information of the plurality of monitoring points and a preset expert system; obtaining core monitoring information and reference monitoring information based on the core monitoring point and the reference monitoring point; fusing the core monitoring information with 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

[0001] The present invention relates to the technical field of tunnel engineering geological disaster early warning, and in particular to a method for early warning of abnormal structural disasters inside a rock mass, specifically to a method, device and electronic equipment for early warning of rock mass disasters in a tunnel engineering. Background Art

[0002] Geological disasters often begin with erosion, damage and force imbalance of local rock and soil structures, especially weak structures or soil-rock interfaces under the action of water; therefore, geological surveys and monitoring and diagnosis of weak structural areas of rock and soil bodies are important means of disaster prevention and mitigation; traditional equipment such as geological radars or acoustic wave detectors can only achieve geological surveys over a large range. For weak interlayers or weak structural surfaces in specific areas of geotechnical engineering, traditional methods are difficult to achieve in terms of accuracy, and cannot achieve visual monitoring and accurate assessment of potential geological disasters, which seriously affects the stability and safety of geotechnical engineering or slope engineering. A new survey method is urgently needed for visual monitoring and accurate early warning of potential geological disasters. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides a tunnel engineering rock disaster early warning method, device and electronic equipment.

[0004] In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0005] In a first aspect, a method for early warning of rock disasters in tunnel engineering is provided, the method comprising: configuring a plurality of monitoring points in the tunnel, providing a geological sensor in the monitoring point to obtain monitoring information of the corresponding monitoring point; obtaining a core monitoring point and a reference monitoring point cooperating with the core monitoring point based on the real-time monitoring information of the plurality of monitoring points and a preset expert system; obtaining core monitoring information and reference monitoring information based on the core monitoring point and the reference monitoring point; fusing the core monitoring information with 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.

[0006] In a first possible implementation of the first aspect, the geological sensor includes a stress sensor, a seepage pressure sensor, a displacement sensor and a temperature sensor, and the stress sensor, the seepage pressure sensor, the displacement sensor and the temperature sensor are used to respectively collect stress data, seepage pressure data, displacement data and temperature data of the tunnel.

[0007] In a second possible implementation of the first aspect, the expert system includes a knowledge graph constructed based on disaster monitoring information obtained by a derived tunnel surrounding rock geological survey device, and the tunnel surrounding rock geological survey device includes a special hole photography system, a side wall photography system, a geological compass tape and drawing tools.

[0008] In combination with the second possible implementation method of the first aspect, in a third possible implementation method, the core monitoring information and the reference monitoring information are fused to obtain target monitoring information, including: obtaining historical data of corresponding monitoring points based on the tunnel surrounding rock geological survey device, assigning values ​​to the geology obtained at the core monitoring points and the reference monitoring points based on the historical data and the change in the monitoring information corresponding to the tunnel, obtaining weight values ​​of the corresponding monitoring points, and fusing the core monitoring information with the reference monitoring information based on the weight values ​​to obtain target monitoring information.

[0009] In combination with 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.

[0010] In combination with the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner, the pre-set early warning identification model includes a neural network model.

[0011] In combination with the fifth possible implementation method of the first aspect, in a sixth possible implementation method, the pre-set early warning identification model is obtained based on a network training method, and the training method includes: obtaining historical data of corresponding monitoring points based on the tunnel surrounding rock geological survey device, determining the geology obtained at the core monitoring points and the reference monitoring points based on the historical data and the change in the monitoring information of the corresponding tunnel, assigning weight values ​​of the corresponding monitoring points, and fusing the core monitoring information with the reference monitoring information based on the weight values ​​to obtain target monitoring information.

[0012] In combination with the sixth possible implementation manner of the first aspect, in the seventh possible implementation manner, the training method specifically includes: using the stress data set, the seepage pressure data set, the displacement data set and the temperature data set as sub-data sets in the training set, and as four inputs of the early warning recognition model; using the four sub-data sets in the training set to train the early warning recognition model until the probability distribution of the early warning recognition model output reaches the target probability distribution, and the corresponding weight value is obtained, and the weight value of the initial model is adjusted to complete the training of the early warning recognition model.

[0013] In the second aspect, an embodiment of the present application also provides a tunnel engineering rock disaster early warning device, including: a monitoring information acquisition module, used to obtain monitoring information of multiple corresponding monitoring points in the tunnel; a monitoring point classification module, used to obtain core monitoring points and reference monitoring points coordinated with the core monitoring points based on the real-time monitoring information of the multiple monitoring points and a pre-set expert system; a monitoring information classification module, used to obtain core monitoring information and reference monitoring information based on the core monitoring points and the reference monitoring points; a monitoring information fusion module, used to fuse the core monitoring information with the reference monitoring information to obtain target monitoring information; an early warning module, used to input the target monitoring information into a pre-set early warning recognition model to output early warning information.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the tunnel engineering rock disaster warning method as described in any one of the above claims when running the executable instructions stored in the memory.

