CN116518945A - Subway tunnel structure safety monitoring method based on measuring robot - Google Patents
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Abstract
The application discloses a subway tunnel structure safety monitoring method based on a measuring robot, belongs to the technical field of traffic safety monitoring, and can be used for intelligent monitoring of subway tunnel structure safety, and the method comprises the following steps: determining a monitoring scheme, configuring measuring scheme point positions, generating sections and monitoring points in batches, and arranging a measuring robot and a prism; three-dimensional coordinate acquisition is carried out on the monitoring points, and acquired data are transmitted to a safety intelligent monitoring platform; performing rough difference removal and curve smoothing on the acquired data; the platform automatically calculates the monitoring data of various monitoring items according to the point position configuration, calculates the current variation and the accumulated variation, sets all levels of early warning thresholds for the current variation and the accumulated variation of each monitoring item according to the operation specification and the project experience, and automatically sends early warning information to the interested person after the monitoring value reaches a preset early warning level so as to realize the full-automatic monitoring of the safety of the tunnel structure.
Description
Technical Field
The application relates to the technical field of traffic safety monitoring, in particular to a subway tunnel structure safety monitoring method based on a measuring robot.
Background
Urban rail transit is a backbone of an urban public transportation system, is an important component of an urban comprehensive transportation system, is critical in safety, and is difficult to acquire accurate monitoring data because urban rail transit trains can generate larger vibration during operation, deformation monitoring of rail transit structures is difficult to acquire, and in order to ensure the operation safety of the rail transit trains, the rail transit networks are in a closed environment, so that monitoring staff can enter a monitoring site to perform operation only in a window period of train shutdown, and therefore higher precision and time requirements are provided for structural safety monitoring in the rail transit operation period.
The traditional track traffic structure safety monitoring is to arrange a plurality of monitoring points on the structure, periodically collect monitoring data of the monitoring points manually and analyze the change trend of the monitoring data, so as to evaluate the safety condition of the structure and adopt various means to prevent the deformation of the structure beyond the safety range. In addition, manually acquired data also need to be returned to the room for internal processing, so that timeliness of monitoring data is sacrificed, and particularly when the structural object is greatly deformed in a short time, deformation data are difficult to accurately acquire in time, and great risks are brought to safety prevention and control of the structural object.
It is necessary to provide a subway tunnel structure safety monitoring method based on a measuring robot to solve the above problems.
It should be noted that the above information disclosed in this background section is only for understanding the background of the present application concept and, therefore, it may contain information that does not constitute prior art.
Disclosure of Invention
Based on the above problems existing in the prior art, the problems to be solved by the present application are: the subway tunnel structure safety monitoring method based on the measuring robot achieves the effects of automatically collecting, analyzing, processing and early warning the information of tunnel structures.
The technical scheme adopted for solving the technical problems is as follows: a subway tunnel structure safety monitoring method based on a measuring robot is applied to monitoring of tunnel structures and comprises the following steps:
step S1: comprehensively determining a monitoring scheme of the safety of the tunnel structure according to the risk level of the tunnel structure, the surrounding environment risk level and the geological condition complexity, wherein the monitoring scheme comprises monitoring types, the number and the positions of monitoring points, monitoring frequencies, deformation early warning grades and early warning information release information;
s2, determining the length of a monitoring tunnel section according to the mileage stake marks of a tunnel starting point and a stopping point in a subway tunnel monitoring range according to a monitoring scheme, generating monitoring sections on a left line tunnel and a right line tunnel of a subway in batches according to the intervals of the monitoring sections in the scheme, setting monitoring points on each monitoring section, wherein the monitoring points are arranged on a vault, a waist, a track bed and a track of the section, and representing the monitoring of a tunnel structure by configuring dynamic combinations of the monitoring points at different positions of the same section;
step S3: according to a monitoring scheme, a measuring robot is deployed on the side wall of a subway tunnel, meanwhile, a prism or a reflecting sheet is installed on the monitoring point, and meanwhile, a plurality of monitoring datum points are uniformly distributed in a relatively stable area of the tunnel and the prism or the reflecting sheet is installed;
step S4, determining three-dimensional coordinates of the monitoring datum point as an initial position of the monitoring datum point by using a total station to measure the high-level control point of the ground outside the tunnel and the monitoring datum point;
s5, using the measuring robot to aim at each monitoring datum point, and completing coordinate positioning of the measuring robot through a rear intersection method according to the initial position coordinates of the monitoring datum points;
step S6, using a measuring robot to automatically monitor the monitoring points and storing horizontal angles and vertical angles of the aiming direction in the monitoring points;
step S7: the method comprises the steps that a measuring robot collects information of monitoring points at regular time and obtains first signals, the first signals represent real-time three-dimensional coordinate information of various monitoring points distributed on a tunnel structure, the first signals are locally stored by using storage equipment, and then the storage equipment transmits the first signals to a safe intelligent monitoring platform through the Internet of things;
step S8: the safety intelligent monitoring platform analyzes the data of the first signal, and automatically calculates the current-period variation and the accumulated variation of monitoring items such as vault settlement, track bed vertical displacement, track bed horizontal displacement, track differential settlement, subway clearance convergence and the like of each monitoring section structure of the tunnel as a second signal according to the combination of monitoring points configured in the platform;
step S9: the security intelligent monitoring platform performs visual display on the second signal to obtain a trend for representing the tunnel structure change at the monitoring point;
step S10: and the safety intelligent monitoring platform establishes an early warning mechanism and performs early warning when the second signal exceeds a preset threshold value.
