Intelligent arch dam deformation monitoring system based on rail robot

Abstract

The invention provides an intelligent arch dam deformation monitoring system based on a rail robot, which belongs to the technical field of hydraulic engineering and comprises an arc-shaped guide rail, a rail moving device, an automatic leveling base, an intelligent total station, a driving device and a data processing module. The intelligent total station is used for collecting deformation data of a plurality of rearview reference points arranged on the top of the arch dam; the driving device is used for driving the rail moving device to slide along the arc-shaped guide rail; the data processing module is used for receiving, analyzing and processing the deformation data, the data processing module adopts a multi-station automatic networking adjustment algorithm to carry out error correction and data adjustment on the deformation data and outputs a deformation monitoring result of the arch dam, and the multi-station automatic networking adjustment algorithm comprises a least square method, a Kalman filtering algorithm and resection adjustment. The arch dam deformation data are automatically collected through the rail robot, high-precision data correction is ensured in combination with a multi-station automatic networking adjustment algorithm, and the reliability and precision of deformation monitoring are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of water conservancy projects, and more specifically, relates to an intelligent deformation monitoring system for arch dams based on rail robots. Background Art

[0002] As an important hydraulic structure, the safety and stability of an arch dam directly affect the overall safety of a water conservancy project. In particular, the deformation monitoring of the dam body is an important means to ensure the long-term stable operation of the arch dam. Traditional arch dam deformation monitoring methods mainly rely on techniques such as leveling survey, intelligent total station, and GPS measurement. Although these techniques can provide deformation data to a certain extent, there are still many problems. First, the use of leveling survey and intelligent total station requires manual operation, which is inefficient and easily affected by weather, environment, and human factors, resulting in insufficient guarantee of the accuracy and stability of measurement results. Second, traditional measurement methods usually can only provide discrete data, cannot achieve real-time and continuous monitoring, and the measurement frequency each time is low, making it difficult to timely track and evaluate the dynamic deformation of the arch dam. Moreover, traditional methods have deficiencies in measurement accuracy and error correction, and are prone to cumulative measurement errors due to changes in external conditions (such as temperature, wind speed, etc.), affecting the reliability of deformation monitoring.

[0003] With the continuous development of intelligent technologies, automated monitoring systems have gradually become the research trend of arch dam deformation monitoring. However, existing automated monitoring systems still face some technical challenges. First, although automated systems can achieve relatively efficient data collection, accurate positioning of equipment remains a difficult problem in a complex environment. The arch dam body and its surrounding environment usually have complex terrain and climatic conditions, which pose challenges to the stability and accuracy of measurement equipment. Second, the monitoring coverage of existing systems is limited, and many automated systems can only cover some back sight reference benchmark points, resulting in the inability to comprehensively and real-time obtain the deformation data of the dam, and unable to meet the requirements of all-round and continuous deformation monitoring of the arch dam. Finally, in the data display and processing process of automated systems, data processing algorithms are still insufficient. Although automated equipment can collect a large amount of data, how to perform data analysis, error correction, and deformation trend prediction in real-time and efficiently still depends on manual intervention, and it is difficult to achieve true intelligent monitoring.

[0004] Therefore, how to combine automated equipment with advanced algorithms for data processing to improve the accuracy, efficiency, and intelligent level of monitoring is a difficult problem that urgently needs to be solved in the current arch dam deformation monitoring technology. Summary of the Invention

[0005] The object of the present invention is to provide an intelligent deformation monitoring system for an arch dam based on an orbital robot, so as to solve at least one of the problems in the prior art such as manual dependence in arch dam deformation monitoring, discrete data, accuracy being interfered, inaccurate positioning of the automation system, limited coverage, and insufficient data processing.

[0006] To achieve the above object, the present invention provides an intelligent deformation monitoring system for an arch dam based on an orbital robot, including:

[0007] An arc-shaped guide rail, fixed on the top of the arch dam;

[0008] An orbital moving device, slidably connected to the arc-shaped guide rail;

[0009] An automatic level base, connected to the orbital moving device;

[0010] An intelligent total station, connected to the automatic level base, and used for collecting deformation data of a plurality of back sight reference benchmarks arranged on the top of the arch dam;

[0011] A driving device, used for driving the orbital moving device to slide along the arc-shaped guide rail; and,

[0012] A data processing module, used for receiving, analyzing and processing the deformation data. The data processing module uses a multi-station automatic networking adjustment algorithm to correct errors and adjust the data of the deformation data, and outputs the deformation monitoring result of the arch dam. The multi-station automatic networking adjustment algorithm includes the least squares method, the Kalman filtering algorithm and the resection adjustment.

[0013] The multi-station automatic networking adjustment algorithm is used to optimize the data accuracy from multiple back sight reference benchmarks. This algorithm can effectively identify and correct systematic errors and random errors in the measurement process. In the adjustment process, according to the relative positions of the back sight reference benchmarks and their accuracy requirements, the weights of the data are dynamically adjusted, combined with the resection adjustment, to optimize the data adjustment and correction effect, so as to improve the accuracy of deformation monitoring. The data processing module can real-time identify and correct measurement errors, automatically update the deformation data, generate deformation curves and deformation trend charts, and provide relevant statistical analysis reports; through the integrated data analysis tool, the data processing module can analyze the data of multiple monitoring points, automatically generate the real-time deformation trend of the arch dam, and display the data through a graphical interface to assist engineers in decision-making. The data update process combines the resection adjustment to ensure the consistency and accuracy of the data in the time series.

[0014] Further, the data processing module processes the deformation data in the following stages:

[0015] In the initialization network establishment stage, the resection adjustment is used to obtain the deformation data of each of the back sight reference benchmarks, and a preliminary control network is established;

[0016] In the stage of processing regularly measured data, the least squares method is used to optimize the static error of the deformation data, eliminate systematic deviation, and obtain the optimal measured point coordinates of each of the back sight reference benchmark points;

[0017] In the stage of real-time dynamic monitoring, in continuous multi-period measurements, the Kalman filtering algorithm is used to fuse the current observation value with the estimated value at the previous moment, correct random errors, and realize the prediction of the dynamic deformation trend.

[0018] Further, in the measurement network, the observed deformation data includes the distance between survey stations, the angle between survey stations, and the azimuth angle of survey stations, and the least squares adjustment is used to correct the error of the observed deformation data.

[0019] Further, in the initialization network construction stage, the resection adjustment eliminates the cumulative error in the measurement process by performing reverse adjustment on the deformation data of multiple back sight reference benchmark points. The resection adjustment corrects the error through the following formula:

[0020]

[0021] where, i represents the i-th observed data point, j represents the j-th known reference point, X is the parameter set of the fitting function, x j is the estimated position coordinate of the j-th reference point, y i is the observed value of the i-th back sight reference benchmark point, f(x i ) is the fitting function, is the reference point position, λ is the resection adjustment correction coefficient;

[0022] Δx is the correction amount, W i is the weight matrix of the i-th back sight reference benchmark point, z i is the observed value of the i-th back sight reference benchmark point, H i is the measurement matrix of the i-th back sight reference benchmark point.

[0023] Further, in the stage of processing regularly measured data, the least squares method optimizes the data through the following formula:

[0024] where,

[0025] w i is the weight of the i-th back sight reference benchmark point, and the weight value is dynamically adjusted according to the reliability and conditions of the back sight reference benchmark point to ensure accurate adjustment and correction of the deformation data.

[0026] Further, in the real-time dynamic monitoring stage, the Kalman filtering algorithm corrects the error in real time through the following correction formula:

[0027]

[0028] where k is the number of the current time step, is the estimated value at the current moment after correction, is the state estimated value at the previous moment k - 1 after correction, x k is the estimated value at the current moment before correction, y k is the current observed value, K k is the Kalman gain, H k is the measurement matrix, is the known position of the reference point, and β is the resection correction coefficient.

[0029] Further, the data processing module has the functions of data storage and management, and can store historical monitoring data for a long time. The data processing module analyzes the historical data through data mining techniques, provides a basis for the long-term safety assessment and maintenance decision-making of the arch dam. The data mining techniques include methods such as cluster analysis and time series analysis. The data mining techniques are used to discover the laws and trends in the historical data and predict the future deformation trend.

[0030] Further, it also includes a wireless communication module and an intelligent early warning module. The intelligent total station and the data processing module perform data transmission through the wireless communication module. The wireless communication module supports an efficient data transmission protocol to ensure stable and real-time data transmission between different back sight reference benchmark points; the communication protocol adopts an efficient data transmission technology based on wireless local area network or Bluetooth, and can realize real-time data transmission and processing within the dam monitoring area; the intelligent early warning module generates a potential risk warning based on the deformation trend predicted by the data processing module, and issues maintenance and safety warnings according to the set warning threshold to assist engineers in taking preventive measures. That is, the intelligent early warning module predicts and issues a warning in advance for possible deformation anomalies by analyzing the change rate and trend of the deformation data to help take corresponding measures in time.

