Tunnel deformation monitoring method and system, readable storage medium and computer

Abstract

The invention provides a tunnel deformation monitoring method and system, a readable storage medium and a computer, and the method comprises the steps: randomly obtaining sample set data from a data collection result, and carrying out the deviation processing of the sample set data and the data collection result through a deviation calculation algorithm, and obtaining the current point cloud data of a tunnel; performing point cloud splicing and point cloud preprocessing on the current point cloud data of the tunnel to obtain a curved surface model of the tunnel, and splitting the curved surface model into multiple pieces of section data according to a preset standard; calculating the gradient according to the width of each monitored target object and the corresponding current settlement data; and calculating the deformation amount corresponding to each section data according to each section data, the corresponding gradient and the current settlement data, and judging the current deformation state of the tunnel based on the determined abnormal section data. According to the method, the current deformation state of the tunnel is judged according to the deformation of the section data, the data utilization rate is increased while the working efficiency is improved, and the engineering safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a tunnel deformation monitoring method, system, readable storage medium and computer. Background Art

[0002] With the improvement of people's living standards and the rapid development of science and technology, urbanization construction projects are also expanding. Various pipeline tunnels are buried under urban roads, and the monitoring of their deformation degree has become an indispensable part of urban construction projects.

[0003] Traditional tunnel deformation monitoring methods usually involve manual physical contact using a steel ruler convergence meter or a balance ruler, and manually judging the measured data. However, traditional tunnel deformation monitoring methods have high requirements for labor and environment. When the tunnel environment is complex, manual measurement will lead to a decrease in measurement accuracy, seriously affecting the accuracy of the data, resulting in large errors in the detection results and affecting the judgment. Summary of the invention

[0004] Based on this, the object of the present invention is to provide a tunnel deformation monitoring method, system, readable storage medium and computer to at least solve the deficiencies in the above-mentioned technology.

[0005] The present invention provides a tunnel deformation monitoring method, comprising: Based on a preset acquisition device, data is collected on the tunnel, and a sample set of data is randomly obtained from the data acquisition result, and the sample set data and the data acquisition result are subjected to deviation processing by using a deviation calculation algorithm to obtain current point cloud data of the tunnel; Performing point cloud stitching and point cloud preprocessing on the current point cloud data of the tunnel to obtain a surface model of the tunnel, and splitting the surface model into a plurality of section data according to a preset standard, wherein each section data contains a corresponding monitoring target object; Collecting data of the monitoring target corresponding to each of the cross-sectional data, and calculating the current settlement data of the tunnel according to the collected data; Obtaining the width of each of the monitoring targets, and calculating the corresponding inclination according to the width of each of the monitoring targets and the corresponding current settlement data; The deformation amount corresponding to each of the cross-sectional data is calculated according to the inclination of each of the cross-sectional data and its corresponding monitoring target and the current settlement data, and abnormal cross-sectional data is determined in each of the cross-sectional data based on the deformation amount limit and the deformation amount corresponding to each of the cross-sectional data, and the current deformation state of the tunnel is judged based on the abnormal cross-sectional data.

[0006] Furthermore, the steps of collecting data on the tunnel based on a preset collection device, randomly acquiring a sample set of data from the data collection result, and performing deviation processing on the sample set of data and the data collection result using a deviation calculation algorithm to obtain the current point cloud data of the tunnel include: Using a preset acquisition device to collect data from the tunnel to obtain a point cloud data set of the tunnel, and randomly sampling from the point cloud data set as sample set data; The sample set data is randomly sampled using a minimum variance estimation algorithm, and deviation calculation is performed based on the random sampling result and the point cloud data set to obtain a corresponding deviation calculation result; A deviation threshold is constructed based on the initial parameters of the tunnel, and the deviation calculation result is compared with the deviation threshold to obtain current point cloud data of the tunnel.