Further, in the step S2, the method for arranging the monitoring points includes:
and generating monitoring sections on the left line and the right line of a tunnel, continuously numbering, laying datum points on each monitoring section as required, and representing various test items concerned by structural safety monitoring by reasonably grouping the datum points.
Further, in step S3, the working range of the measuring robot is reasonably determined according to the performance of the measuring robot, and for tunnels with longer monitoring sections or poor viewing conditions, multiple groups of measuring robots are usually required to be arranged, each robot is responsible for monitoring points on the section of the tunnel in the respective range, and meanwhile, a reasonable monitoring reference point is required to be arranged for each measuring robot so as to complete the rear intersection of the measuring robots.
Further, in the step S4, the joint measurement method includes the following steps of:
a) Selecting stable positions on the ground outside the influence range of the tunnel to set a plurality of high-level planes and elevation control points, and determining three-dimensional coordinates of the high-level control points through RTK and second-level measurement methods;
b) The three-dimensional coordinates of two high-level control points are selected to complete the rear intersection of the space through a total station free standing method, then the remaining high-level control points are positioned and compared with the known three-dimensional coordinates of the high-level control points, and the accuracy of the rear intersection is determined through rechecking comparison;
c) According to the viewing conditions inside and outside the tunnel, positioning transition points are distributed, and three-dimensional coordinates of the positioning transition points are determined by using a total station which completes the rear intersection;
d) And c, sequentially determining the three-dimensional coordinates of each monitoring datum point in the tunnel by adopting the same method as the steps b and c, and preparing for the automatic measurement of the measuring robot.
Further, in step S6, firstly, the monitoring points in the monitoring range of each measuring robot are calibrated in sequence manually, coordinate measurement of the monitoring points is performed, and learning measurement of the measuring robot is realized, so that on one hand, the measuring robot stores horizontal angles and vertical angles when calibrating each monitoring point, self-service tracking measurement is conveniently performed in a later stage, on the other hand, initial three-dimensional coordinates of each monitoring point are determined, and initial contrast data is provided for deformation of the follow-up evaluation monitoring points.
Further, in step S7, the storage device transmits the first signal to the security intelligent monitoring platform through the internet of things, and is provided with a network disconnection protection mechanism, where the network disconnection protection mechanism is used for preventing the first signal from being lost in the transmission process.
Further, in the step S8, the data parsing of the first signal further includes:
a) Preprocessing the first signal, wherein the preprocessing comprises rough difference discrimination and rejection of monitoring data;
b) Performing data interpolation on the first signal, wherein the data interpolation applies a space-time correlation principle to interpolate missing data;
c) And carrying out smoothing treatment on the first signal to obtain a second signal of the analyzed tunnel structure test item change.
Further, in the process of preprocessing the first signal, the three-time standard deviation method is used for removing abnormal values, in the process of performing data interpolation on the first signal, the measured value of the point close to the measuring point on the same cross section is adopted for estimating information of a target point, in the process of performing smoothing on the first signal, the moving average method is adopted for performing smoothing, the average value of each numerical value is calculated in sequence, the numerical value of the middle point is replaced, and the data sequence is free from random interference.