[0031] Further, it also includes a protective cover, and the protective cover includes a first part and a second part symmetrically arranged;

[0032] The track moving device includes a slider and a cylinder connected to each other. The slider is slidably connected to the arc-shaped guide rail. A cavity is provided inside the cylinder, and a rotating shaft penetrating the cavity inside the cylinder is provided on the cylinder, and at least two rotating shafts are symmetrically provided; the exposed end of each rotating shaft is fixedly connected with a connecting rod, and one end located inside the cavity is fixedly connected with a gear. The other ends of at least two connecting rods are respectively connected to the first part and the second part;

[0033] An electric push rod is provided inside the cavity, and racks are symmetrically provided on both sides of the outer wall of the electric push rod; at least two gears are symmetrically arranged on both sides of the rack, and the rack is in meshing transmission with the gear.

[0034] Furthermore, an energy storage device is further included. A photovoltaic panel and an intelligent atmospheric temperature, humidity and pressure measuring instrument are connected to the protective cover. The energy storage device is used to store the electric energy converted by the photovoltaic panel. The intelligent atmospheric temperature, humidity and pressure measuring instrument is used to measure the temperature, humidity and pressure of the air in real time. The energy storage device is used to supply power to the driving device, the intelligent total station, the electric push rod, the intelligent atmospheric temperature, humidity and pressure measuring instrument and the data processing module.

[0035] Compared with the prior art, the present invention has the following technical effects:

[0036] An intelligent deformation monitoring system for an arch dam based on a track robot of the present invention fixes an arc-shaped guide rail and a track moving device that fits the curved surface of the arch dam crest on the arch dam crest, and sets the intelligent total station on the track moving device. In this way, when the driving device drives the track moving device to slide along the arc-shaped guide rail, the intelligent total station can be synchronously driven to freely move at different positions on the arch dam crest, eliminating the need for manual movement of the intelligent total station, improving the monitoring efficiency, and effectively solving the problems of low data collection efficiency, insufficient accuracy and excessive human intervention in the traditional arch dam deformation monitoring method. In addition, the arc-shaped guide rail is arranged along the arch dam crest, so that the monitoring coverage range of the intelligent total station can cover all the back sight reference points on the arch dam crest, and the deformation data of the dam can be comprehensively and real-time obtained, meeting the requirements for all-round and continuous deformation monitoring of the arch dam. On the other hand, the intelligent deformation monitoring system for an arch dam based on a track robot of the present invention can not only automatically collect and real-time transmit the deformation data of multiple back sight reference points, but also perform high-precision correction on the collected deformation data through the data processing module, ensuring the consistency and accuracy of the deformation monitoring data; the data processing module adopts a multi-station automatic networking adjustment algorithm, which can effectively optimize the data accuracy from different back sight reference points, identify and correct the errors that may occur during the measurement process, such as systematic errors and random errors, thereby improving the reliability and accuracy of the monitoring results. Combined with the resection adjustment, the monitoring system can further eliminate the cumulative errors, improve the stability and consistency of the measurement data, and enhance the accuracy of the deformation monitoring. Brief Description of the Drawings

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 Figure 8 is a schematic diagram of the overall structure of an intelligent deformation monitoring system for an arch dam based on an orbital robot in the state where the protective cover is closed;

[0039] Figure 2 Figure 12 is a schematic diagram of the overall structure of an intelligent deformation monitoring system for an arch dam based on an orbital robot in the state where the protective cover is opened;

[0040] Figure 3 For Figure 2 the rear view structure diagram;

[0041] Figure 4 Figure 22 is a partial structure diagram of an electric push rod driving the protective cover to act provided by an embodiment of the present invention;

[0042] Figure 5 Figure 26 is a top view structure diagram of the layout of an intelligent deformation monitoring system for an arch dam based on an orbital robot provided by an embodiment of the present invention on the arch dam;

[0043] Figure 6 Figure 30 is a schematic diagram of the data transmission process between the data processing module and the intelligent total station provided by an embodiment of the present invention;

[0044] Figure 7 Figure 34 is a schematic diagram of the working principle of the multi-station automatic networking adjustment algorithm provided by an embodiment of the present invention;

[0045] Figure 8 Figure 38 is a schematic diagram of the working process of the intelligent early warning module provided by an embodiment of the present invention;

[0046] Figure 9 Figure 42 is a schematic diagram of the data storage and management function provided by an embodiment of the present invention;

[0047] Figure 10 Figure 46 is a schematic diagram of the simulated deformation of the arch dam in different periods provided by an embodiment of the present invention. The shape of the curve in the figure represents the form of the arch dam, and the horizontal and vertical coordinates in the figure have no actual meaning.

[0048] Among them, the reference numerals in the figures are as follows:

[0049] 1. Arc-shaped guide rail, 2. Track moving device, 3. Intelligent total station, 4. Photovoltaic panel, 5. Protective cover, 6. Connecting rod, 7. Rotating shaft, 8. Intelligent atmospheric temperature, humidity and pressure measuring instrument, 9. Automatic level base, 10. Gear, 11. Electric push rod, 12. Rack, 201. Slide block, 202. Cylinder, 203. Cavity, 501. First part, 502. Second part. Detailed implementation manners

[0050] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0051] The embodiment of the present invention provides an intelligent deformation monitoring system for an arch dam based on a track robot, as Figures 1 - 10 shown, which includes: an arc-shaped guide rail 1, a track moving device 2, an automatic level base 9, an intelligent total station 3, a driving device and a data processing module. The arc-shaped guide rail 1 fits the curved surface of the top of the arch dam, and the arc-shaped guide rail 1 is laid along the top of the arch dam and fixed on the top of the arch dam; the track moving device 2 is slidably connected to the arc-shaped guide rail 1; the automatic level base 9 is connected to the track moving device 2; the intelligent total station 3 is connected to the automatic level base 9 and is used to collect the deformation data of a plurality of back sight reference benchmark points set on the top of the arch dam. The collected deformation data is the three-dimensional coordinate data of the back sight reference benchmark point, including the horizontal displacement, longitudinal displacement and elevation change of the point; the driving device is used to drive the track moving device 2 to slide along the arc-shaped guide rail 1. The data processing module is used to receive, analyze and process the deformation data. The data processing module uses a multi-station automatic networking adjustment algorithm to correct the errors and adjust the data of the deformation data, and outputs the deformation monitoring result of the arch dam. The multi-station automatic networking adjustment algorithm includes the least squares method, the Kalman filtering algorithm and the resection adjustment.

[0052] In this embodiment, an automatic level base 9 is provided between the rail moving device 2 and the intelligent total station 3. The intelligent total station 3 is connected to the rail moving device 2 through the automatic level base 9. When the rail moving device 2 moves to a new measurement point, the automatic level base 9 can detect the inclination state of the intelligent total station 3 and automatically adjust it through the internal electric leveling mechanism, so that the intelligent total station 3 maintains a horizontal state, ensuring the measurement accuracy. In this way, in the case of uneven ground, slight deformation of the arc-shaped guide rail 1, or minor errors generated during the operation of the rail moving device 2, the automatic level base 9 can quickly adjust the intelligent total station 3, compensate for the minor inclination, and improve the adaptability of the monitoring device in harsh environments. In this way, the monitoring device can automatically adjust the horizontal state of the intelligent total station 3, ensure the measurement accuracy, and at the same time improve the adaptability of the system, enabling it to perform stable measurements in complex environments. The structure of the automatic level base 9 and the above functions it can achieve are prior art and will not be elaborated in this embodiment.

[0053] The driving device can drive the rail moving device 2 to slide along the arc-shaped guide rail 1. The specific connection structure among the driving device, the rail moving device 2, and the arc-shaped guide rail 1 can adopt the conventional setting structure in existing rail robots and will not be elaborated in this embodiment.

[0054] Furthermore, the monitoring system of this embodiment further includes a protective cover 5. The protective cover 5 includes a first part 501 and a second part 502 that are symmetrically arranged; the rail moving device 2 includes a slider 201 and a cylinder 202 that are connected to each other. The slider 201 is slidably connected to the arc-shaped guide rail 1. A cavity 203 is provided inside the cylinder 202. A rotating shaft 7 passing through the internal cavity 203 of the cylinder 202 is provided on the cylinder 202. At least two rotating shafts 7 are symmetrically provided; a connecting rod 6 is fixedly connected to the exposed end of each rotating shaft 7, and a gear 10 is fixedly connected to the end located inside the cavity 203. The other ends of at least two connecting rods 6 are respectively connected to the first part 501 and the second part 502; an electric push rod 11 is provided inside the cavity 203. Rack teeth 12 are symmetrically provided on both sides of the outer wall of the electric push rod 11; at least two gears 10 are symmetrically arranged on both sides of the rack teeth 12, and the rack teeth 12 are in meshing transmission with the gears 10.