[0007] Further, the current point cloud data of the tunnel is subjected to point cloud stitching and point cloud preprocessing to obtain a surface model of the tunnel, and the surface model is split into a plurality of section data according to a preset standard, wherein each section data contains a corresponding monitoring target object, the step comprising: Constructing an independent coordinate system of the acquisition device, and performing point cloud splicing on the current point cloud data of the tunnel based on the independent coordinate system to obtain point cloud splicing data; Using a noise point screening algorithm to screen out noise point data in the point cloud splicing data, and performing fitting transformation on the screened point cloud splicing data to obtain a surface model of the tunnel; A corresponding splitting standard is constructed according to the position of the monitoring target object in the independent coordinate system, and the surface model is split into a plurality of cross-sectional data based on the splitting standard.

[0008] Furthermore, the step of collecting data of the monitoring target corresponding to each of the cross-sectional data and calculating the current settlement data of the tunnel according to the collected data includes: Collecting data on the monitoring target objects corresponding to each of the cross-sectional data to obtain current elevation data of each of the monitoring target objects; The initial elevation data of each of the monitoring targets is acquired, and the current settlement data of each of the monitoring targets is calculated based on the current elevation data of each of the monitoring targets and the initial elevation data of each of the monitoring targets.

[0009] The present invention also provides a tunnel deformation monitoring system, comprising: A data acquisition module, used to collect data from the tunnel based on a preset acquisition device, and randomly obtain a sample set of data from the data acquisition result, and perform deviation processing on the sample set data and the data acquisition result using a deviation calculation algorithm to obtain current point cloud data of the tunnel; A point cloud processing module, used for performing point cloud splicing and point cloud preprocessing on the current point cloud data of the tunnel to obtain a surface model of the tunnel, and splitting the surface model into a plurality of section data according to a preset standard, wherein each section data contains a corresponding monitoring target object; A settlement data processing module, used to collect data of the monitoring target corresponding to each of the cross-sectional data, and calculate the current settlement data of the tunnel according to the collected data; An inclination calculation module, used to obtain the width of each of the monitoring targets, and calculate the corresponding inclination according to the width of each of the monitoring targets and the corresponding current settlement data; A state monitoring module is used to calculate the deformation corresponding to each section data based on the inclination of each section data and its corresponding monitoring target and the current settlement data, and to determine abnormal section data in each section data based on the deformation limit and the deformation corresponding to each section data, and to judge the current deformation state of the tunnel based on the abnormal section data.

[0010] Furthermore, the data acquisition module includes: A data acquisition unit, used to acquire data from the tunnel using a preset acquisition device to obtain a point cloud data set of the tunnel, and randomly sample the point cloud data set as sample set data; A deviation calculation unit, used to randomly sample the sample set data using a minimum variance estimation algorithm, and perform deviation calculation based on the random sampling result and the point cloud data set to obtain a corresponding deviation calculation result; The result comparison unit is used to construct a deviation threshold based on the initial parameters of the tunnel, and compare the deviation calculation result with the deviation threshold to obtain the current point cloud data of the tunnel.

[0011] Furthermore, the point cloud processing module includes: A point cloud stitching unit, used to construct an independent coordinate system of the acquisition device, and perform point cloud stitching on the current point cloud data of the tunnel based on the independent coordinate system to obtain point cloud stitching data; A fitting conversion unit, used to filter out noise data in the point cloud splicing data by using a noise filtering algorithm, and to perform fitting conversion on the filtered point cloud splicing data to obtain a surface model of the tunnel; The data splitting unit is used to construct a corresponding splitting standard according to the position of the monitoring target object in the independent coordinate system, and split the surface model into multiple cross-sectional data based on the splitting standard.

[0012] Furthermore, the settlement data processing module includes: An elevation data acquisition unit, used to collect data on the monitoring target object corresponding to each of the cross-sectional data, so as to obtain the current elevation data of each of the monitoring target objects; The settlement data processing unit is used to obtain the initial elevation data of each of the monitoring targets, and calculate the current settlement data of each of the monitoring targets based on the current elevation data of each of the monitoring targets and the initial elevation data of each of the monitoring targets.