Further, in step S9, when the security intelligent monitoring platform performs visual display on the second signal, the second signal is subjected to time series analysis, and the analysis result is output in the form of a change trend chart.
Further, in step S10, after the safety intelligent monitoring platform establishes the early warning mechanism, an alarm level is set, and when the monitored data variation is greater than the preset threshold, a corresponding early warning is sent.
The beneficial effects of this application lie in: the section and the monitoring points are generated in batches, the measuring robot and the prism are arranged, three-dimensional coordinate acquisition is carried out on the monitoring points, acquired data are transmitted to the safe intelligent platform, the acquired data are processed, different monitoring points can be dynamically configured during data processing, after the acquired data are processed, all levels of early warning threshold values are set for the current variation and the accumulated variation of each measuring item according to the operation specification and project experience, and after the monitoring value reaches a preset early warning level, early warning information is automatically sent, so that the safety monitoring of the tunnel structure is realized.
In addition to the objects, features, and advantages described above, there are other objects, features, and advantages of the present application. The present application will be described in further detail with reference to the drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a schematic flow chart of a subway tunnel structure safety monitoring method based on a measuring robot in the application;
fig. 2 is a schematic diagram of a monitoring point layout.
Detailed Description
It should be noted that, without conflict, the embodiments and features in the embodiments may be combined with each other, and the present application will be described in detail with reference to the drawings and the embodiments.
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
As shown in fig. 1, the present application provides a subway tunnel structure safety monitoring method based on a measurement robot, which includes:
the structural safety monitoring scheme is reasonably designed according to the risk level of the rail transit structural object, the surrounding environment risk level and the geological condition complexity, and specifically comprises a monitoring project, a monitoring range, a point distribution requirement, a monitoring method, a monitoring time, a monitoring frequency, a field monitoring operation period, personnel and equipment installation, an in-out field requirement and the like.
Step S1: according to the monitoring scheme, safety monitoring sections of the subway tunnel structure are generated in batches in a monitoring range, monitoring points are set for each section, and the custom configuration of various monitoring items is realized through the combination of the monitoring points.
And arranging various monitoring points on each section structure of the tunnel according to the determined monitoring scheme, monitoring reference points in a stable area inside the tunnel, and arranging high-grade control points outside the range of a subway protection area outside the tunnel. The three-dimensional coordinates of the high-level control point are determined by adopting an RTK or guiding measurement method. And then, carrying out joint measurement on the high-level control points and the monitoring datum points by a wire measurement method to obtain the three-dimensional coordinates of each monitoring datum point.
Step S2: and selecting proper positions on the side wall of the subway tunnel according to the length, radian and the viewing conditions of the subway tunnel, arranging a plurality of measuring robots, wherein each robot is responsible for monitoring a part of monitoring points of the subway section, and all the measuring robots realize the coverage of all the monitoring points in a monitoring range. And then determining the working space reference of each measuring robot by adopting a method of rear intersection of the measuring robot on the monitoring datum points with known coordinates. And finally, carrying out learning measurement configuration work by the measuring robot, operating the prisms where the monitoring points are located by the measuring robot one by one, and recording the horizontal angle and the vertical angle of the monitoring points by the monitoring system. After the configuration is completed, the measuring robot can perform automatic monitoring work according to the preset monitoring frequency.
Specifically, as shown in fig. 2, in the embodiment of the present invention, monitoring sections are generated in batches on the left line and the right line of a tunnel and numbered, 5 reference points are arranged on each section, and are respectively located at A, B, C, D, E in the figure, and information acquisition on the monitoring sites is achieved through different combinations, wherein a major arc part formed by CDs is a tunnel section, two points of C, D are located on rails on two sides of a subway rail surface, a point E is located at the top of the tunnel, and a point AB is located at the waist of the tunnel.
Step S3: and information acquisition is carried out on the monitoring points by using the measuring robot, a first signal is obtained, the first signal is used for monitoring the space three-dimensional coordinates of the moment of monitoring of each monitoring point, and the monitoring of a plurality of measuring items can be realized by configuring the monitoring points.
Specifically, the monitoring items comprise horizontal displacement monitoring, vertical displacement monitoring, tunnel clearance convergence monitoring, rail surface elevation monitoring, track bed horizontal displacement, track horizontal height difference, longitudinal height difference monitoring and other data. In the embodiment, the A monitoring point and the B monitoring point are positioned at the waist of the structure, the combination is used for realizing tunnel clearance convergence monitoring, the E monitoring point and the C monitoring point are used for realizing vault vertical displacement monitoring, and the C, D two monitoring points are used for realizing track horizontal height difference and longitudinal height difference and track bed horizontal displacement monitoring.
