CN117194868A - Surrounding rock deformation monitoring method and device, electronic equipment and storage medium - Google Patents
Surrounding rock deformation monitoring method and device, electronic equipment and storage medium Download PDFInfo
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- 239000011435 rock Substances 0.000 title claims abstract description 120
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Abstract
The application provides a surrounding rock deformation monitoring method, a surrounding rock deformation monitoring device, electronic equipment and a storage medium. Comprising the following steps: acquiring deformation data acquired at each acquisition point on the surface of the surrounding rock of the tunnel at the target acquisition moment; determining a deformation incentive corresponding to each acquisition point based on deformation data acquired by each acquisition point; constructing a space deformation area matrix corresponding to the surface of the tunnel surrounding rock based on the deformation inducements corresponding to each acquisition point; and determining deformation risk grades corresponding to any monitoring points on the surface of the tunnel surrounding rock based on the space deformation area matrix at the target acquisition moment. Therefore, the deformation risk level of any monitoring point on the surface of the surrounding rock of the tunnel can be determined through the space deformation area matrix, so that the comprehensive monitoring of the surface of the surrounding rock of the tunnel is realized, and further, the accurate positioning of the deformation part of the surrounding rock is realized.
Description
Technical Field
The application relates to the technical field of geotechnical engineering, in particular to a surrounding rock deformation monitoring method, a surrounding rock deformation monitoring device, electronic equipment and a storage medium.
Background
Along with the development of economic level and technological level, geotechnical engineering is more focused on surrounding rock deformation and stress monitoring of supporting structures in the construction process, and disaster caused by surrounding rock deformation is reduced by a local point position monitoring method. However, most of the current monitoring methods monitor and analyze data of certain points on the surface of the surrounding rock through sensors, dead zones exist in non-monitoring areas, the whole surface of the surrounding rock cannot be monitored and analyzed, and accurate positioning of deformation parts of the surrounding rock is difficult to achieve.
Disclosure of Invention
The embodiment of the application aims to provide a surrounding rock deformation monitoring method, device, electronic equipment and storage medium, which are used for solving the problems that the whole surrounding rock surface cannot be monitored and analyzed, and the surrounding rock deformation part cannot be accurately positioned easily. The specific technical scheme is as follows:
in a first aspect, there is provided a method of monitoring deformation of a surrounding rock, the method comprising:
acquiring deformation data acquired at each acquisition point on the surface of the surrounding rock of the tunnel at the target acquisition moment;
determining a deformation incentive corresponding to each acquisition point based on deformation data acquired by each acquisition point;
constructing a space deformation area matrix corresponding to the surface of the tunnel surrounding rock based on the deformation inducements corresponding to each acquisition point;
and determining deformation risk grades corresponding to any monitoring points on the surface of the tunnel surrounding rock based on the space deformation area matrix at the target acquisition moment.
In one possible implementation manner, the determining the deformation incentive corresponding to each acquisition point based on the deformation data acquired by each acquisition point includes:
inputting deformation data corresponding to each acquisition point into a pre-trained prediction model for outputting corresponding deformation inducements by the prediction model;
the prediction model is a neural network model obtained by training with sample deformation data as input and known deformation inducements corresponding to the sample deformation data as target output.
In one possible implementation manner, the constructing a spatial deformation area matrix corresponding to the tunnel surrounding rock surface based on the deformation inducements corresponding to each acquisition point includes:
inputting deformation inducements corresponding to the acquisition points into a pre-trained mechanical model aiming at each acquisition point so as to output a region space deformation area matrix corresponding to a region where the acquisition points are positioned by the mechanical model;
and carrying out fusion treatment on the regional spatial deformation area matrix corresponding to all the acquisition points to obtain the spatial deformation area matrix corresponding to the tunnel surrounding rock surface.
In one possible embodiment, the deformation data includes axial force, displacement, inclination angle.
In one possible implementation manner, the spatial deformation area matrix includes a parameter area matrix corresponding to at least one parameter type, and the determining, based on the spatial deformation area matrix, a deformation risk level corresponding to the monitoring point at the target acquisition time includes:
determining type weights corresponding to the parameter area matrixes aiming at each parameter area matrix, and determining deformation parameters corresponding to the monitoring points in the parameter area matrixes;
calculating the product of the type weight and the deformation parameter to obtain a parameter risk assessment score corresponding to the parameter area matrix;
carrying out summation operation on the parameter risk assessment scores corresponding to all the parameter area matrixes to obtain a target risk assessment score;
and determining the deformation risk level corresponding to the monitoring point at the target acquisition time according to the corresponding relation between the preset risk assessment score and the risk level.