[0013] The present invention also provides a readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned tunnel deformation monitoring method is implemented.

[0014] The present invention also provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned tunnel deformation monitoring method when executing the computer program.

[0015] The tunnel deformation monitoring method, system, readable storage medium and computer of the present invention collect data on the tunnel and use a deviation calculation algorithm to perform deviation processing on the data collection results to obtain the current point cloud data of the tunnel, and improve the accuracy of the data through deviation processing; use the current point cloud data to perform point cloud processing to construct a surface model of the tunnel, use the cross-sectional data split by the surface model to calculate the collected data of the monitoring target object to obtain the current settlement data of the tunnel, use the settlement data and the data of the monitoring target object to determine the deformation of the cross-sectional data, and use the deformation of the cross-sectional data to determine the current deformation state of the tunnel, thereby improving work efficiency and increasing data utilization to ensure project safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a tunnel deformation monitoring method in a first embodiment of the present invention; Figure 2 for Figure 1 Detailed flow chart of step S101; Figure 3 for Figure 1 Detailed flow chart of step S102; Figure 4 for Figure 1 Detailed flow chart of step S103; Figure 5is a structural block diagram of a tunnel deformation monitoring system in a second embodiment of the present invention; Figure 6 FIG. 4 is a structural block diagram of a computer in a third embodiment of the present invention.

[0017] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0018] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0020] Embodiment 1 See also Figure 1 , which shows a tunnel deformation monitoring method in a first embodiment of the present invention, and the method specifically includes steps S101 to S105: S101, collecting data on the tunnel based on a preset collection device, and randomly obtaining a sample set of data from the data collection result, and performing deviation processing on the sample set of data and the data collection result using a deviation calculation algorithm to obtain current point cloud data of the tunnel; For further information, see Figure 2 , the step S101 specifically includes S1011~S1013: S1011, using a preset acquisition device to collect data on the tunnel to obtain a point cloud data set of the tunnel, and randomly sampling from the point cloud data set as sample set data; S1012, randomly sampling the sample set data using a minimum variance estimation algorithm, and performing deviation calculation based on the random sampling result and the point cloud data set to obtain a corresponding deviation calculation result; S1013, constructing a deviation threshold based on the initial parameters of the tunnel, and comparing the deviation calculation result with the deviation threshold to obtain current point cloud data of the tunnel.

[0021] In a specific implementation, the tunnel is scanned using a collection device pre-installed in the tunnel, and a preset fixed point in the tunnel is used as a reference point to construct a point cloud data set of the tunnel, wherein the collection device is a laser scanner or a total station, and is installed one at a distance of 1 km and evenly distributed in the tunnel. In some optional embodiments, the collection device may also be a device with a point cloud data collection function; Specifically, random sampling is performed from the point cloud data set collected above to obtain a sample set of data, and a cylindrical surface model of the random sampling data is estimated using minimum variance, and then the calculated model data and the point cloud data set are subjected to deviation calculation to obtain a corresponding deviation value; The deviation threshold is calculated using the preset mathematical model and the initial parameters of the tunnel, and the calculated deviation value is compared with the deviation threshold. If the deviation value is greater than the deviation threshold, the corresponding point cloud data is discarded. When the deviation value is less than the deviation threshold, the data is retained, and all the retained point cloud data are repeatedly randomly sampled. After continuously iterating the preset iteration threshold, the final model data of the tunnel, that is, the current point cloud data, is obtained.