In this embodiment, the monitoring devices of the monitoring points are connected with DTU devices, and are powered by a unified power supply line, the DTU devices configure acquisition parameters for the monitoring devices, and aggregate data acquired by the monitoring devices arranged at the monitoring points
Step S4: after the acquired first signal is locally stored by using the storage device, the storage device transmits the first signal to the safety intelligent monitoring platform through the Internet of things;
specifically, the storage is divided into local storage and cloud storage, wherein the cloud storage process includes a data communication process, in this embodiment, the communication process can upload monitoring data through 5G or internet of things, and establish a network disconnection protection mechanism to prevent data from being lost when the network condition is bad, and the network disconnection protection mechanism is based on the current traditional data transmission network disconnection protection method, and specifically refers to the technical scheme in the chinese patent of invention with publication number CN 100550848C.
Step S5: the safety intelligent monitoring platform performs data analysis on the first signal and obtains a second signal;
specifically, in the process of data analysis, firstly, the monitoring data needs to be preprocessed, the preprocessing process comprises coarse difference judgment and rejection of the monitoring data, in the preprocessing process, a large amount of monitoring data needs to be used, the data amount of each monitoring point is large, and three times of standard deviation is used as a standard for determining the choice of the monitoring data.
Specifically, according to the normal distribution rule of the random variable, in the multiple values, the probability that the measured value falls between the negative three-time standard deviation and the positive three-time standard deviation is 99.73%, so that the data falling outside the range is removed, and when the method is applied to the embodiment, the abnormal value is removed from the data acquired by each monitoring point by using the three-time standard deviation method.
After preprocessing is finished, because the condition of data missing can occur in the data acquisition process, if data interpolation is not performed, the missing of effective data can influence the monitoring result, therefore, the data interpolation is required to be performed on the preprocessed monitoring data, specifically, the data interpolation method applies the space-time correlation principle to interpolate the missing data, in the practical application process, the measured values of the same type of monitoring points arranged on the same section of the tunnel are subjected to the same environmental load, and therefore, the measured values of the adjacent measuring points of the same section can be used for estimating the information of the target point.
The data analysis process further comprises smoothing processing of the monitored data, and because the collected data can be affected in multiple aspects, such as placement errors of equipment, tunnel surface deformation and the like, distortion points exist in the actually monitored data, the distortion points can cause wavy jitter of the data, and influence on subsequent data analysis is caused, so that the smoothing processing is needed, a specific smoothing processing method is a sliding average method, and the principle is that the average value of each numerical value is calculated in sequence to replace the numerical value of a middle point, so that the data sequence gets rid of random interference.
Step S6: the safety intelligent monitoring platform performs visual display on the second signal to obtain a third signal representing the trend of the tunnel structure change at the monitoring point;
specifically, the processed data includes various types of data, such as horizontal displacement data, vertical displacement data and tunnel clearance convergence data, time series analysis is performed on the data of the types, the analyzed data is displayed in the form of a change trend graph, specifically, the change trend graph includes a horizontal axis and a vertical axis, wherein the horizontal axis represents a time series, specifically, a set sampling period, the vertical axis is monitoring data, each sampling period corresponds to a group of monitoring data, and the change trend of each monitoring point can be obtained after connection.
Step S7: the safety intelligent monitoring platform establishes an early warning mechanism and carries out early warning when the third signal exceeds a preset threshold value;
specifically, the early warning mechanism is also provided with an alarm level, when the variation of the monitored data is larger than the preset threshold value, early warning is sent out, and alarm processing is carried out.
The alarm level comprises three levels of a, b and c, wherein when the second signal is greater than 70% of a preset threshold value, a level a early warning is sent out, when the second signal is greater than 80% of the preset threshold value, a level b early warning is sent out, and when the second signal is greater than 100% of the preset threshold value, a level c early warning is sent out.
Step S8: and the manual inspection is performed for rechecking, so that the normal and stable operation of the safety intelligent monitoring platform is ensured.