[0055] In this embodiment, the two parts on the left and right of the protective cover 5, namely the first part 501 and the second part 502, cover the intelligent total station 3 above it through the action of the electric push rod 11. Specifically, the electric push rod 11 serves as the power source. The electric push rod 11 drives the rack teeth 12 to move up and down, and the rack teeth 12 move smoothly in the vertical direction inside the cylinder 202. The rack teeth 12 are meshingly connected with the gears 10 on both sides of it. The gears 10 are fixedly installed on the horizontally arranged rotating shaft 7. As the rack teeth 12 move up or down, it drives the gears 10 to rotate synchronously, and then drives the two rotating shafts 7 on both sides to rotate in opposite directions.

[0056] Since the rotating shaft 7 is fixedly connected to the connecting rod 6 at its end, that is, both ends of each connecting rod 6 are respectively connected to the rotating shaft 7 and the protective cover 5. Driven by the rotating shaft 7, the connecting rod 6 simultaneously undergoes an angular change, driving the protective cover 5 to open and close synchronously. The entire opening and closing process has a stable structure and smooth movement. The protective cover 5 can be flexibly opened or closed according to the operating state of the intelligent total station 3, realizing effective shielding and protection of the intelligent total station 3.

[0057] Furthermore, in this embodiment, four rotating shafts 7 are symmetrically arranged on the column body 202. Both ends of each rotating shaft 7 penetrate through the opposite sides of the column body 202 and extend out. An exposed end of each rotating shaft 7 is fixedly connected to a connecting rod 6. The four rotating shafts 7 are symmetrically arranged on both sides of the rack 12. The four connecting rods 6 located on the same side of the rack 12 are connected to the first part 501 or the second part 502 of the protective cover 5. In this way, both the first part 501 and the second part 502 of the protective cover 5 are connected to the rotating shaft 7 through two pairs of connecting rods 6, forming a parallelogram linkage mechanism, which can make the entire opening and closing process of the protective cover 5 more stable, the movement smoother, without jamming, reduce mechanical wear, and extend the service life of the device.

[0058] The electric push rod 11 in this embodiment adopts a motor-driven screw rod mechanism, which converts rotational motion into stable and controllable linear motion. Its thrust output is stable and the response is sensitive, and it can accurately control the up and down displacement of the rack 12, thereby ensuring the accuracy and synchronism of the opening and closing of the protective cover 5. In this embodiment, the electric push rod 11 drives the rack 12 to drive the gear 10, the rotating shaft 7, the connecting rod 6, and the protective cover 5 to act in sequence, realizing the automatic opening and closing of the protective cover 5, replacing the traditional manual operation method, greatly improving the protection response efficiency, and meeting the requirements of rapid deployment and withdrawal.

[0059] In addition, the protective cover 5 of this embodiment can also be optionally equipped with a limit device and a damping mechanism to ensure that the protective cover 5 automatically stops at the limit position to prevent overstretching or collision; at the same time, the current state of the protective cover 5 can be fed back through a sensor, and combined with the control system to realize automatic opening and closing and safety linkage control. The overall structure has both protection, stability and intelligent control capabilities, and is suitable for the protection requirements of the intelligent total station 3 operating in harsh environments such as the wild or dusty and rainy conditions.

[0060] Furthermore, the monitoring device of this embodiment further includes an energy storage device. A photovoltaic panel 4 is connected to the protective cover 5. The energy storage device is used to store the electric energy converted by the photovoltaic panel 4, and the energy storage device is used to supply power to the driving device, the intelligent total station 3, and the electric push rod 11. In this way, the monitoring device of this embodiment can be powered by solar energy without connecting to an external power supply. The use of the photovoltaic panel 4 not only reduces the dependence on external power supplies, but also improves the stability and autonomy of the system, which is especially suitable for relatively remote monitoring scenarios such as arch dams, ensuring that the system can continue to operate stably without an external power supply.

[0061] Further, the monitoring device of this embodiment further includes an intelligent atmospheric temperature, humidity and pressure measuring instrument 8 for measuring the temperature, humidity and air pressure of the air in real time, and the energy storage device is also used to supply power to the intelligent atmospheric temperature, humidity and pressure measuring instrument 8. The intelligent atmospheric temperature, humidity and pressure measuring instrument 8 can measure the temperature, humidity and air pressure of the air in real time, so as to predict the climatic conditions at the top of the arch dam and determine whether it is suitable to activate the monitoring device of this embodiment for arch dam deformation monitoring. Further, the intelligent atmospheric temperature, humidity and pressure measuring instrument 8 of this embodiment is connected to the protective cover 5. Specifically, it can be connected to the triangular roof of the protective cover 5.

[0062] Further, the monitoring device of this embodiment further includes a control device. The control device is used to receive the monitoring data of the intelligent atmospheric temperature, humidity and pressure measuring instrument 8 and control the action of the electric push rod 11. The control device can automatically control the action of the electric push rod 11 according to the received temperature, humidity and air pressure data of the air, realizing the intelligent control of the electric push rod 11 without manual intervention. For example, when the control device predicts that adverse weather such as rain and snow will occur in the future according to the received temperature, humidity and air pressure data of the air, it can control the electric push rod 11 to act to close the protective cover 5 to protect the intelligent total station instrument 3; if it is predicted that the future weather is suitable for deformation monitoring, then control the electric push rod 11 to act in the reverse direction to open the protective cover 5 so that the intelligent total station instrument 3 can carry out normal monitoring work. Further, the control device of this embodiment is also used to control the action of the driving device to control the sliding path of the track moving device 2 on the arc-shaped guide rail 1. For example, the control device can control the track moving device 2 to slide at equal intervals or back and forth at a predetermined time interval on the arc-shaped guide rail 1, and the control device can also be powered by the energy storage device.

[0063] In the non-measurement state of the monitoring device of this embodiment, the intelligent total station instrument 3 is in a closed protection state, and when measurement is required, the control device controls the protective cover 5 to automatically open, so that the intelligent total station instrument 3 enters the working mode. This can not only effectively avoid the influence of environmental factors such as dust, rain and sand on the intelligent total station instrument 3, but also extend the service life of the intelligent total station instrument 3 and improve the long-term reliability of the equipment.

[0064] In addition, the protective cover 5 of this embodiment is provided with an intelligent atmospheric temperature, humidity and pressure measuring instrument 8 for monitoring environmental conditions. The intelligent atmospheric temperature, humidity and pressure measuring instrument 8 has a weather monitoring function. In case of bad weather (such as heavy rain, strong wind, extreme high and low temperatures, etc.), the protective cover 5 can be automatically triggered to close through the control device to ensure that the intelligent total station instrument 3 is protected from bad environments. When the weather is good, the control device controls the protective cover 5 to automatically open, so that the intelligent total station instrument 3 is in a normal measurement state to ensure the normal progress of the measurement task.

[0065] The monitoring system of this embodiment provides safety protection for the intelligent total station 3 by setting up a protective cover 5, and improves the intelligence level by combining the intelligent atmospheric temperature, humidity and pressure measuring instrument 8 and the photovoltaic panel 4 for power supply, which can improve the measurement accuracy, enhance the durability of the equipment, and realize long-term unattended automatic monitoring.

[0066] In this embodiment, multiple rear-view reference datum points are arranged on the mountains on both sides of the arch dam to obtain deformation data of the arch dam surface at different positions. The arrangement of the rear-view reference datum points takes into account the force distribution characteristics and key deformation areas of the arch dam. Through the reasonably distributed rear-view reference datum points, the overall deformation trend and local abnormal conditions of the arch dam surface can be fully and accurately reflected.

[0067] The arc guide 1 is used to guide the track moving device 2 to slide and move correspondingly between multiple rear-view reference datum points. The setting of the arc guide 1 ensures that the track moving device 2 can accurately and stably move to the measuring point position of each rear-view reference datum point, thereby ensuring the consistency and stability of the measurement data of the intelligent total station 3. The track moving device 2 is precisely positioned through an electric drive and automatic control system, and the intelligent total station 3 is installed on it. The track moving device 2 and the arc guide 1 are synchronized through a high-precision positioning sensor to ensure the accuracy and efficiency of the measurement.

[0068] The intelligent total station 3 is arranged on the track moving device 2. When the track moving device 2 drives the intelligent total station 3 to move and measure between different rear-sight reference datum points through the arc guide rail 1, the deformation data of the arch dam is collected in real time by the intelligent total station 3, and the data is transmitted to the data processing module. The intelligent total station 3 adopts high-precision laser measurement technology, which can quickly and accurately obtain distance and angle data, ensuring that the deformation data of each rear-sight reference datum point can accurately reflect the real-time status of the arch dam. In this way, automatic measurement can be performed at different positions by one intelligent total station 3, which can save equipment costs and manpower.