In one possible implementation manner, after the constructing the spatial deformation area matrix corresponding to the tunnel surrounding rock surface based on the deformation inducement corresponding to each acquisition point, the method further includes:
predicting the spatial deformation area matrix by using a pre-trained prediction model to obtain a time deformation area matrix corresponding to the prediction moment;
and determining the deformation risk level corresponding to any monitoring point on the surface of the tunnel surrounding rock based on the time deformation area matrix.
In one possible implementation manner, after determining the deformation risk level corresponding to the monitoring point at the target acquisition time based on the spatial deformation area matrix, the method further includes:
constructing a three-dimensional model corresponding to the surface of the tunnel surrounding rock;
determining a rendering mode corresponding to the deformation risk level;
and rendering the position corresponding to the monitoring point on the three-dimensional model according to the rendering mode.
In a second aspect, there is provided a surrounding rock deformation monitoring device, the device comprising:
the acquisition module is used for acquiring deformation data acquired at each acquisition point on the surface of the tunnel surrounding rock at the target acquisition moment;
the first determining module is used for determining a deformation incentive corresponding to each acquisition point based on deformation data acquired by each acquisition point;
the construction module is used for constructing a space deformation area matrix corresponding to the surface of the tunnel surrounding rock based on the deformation inducements corresponding to each acquisition point;
the second determining module is used for determining deformation risk grades corresponding to any monitoring point on the surface of the tunnel surrounding rock based on the space deformation area matrix at the target acquisition moment.
In one possible embodiment, the first determining module is configured to:
inputting deformation data corresponding to each acquisition point into a pre-trained prediction model for outputting corresponding deformation inducements by the prediction model;
the prediction model is a neural network model obtained by training with sample deformation data as input and known deformation inducements corresponding to the sample deformation data as target output.
In one possible embodiment, the building block is configured to:
inputting deformation inducements corresponding to the acquisition points into a pre-trained mechanical model aiming at each acquisition point so as to output a region space deformation area matrix corresponding to a region where the acquisition points are positioned by the mechanical model;
and carrying out fusion treatment on the regional spatial deformation area matrix corresponding to all the acquisition points to obtain the spatial deformation area matrix corresponding to the tunnel surrounding rock surface.
In one possible embodiment, the deformation data includes axial force, displacement, inclination angle.
In one possible implementation manner, the spatial deformation area matrix includes a parameter area matrix corresponding to at least one parameter type, and the second determining module is configured to:
determining type weights corresponding to the parameter area matrixes aiming at each parameter area matrix, and determining deformation parameters corresponding to the monitoring points in the parameter area matrixes;
calculating the product of the type weight and the deformation parameter to obtain a parameter risk assessment score corresponding to the parameter area matrix;
carrying out summation operation on the parameter risk assessment scores corresponding to all the parameter area matrixes to obtain a target risk assessment score;
and determining the deformation risk level corresponding to the monitoring point at the target acquisition time according to the corresponding relation between the preset risk assessment score and the risk level.
In one possible embodiment, the apparatus further comprises a prediction module for:
predicting the spatial deformation area matrix by using a pre-trained prediction model to obtain a time deformation area matrix corresponding to the prediction moment;
and determining the deformation risk level corresponding to any monitoring point on the surface of the tunnel surrounding rock based on the time deformation area matrix.
In one possible implementation, the apparatus further includes a rendering module configured to:
constructing a three-dimensional model corresponding to the surface of the tunnel surrounding rock;
determining a rendering mode corresponding to the deformation risk level;
and rendering the position corresponding to the monitoring point on the three-dimensional model according to the rendering mode.
In a third aspect, an electronic device is provided, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of the first aspects when executing a program stored on a memory.
In a fourth aspect, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of the first aspects.
In a fifth aspect, there is provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform any of the above-described methods of monitoring deformation of surrounding rock.