[0022] S102, performing point cloud stitching and point cloud preprocessing on the current point cloud data of the tunnel to obtain a surface model of the tunnel, and splitting the surface model into a plurality of section data according to a preset standard, wherein each section data contains a corresponding monitoring target object; For further information, see Figure 3 , the step S102 specifically includes steps S1021 to S1023: S1021, constructing an independent coordinate system of the acquisition device, and performing point cloud splicing on the current point cloud data of the tunnel based on the independent coordinate system to obtain point cloud splicing data; S1022, using a noise screening algorithm to screen out noise data in the point cloud splicing data, and performing fitting transformation on the screened point cloud splicing data to obtain a surface model of the tunnel; S1023: construct a corresponding splitting standard according to the position of the monitoring target object in the independent coordinate system, and split the surface model into multiple cross-sectional data based on the splitting standard.

[0023] In a specific implementation, an independent coordinate system of the above-mentioned acquisition device is constructed, and based on the independent coordinate system of each acquisition device, the current point cloud data of the tunnel is targeted and spliced ​​into the same coordinate system to obtain point cloud splicing data; Specifically, a noise removal algorithm is used to remove noise from the point cloud splicing data, wherein the noise data includes point cloud data of railings, water pipes, air ducts, etc. The screened point cloud splicing data is fitted and converted into an overall surface model of the tunnel.

[0024] A corresponding monitoring target object is pre-set in the independent coordinate system of each acquisition device, and the monitoring target object is fixed inside the tunnel. For example, marking points are set at the arch top, arch bottom, and arch waist of the tunnel, and a monitoring target object is fixed at each marking point. The surface of the tunnel is quadruped according to the position of the monitoring target object in the independent coordinate system of each acquisition device, so as to split the surface model obtained above into multiple section data.

[0025] S103, collecting data on the monitoring target objects corresponding to each of the cross-sectional data, and calculating the current settlement data of the tunnel according to the collected data; For further information, see Figure 4 , the step S103 specifically includes steps S1031~S1032: S1031, collecting data on the monitoring target object corresponding to each of the cross-sectional data to obtain current elevation data of each of the monitoring target objects; S1032, acquiring initial elevation data of each of the monitored targets, and calculating current settlement data of each of the monitored targets based on current elevation data of each of the monitored targets and the initial elevation data of each of the monitored targets.

[0026] In the specific implementation, data collection is performed on the monitoring target corresponding to each section data to obtain the current elevation data of the monitoring target, obtain the initial elevation data of each monitoring target, and calculate the current settlement data of each monitoring target based on the obtained current elevation data and initial elevation data: ; In the formula, Indicates Current settlement data of the monitoring target, For the The current elevation data of the monitored object, For the The initial elevation data of the monitoring target, Indicates The concentrated load at the location of the monitoring target, Indicates The load transfer coefficient at the location of the monitoring target.

[0027] S104, obtaining the width of each of the monitoring targets, and calculating the corresponding inclination according to the width of each of the monitoring targets and the corresponding current settlement data; In specific implementation, the width of the monitoring target is obtained, and the inclination corresponding to the monitoring target is calculated according to the width of the monitoring target and its corresponding current settlement data: ; In the formula, Indicates The width of the monitoring target, Indicates The error coefficient of a monitored target is a constant.

[0028] S105, calculating the deformation amount corresponding to each of the cross-sectional data according to the inclination of each of the cross-sectional data and its corresponding monitoring target and the current settlement data, and determining abnormal cross-sectional data in each of the cross-sectional data based on the deformation amount limit and the deformation amount corresponding to each of the cross-sectional data, and judging the current deformation state of the tunnel based on the abnormal cross-sectional data.

[0029] In the specific implementation, a deformation calculation model is constructed, and the cross-sectional data obtained above and the inclination and current settlement data of the monitored object are imported into the deformation calculation model to obtain corresponding calculated values, and the calculated values ​​are subjected to regression analysis and fitting, and the fitting results are compared with the preset standard results to obtain the deformation corresponding to each cross-sectional data, define the deformation limit of each cross-sectional data, and compare the deformation corresponding to each cross-sectional data with the deformation limit to determine the abnormal cross-sectional data. In this embodiment, when the deformation of the arch crown data and the arch bottom data in the cross-sectional data is less than 10 mm, the deformation of the arch waist is less than 7 mm, the deformation of the segment diameter is less than 24 mm, and the deformation of a single segment is less than 12 mm, it means that the cross-sectional data is normal. When a certain cross-sectional data does not meet the above requirements, it is marked as abnormal cross-sectional data, and then the abnormal cross-sectional data is input into the tunnel model for data processing to determine the current deformation state of the tunnel.