[0069] The data processing module is electrically connected to the intelligent total station 3, and is used to receive, analyze and process the measurement data, perform error correction and data adjustment, and finally output the deformation monitoring results of the arch dam. The adjustment algorithms adopted by the data processing module include the least squares method, the Kalman filter algorithm and the rear intersection adjustment. The data processing module can perform error correction and dynamic adjustment optimization on the collected original data, and eliminate the influence of measurement errors, environmental interference and other factors. After adjustment, the final output is a high-precision deformation monitoring result of the arch dam, including deformation curves, trend graphs, local deformation anomaly graphs and related statistical analysis reports. These data can provide strong support for the safety assessment and maintenance decision-making of the arch dam.

[0070] During operation of the monitoring system of this embodiment, the track moving device 2 is set to move at fixed time intervals, so that the track moving device 2 drives the intelligent total station 3 to make equidistant displacements and stop accurately at each measuring point. When the track moving device 2 stops, the intelligent total station 3 immediately starts measurement in combination with the rear-sight reference datum point, stores the position information of the current point, and collects the three-dimensional coordinate data of the target point on the surface of the arch dam through the intelligent total station 3, including X (horizontal displacement), Y (longitudinal displacement), and Z (elevation change). Then, repeated measurements are performed by setting fixed time intervals (such as daily, weekly or monthly), and data is accumulated for a long time. Finally, after repeated measurements, the linear displacement deformation data of the arch dam at different periods can be obtained to reflect the movement trend of the dam body.

[0071] The driving device of this embodiment can use any control method in Table 1 below to drive the track moving device 2 to move:

[0072] Table 1

[0073]

[0074]

[0075] In a specific control method, the track moving device 2 is set to move once every 2 meters and stop for 10 seconds; a PLC controller or edge computing terminal (such as Raspberry Pi, Jetson) is used to control the overall process. Communication method: RS232 / 485 serial port command (commonly used in industrial scenarios); TCP / IP or wireless communication (suitable for distributed and remote control); the intelligent total station 3 automatically aligns and measures after receiving the "measurement command".

[0076] This embodiment uses data from multiple measurement points to draw a three-dimensional displacement curve of the intelligent total station 3 at each time point, and extracts key deformation data, such as the deformation characteristics of the dam surface. Then, through mathematical fitting methods (such as least squares method, spline interpolation), the rear-view reference datum point data of different periods are fitted into a continuous arch dam morphology curve, and a deformation model of the dam body is established. Then, the differences in the arch dam curves at different time points (such as 1 month, 3 months, 1 year, and 3 years) are compared, thereby inferring the deformation trend of the arch dam.

[0077] For example: short-term deformation (weeks to months): may be related to changes in temperature, rainfall or reservoir water level; long-term deformation (several years): may reflect problems such as creep, settlement, and structural deformation of the dam body.

[0078] If the difference between the curves in different time periods exceeds the safety threshold, it indicates that the arch dam may have abnormal deformation. The system can automatically issue an alarm to remind the engineering management personnel to take measures, such as reinforcement, pressure release, and seepage monitoring. At the same time, combined with long-term monitoring data, the future deformation trend of the arch dam can be predicted, and maintenance measures can be taken in advance to improve the safety and service life of the arch dam.

[0079] The monitoring system of this embodiment drives the intelligent total station 3 through the track moving device 2 for fixed-point interval measurement. After multiple measurements, the linear displacement deformation data of the arch dam is obtained, and then the deformation curve of the arch dam is fitted. By comparing the curves in different time periods, the deformation trend of the arch dam can be accurately analyzed, and an alarm can be provided when an abnormal situation occurs. The monitoring system of this embodiment not only improves the measurement accuracy but also realizes intelligent and unmanned monitoring, which is an important technical means for the long-term safety assessment and disaster prevention warning of arch dams.

[0080] The monitoring system of this embodiment is based on the track moving device 2 for automated measurement, combined with intelligent data processing algorithms, reducing the possibility of manual intervention and operation errors. The track moving device 2 can automatically move according to the preset path, ensuring the efficiency and accuracy of monitoring, reducing the workload of manual inspections, and being able to operate continuously in a complex environment. At the same time, through the movement of the track moving device 2 and the precise measurement of the intelligent total station 3, combined with various adjustment algorithms (such as the least squares method, Kalman filtering algorithm, and resection adjustment) adopted by the data processing module, systematic errors and environmental interferences can be effectively eliminated, significantly improving the accuracy of the arch dam deformation data. This high-precision monitoring can provide reliable data support for engineers, thus discovering potential safety hazards of the arch dam in advance.

[0081] The multi-station automatic network adjustment algorithm adopted by the data processing module of this embodiment aims to optimize the accuracy of arch dam deformation monitoring by integrating data from multiple back sight reference points and effectively correct various errors in the measurement process. The core of this algorithm includes the least squares method, Kalman filtering algorithm, and resection adjustment, which are used to optimize the data accuracy from multiple back sight reference points. The multi-station automatic network adjustment algorithm can effectively identify and correct systematic errors and random errors in the measurement process.

[0082] In this embodiment, first, an observation equation is established: Multiple stations (unknown points) form a free network through angle measurement, distance measurement, or azimuth angle, and its mathematical model is usually expressed as:

[0083] V = AX - L

[0084] Where, V is the error vector (residual of the observed value); A is the coefficient matrix (partial derivative matrix); X is the unknown to be solved (correction value of the station coordinates); L is the observed value vector (calculated from the observed data and initial estimate).

[0085] In the observation network, the observation types include:

[0086] 1. Side length observation (distance between stations):

[0087]

[0088] where X i , Y i are the coordinates of the i-th known point (control point);

[0089] X j , Y j are the coordinates of the j-th known or unknown point;

[0090] S ij is the distance observation value from point i to point j.

[0091] 2. Angle observation (angle between stations):

[0092]

[0093] where θ ijk is the angle observation value; X i , Y i are the coordinates of point i; X j , Y j are the coordinates of the observation station j;

[0094] X k , Y k are the coordinates of point k.

[0095] 3. Azimuth observation:

[0096]

[0097] where α ij is the azimuth from point i to point j; X i , Y i are the coordinates of point i; X j , Y j are the coordinates of point j.

[0098] Since all observed values have errors, in this embodiment, the data processing module corrects the errors of the observed values in the observation equation. Specifically, weighted least squares is used to perform weighted fusion on multi-observed data to control error propagation, so as to solve the problem of poor consistency of multi-station observed data; resection adjustment is used to correct the initial position of the observation station to eliminate systematic errors, so as to solve the problem that the station position error affects the solution of the target point; the Kalman filter algorithm is used to update the state estimate in real time to eliminate noise and predict the deformation trend, so as to solve the problems of dynamic environment interference and observed value jitter. The data processing module of the embodiment of the present invention combines the weighted least squares method, the Kalman filter algorithm and resection adjustment, and uses the collaborative processing of the three algorithms to construct an error correction system combining dynamic + static + inverse solution, effectively solving the problem of long-term instability of arch dam deformation data.

[0099] For example, the least squares adjustment of the observed values is carried out as follows;

[0100] Establish a linearized equation: Since the measurement equation is usually non-linear, it needs to be linearized by Taylor expansion:

[0101] f(X + ΔX) ≈ f(X) + J·ΔX

[0102] Where: f(X) is the non-linear function model of the observed value, representing the theoretical function of the measured value with respect to the unknown parameter X; X is the unknown parameter vector (the initial coordinate estimates of multiple survey stations);

[0103] is the Jacobian matrix (partial derivative matrix); ΔX is the coordinate correction amount to be solved.

[0104] Expand it into matrix form: A·ΔX = L - f(X)

[0105] Where: A is the Jacobian matrix, ΔX is the coordinate correction amount to be solved, and L - f(X) is the observation residual; L is the vector of measured observed values, such as measured angles, side lengths, azimuth angles, etc.

[0106] Least squares adjustment solution: That is, use the least squares estimation to solve the optimal coordinate correction amount:

[0107] ΔX = (A T PA) -1 A T PL

[0108] Where: P is the weight matrix (usually determined by the measurement accuracy), and A T PA is the normal equation matrix.

[0109] The final corrected coordinates of the unknown point are:

[0110] X new= X old + ΔX

[0111] Wherein, X new is the corrected point coordinate, and X old is the point coordinate before correction.

[0112] The least squares method used in this embodiment is a classical adjustment method, which can solve the optimal solution of the system by minimizing the sum of squares of measurement errors. Its basic idea is to construct a function of the sum of squares of errors and determine the optimal measured value by optimizing this function. Since it can effectively weight the observed data of each backsight reference benchmark point and optimize and correct the errors in combination with the accuracy requirements of the backsight reference benchmark point, the basic formula of the least squares method is as follows:

[0113]

[0114] Wherein, f(x) is the sum of squares of errors, w i is the weight of the i-th backsight reference benchmark point, and y i is the measured value of the i-th backsight reference benchmark point, is the value predicted according to the model, and x is the parameter to be optimized.