The embodiment of the application has the beneficial effects that:
the embodiment of the application provides a surrounding rock deformation monitoring method, device, electronic equipment and storage medium, wherein in the embodiment of the application, firstly, deformation data acquired by each acquisition point on the surface of a tunnel surrounding rock at the target acquisition moment are acquired, then, based on the deformation data acquired by each acquisition point, deformation inducements corresponding to each acquisition point are determined, a spatial deformation area matrix corresponding to the surface of the tunnel surrounding rock is constructed based on the deformation inducements corresponding to each acquisition point, and finally, for any monitoring point on the surface of the tunnel surrounding rock, the deformation risk grade corresponding to the monitoring point at the target acquisition moment is determined based on the spatial deformation area matrix. Therefore, the deformation risk level of any monitoring point on the surface of the surrounding rock of the tunnel can be determined through the space deformation area matrix, so that the comprehensive monitoring of the surface of the surrounding rock of the tunnel is realized, and further, the accurate positioning of the deformation part of the surrounding rock is realized.
Of course, it is not necessary for any one product or method of practicing the application to achieve all of the advantages set forth above at the same time.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which the figures of the drawings are not to be taken in a limiting sense, unless otherwise indicated.
FIG. 1 is a flow chart of a method for monitoring deformation of surrounding rock according to an embodiment of the present application;
FIG. 2 is a schematic diagram of predicting deformation data in a corresponding region by a mechanical model;
FIG. 3 is a flowchart of another method for monitoring deformation of surrounding rock according to an embodiment of the present application;
FIG. 4 is a flowchart of another method for monitoring deformation of surrounding rock according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a surrounding rock deformation monitoring device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The following disclosure provides many different embodiments, or examples, for implementing different structures of the application. In order to simplify the present disclosure, components and arrangements of specific examples are described below. They are, of course, merely examples and are not intended to limit the application. Furthermore, the present application may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed.
The following will describe a detailed description of a method for monitoring deformation of surrounding rock according to an embodiment of the present application with reference to specific embodiments, as shown in fig. 1, the specific steps are as follows:
s101, acquiring deformation data acquired at each acquisition point on the surface of the surrounding rock of the tunnel at the target acquisition moment.
Deformation data refer to data for causing deformation of the surface of the surrounding rock of the tunnel, and specifically comprise axial force, displacement and inclination angle. The axial force refers to a pulling force or a pushing force acting on the inner wall of the surrounding rock of the tunnel along the axial direction of the surrounding rock of the tunnel, and the axial force is along the axial direction of the surrounding rock of the tunnel, and can be either compressive force or tensile force. Displacement refers to the displacement or deformation of the rock mass at a location on the surface of the surrounding rock of the tunnel. And the inclination angle is used for representing the inclination angle of the surrounding rock surface of the tunnel relative to the horizontal line.
In practical application, can set up a plurality of acquisition points on tunnel country rock surface, through arranging monitoring devices (including axial force sensor, displacement sensor and inclination sensor) in every acquisition point department, gather corresponding deformation data. The axial force sensor is used for acquiring axial force at a corresponding acquisition point; the displacement sensor is used for acquiring displacement at a corresponding acquisition point; and the inclination angle sensor is used for acquiring the inclination angle of the corresponding acquisition point.
In the embodiment of the application, after deformation data at corresponding acquisition points are acquired through a plurality of sensors, the data can be transmitted to a DTU (Data Transfer unit, data transmission unit) of a computer for carrying out subsequent calculation through a built-in LoRaWAN (Long Range Wide Area Network, long-distance wide area network) communication protocol, after the deformation data are processed, the deformation data are uploaded to a server through a 4G/5G technology by the DTU, and after comprehensive processing, the deformation data are displayed in real time at a terminal.
In application, after receiving the initial data uploaded by the monitoring device (i.e., the data directly collected by the monitoring device), the initial data may be further preprocessed to obtain deformation data for calculation. The preprocessing specifically comprises deleting data points which deviate from a normal range obviously; for the data points with data missing, supplementing the values of the data points by using the average value of the values acquired at the time before and after the position of the data points; the middle section of data of the output data is replaced according to the middle value by adopting a median filtering method for noise points in the data; the smoothing process is achieved by smoothing the sequence by calculating a running average of the data sequence. Therefore, the abnormal data is processed, the missing data is supplemented, noise is removed, and the abnormal data is smoothed, so that the deformed data is more continuous and smooth.
In order to cope with a complex tunnel construction environment, signal repeaters may be arranged at appropriate positions in order to avoid being affected by interruption of transmission signals due to rock shielding. And meanwhile, in order to avoid the threat of blasting and other factors on damage to the power supply line, equipment used in the tunnel adopts a power supply method.
S102, determining deformation inducements corresponding to the acquisition points based on the deformation data acquired by the acquisition points.