[0030] In summary, the tunnel deformation monitoring method in the above-mentioned embodiment of the present invention collects data on the tunnel, and uses a deviation calculation algorithm to perform deviation processing on the data collection results to obtain the current point cloud data of the tunnel, and improves the accuracy of the data through deviation processing; uses the current point cloud data to perform point cloud processing to construct a surface model of the tunnel, and uses the cross-sectional data split by the surface model to calculate the collected data of the monitoring target object to obtain the current settlement data of the tunnel, and uses the settlement data and the data of the monitoring target object to determine the deformation of the cross-sectional data, and uses the deformation of the cross-sectional data to determine the current deformation state of the tunnel, thereby improving work efficiency while increasing data utilization and ensuring project safety.

[0031] Embodiment 2 Another aspect of the present invention is to provide a tunnel deformation monitoring system. Figure 5 , which shows a tunnel deformation monitoring system in a second embodiment of the present invention, the system comprises: The data acquisition module 11 is used to collect data from the tunnel based on a preset acquisition device, and randomly obtain a sample set of data from the data acquisition result, and perform deviation processing on the sample set data and the data acquisition result using a deviation calculation algorithm to obtain the current point cloud data of the tunnel; Furthermore, the data acquisition module 11 includes: A data acquisition unit, used to acquire data from the tunnel using a preset acquisition device to obtain a point cloud data set of the tunnel, and randomly sample the point cloud data set as sample set data; A deviation calculation unit, used to randomly sample the sample set data using a minimum variance estimation algorithm, and perform deviation calculation based on the random sampling result and the point cloud data set to obtain a corresponding deviation calculation result; The result comparison unit is used to construct a deviation threshold based on the initial parameters of the tunnel, and compare the deviation calculation result with the deviation threshold to obtain the current point cloud data of the tunnel.

[0032] The point cloud processing module 12 is used to perform point cloud splicing and point cloud preprocessing on the current point cloud data of the tunnel to obtain a surface model of the tunnel, and split the surface model into a plurality of section data according to a preset standard, wherein each section data contains a corresponding monitoring target object; Furthermore, the point cloud processing module 12 includes: A point cloud stitching unit, used to construct an independent coordinate system of the acquisition device, and perform point cloud stitching on the current point cloud data of the tunnel based on the independent coordinate system to obtain point cloud stitching data; A fitting conversion unit, used to filter out noise data in the point cloud splicing data by using a noise filtering algorithm, and to perform fitting conversion on the filtered point cloud splicing data to obtain a surface model of the tunnel; The data splitting unit is used to construct a corresponding splitting standard according to the position of the monitoring target object in the independent coordinate system, and split the surface model into multiple cross-sectional data based on the splitting standard.

[0033] The settlement data processing module 13 is used to collect data of the monitoring target corresponding to each of the cross-sectional data, and calculate the current settlement data of the tunnel according to the collected data; Furthermore, the settlement data processing module 13 includes: An elevation data acquisition unit, used to collect data on the monitoring target object corresponding to each of the cross-sectional data, so as to obtain the current elevation data of each of the monitoring target objects; The settlement data processing unit is used to obtain the initial elevation data of each of the monitoring targets, and calculate the current settlement data of each of the monitoring targets based on the current elevation data of each of the monitoring targets and the initial elevation data of each of the monitoring targets.