[0115] The least squares method solves the optimal parameter x by optimizing the above sum-of-squares-of-errors function, thereby realizing the adjustment of data.

[0116] In order to avoid the overall translation or rotation of the free network, constraints need to be introduced. The common methods include: fixing the coordinates of certain known control points and applying translation and rotation constraints. For example, applying a fixed constraint to a certain reference point (X0, Y0):

[0117] X i = X i,固定 , Y i = Y i,固定

[0118] The Lagrange multiplier method is introduced into the matrix to solve the constraint problem and adjust the normal equation:

[0119]

[0120] Where: C is the constraint matrix, d is the constraint vector, and λ is the Lagrange multiplier; after solving for ΔX, update the coordinates of the unknown points.

[0121] Since the measurement equation is non-linear, the solution process requires iteration:

[0122] Initial estimate (using approximate coordinates as the initial value), calculate the error equation (construct A, L), solve the adjustment (calculate ΔX), update the coordinates, and check the convergence (if ||ΔX|| is less than the threshold, stop).

[0123] Generally, the convergence criterion is:

[0124] max(|ΔX|) < ε

[0125] Error evaluation: After the adjustment is completed, it is necessary to calculate the covariance matrix to evaluate the accuracy of the coordinates of the unknown points:

[0126] C X = (A T PA) -1

[0127] The standard error of each unknown point is calculated as follows:

[0128]

[0129] In addition, it is also necessary to calculate the variance of unit weight:

[0130]

[0131] where: n is the number of observation equations, u is the number of unknown parameters. If is too large, it indicates that the observation error is large, and it may be necessary to re-measure or adjust the weights.

[0132] Calculation example: Suppose there are 3 survey stations A, B, C, and 2 unknown points P, Q, and the following are provided:

[0133] Angle observations θ PA , θ PB , θ QA , θ QB ; distance observations S AP , S BP , S AQ , S BQ ;

[0134] The Jacobian matrix A and the observation vector L can be constructed, and then X P , Y P , X Q , Y Q .

[0135] The Kalman filter algorithm is a recursive optimal estimation method based on a dynamic model. When processing real-time data, it can continuously update the estimated value according to the state of the system and the observation data, reducing the impact of errors on the measurement results. The Kalman filter algorithm is especially suitable for processing dynamic data with noise, and can effectively improve the real-time performance and accuracy of the monitoring system. The Kalman filter can continuously update the estimated value according to the state of the system and the observation data, thereby reducing the impact of measurement errors on the monitoring results. The core idea of the Kalman filter is based on the weighted average of the current estimated value and the observed value for gradual correction; the state update formula of the Kalman filter is:

[0136] x k = x k-1 + K k (z k - H k x k-1 )

[0137] where x k is the estimated value at the current moment, K k is the Kalman gain, z k is the current observation value, H k is the measurement matrix, and x k-1 is the estimated value at the previous moment.

[0138] The calculation formula for the Kalman gain K k is as follows:

[0139]

[0140] where P k-1 is the estimated covariance at the previous moment, and R is the observation noise covariance.

[0141] By combining the Kalman filtering algorithm and resection adjustment, the error is corrected in real time through the following correction formula:

[0142]

[0143] where k is the current time step number, that is, "the k-th moment" or "the k-th observation period", which is used to identify the current moment being processed in the sequential estimation;

[0144] is the estimated value of the state at the previous moment k - 1 after correction, that is, the prediction or estimation result of the state of the system at the k - 1 moment, which is the basis for the update at the current moment;

[0145] is the estimated value at the current moment after correction, y k is the current observation value, K k is the Kalman gain, H k is the measurement matrix, is the known position of the reference point, and β is the resection correction coefficient, which is used to reduce the error accumulation caused by sensor errors and environmental interference during the measurement process.

[0146] The advantage of the Kalman filtering algorithm lies in its real-time performance and self-adaptability, which can effectively process data with noise and predict and correct future measurement values.

[0147] Resection adjustment, as an error correction method specifically for multi-backsight reference benchmark data, can eliminate the cumulative error in the measurement process through reverse adjustment. This technology is particularly suitable for occasions with high measurement accuracy requirements. By analyzing the measured data backward, correcting the error based on the adjustment coefficient, and combining the relative positions of the backsight reference benchmarks, the data accuracy is gradually optimized. Through this method, the influence of measurement errors on the overall deformation monitoring results can be effectively reduced, ensuring that the final output deformation data has high accuracy and high credibility. Resection adjustment eliminates the cumulative error in the measurement process by performing reverse adjustment on the multi-backsight reference benchmark data, and the error correction is specifically carried out through the following formula:

[0148]

[0149] where i represents the i-th observed data point, j represents the j-th known reference point, X is the set of parameters of the fitting function (or the vector of model parameters to be optimized), that is, the variable solved by minimizing the objective function, x j is the estimated position coordinate of the j-th reference point, y i is the observed value of the i-th backsight reference benchmark, f(x i ) is the fitting function, is the reference point position, λ is the adjustment coefficient, and the coefficient is dynamically adjusted according to the measurement accuracy and error characteristics to reduce the influence of errors on the overall data accuracy.

[0150] At the same time, the key of this technology lies in optimizing the data by calculating the error correction coefficient based on the relative position relationship between multiple backsight reference benchmarks according to the data of multiple backsight reference benchmarks;

[0151] The basic formula of resection adjustment is as follows:

[0152]

[0153] where Δx is the correction amount, W i is the weight matrix of the i-th backsight reference benchmark, z i is the observed value of the i-th backsight reference benchmark, H i is the measurement matrix of the i-th backsight reference benchmark.

[0154] In this embodiment, the data processing module combines the least squares method, Kalman filtering, and resection adjustment. By dynamically optimizing the data accuracy of each back sight reference benchmark point, it can effectively reduce systematic errors and random errors, thereby improving the overall accuracy of the arch dam deformation monitoring system. These intelligent algorithms (least squares method, Kalman filtering, resection adjustment) adopted in this embodiment are not mutually exclusive, but rather tools with different focuses that can be used in combination. In a high-precision, long-term, and dynamically updated data system such as arch dam deformation monitoring, the three algorithms are used in combination to make them each perform their own functions and cooperate for optimization to achieve higher accuracy and robustness.

[0155] Among them, resection adjustment is used in the fixed-point layout stage of the intelligent total station 3 to calculate the initial station coordinates. Resection adjustment has the characteristics of simplicity and single-point solution, and is suitable for fixed-point layout or new station positioning; the least squares method is used in the adjustment stage of static network measurement data, and the overall solution of the measurement network is optimized through multi-point observation joint adjustment. It has the characteristics of classical static error optimization and can reduce systematic errors overall; the Kalman filtering algorithm is used in the dynamic monitoring stage of arch dam deformation. It is particularly suitable for time series data processing and can be used for dynamic time series data estimation, filtering, and prediction. It has the characteristics of real-time update and can effectively process system drift and noise.

[0156] The following uses a specific embodiment to illustrate how the data processing module of this embodiment combines the three algorithms to collaboratively eliminate deformation data errors:

[0157] 1. Initial network construction stage:

[0158] Use the resection adjustment algorithm to quickly obtain the initial coordinates of the station; establish a preliminary control network structure.

[0159] 2. Periodic measurement data processing stage:

[0160] All the data of the observation points enter the least squares adjustment model for static error optimization to eliminate systematic deviations; obtain a set of "optimal" measuring point coordinates.

[0161] 3. Real-time dynamic monitoring stage:

[0162] Introduce the Kalman filtering algorithm in continuous multi-period measurements to fuse the current observation value with the estimated value of the previous moment; correct random errors; achieve prediction and filtering of dynamic deformation trends; and adapt to the non-linear response of arch dam deformation or external environmental disturbances.

[0163] For example: Use the track moving device 2 to drive the intelligent total station 3 to continuously measure the arch dam. At each new measurement point, use the resection adjustment algorithm to determine the current station position; after a complete round of measurement, use the least squares method to jointly adjust the observed values of all points by resection to remove redundant errors; send the adjusted results obtained from each measurement into the Kalman filter for time series optimization to predict the change trend at the next moment, realizing dynamic deformation monitoring and early warning.

[0164] In a specific observation scenario, there are three backsight reference points A, B, and C (known coordinates); three stations M1, M2, and M3, collecting the position of the target point P; measure once a day, monitoring period: 7 days.

[0165] The data processing module corrects the observation data according to the following steps:

[0166] ① Use resection adjustment to find the position of station M1;

[0167] M1 measures the angles of points A, B, and C: θ1A = 45.2°, θ1B = 60.1°, θ1C = 74.3°;

[0168] Use the inverse solution formula to find the spatial position of M1, with an error < ±2m;

[0169] Utilize resection adjustment to solve the global offset problem caused by "station installation error", improve the geometric accuracy of the station, control the error propagation source, and reduce the systematic error of the station.