The deformation inducement is a factor that induces deformation of surrounding rock, for example, groundwater level change, earthquake force, external load application, temperature change, etc. These factors can affect the displacement, inclination, axial force and other parameters of the surrounding rock, resulting in deformation of the surrounding rock.
In the embodiment of the present application, based on the deformation data collected by each collection point, determining the deformation inducement corresponding to each collection point may include:
and inputting deformation data corresponding to each acquisition point into a pre-trained prediction model to output corresponding deformation inducements by the prediction model, wherein the prediction model is a neural network model which is obtained by training by taking sample deformation data as input and taking known deformation inducements corresponding to the sample deformation data as target output.
The neural network model may be a BP (Back Propagation) network model, and specifically, the neural network model may be trained through the following steps.
The historically collected sample deformation data is used as the input of a model, and simultaneously, the corresponding known deformation inducement is provided as the target output to train the model, and the weight and the bias of the neural network are optimized to establish a prediction model capable of accurately predicting the deformation inducement.
And S103, constructing a space deformation area matrix corresponding to the surface of the tunnel surrounding rock based on the deformation inducements corresponding to each acquisition point.
The spatial deformation area matrix comprises a spatial axial force area matrix, a spatial displacement area matrix and a spatial inclination area matrix.
Specifically, based on the deformation inducement corresponding to each acquisition point, the implementation of constructing the spatial deformation area matrix corresponding to the tunnel surrounding rock surface may include:
and inputting deformation inducements corresponding to the acquisition points into a pre-trained mechanical model aiming at each acquisition point, outputting a regional spatial deformation area matrix corresponding to the region where the acquisition points are located by the mechanical model, and carrying out fusion processing on the regional spatial deformation area matrix corresponding to all the acquisition points to obtain the spatial deformation area matrix corresponding to the tunnel surrounding rock surface.
The mechanical model is a model constructed by experimental data in advance, and can estimate deformation data corresponding to each position in a region according to deformation inducements corresponding to the region. In order to evaluate the accuracy and reliability of the model, model prediction data XM may be comparison fitted to actual survey point data XMR. If the XM is substantially identical to the XMR, i.e., the degree of fit is high, on the correct premise of the model, the model is considered trustworthy. If the deviation between the model prediction data XM and the actual monitoring data XMR is too large, the model is considered to be optimized, and workers are informed of optimizing the model, so that the control and early warning of deformation behavior in engineering practice can be reliably supported.
Based on the above, the mechanical model may output an area deformation area matrix corresponding to the area where each acquisition point is located according to the deformation cause corresponding to each acquisition point, that is, each area deformation area matrix includes deformation data corresponding to each position in the corresponding area. Here, the area deformation area matrix includes an area axial force area matrix, an area displacement area matrix and an area inclination area matrix, and the area axial force area matrix includes axial force data corresponding to each position in the corresponding area, the area displacement area matrix includes displacement data corresponding to each position in the corresponding area, and the area inclination area matrix includes inclination angle data corresponding to each position in the corresponding area.
Combining the regional axial force area matrixes corresponding to all the regions together to obtain a spatial axial force area matrix corresponding to the surface of the surrounding rock of the tunnel; combining the area displacement area matrixes corresponding to all areas to obtain a space displacement area matrix corresponding to the surface of the surrounding rock of the tunnel; and combining the regional inclination area matrixes corresponding to all the regions to obtain the spatial inclination area matrixes corresponding to the surfaces of the surrounding rocks of the tunnel.
In this way, the spatial axial force area matrix contains axial force data of any position point on the surface of the tunnel surrounding rock, the data of the corresponding acquisition point in the matrix is the axial force data actually acquired by the sensor, and the data of other points except the acquisition point are the axial force data estimated by the deformation inducement. The spatial displacement area matrix contains displacement data of any position point on the surface of the tunnel surrounding rock, the data of the corresponding acquisition point in the matrix is the displacement data actually acquired by the sensor, and the data of other points except the acquisition point are the displacement data estimated by deformation inducement. The space inclination area matrix comprises inclination angle data of any position point on the surface of the tunnel surrounding rock, the data of the corresponding acquisition point in the matrix is the inclination angle data actually acquired by the sensor, and the data of other points except the acquisition point are the inclination angle data estimated by deformation inducement.
In the application, there is a case that the areas corresponding to the plurality of acquisition points overlap, and for each position in the overlapping area, in the process of combining the strain area matrixes of all the areas, the position corresponds to a plurality of deformation data, and at this time, an average value of the plurality of deformation data can be used as the deformation data finally corresponding to the position.