[0034] The inclination calculation module 14 is used to obtain the width of each monitoring target object and calculate the corresponding inclination according to the width of each monitoring target object and its corresponding current settlement data; The state monitoring module 15 is used to calculate the deformation corresponding to each section data according to the inclination of each section data and its corresponding monitoring target and the current settlement data, and to determine abnormal section data in each section data based on the deformation limit and the deformation corresponding to each section data, and to judge the current deformation state of the tunnel based on the abnormal section data.

[0035] The functions or operation steps implemented when the above modules and units are executed are generally the same as those in the above method embodiments, and will not be repeated here.

[0036] The tunnel deformation monitoring system provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the system embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0037] Embodiment 3 The present invention also provides a computer, see Figure 6 , shown is a computer in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned tunnel deformation monitoring method is implemented.

[0038] The memory 10 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of a computer, such as a hard disk of the computer. In other embodiments, the memory 10 may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Further, the memory 10 may also include both an internal storage unit of the computer and an external storage device. The memory 10 may be used not only to store application software and various types of data installed in the computer, but also to temporarily store data that has been output or is to be output.

[0039] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs, etc.

[0040] It should be pointed out that Figure 6 The structure shown does not constitute a limitation on the computer. In other embodiments, the computer may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0041] The embodiment of the present invention further provides a readable storage medium on which a computer program is stored. When the program is executed by a processor, the tunnel deformation monitoring method as described above is implemented.

[0042] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0043] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0044] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0045] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0046] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A tunnel deformation monitoring method, characterized in that: include: Based on a preset acquisition device, data is collected on the tunnel, and a sample set of data is randomly obtained from the data acquisition result, and the sample set data and the data acquisition result are subjected to deviation processing by using a deviation calculation algorithm to obtain current point cloud data of the tunnel; Performing point cloud stitching and point cloud preprocessing on the current point cloud data of the tunnel to obtain a surface model of the tunnel, and splitting the surface model into a plurality of section data according to a preset standard, wherein each section data contains a corresponding monitoring target object; Collecting data of the monitoring target corresponding to each of the cross-sectional data, and calculating the current settlement data of the tunnel according to the collected data; Obtaining the width of each of the monitoring targets, and calculating the corresponding inclination according to the width of each of the monitoring targets and the corresponding current settlement data; The deformation amount corresponding to each of the cross-sectional data is calculated according to the inclination of each of the cross-sectional data and its corresponding monitoring target and the current settlement data, and abnormal cross-sectional data is determined in each of the cross-sectional data based on the deformation amount limit and the deformation amount corresponding to each of the cross-sectional data, and the current deformation state of the tunnel is judged based on the abnormal cross-sectional data.

2. The tunnel deformation monitoring method according to claim 1, characterized in that: The steps of collecting data on the tunnel based on a preset collection device, randomly acquiring a sample set of data from the data collection result, and performing deviation processing on the sample set of data and the data collection result using a deviation calculation algorithm to obtain the current point cloud data of the tunnel include: Using a preset acquisition device to collect data from the tunnel to obtain a point cloud data set of the tunnel, and randomly sampling from the point cloud data set as sample set data; The sample set data is randomly sampled using a minimum variance estimation algorithm, and deviation calculation is performed based on the random sampling result and the point cloud data set to obtain a corresponding deviation calculation result; A deviation threshold is constructed based on the initial parameters of the tunnel, and the deviation calculation result is compared with the deviation threshold to obtain current point cloud data of the tunnel.

3. The tunnel deformation monitoring method according to claim 1, characterized in that: The steps of performing point cloud stitching and point cloud preprocessing on the current point cloud data of the tunnel to obtain a surface model of the tunnel, and splitting the surface model into a plurality of section data according to a preset standard, wherein each section data contains a corresponding monitoring target object, include: Constructing an independent coordinate system of the acquisition device, and performing point cloud splicing on the current point cloud data of the tunnel based on the independent coordinate system to obtain point cloud splicing data; Using a noise point screening algorithm to screen out noise point data in the point cloud splicing data, and performing fitting transformation on the screened point cloud splicing data to obtain a surface model of the tunnel; A corresponding splitting standard is constructed according to the position of the monitoring target object in the independent coordinate system, and the surface model is split into a plurality of cross-sectional data based on the splitting standard.