[0170] ② Conduct joint adjustment of the target point P by multiple stations (weighted least squares method);

[0171] Observation station Observed value XP (m) Standard deviation σ (mm) Weight wi M1 120.02 ±0.9 1.23 M2 120.05 ±1.2 0.98 M3 119.94 ±1.5 0.72

[0172] Use:

[0173] ΔX = (A T PA) -1 A T PL

[0174] Where: P is the weight matrix (usually determined by the measurement accuracy), A T PA is the normal equation matrix, ΔX is the coordinate correction amount to be solved, and L is the residual vector of the observed value and the function value;

[0175] Point P is synthesized from the different positions measured by multiple stations A, B, and C into a unique optimal solution, and the optimal position estimate of P is 120.01m.

[0176] Utilize the weighted least squares method to reasonably allocate the weights of the stations, exclude the influence of abnormal stations, improve the coordinate accuracy of the target point, and offset the error interference caused by data quality fluctuations and environmental factors.

[0177] ③ Continuously measure for 7 days, and use the Kalman filter to update the position of point P:

[0178] Date Original observation (m) Prediction before filtering (m) Updated value after filtering (m) D1 120.01 - 120.01 D2 120.02 120.01 120.015 D3 120.06 (jitter) 120.015 120.03 D4 120.05 120.03 120.04

[0179] Use the Kalman filter update formula to filter out environmental errors such as accidental noise, instrument jitter, thermal expansion and contraction: x k = x k-1 + K k (z k - H k x k-1 )

[0180] Obtain the "latest state" and "deformation trend prediction" of the target point P.

[0181] In this embodiment, the Kalman filter algorithm is used to smooth the data in real time, suppress the mutation points, improve the continuity and real-time performance of the observed data, and reduce the dynamic drift and environmental disturbance, such as the errors caused by water level fluctuations and instrument instability caused by temperature differences.

[0182] In this embodiment, the application of the weighted least squares method in multi-station adjustment can optimize the weighted correction of data by dynamically adjusting the weight values of each back sight reference benchmark point according to its reliability and error characteristics. The formula of the weighted least squares method is:

[0183]

[0184] Among them, f(x) is the weighted least squares objective function, which is used to measure the weighted sum of squares of the errors between all observed values and the fitted values, and is the function that needs to be minimized in the fitting. y i is the actual observed value (such as measurement coordinates, distances, angles, etc.) of the i-th back sight reference benchmark point, obtained by the measuring instrument. is the theoretical fitted value calculated for the i-th point based on the current model parameters x, that is, the predicted value or model value of y i , and w i is the weight of the i-th back sight reference benchmark point, and the weight value is dynamically adjusted according to the reliability of the back sight reference benchmark point and the measurement conditions.

[0185] In the embodiment of the present invention, by combining the resection adjustment, the weighted least squares method, and the Kalman filter algorithm, the data processing module can achieve high-precision and high-efficiency error correction when processing data from multiple back sight reference benchmark points, and greatly improve the real-time performance and accuracy of the monitoring system. For the deformation monitoring task, the data processing module can flexibly adjust the algorithm parameters according to the on-site environment and measurement conditions, and continuously optimize the adjustment effect.

[0186] The three algorithms ( resection adjustment, weighted least squares method, Kalman filter algorithm ) in the embodiments of the present invention are nested, called, and run collaboratively according to different stages of the measurement process to construct a dynamically optimized data processing system, improving measurement accuracy, robustness, and real-time performance, and being particularly applicable to the identification and early warning of trend micro-deformations in continuous deformation monitoring tasks.

[0187] In the embodiments of the present invention, by jointly using three algorithms, in the initial stage of station positioning initialization, resection adjustment can be used to quickly locate the station and form the basic structure of the measurement network; in the multi-station data fusion stage, the weighted least squares method can be used to improve the adjustment accuracy, strongly suppress outliers, and support multi-source redundant observations; in the stage of continuous deformation prediction of the arch dam, the Kalman filter algorithm can be used to optimize time continuity and improve real-time performance and dynamic tracking ability.

[0188] The monitoring system in the embodiments of the present invention can automatically adjust weights and algorithm parameters according to the state of each reference point; simultaneously consider spatial geometric accuracy + data redundancy processing + time series prediction ability in real time; greatly improve the monitoring accuracy, robustness, and intelligent level of the system.

[0189] The core advantage of the multi-station automatic networking adjustment algorithm in the embodiments of the present invention is that it can dynamically adjust the data weights in the adjustment process according to the accuracy requirements and relative position relationships of different back sight reference benchmark points, and eliminate the accumulation of systematic errors through resection adjustment. This algorithm can effectively improve the accuracy and reliability of arch dam deformation monitoring and reduce the interference of external factors on the measurement results.

[0190] In addition, the Kalman filter algorithm combined with resection adjustment can perform real-time correction and update of data, and is particularly applicable to the arch dam deformation monitoring task with dynamic characteristics. By comprehensively considering multi-source data and systematic errors, this algorithm can provide real-time and reliable monitoring results under high-precision requirements, providing a scientific basis for the safety assessment of the arch dam.

[0191] In practical applications, the data adjustment process not only needs to consider the actual position and accuracy requirements of the backsight reference benchmark points, but also must dynamically adjust the weights of the data according to the relative positions of the backsight reference benchmark points and their impacts on the overall measurement accuracy. This is because the relative positions between the backsight reference benchmark points have a crucial impact on the accuracy and stability of the data. Through a reasonable weighting strategy, it is possible to flexibly adjust the contribution degrees of the data according to the reliability, importance of each backsight reference benchmark point, and the characteristics of its location. Especially for points at different positions within the measurement area, some points may be less affected by factors such as environmental conditions and external interferences, and the data is more reliable; while the backsight reference benchmark points at some positions may be more affected, and the credibility of their data is lower. By adjusting the weights of these points, the negative impacts on the overall measurement results can be reduced, and the accuracy and stability of the data can be further improved.

[0192] In addition, by combining resection adjustment for data correction, errors caused by sensor errors, environmental changes or other external factors can be eliminated through reverse correction of the measurement data, thereby optimizing the deformation monitoring results. Specifically, resection adjustment uses the method of reverse adjustment to eliminate the cumulative errors that occur during the measurement process through the intersection correction of the backsight reference benchmark point data. This process is particularly suitable for deformation monitoring with high-precision requirements and can effectively reduce the impacts of external environmental changes and sensor errors on the measurement results. By adding corrections for the errors of each backsight reference benchmark point during the adjustment process, it can be ensured that the finally output deformation data has higher accuracy and credibility.

[0193] In this way, the adjustment algorithm effectively solves the systematic errors and random errors that occur during the data synthesis process of multiple backsight reference benchmark points. Systematic errors may stem from factors such as calibration problems of measurement equipment and external interferences, while random errors usually manifest as random fluctuations caused by factors such as environmental changes and measurement errors. Through precise correction of these errors, the adjustment algorithm can significantly improve the reliability, accuracy and real-time performance of the deformation monitoring system. This can not only help engineers obtain more accurate data during the monitoring process, but also ensure the stability of the deformation monitoring system, making the monitoring results more scientific and operable.

[0194] For example, during the monitoring of an arch dam, due to environmental changes around the dam, such as temperature, humidity, or water level fluctuations, etc., it may lead to an increase in the errors of the measuring equipment. At this time, the data adjustment algorithm, through the combination of the least squares method, Kalman filtering, and resection adjustment, etc., can correct these errors in real time. The Kalman filtering algorithm, as an adaptive filtering method, can continuously update the estimated value in a dynamic environment, handle problems such as sensor noise or data loss, and improve the real-time performance of the system. Combining resection adjustment can further optimize the correction effect, reduce the influence caused by systematic errors or environmental changes, and ultimately make the monitoring results more stable and reliable.

[0195] Through the reasonable combination of algorithms and technologies, the deformation monitoring system can provide more accurate and stable deformation data in the actual monitoring of the dam. These data provide a solid scientific basis for the safety management, long-term maintenance decision-making, and engineering optimization of the dam. Specifically, accurate deformation monitoring data can help engineers identify potential structural problems or deformation trends, so as to take preventive measures and effectively avoid the occurrence of disaster accidents. In addition, the monitoring results can also provide important support for the operation and management of the dam, such as providing references for the arrangement of maintenance cycles, the formulation of structural reinforcement measures, and the prediction of deformation trends, ensuring the safety and long-term stability of the dam operation.

[0196] During the deformation monitoring process, as the number of backsight reference points continuously increases, the adjustment algorithm can automatically adapt to the new measurement data, adjust the weights of the data in real time, and make the correction of the monitoring data more accurate. This adaptive data processing ability enables the system to maintain a high degree of accuracy and stability when dealing with complex and changing environmental conditions, ensuring the scientific nature and reliability of the monitoring results. Through continuous iterative optimization, the system can also continuously improve the measurement accuracy with the accumulation of monitoring data and the change of environmental conditions, and ultimately provide comprehensive support for the whole life cycle management of the dam.