It should be noted that, in application, the monitoring devices (i.e., the axial force sensor, the displacement sensor and the inclination sensor) need to be arranged according to the estimation range of the mechanical model, that is, at least one monitoring device is arranged in each prediction area, so that the constructed spatial deformation area matrix can be ensured to cover the whole surface of the surrounding rock of the tunnel.
As shown in fig. 2, a schematic diagram of predicting deformation data of each position in the corresponding region by using a mechanical model is shown. Each sensor module comprises an axial force sensor, a displacement sensor and an inclination sensor, and the coverage area of an arrow is the presumption range of a mechanical model (namely a 'source force (YLM)' model in the drawing).
S104, aiming at any monitoring point on the surface of the tunnel surrounding rock, determining a deformation risk level corresponding to the monitoring point at the target acquisition moment based on the space deformation area matrix.
The monitoring point can be any point on the surface of the surrounding rock of the tunnel, including an acquisition point where the monitoring device is placed and other points where the monitoring device is not placed.
In the embodiment of the present application, based on the spatial deformation area matrix, the specific implementation of determining the deformation risk level corresponding to the monitoring point at the target acquisition time may include:
a1, determining type weights corresponding to the parameter area matrixes according to each parameter area matrix, and determining deformation parameters corresponding to the monitoring points in the parameter area matrixes;
a2, calculating the product of the type weight and the deformation parameter to obtain a parameter risk assessment score corresponding to the parameter area matrix;
step A3, carrying out summation operation on the parameter risk assessment scores corresponding to all the parameter area matrixes to obtain a target risk assessment score;
and step A4, determining the deformation risk level corresponding to the monitoring point at the target acquisition moment according to the corresponding relation between the preset risk assessment score and the risk level.
The spatial deformation area matrix comprises at least one parameter area matrix corresponding to a parameter type, namely, a spatial axial force area matrix corresponding to an axial force parameter, a spatial displacement area matrix corresponding to a displacement parameter and a spatial inclination area matrix corresponding to an inclination angle parameter.
And the type weights are used for representing the influence degree of different parameter types on the deformation at the corresponding positions, and the higher the influence degree is, the higher the corresponding type weights are.
Specifically, the target risk assessment score of each monitoring point can be calculated by the following formula:
f is a target risk assessment score, K is a risk assessment weight matrix (including type weights corresponding to each parameter type), and W is a risk assessment factor (i.e., deformation parameters corresponding to different parameter types corresponding to the monitoring points, i.e., axial force, displacement, inclination angle).
And further, determining the deformation risk level corresponding to the monitoring point at the target acquisition time according to the corresponding relation between the preset risk assessment score and the risk level. Therefore, comprehensive monitoring of the surface of the surrounding rock of the tunnel is realized.
In another embodiment, when the deformation risk level corresponding to any monitoring point is higher than the preset level, the position of the monitoring point is easy to generate deformation risk, and at this time, the staff can be prompted to process in time by sending alarm information, so that safety accidents are avoided.
In addition, in another embodiment, different alarm information can be sent according to different deformation risk levels corresponding to the monitoring points, so that the staff can know the deformation risk levels corresponding to the monitoring points in time through the alarm information, and the staff can be assisted to make corresponding measures in time.
In the embodiment of the application, firstly, deformation data acquired by each acquisition point on the surface of the surrounding rock of a tunnel at the target acquisition moment is acquired, then, based on the deformation data acquired by each acquisition point, a deformation incentive corresponding to each acquisition point is determined, and based on the deformation incentive corresponding to each acquisition point, a spatial deformation area matrix corresponding to the surface of the surrounding rock of the tunnel is constructed, and finally, for any monitoring point on the surface of the surrounding rock of the tunnel, a deformation risk level corresponding to the monitoring point at the target acquisition moment is determined based on the spatial deformation area matrix. Therefore, the deformation risk level of any monitoring point on the surface of the surrounding rock of the tunnel can be determined through the space deformation area matrix, so that the comprehensive monitoring of the surface of the surrounding rock of the tunnel is realized, and further, the accurate positioning of the deformation part of the surrounding rock is realized.
Referring to fig. 3, a flowchart of an embodiment of another method for monitoring deformation of surrounding rock is provided in an embodiment of the present application. As shown in fig. 3, the process may include the steps of:
s301, predicting the spatial deformation area matrix by using a pre-trained prediction model to obtain a time deformation area matrix corresponding to the prediction moment;
s302, determining deformation risk levels corresponding to monitoring points at the predicted moment based on the time deformation area matrix aiming at any monitoring point on the surface of the tunnel surrounding rock.