Specifically, a prism or a reflecting sheet is arranged on a monitoring point of a subway tunnel section structure, and in the tunnel maintenance process, the prism and the reflecting sheet can be possibly touched by maintenance workers, so that position sending offset is caused. When the offset is smaller, the measuring robot can track the offset prism or reflector plate, but the offset is mistakenly treated as deformation of the monitoring point, and rough difference judgment and restoration can be carried out during data preprocessing, but in order to ensure accurate monitoring data, manual inspection can be carried out at the moment to further confirm the deformation reason. If the offset is larger, the measuring robot cannot track the offset prism or the transmitting sheet, but in order to save the continuity of the monitoring data, part of monitoring points are usually required to be supplemented to replace damaged monitoring points, and at the moment, manual inspection and restoration of the monitoring points are also required.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. The method for fully automatically monitoring the safety of the subway tunnel structure based on the measuring robot is applied to deformation monitoring of the subway tunnel structure constructed by a shield method and is characterized by comprising the following steps of:
step S1: comprehensively determining a monitoring scheme of the safety of the tunnel structure according to the risk level of the tunnel structure, the surrounding environment risk level and the geological condition complexity, wherein the monitoring scheme comprises monitoring types, the number and the positions of monitoring points, monitoring frequencies, deformation early warning grades and early warning information release information;
s2, determining the length of a monitoring tunnel section according to the mileage stake marks of a tunnel starting point and a stopping point in a subway tunnel monitoring range according to a monitoring scheme, generating monitoring sections on a left line tunnel and a right line tunnel of a subway in batches according to the intervals of the monitoring sections in the scheme, setting monitoring points on each monitoring section, wherein the monitoring points are arranged on a vault, a waist, a track bed and a track of the section, and representing the monitoring of a tunnel structure by configuring dynamic combinations of the monitoring points at different positions of the same section;
step S3: according to a monitoring scheme, a measuring robot is deployed on the side wall of a subway tunnel, meanwhile, a prism or a reflecting sheet is installed on the monitoring point, and meanwhile, a plurality of monitoring datum points are uniformly distributed in a relatively stable area of the tunnel and the prism or the reflecting sheet is installed;
step S4, determining three-dimensional coordinates of the monitoring datum point as an initial position of the monitoring datum point by using a total station to measure the high-level control point of the ground outside the tunnel and the monitoring datum point;
s5, using the measuring robot to aim at each monitoring datum point, and completing coordinate positioning of the measuring robot through a rear intersection method according to the initial position coordinates of the monitoring datum points;
step S6, using a measuring robot to automatically monitor the monitoring points and storing horizontal angles and vertical angles of the aiming direction in the monitoring points;
step S7: the method comprises the steps that a measuring robot collects information of monitoring points at regular time and obtains first signals, the first signals represent real-time three-dimensional coordinate information of various monitoring points distributed on a tunnel structure, the first signals are locally stored by using storage equipment, and then the storage equipment transmits the first signals to a safe intelligent monitoring platform through the Internet of things;
step S8: the safety intelligent monitoring platform analyzes the data of the first signal, and automatically calculates the current-period variation and the accumulated variation of monitoring items such as vault settlement, track bed vertical displacement, track bed horizontal displacement, track differential settlement, subway clearance convergence and the like of each monitoring section structure of the tunnel as a second signal according to the combination of monitoring points configured in the platform;
step S9: the security intelligent monitoring platform performs visual display on the second signal to obtain a trend for representing the tunnel structure change at the monitoring point;
step S10: and the safety intelligent monitoring platform establishes an early warning mechanism and performs early warning when the second signal exceeds a preset threshold value.
2. The subway tunnel structure safety monitoring method based on the measuring robot according to claim 1, wherein the method comprises the following steps: in the step S2, the method for arranging the monitoring points includes:
and generating monitoring sections on the left line and the right line of a tunnel, continuously numbering, laying datum points on each monitoring section as required, and representing various test items concerned by structural safety monitoring by reasonably grouping the datum points.
3. The subway tunnel structure safety monitoring method based on the measuring robot according to claim 1, wherein the method comprises the following steps: in step S3, the working range of the measuring robot is reasonably determined according to the performance of the measuring robot, and for tunnels with longer monitoring sections or poor viewing conditions, multiple groups of measuring robots are generally required to be arranged, each robot is responsible for monitoring points on the section of the tunnel in the respective range, and meanwhile, a reasonable monitoring reference point is required to be arranged for each measuring robot so as to complete the rear intersection of the measuring robots.