[0197] In short, through the application of accurate adjustment algorithms and resection adjustment, the automatic arch dam deformation measurement system can effectively overcome the errors existing in the measurement process, provide high-precision deformation data, ensure good stability and accuracy of the system in the real-time monitoring process, and thus provide a scientific and reliable basis for the safety assessment and maintenance decision-making of the dam.

[0198] The data processing module can identify and correct measurement errors in real time, automatically update the deformation data, generate deformation curves, deformation trend graphs, and provide relevant statistical analysis reports;

[0199] Through the integrated data analysis tools, the data processing module can analyze the data of multiple monitoring points, automatically generate the real-time deformation trend of the arch dam, and display the data through a graphical interface to assist engineers in making decisions. The data update process incorporates resection adjustment to ensure the consistency and accuracy of the data in the time series.

[0200] In the arch dam deformation monitoring system, the main task of the data processing module is to analyze, correct, and update the monitoring data in real time. This module can identify and correct the errors generated during the measurement process in real time to ensure that the finally output data has high precision and high reliability. Through the integrated data analysis tools, the data processing module can comprehensively analyze the data of multiple monitoring points and automatically generate deformation curves and deformation trend diagrams to visually display the real-time deformation state of the arch dam, thus assisting engineers in making timely and accurate decisions.

[0201] To achieve this goal, the data processing module adopts a variety of data processing algorithms, especially the combined use of weighted least squares method, Kalman filtering, and resection adjustment. These algorithms can effectively process the data generated by multiple back sight reference benchmark points, optimize the adjustment and error correction process of the data, improve the accuracy of the data, and ensure the consistency and reliability of the data in the time series. Especially resection adjustment, it can dynamically adjust the errors and eliminate the cumulative errors generated during the measurement process, thus ensuring the accuracy of the deformation monitoring results.

[0202] Through the integrated real-time data analysis tools, the data processing module can process the data from different monitoring points and generate the real-time deformation trend of the arch dam. These tools automatically identify and correct the anomalies in the data based on the geographical locations of the back sight reference benchmark points, sensor characteristics, and real-time data. At the same time, through the graphical interface, users can visually view various forms of data presentations such as deformation curves and trend diagrams, which is convenient for engineers to quickly understand the deformation state of the arch dam. These data not only contribute to real-time monitoring but also provide a decision-making basis for the long-term safety management of the dam;

[0203] During the deformation monitoring process, the data processing module adopts resection adjustment. By inversely adjusting the observation data of multiple back sight reference benchmark points, it effectively reduces the influence caused by factors such as systematic errors, sensor errors, and environmental changes. This process ensures the consistency of the data in the time series, enabling the deformation data to maintain high precision and high stability during long-term monitoring.

[0204] The weighted least squares method is one of the key error correction tools in the data processing module. This method dynamically adjusts the weights of different back sight reference benchmark points, and performs weighted adjustment on the data according to the reliability, conditions, and accuracy requirements of the back sight reference benchmark points. The weighted least squares method can make more effective use of the data of each back sight reference benchmark point. Especially when some back sight reference benchmark points may be affected by external interference or equipment errors, by adjusting the weights, it ensures that the error correction process is more accurate.

[0205] The formula of the weighted least squares method is as follows:

[0206] A T WAΔx=A T Wb

[0207] Where A is the measurement design matrix, W is the weight matrix, Δx is the unknown correction quantity to be solved, and b is the error term of the observed quantity. The weight matrix W is dynamically adjusted according to the reliability and conditions of the back sight reference benchmark points to ensure more accurate adjustment and correction of the deformed data.

[0208] The data processing module uses the weighted least squares method for error correction. The weighted least squares method optimizes the data through the following formula:

[0209]

[0210] Where, w i is the weight of the i-th back sight reference benchmark point, λ is the resection adjustment correction coefficient, and the weight value w i is dynamically adjusted according to the error variance, stability, measurement conditions, etc. of each back sight reference benchmark point, making the reliable points have higher weights and the abnormal points have less influence, ensuring accurate adjustment and correction of the deformed data.

[0211] By dynamically adjusting the weights of different back sight reference benchmark points, the weighted least squares method can effectively solve the error problems caused by the low data accuracy of some back sight reference benchmark points, ensuring the reliability of the overall monitoring results. The determination of the weight value is based on the reliability, measurement accuracy, environmental conditions, and other relevant factors of the back sight reference benchmark points, which directly affect the quality of the data and the final monitoring results. Through a reasonable weighting strategy, the system can optimize the data correction effect, thereby improving the accuracy of the deformation monitoring system.

[0212] Resection adjustment, as an error correction method for multi-backsight reference benchmark data, mainly eliminates the cumulative error in the measurement process through reverse adjustment. This technology is particularly suitable for occasions with high precision requirements. By analyzing the measured data in reverse, correcting the error according to the adjustment coefficient, and combining with the relative position of the backsight reference benchmark, the data accuracy is gradually optimized. Through this method, the influence of measurement error on the overall deformation monitoring result can be effectively reduced, ensuring that the final output deformation data has higher accuracy and credibility.

[0213] The formula for resection adjustment is as follows:

[0214] X corrected = X raw - C·ΔX

[0215] Where X corrected is the corrected data, X raw is the original data, C is the adjustment coefficient matrix, and ΔX is the error amount to be corrected. Through the dynamic correction of the error, resection adjustment can effectively eliminate the deviation caused by factors such as sensor error and environmental change, thereby optimizing the deformation monitoring result.

[0216] In the embodiment of the present invention, the intelligent total station 3 and the data processing module perform data transmission through a wireless communication module to achieve real-time and stable data transmission. The wireless communication module adopts an efficient data transmission protocol to ensure that the data between different backsight reference benchmarks can be transmitted quickly and accurately. This communication method not only improves the efficiency of data acquisition, but also reduces the complexity of laying traditional wired transmission lines, and reduces the cost and difficulty of on-site construction.

[0217] The wireless communication module supports efficient data transmission technologies based on wireless local area network (WLAN) or Bluetooth. The wireless local area network can provide a wide coverage range, which is suitable for multi-point real-time data transmission within the dam monitoring area to ensure that the measurement data at different positions can be quickly transmitted to the data processing center for analysis. The Bluetooth technology is suitable for low-power transmission within a short distance, which is suitable for data exchange between local areas or devices, and has low latency to ensure the real-time nature of the data.

[0218] Through these efficient data transmission technologies, the wireless communication module can realize real-time data acquisition and processing within the dam monitoring area, enabling engineers to grasp the deformation situation and other key parameters of the arch dam at any time and respond promptly to possible deformation anomalies. The real-time data transmission ability of the system provides a solid foundation for the prediction and early warning of deformation trends, improving the response speed and accuracy of the monitoring system.

[0219] In addition, the stability and efficiency of the wireless communication module ensure good data transmission even in harsh environments, guaranteeing the reliability of the system during the long-term operation of the dam. Ultimately, the accurate transmission and timely processing of data provide strong support for the safety management and decision-making of the dam, further enhancing the safety and maintenance efficiency of the arch dam.

[0220] The monitoring system of the embodiment of the present invention further includes an intelligent early warning module. Based on the real-time analysis of deformation monitoring data, the intelligent early warning module can automatically identify and predict the deformation trend of the arch dam, thereby generating a potential risk warning. Through in-depth analysis of real-time data, combined with the deformation trend and change rate, the system can effectively identify potential risk areas and issue timely warnings based on the set warning thresholds, helping engineers take preventive measures.

[0221] The core functions of this module include data trend prediction and risk assessment. When the deformation trend changes, the system will calculate key parameters such as deformation speed and acceleration in real time to predict the future deformation development. For example, when the deformation speed exceeds the threshold or the deformation direction is abnormal, the system will trigger an alarm to remind engineers to check and handle potential hidden dangers in a timely manner.

[0222] The early warning module uses intelligent technologies such as time series analysis, regression analysis, and machine learning models, which can accurately capture early anomalies and predict future deformation trends. By comparing with historical data, the system continuously optimizes the early warning model to improve the accuracy and reliability of the prediction.

[0223] In addition, the module supports the setting of multiple warning thresholds, adjusts the warning criteria for different monitoring areas or deformation characteristics, ensuring that there is no over-warning and that real safety hazards can be captured in a timely manner. The warning information is displayed through a visual interface to help engineers quickly analyze the current risks and take countermeasures, such as arranging inspections or starting reinforcement procedures.

[0224] In summary, through real-time monitoring, analysis, and prediction, the intelligent early warning module improves the response speed and safety of the deformation monitoring system, provides strong decision-making support for dam maintenance and safety management, and ensures the long-term stable operation of the arch dam.