[0015] In the technical solution provided by the embodiment of the present application, the optimal monitoring point is obtained based on multiple monitoring points and an expert system for multiple monitoring points in the tunnel surrounding rock, and the monitoring information obtained based on the optimal monitoring point and the monitoring information of multiple reference monitoring points are integrated to obtain the target monitoring information. And the target monitoring information is processed based on the early warning recognition model to obtain the final early warning information. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] The methods, systems and / or programs in the accompanying drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example numbers represent similar mechanisms in the various views of the accompanying drawings.

[0018] Figure 1 It is a schematic block diagram of the device provided in an embodiment of the present application.

[0019] Figure 2 It is a flow chart of a tunnel engineering rock disaster early warning method according to some embodiments of the present application. DETAILED DESCRIPTION

[0020] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0021] In the following detailed description, numerous specific details are set forth by way of example in order to provide a comprehensive understanding of the relevant guidance. However, it will be apparent to those skilled in the art that the present application may be practiced without these details. In other cases, well-known methods, procedures, systems, compositions and / or circuits have been described at a relatively high level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.

[0022] Flowcharts are used in the present application to illustrate the execution process performed by the system according to the embodiment of the present application. It should be clearly understood that the execution process of the flowchart may not be performed in order. On the contrary, these execution processes may be performed in reverse order or simultaneously. In addition, at least one other execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0023] The technical solution provided in the embodiment of the present application mainly obtains warning information by constructing a warning recognition model to process real-time data in the tunnel, and realizes early warning of abnormal information in the tunnel through computer processing technology.

[0024] An embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes a rock disaster warning method for tunnel engineering to identify and judge forecast information in abnormal structures within the tunnel rock mass.

[0025] In this embodiment, the terminal may be a server, and the physical structure of the server includes a memory, a processor, and a communication unit. The memory, the processor, and the communication unit are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components may be electrically connected to each other via one or more communication buses or signal lines. The memory is used to store specific information and programs, and the communication unit is used to send the processed information to the corresponding user end.

[0026] In this embodiment, the storage module is divided into two storage areas, one of which is a program storage unit and the other is a data storage unit. The program storage unit is equivalent to the firmware area, and the read and write permissions of this area are set to read-only mode, and the data stored in it cannot be erased or changed. The data in the data storage unit can be erased or read and written. When the capacity of the data storage area is full, the newly written data will overwrite the earliest historical data.

[0027] Among them, the memory can be, but is not limited to, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0028] The processor may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0029] See also Figure 2 In this embodiment, the rock mass disaster early warning method for the running tunnel engineering includes the following specific methods:

[0030] Step S210: multiple monitoring points are configured in the surrounding rock of the tunnel, and geological sensors are arranged in the monitoring points to obtain monitoring information of the corresponding monitoring points.

[0031] In this embodiment, multiple monitoring points configured in the tunnel surrounding rock are used to obtain monitoring information of the tunnel surrounding rock, wherein the multiple monitoring points can be set based on the monitoring result target. In addition, in this embodiment, 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 to collect stress data, osmotic pressure data, displacement data and temperature data of the tunnel respectively.

[0032] Step S220. Based on the real-time monitoring information of the plurality of monitoring points and a preset expert system, a core monitoring point and a reference monitoring point coordinated with the core monitoring point are acquired.

[0033] In this embodiment, the expert system includes a knowledge graph constructed based on disaster monitoring information obtained by a derived tunnel surrounding rock geological survey device, and the tunnel surrounding rock geological survey device includes a special hole photography system, a side wall photography system, a geological compass tape and a drawing tool.

[0034] The multiple devices and equipment are connected based on the configuration mode of the corresponding lines to obtain the final connection line, so as to obtain the target data in the tunnel surrounding rock. And a knowledge graph is constructed based on the obtained target data, wherein the construction of the knowledge graph is based on big data, so the target data for each target data is based on the target data corresponding to the derived tunnel.

[0035] Step S230: Acquire core monitoring information and reference monitoring information based on the core monitoring point and the reference monitoring point.

[0036] In this embodiment, the monitoring information includes stress data, seepage pressure data, displacement data and temperature data.

[0037] Step S240: Fusing the core monitoring information with the reference monitoring information to obtain target monitoring information.