S301 and S302 are collectively described below:
and the prediction model is used for predicting deformation data at a certain moment in the future based on the deformation data corresponding to each monitoring point in the spatial deformation area matrix.
Specifically, a predictive model may be constructed using Bi-LSTM logic algorithms. Bi-LSTM is a recurrent neural network capable of capturing time series information, features are extracted by introducing hidden states in both the forward and backward directions. In application, the training set can be used for training the model, and the model parameters are optimized. Model weights and biases are updated continuously through a back-propagation algorithm during training, according to a defined loss function. And verifying the model obtained through training by using the verification set, and evaluating the performance of the model. The verification process may use some evaluation criteria such as accuracy, recall, F1 value, etc.
In the embodiment of the present application, after S103, a spatial axial force area matrix, a spatial displacement area matrix, and a spatial inclination area matrix corresponding to the target acquisition time are respectively predicted in a time dimension by using a prediction model, so as to obtain a spatial axial force area matrix, a spatial displacement area matrix, and a spatial inclination area matrix corresponding to the prediction time, and further, a deformation risk level corresponding to any monitoring point at the prediction time is obtained by using the spatial axial force area matrix, the spatial displacement area matrix, and the spatial inclination area matrix corresponding to the prediction time, and performing the calculation process in step S104.
Through the flow shown in fig. 3, the spatial deformation area matrix can be predicted by using a prediction model, so as to obtain a time deformation area matrix corresponding to the prediction moment, and the deformation risk level corresponding to any monitoring point at the prediction moment is determined based on the time deformation area matrix corresponding to the prediction moment. Therefore, the risk prediction of the surrounding rock surface of the tunnel at a certain moment in the future is realized, so that workers can find risks in advance, and loss is reduced.
Referring to fig. 4, a flowchart of an embodiment of another method for monitoring deformation of surrounding rock is provided in an embodiment of the present application. As shown in fig. 4, the process may include the steps of:
s401, constructing a three-dimensional model corresponding to the surface of the tunnel surrounding rock.
In the embodiment of the application, an unmanned aerial vehicle three-dimensional modeling technology can be utilized, a small unmanned aerial vehicle is used for carrying a laser ranging instrument, three-dimensional laser points and three-dimensional laser line scanning are used for acquiring the characteristics of each point surface in a tunnel in detail, an independent three-dimensional coordinate system of the tunnel is constructed, the detailed coordinates of each acquisition point position are acquired, and then a three-dimensional model corresponding to the surrounding rock surface of the tunnel is constructed on a cloud platform through a digital twin technology based on the three-dimensional coordinate system and the detailed coordinates of the acquisition point positions. The deformation three-dimensional model can map deformation data of corresponding position coordinates, so that a user can know the deformation condition of each position of the surrounding rock surface of the tunnel conveniently through the deformation three-dimensional model.
S402, determining a rendering mode corresponding to the deformation risk level.
And S403, rendering the positions corresponding to the monitoring points on the three-dimensional model according to the rendering mode.
S402 and S403 are collectively described below:
the rendering method is a method of rendering a position on the three-dimensional model, and includes, for example, rendering with green, rendering with yellow, and the like.
In the embodiment of the application, the corresponding relation between different risk levels and different rendering modes is preset, for example, the risk-free monitoring points are rendered by green, the high-risk monitoring points are rendered by yellow, and the like. Based on the above, for any monitoring point, after determining the deformation risk level corresponding to the monitoring point, the rendering mode corresponding to the monitoring point can be determined according to the corresponding relation between the risk level and the rendering mode, and then the position corresponding to the monitoring point on the three-dimensional model is rendered according to the rendering mode.
Through the flow shown in fig. 4, positions with different risk levels can be rendered on the three-dimensional model corresponding to the surface of the surrounding rock of the tunnel by using different rendering modes, so that a user can intuitively know the deformation risk of each position through the three-dimensional model.
Based on the same technical concept, the embodiment of the application also provides a surrounding rock deformation monitoring device, as shown in fig. 5, which comprises:
the acquisition module 501 is configured to acquire deformation data acquired at each acquisition point on the surface of the surrounding rock of the tunnel at the target acquisition moment;
a first determining module 502, configured to determine a deformation cause corresponding to each acquisition point based on deformation data acquired by each acquisition point;
a construction module 503, configured to construct a spatial deformation area matrix corresponding to the tunnel surrounding rock surface based on the deformation inducements corresponding to each acquisition point;
and a second determining module 504, configured to determine, for any monitoring point on the surface of the tunnel surrounding rock, a deformation risk level corresponding to the monitoring point at the target acquisition time based on the spatial deformation area matrix.