4. The subway tunnel structure safety monitoring method based on the measuring robot according to claim 1, wherein the method comprises the following steps: in the step S4, the joint measurement method includes the following steps:
a) Selecting stable positions on the ground outside the influence range of the tunnel to set a plurality of high-level planes and elevation control points, and determining three-dimensional coordinates of the high-level control points through RTK and second-level measurement methods;
b) The three-dimensional coordinates of two high-level control points are selected to complete the rear intersection of the space through a total station free standing method, then the remaining high-level control points are positioned and compared with the known three-dimensional coordinates of the high-level control points, and the accuracy of the rear intersection is determined through rechecking comparison;
c) According to the viewing conditions inside and outside the tunnel, positioning transition points are distributed, and three-dimensional coordinates of the positioning transition points are determined by using a total station which completes the rear intersection;
d) And c, sequentially determining the three-dimensional coordinates of each monitoring datum point in the tunnel by adopting the same method as the steps b and c, and preparing for the automatic measurement of the measuring robot.
5. The subway tunnel structure safety monitoring method based on the measuring robot according to claim 1, wherein the method comprises the following steps: in step S6, firstly, the monitoring points in the monitoring range of each measuring robot are manually and sequentially calibrated, coordinate measurement of the monitoring points is performed, learning measurement of the measuring robot is realized, on one hand, the measuring robot is enabled to store horizontal angles and vertical angles when the monitoring points are calibrated, self-service tracking measurement is conveniently performed in a later stage, on the other hand, initial three-dimensional coordinates of the monitoring points are determined, and initial comparison data are provided for deformation of the follow-up evaluation monitoring points.
6. The subway tunnel structure safety monitoring method based on the measuring robot according to claim 1, wherein the method comprises the following steps: in step S7, the storage device transmits the first signal to the secure intelligent monitoring platform through the internet of things, and is provided with a network disconnection protection mechanism, where the network disconnection protection mechanism is used for preventing the first signal from being lost in the transmission process.
7. The subway tunnel structure safety monitoring method based on the measuring robot according to claim 1, wherein the method comprises the following steps: in the step S8, the data parsing of the first signal further includes:
a) Preprocessing the first signal, wherein the preprocessing comprises rough difference discrimination and rejection of monitoring data;
b) Performing data interpolation on the first signal, wherein the data interpolation applies a space-time correlation principle to interpolate missing data;
c) And carrying out smoothing treatment on the first signal to obtain a second signal of the analyzed tunnel structure test item change.
8. The method for monitoring the urban rail transit operation period structure safety intelligent monitoring platform according to claim 1, which is characterized in that: and removing abnormal values by using a triple standard difference method in the process of preprocessing the first signal, estimating information of a target point by adopting a measured value of a point close to the same cross section in the process of carrying out data interpolation on the first signal, and carrying out smoothing processing by using a moving average method in the process of carrying out smoothing processing on the first signal, sequentially calculating the average value of each numerical value, replacing the numerical value of a middle point, so that the data sequence gets rid of random interference.
9. The subway tunnel structure safety monitoring method based on the measuring robot according to claim 1, wherein the method comprises the following steps: in step S9, when the security intelligent monitoring platform performs visual display on the second signal, the second signal is subjected to time series analysis, and the analysis result is output in the form of a change trend chart.
10. The subway tunnel structure safety monitoring method based on the measuring robot according to claim 1, wherein the method comprises the following steps: in step S10, after the safety intelligent monitoring platform establishes the early warning mechanism, an alarm level is set, and when the monitored data variation is greater than the preset threshold, a corresponding early warning is sent.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116973522A (en) * | 2023-09-22 | 2023-10-31 | 深圳市天地互通科技有限公司 | Integrated automatic atmosphere monitoring control system |
| CN117473234A (en) * | 2023-12-28 | 2024-01-30 | 广州地铁设计研究院股份有限公司 | Deformation monitoring data preprocessing method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116973522A (en) * | 2023-09-22 | 2023-10-31 | 深圳市天地互通科技有限公司 | Integrated automatic atmosphere monitoring control system |
| CN117473234A (en) * | 2023-12-28 | 2024-01-30 | 广州地铁设计研究院股份有限公司 | Deformation monitoring data preprocessing method, device, equipment and storage medium |
| CN117473234B (en) * | 2023-12-28 | 2024-04-30 | 广州地铁设计研究院股份有限公司 | Deformation monitoring data preprocessing method, device, equipment and storage medium |
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