[0038] In this embodiment, the processing method of this process includes: obtaining historical data of corresponding monitoring points based on the tunnel surrounding rock geological survey device, determining the geology obtained at the core monitoring points and the reference monitoring points based on the historical data and the change in the monitoring information of the corresponding tunnel, and obtaining the weight value of the corresponding monitoring point; based on the weight value, the core monitoring information and the reference monitoring information are integrated to obtain the target monitoring information.

[0039] Step S250: Input the target monitoring information into a preset warning recognition model to output warning information.

[0040] 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 from the target monitoring information, inputting the target monitoring information into the initial model, obtaining a feature vector of the target monitoring information, obtaining a probability distribution based on the feature vector and a probability density function, and adjusting the initial model weight value to the target probability distribution based on the probability distribution.

[0041] In this embodiment, for the probability distribution of abnormal structures, the trained model can achieve the early acquisition of abnormal geological structure information in the monitoring information, thereby realizing the prediction results of the corresponding information. And this information is sent to the corresponding user end for engineering personnel to grasp the information.

[0042] Among them, for the application scenario in this embodiment, the training process is mainly as follows:

[0043] The stress data set, the seepage pressure data set, the displacement data set and the temperature data set are respectively used as sub-data sets in the training set, and as four inputs of the early warning recognition model; the early warning recognition model is trained using the four sub-data sets in the training set until the probability distribution of the early warning recognition model output reaches the target probability distribution, the corresponding weight value is obtained, and the weight value of the initial model is adjusted to complete the training of the early warning recognition model.

[0044] Also, see Figure 1 In this embodiment, a tunnel advanced geological prediction device 100 is configured to execute the above method, and a monitoring information acquisition module 110 is used to obtain monitoring information of multiple corresponding monitoring points in the tunnel. A monitoring point classification module 120 is used to obtain core monitoring points and reference monitoring points that cooperate with the core monitoring points based on the real-time monitoring information of the multiple monitoring points and a preset expert system. A monitoring information classification module 130 is used to obtain core monitoring information and reference monitoring information based on the core monitoring points and the reference monitoring points. A monitoring information fusion module 140 is used to fuse the core monitoring information with the reference monitoring information to obtain target monitoring information. A forecast module 150 is used to input the target monitoring information into a preset early warning recognition model to output early warning information.

[0045] It should be understood that, for technical terms that are not explained in the above content, those skilled in the art can unambiguously determine their meanings by deducing from the above disclosed content. For example, for some terms such as thresholds and coefficients, those skilled in the art can deduce and determine them based on the logical relationship between the previous and the next terms. The value ranges of these numerical values ​​can be selected according to actual conditions, such as 0.1~1, 1~10, and 50~100, which are not limited here.

[0046] Those skilled in the art can unambiguously determine some preset, benchmark, predetermined, set and preferred labeled technical features / technical terms based on the above disclosed content, such as thresholds, threshold intervals, threshold ranges, etc. For some unexplained technical feature terms, those skilled in the art are fully capable of reasonably and unambiguously deducing based on the logical relationship of the context, so as to clearly and completely implement the above technical solutions. The prefixes of unexplained technical feature terms, such as "first", "second", "example", "target", etc., can be unambiguously deduced and determined based on the context. The suffixes of unexplained technical feature terms, such as "set", "list", etc., can also be unambiguously deduced and determined based on the context.

[0047] The above contents disclosed in the embodiments of the present application are clear and complete for those skilled in the art. It should be understood that the process of deducing and analyzing the unexplained technical terms based on the above disclosed contents by those skilled in the art is based on the contents recorded in the present application, and therefore the above contents are not a judgment on the creativity of the overall solution.

[0048] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0049] At the same time, the present application uses specific terms to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different parts of this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in at least one embodiment of the present application can be appropriately combined.

[0050] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component" or "system". In addition, various aspects of the present application may be represented as a computer product located in at least one computer-readable medium, the product including a computer-readable program code.

[0051] A computer readable signal medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may have a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. A computer readable signal medium may be any computer readable medium other than a computer readable storage medium, which may be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit a program for use. The program code on the computer readable signal medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar medium, or a combination of any of the above mediums.

[0052] The computer program code required for the execution of various aspects of the present application can be written in any combination of one or more programming languages, including object-oriented programming, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., or similar conventional programming languages, such as "C" programming language, Visual Basic, Fortran2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy or other programming languages. The programming code can be executed entirely on the user's computer, or as an independent software package on the user's computer, or partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0053] In addition, unless expressly stated in the scope of the patent application, the order of the processing elements and sequences described in this application, the use of digital letters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached patent scope is not limited to the disclosed embodiments. On the contrary, the scope of the patent application is intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of the present application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0054] It should also be understood that in order to simplify the description disclosed in this application and thus help understand at least one embodiment of the invention, in the above description of the embodiments of the application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of the application are more than the features mentioned in the claims. In fact, the features of the embodiment are less than all the features of the single embodiment disclosed above.