In one possible embodiment, the first determining module is configured to:
inputting deformation data corresponding to each acquisition point into a pre-trained prediction model for outputting corresponding deformation inducements by the prediction model;
the prediction model is a neural network model obtained by training with sample deformation data as input and known deformation inducements corresponding to the sample deformation data as target output.
In one possible embodiment, the building block is configured to:
inputting deformation inducements corresponding to the acquisition points into a pre-trained mechanical model aiming at each acquisition point so as to output a region space deformation area matrix corresponding to a region where the acquisition points are positioned by the mechanical model;
and carrying out fusion treatment on the regional spatial deformation area matrix corresponding to all the acquisition points to obtain the spatial deformation area matrix corresponding to the tunnel surrounding rock surface.
In one possible embodiment, the deformation data includes axial force, displacement, inclination angle.
In one possible implementation manner, the spatial deformation area matrix includes a parameter area matrix corresponding to at least one parameter type, and the second determining module is configured to:
determining type weights corresponding to the parameter area matrixes aiming at each parameter area matrix, and determining deformation parameters corresponding to the monitoring points in the parameter area matrixes;
calculating the product of the type weight and the deformation parameter to obtain a parameter risk assessment score corresponding to the parameter area matrix;
carrying out summation operation on the parameter risk assessment scores corresponding to all the parameter area matrixes to obtain a target risk assessment score;
and determining the deformation risk level corresponding to the monitoring point at the target acquisition time according to the corresponding relation between the preset risk assessment score and the risk level.
In one possible embodiment, the apparatus further comprises a prediction module for:
predicting the spatial deformation area matrix by using a pre-trained prediction model to obtain a time deformation area matrix corresponding to the prediction moment;
and determining the deformation risk level corresponding to any monitoring point on the surface of the tunnel surrounding rock based on the time deformation area matrix.
In one possible implementation, the apparatus further includes a rendering module configured to:
constructing a three-dimensional model corresponding to the surface of the tunnel surrounding rock;
determining a rendering mode corresponding to the deformation risk level;
and rendering the position corresponding to the monitoring point on the three-dimensional model according to the rendering mode.
In the embodiment of the application, firstly, deformation data acquired by each acquisition point on the surface of the surrounding rock of a tunnel at the target acquisition moment is acquired, then, based on the deformation data acquired by each acquisition point, a deformation incentive corresponding to each acquisition point is determined, and based on the deformation incentive corresponding to each acquisition point, a spatial deformation area matrix corresponding to the surface of the surrounding rock of the tunnel is constructed, and finally, for any monitoring point on the surface of the surrounding rock of the tunnel, a deformation risk level corresponding to the monitoring point at the target acquisition moment is determined based on the spatial deformation area matrix. Therefore, the deformation risk level of any monitoring point on the surface of the surrounding rock of the tunnel can be determined through the space deformation area matrix, so that the comprehensive monitoring of the surface of the surrounding rock of the tunnel is realized, and further, the accurate positioning of the deformation part of the surrounding rock is realized.
Based on the same technical concept, the embodiment of the present application further provides an electronic device, as shown in fig. 6, including a processor 111, a communication interface 112, a memory 113 and a communication bus 114, where the processor 111, the communication interface 112, and the memory 113 perform communication with each other through the communication bus 114,
a memory 113 for storing a computer program;
the processor 111 is configured to execute a program stored in the memory 113, and implement the following steps:
acquiring deformation data acquired at each acquisition point on the surface of the surrounding rock of the tunnel at the target acquisition moment;
determining a deformation incentive corresponding to each acquisition point based on deformation data acquired by each acquisition point;
constructing a space deformation area matrix corresponding to the surface of the tunnel surrounding rock based on the deformation inducements corresponding to each acquisition point;
and determining deformation risk grades corresponding to any monitoring points on the surface of the tunnel surrounding rock based on the space deformation area matrix at the target acquisition moment.
The communication bus mentioned above for the electronic devices may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface is used for communication between the electronic device and other devices.
The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
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 (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In yet another embodiment of the present application, there is also provided a computer readable storage medium having stored therein a computer program which when executed by a processor implements the steps of any of the above-described surrounding rock deformation monitoring methods.