[0225] The data processing module of the embodiment of the present invention has powerful data storage and management functions. It can store the historical deformation data of multiple monitoring points for a long time and ensure the integrity, security, and consistency of the data, providing a solid foundation for later deformation analysis. These historical data not only support the long-term safety assessment of the arch dam but also provide a scientific basis for maintenance decision-making. Through an efficient filing, backup, and management mechanism, the risks of data loss, damage, or tampering are avoided.

[0226] On this basis, the data processing module of this embodiment uses data mining technology to deeply analyze historical data and discovers the laws and trends in the deformation data. By methods such as cluster analysis, time series analysis, and regression analysis, it reveals deformation characteristics, helps identify deformation patterns, and predict future trends. For example, time series analysis can capture the change laws of deformation trends, predict the future deformation rate and direction of the arch dam, and identify potential risks in advance.

[0227] In addition, the data processing module also extracts long-term deformation trends through trend analysis, helps engineers evaluate the overall safety status of the dam, reveals structural weaknesses or stress accumulation areas, and supports maintenance decisions. Finally, the analysis results are presented through visual charts, such as the deformation rate change curve, to help engineers understand the long-term deformation trends and make scientific predictions and plans.

[0228] By combining data storage and management functions, data mining technology, and predictive analysis, the data processing module provides strong support for the safety assessment and maintenance decisions of the arch dam, greatly improving the scientificity and accuracy of dam management.

[0229] The working principle of the monitoring system of this embodiment is as follows:

[0230] Through components such as the track moving device 2, intelligent total station 3, data processing module, and intelligent warning module, automatic acquisition and processing of deformation data at different positions on the surface of the arch dam are realized. First, the monitoring system arranges multiple back sight reference points on the mountains on both sides of the arch dam to obtain the deformation data on the surface of the arch dam. The intelligent total station 3 is installed on the track moving device 2, and the track moving device 2 slides on the fixed arc-shaped guide rail 1 on the top of the arch dam through the driving device, driving the intelligent total station 3 to move and measure between different back sight reference points, and real-time collecting deformation data. The collected deformation data is transmitted to the data processing module through the wireless communication module to ensure stable and real-time transmission of data between different back sight reference points.

[0231] After receiving the deformation data, the data processing module uses the multi-station automatic network adjustment algorithm to correct the data. This algorithm combines the least squares method, Kalman filtering algorithm, and resection adjustment. By dynamically adjusting the data weights, it optimally corrects the errors of each back sight reference point. The adjustment algorithm effectively eliminates systematic errors and random errors according to the relative positions and accuracy requirements of the back sight reference points, further improving the accuracy and stability of the deformation data. The whole process ensures the accuracy of the monitoring data and can automatically generate deformation trend charts and curve charts to assist engineers in making decisions on the real-time deformation of the arch dam.

[0232] In addition, the data processing module also has an intelligent early warning function. By analyzing the change rate and trend of the deformation data, the intelligent early warning module can identify potential deformation anomalies in advance and issue a safety warning when the set threshold is reached, reminding the engineer to take preventive measures. At the same time, the data processing module stores historical data for a long time and conducts mining analysis. By using technologies such as cluster analysis and time series analysis, it discovers the laws and trends in the historical data, helps predict future deformation trends, and provides data support for the long-term safety assessment and maintenance decision-making of the arch dam. The monitoring system of this embodiment can achieve accurate, efficient and stable dam deformation monitoring, ensuring the safe operation of the dam.

[0233] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

Claims

1. An intelligent deformation monitoring system for arch dams based on orbital robots, characterized in that, Comprising: An arc-shaped guide rail, fixed on the top of the arch dam; A rail moving device, slidably connected to the arc-shaped guide rail; An automatic level base, connected to the rail moving device; An intelligent total station, connected to the automatic level base, for collecting deformation data of a plurality of back sight reference benchmark points provided on the top of the arch dam; A driving device, for driving the rail moving device to slide along the arc-shaped guide rail; And, A data processing module, for receiving, analyzing and processing the deformation data. The data processing module uses a multi-station automatic networking adjustment algorithm to correct errors and adjust data for the deformation data, and outputs the deformation monitoring result of the arch dam. The multi-station automatic networking adjustment algorithm includes the least squares method, the Kalman filter algorithm and the resection adjustment.

2. The intelligent deformation monitoring system of an arch dam based on an orbital robot according to claim 1, wherein The data processing module processes the deformation data in the following stages: In the initialization network establishment stage, the resection adjustment is used to obtain the deformation data of each of the back sight reference benchmark points, and a preliminary control network is established; In the regular measurement data processing stage, the least squares method is used to optimize the static error of the deformation data to obtain the optimal measurement point coordinates of each of the back sight reference benchmark points; In the real-time dynamic monitoring stage, the Kalman filter algorithm is used in continuous multi-period measurements to fuse the current observation value with the estimated value of the previous moment, correct the random error, and realize the prediction of the dynamic deformation trend.

3. The intelligent deformation monitoring system for arch dams based on track robots according to claim 2, wherein In the measurement network, the observed deformation data includes the distance between stations, the angle between stations and the azimuth of the station, and the least squares adjustment is used to correct the error of the deformation data.

4. The intelligent deformation monitoring system for arch dams based on rail robots according to claim 2, characterized in that, In the initialization network establishment stage, the resection adjustment eliminates the cumulative error in the measurement process by performing inverse adjustment on the deformation data of a plurality of the back sight reference benchmark points. The resection adjustment corrects the error through the following formula: where \(i\) represents the \(i\)-th observed data point, \(j\) represents the \(j\)-th known reference point, \(X\) is the set of parameters of the fitting function, and \(x\) j is the estimated position coordinate of the \(j\)-th reference point, and \(y\) i is the observed value of the \(i\)-th back sight reference datum point, and \(f(x\) i ) is the fitting function, is the reference point position, and \(\lambda\) is the resection adjustment correction coefficient; Δx is the correction amount, and W i is the weight matrix of the i-th rear sight reference benchmark point, and z i is the observed value of the i-th rear sight reference benchmark point, and H i is the measurement matrix of the i-th rear sight reference benchmark point.

5. The intelligent deformation monitoring system for arch dams based on rail robots according to claim 4, characterized in that, In the regular measurement data processing stage, the least squares method optimizes the data through the following formula: Among them, w i is the weight of the i-th rear-view reference datum point.

6. The intelligent deformation monitoring system for arch dams based on rail robots according to claim 4, wherein, In the real-time dynamic monitoring stage, the Kalman filter algorithm corrects the error in real time through the following correction formula: where k is the current time step number, is the estimated value at the current time after correction, is the state estimated value at the previous time step k - 1 after correction, x k is the estimated value at the current time before correction, y k is the current observed value, K k is the Kalman gain, H k is the measurement matrix, is the known position of the reference point, and β is the resection correction coefficient.

7. An intelligent deformation monitoring system for arch dams based on rail robots according to any one of claims 1-6, characterized in that, The data processing module has a data storage and management function. The data processing module analyzes historical data through data mining techniques. The data mining techniques include cluster analysis and time series analysis. The data mining techniques are used to discover the laws and trends in historical data and predict the future deformation trend.

8. The intelligent deformation monitoring system for arch dams based on track robots according to claim 7, characterized in that, It further includes a wireless communication module and an intelligent warning module. Data is transmitted between the intelligent total station and the data processing module through the wireless communication module; the intelligent warning module generates a potential risk warning based on the deformation trend predicted by the data processing module, and issues a maintenance and safety warning according to the set warning threshold.

9. An intelligent deformation monitoring system for an arch dam based on an orbital robot according to any one of claims 1-6, characterized in that, It further includes a protective cover, and the protective cover includes a first part and a second part that are symmetrically arranged; The described track moving device includes a slider and a cylinder connected to each other. The slider is slidably connected to the arc-shaped guide rail. A cavity is provided inside the cylinder, and a rotating shaft penetrating the cavity inside the cylinder is provided on the cylinder, and at least two rotating shafts are symmetrically provided; the exposed end of each rotating shaft is fixedly connected to a connecting rod, and one end located inside the cavity is fixedly connected to a gear. The other ends of at least two connecting rods are respectively connected to the first part and the second part; An electric push rod is provided inside the cavity, and racks are symmetrically provided on both sides of the outer wall of the electric push rod; at least two gears are symmetrically arranged on both sides of the rack, and the rack is in meshing transmission with the gear.

10. The intelligent deformation monitoring system for arch dams based on rail robots according to claim 9, characterized in that, It further includes an energy storage device. A photovoltaic panel and an intelligent atmospheric temperature, humidity and pressure measuring instrument are connected to the protective cover. The energy storage device is used to store the electric energy converted by the photovoltaic panel. The intelligent atmospheric temperature, humidity and pressure measuring instrument is used to measure the temperature, humidity and pressure of the air in real time. The energy storage device is used to supply power to the driving device, the intelligent total station, the electric push rod, the intelligent atmospheric temperature, humidity and pressure measuring instrument and the data processing module.

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