Claims

1. A tunnel engineering rock mass disaster early warning method, characterized in that: The method comprises: A plurality of monitoring points are configured in the tunnel, and a geological sensor is arranged in the monitoring point to obtain monitoring information of the corresponding monitoring point; 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 to respectively collect stress data, osmotic pressure data, displacement data and temperature data of the tunnel; Acquire core monitoring points and reference monitoring points coordinated with the core monitoring points based on the real-time monitoring information of the multiple monitoring points and a preset expert system; Acquire core monitoring information and reference monitoring information based on the core monitoring point and the reference monitoring point; Fusion of the core monitoring information with the reference monitoring information to obtain target monitoring information; The target monitoring information is input into a preset early warning recognition model to output the early warning information; the preset early warning recognition model includes a neural network model, 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 a feature vector of the target monitoring information, obtaining a probability distribution based on the feature vector and a probability density function, and adjusting the weight value of the initial model to a target probability distribution based on the probability distribution; the training method specifically includes: using a stress data set, a seepage pressure data set, a displacement data set, and a temperature data set as sub-data sets in a training set, respectively, and as four inputs of a early warning recognition model; using the four sub-data sets in the training set to train the early warning recognition model until the probability distribution output by the early warning recognition model reaches the target probability distribution, obtaining a corresponding weight value, and adjusting the weight value of the initial model to complete the training of the early warning recognition model.

2. The tunnel engineering rock mass disaster early warning method according to claim 1, characterized in that: The expert system includes a knowledge graph constructed based on disaster monitoring information obtained by a derived tunnel surrounding rock geological survey device, and the tunnel surrounding rock geological survey device includes a special hole photography system, a side wall photography system, a geological compass tape and a drawing tool.

3. The tunnel engineering rock mass disaster early warning method according to claim 2, characterized in that: The core monitoring information is integrated with the reference monitoring information to obtain target monitoring information, including: Based on the tunnel surrounding rock geological survey device, historical data of corresponding monitoring points are obtained, and based on the historical data and the change in the monitoring information of the corresponding tunnel, the geology obtained at the core monitoring points and the reference monitoring points is assigned to obtain weight values ​​of the corresponding monitoring points, and based on the weight values, the core monitoring information and the reference monitoring information are fused to obtain target monitoring information.

4. The tunnel engineering rock mass disaster early warning method according to claim 3 is characterized in that: The target monitoring information includes specific hole image data, stress data, seepage pressure data, displacement data and temperature data.

5. A tunnel engineering rock disaster early warning device, characterized in that: include: A monitoring information acquisition module, used to acquire monitoring information of a plurality of corresponding monitoring points in the tunnel; The monitoring information is obtained through geological sensors, which include stress sensors, seepage pressure sensors, displacement sensors and temperature sensors, and the stress sensors, seepage pressure sensors, displacement sensors and temperature sensors are used to collect stress data, seepage pressure data, displacement data and temperature data of the tunnel respectively; A monitoring point classification module, used for acquiring core monitoring points and reference monitoring points coordinated with the core monitoring points based on the real-time monitoring information of the plurality of monitoring points and a preset expert system; A monitoring information classification module, used to obtain core monitoring information and reference monitoring information based on the core monitoring points and the reference monitoring points; A monitoring information fusion module, used to fuse the core monitoring information with the reference monitoring information to obtain target monitoring information; An early warning module, used to input the target monitoring information into a preset early warning recognition model to output early warning information; The pre-set early warning identification model includes a neural network model, and the pre-set early warning identification model is obtained based on a network training method, and the training method includes: extracting features from the target monitoring information, inputting the target monitoring information into the initial model, obtaining a feature vector of the target monitoring information, obtaining a probability distribution based on the feature vector and a probability density function, and adjusting the weight value of the initial model to the target probability distribution based on the probability distribution; the training method specifically includes: using a stress data set, a seepage pressure data set, a displacement data set, and a temperature data set as sub-data sets in a training set, respectively, and as four inputs of a early warning identification model; using the four sub-data sets in the training set to train the early warning identification model until the probability distribution output by the early warning identification model reaches the target probability distribution, obtaining corresponding weight values, and adjusting the weight values ​​of the initial model to complete the training of the early warning identification model.

6. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; The processor is used to implement the tunnel engineering rock disaster early warning method as described in any one of claims 1 to 4 when running the executable instructions stored in the memory.

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