In yet another embodiment of the present application, there is also provided a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods of monitoring deformation of surrounding rock of the above embodiments.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Based on such understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the related art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in the respective embodiments or some parts of the embodiments.
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "includes," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless an order of performance is explicitly stated. It should also be appreciated that additional or alternative steps may be used.
The foregoing is only a specific embodiment of the application to enable those skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for monitoring deformation of surrounding rock, the method comprising:
acquiring deformation data acquired at each acquisition point on the surface of the surrounding rock of the tunnel at the target acquisition moment;
determining a deformation incentive corresponding to each acquisition point based on deformation data acquired by each acquisition point;
constructing a space deformation area matrix corresponding to the surface of the tunnel surrounding rock based on the deformation inducements corresponding to each acquisition point;
and determining deformation risk grades corresponding to any monitoring points on the surface of the tunnel surrounding rock based on the space deformation area matrix at the target acquisition moment.
2. The method of claim 1, wherein determining a deformation cause for each of the acquisition points based on deformation data acquired by each of the acquisition points comprises:
inputting deformation data corresponding to each acquisition point into a pre-trained prediction model for outputting corresponding deformation inducements by the prediction model;
the prediction model is a neural network model obtained by training with sample deformation data as input and known deformation inducements corresponding to the sample deformation data as target output.
3. The method according to claim 1, wherein the constructing a spatial deformation area matrix corresponding to the tunnel surrounding rock surface based on the deformation inducements corresponding to each acquisition point comprises:
inputting deformation inducements corresponding to the acquisition points into a pre-trained mechanical model aiming at each acquisition point so as to output a region space deformation area matrix corresponding to a region where the acquisition points are positioned by the mechanical model;
and carrying out fusion treatment on the regional spatial deformation area matrix corresponding to all the acquisition points to obtain the spatial deformation area matrix corresponding to the tunnel surrounding rock surface.
4. The method of claim 1, wherein the deformation data comprises axial force, displacement, tilt angle.
5. The method of claim 1, wherein the spatial deformation area matrix includes a parameter area matrix corresponding to at least one parameter type, and the determining, based on the spatial deformation area matrix, a deformation risk level corresponding to the monitoring point at a target acquisition time includes:
determining type weights corresponding to the parameter area matrixes aiming at each parameter area matrix, and determining deformation parameters corresponding to the monitoring points in the parameter area matrixes;
calculating the product of the type weight and the deformation parameter to obtain a parameter risk assessment score corresponding to the parameter area matrix;
carrying out summation operation on the parameter risk assessment scores corresponding to all the parameter area matrixes to obtain a target risk assessment score;
and determining the deformation risk level corresponding to the monitoring point at the target acquisition time according to the corresponding relation between the preset risk assessment score and the risk level.
6. The method according to claim 1, wherein after constructing the spatial deformation area matrix corresponding to the tunnel surrounding rock surface based on the deformation inducement corresponding to each acquisition point, the method further comprises:
predicting the spatial deformation area matrix by using a pre-trained prediction model to obtain a time deformation area matrix corresponding to the prediction moment;
and determining the deformation risk level corresponding to any monitoring point on the surface of the tunnel surrounding rock based on the time deformation area matrix.
7. The method of claim 1, wherein after determining the deformation risk level corresponding to the monitoring point at the target acquisition time based on the spatial deformation area matrix, further comprises:
constructing a three-dimensional model corresponding to the surface of the tunnel surrounding rock;
determining a rendering mode corresponding to the deformation risk level;
and rendering the position corresponding to the monitoring point on the three-dimensional model according to the rendering mode.
8. A surrounding rock deformation monitoring device, the device comprising:
the acquisition module is used for acquiring deformation data acquired at each acquisition point on the surface of the tunnel surrounding rock at the target acquisition moment;
the first determining module is used for determining a deformation incentive corresponding to each acquisition point based on deformation data acquired by each acquisition point;
the construction module is used for constructing a space deformation area matrix corresponding to the surface of the tunnel surrounding rock based on the deformation inducements corresponding to each acquisition point;
the second determining module is used for determining deformation risk grades corresponding to any monitoring point on the surface of the tunnel surrounding rock based on the space deformation area matrix at the target acquisition moment.
9. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
a memory for storing a computer program;
a processor for carrying out the method steps of any one of claims 1-7 when executing a program stored on a memory.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of claims 1-7.
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