4. The tunnel deformation monitoring method according to claim 1, characterized in that: The steps of collecting data of the monitoring target objects corresponding to each of the cross-sectional data and calculating the current settlement data of the tunnel according to the collected data include: Collecting data on the monitoring target objects corresponding to each of the cross-sectional data to obtain current elevation data of each of the monitoring target objects; The initial elevation data of each of the monitoring targets is acquired, and the current settlement data of each of the monitoring targets is calculated based on the current elevation data of each of the monitoring targets and the initial elevation data of each of the monitoring targets.

5. A tunnel deformation monitoring system, characterized in that: include: A data acquisition module, used to collect data from the tunnel based on a preset acquisition device, and randomly obtain a sample set of data from the data acquisition result, and perform deviation processing on the sample set data and the data acquisition result using a deviation calculation algorithm to obtain current point cloud data of the tunnel; A point cloud processing module, used for performing point cloud splicing and point cloud preprocessing on the current point cloud data of the tunnel to obtain a surface model of the tunnel, and splitting the surface model into a plurality of section data according to a preset standard, wherein each section data contains a corresponding monitoring target object; A settlement data processing module, used to collect data of the monitoring target corresponding to each of the cross-sectional data, and calculate the current settlement data of the tunnel according to the collected data; An inclination calculation module, used to obtain the width of each of the monitoring targets, and calculate the corresponding inclination according to the width of each of the monitoring targets and the corresponding current settlement data; A state monitoring module is used to calculate the deformation corresponding to each section data based on the inclination of each section data and its corresponding monitoring target and the current settlement data, and to determine abnormal section data in each section data based on the deformation limit and the deformation corresponding to each section data, and to judge the current deformation state of the tunnel based on the abnormal section data.

6. The tunnel deformation monitoring system according to claim 5, characterized in that: The data acquisition module comprises: A data acquisition unit, used to acquire data from the tunnel using a preset acquisition device to obtain a point cloud data set of the tunnel, and randomly sample the point cloud data set as sample set data; A deviation calculation unit, used to randomly sample the sample set data using a minimum variance estimation algorithm, and perform deviation calculation based on the random sampling result and the point cloud data set to obtain a corresponding deviation calculation result; The result comparison unit is used to construct a deviation threshold based on the initial parameters of the tunnel, and compare the deviation calculation result with the deviation threshold to obtain the current point cloud data of the tunnel.

7. The tunnel deformation monitoring system according to claim 5, characterized in that: The point cloud processing module includes: A point cloud stitching unit, used to construct an independent coordinate system of the acquisition device, and perform point cloud stitching on the current point cloud data of the tunnel based on the independent coordinate system to obtain point cloud stitching data; A fitting conversion unit, used to filter out noise data in the point cloud splicing data by using a noise filtering algorithm, and to perform fitting conversion on the filtered point cloud splicing data to obtain a surface model of the tunnel; The data splitting unit is used to construct a corresponding splitting standard according to the position of the monitoring target object in the independent coordinate system, and split the surface model into multiple cross-sectional data based on the splitting standard.

8. The tunnel deformation monitoring system according to claim 5, characterized in that: The settlement data processing module comprises: An elevation data acquisition unit, used to collect data on the monitoring target object corresponding to each of the cross-sectional data, so as to obtain the current elevation data of each of the monitoring target objects; The settlement data processing unit is used to obtain the initial elevation data of each of the monitoring targets, and calculate the current settlement data of each of the monitoring targets based on the current elevation data of each of the monitoring targets and the initial elevation data of each of the monitoring targets.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the tunnel deformation monitoring method as described in any one of claims 1 to 4 is implemented.

10. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the tunnel deformation monitoring method according to any one of claims 1 to 4 is implemented.

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