Automatic slope monitoring method and system

Abstract

The invention discloses an automatic slope monitoring method and system, and the method comprises the steps: transforming an existing stable observation pillar of a slope opposite bank as a double observation station, and installing a high-precision measurement robot with a self-adaptive cooperation module; an inclination angle sensor and a wireless signal feedback unit are integrated on the latticed measuring point prism to transmit attitude and signal intensity information; after observation station orientation and parameter configuration are completed, the measurement robot and the measurement points cooperate in a bidirectional mode, observation parameters are dynamically adjusted, displacement data are calculated through an intersection method, and the displacement data are transmitted to a monitoring center through optical fibers to be verified and corrected. According to the invention, the problems of low monitoring efficiency, large error, incomplete coverage, insufficient precision, poor adaptability, insufficient stability and high operation and maintenance cost in the prior art are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of slope monitoring technology, and in particular to an automated slope monitoring method and system. Background Technology

[0002] As core infrastructure for energy supply and water resource regulation, the stability of high slope structures in large-scale hydropower projects directly affects the overall safe operation of the project and the safety of life and property in the surrounding areas. Especially for hydropower stations with large drainage areas, significant slope elevation differences, and complex geological conditions, high slopes are prone to minute deformations under long-term water erosion, geological tectonic activity, and operational loads. Failure to detect these deformations in a timely manner can lead to major safety accidents such as landslides and collapses. Therefore, monitoring the external deformation of high slopes is a crucial aspect of the safety management of hydropower projects.

[0003] Currently, there are two main monitoring methods in the field of high slope deformation monitoring at hydropower stations. One is the traditional manual monitoring method, which involves setting up shared horizontal and vertical displacement measuring points in areas such as slope walkways and using optical instruments for periodic manual observation. This method requires staff to be on-site, which is not only labor-intensive and inefficient, but also has a long monitoring cycle, making it difficult to achieve real-time dynamic monitoring and timely capture of short-term sudden slope deformation. Furthermore, manual observation is susceptible to the influence of operator skill and environmental factors, inevitably introducing human error and making it difficult to guarantee the reliability and consistency of monitoring data. Taking the monitoring of the left bank high slope of a large hydropower station as an example, such slopes often have dozens or even hundreds of grid-like measuring points set up along multiple walkways. Manually observing each point requires a significant amount of time and manpower, and manual inspection also poses certain safety risks in high-altitude and steep terrain conditions.

[0004] Another type is the gradually developing automated monitoring technology, which mainly uses a single measuring device or a single station layout for monitoring. However, this type of technology still has many limitations: First, the monitoring coverage of a single measuring device or a single station is limited. For measuring points distributed in a grid pattern across multiple trails with a wide coverage area, monitoring blind spots are likely to occur, making it difficult to achieve full coverage of the entire slope without dead angles. Second, the terrain of high slopes is complex, and visibility is greatly affected by terrain obstruction. A single station layout often cannot guarantee effective visibility with all measuring points, leading to a decrease in monitoring accuracy or even failure to complete observations at some points. Third, the accuracy indicators of existing automated monitoring equipment still fall short of the requirements of hydropower engineering specifications, especially in the synchronous monitoring of horizontal and vertical displacements, where data errors are difficult to meet the requirements for high-precision safety early warning. Fourth, for existing manual monitoring points already deployed in existing hydropower stations, existing automated retrofit schemes lack standardized design. The compatibility between new equipment and existing observation facilities is poor, often requiring large-scale construction of civil engineering facilities. This not only makes the retrofit difficult and time-consuming but also increases project costs, making it difficult to adapt to the upgrade and retrofit requirements of existing projects.

[0005] Furthermore, existing automated monitoring systems suffer from insufficient stability and adaptability during operation. Some systems rely on a single communication method, making them susceptible to signal interference or line faults in complex mountainous environments, leading to data transmission interruptions. The power supply system lacks redundancy, failing to cope with power fluctuations under extreme weather conditions. Simultaneously, monitoring equipment is constantly exposed to outdoor environments, making it prone to malfunctions or accuracy drift due to rain, dust, and temperature changes. Existing solutions lack effective self-calibration and maintenance mechanisms, further impacting the continuity and reliability of monitoring data. In summary, existing high slope deformation monitoring technologies for hydropower stations are inadequate in terms of monitoring efficiency, coverage, measurement accuracy, adaptability, and operational stability, failing to meet the actual needs of high slope safety monitoring in large hydropower projects. Therefore, a monitoring technology solution that achieves high precision, full coverage, automation, and adaptability to existing facility upgrades is urgently needed. Summary of the Invention

[0006] The purpose of this invention is to provide an automated slope monitoring method and system to solve the technical problems of low efficiency, large error, and high safety risk in manual monitoring of high slope deformation monitoring in existing large hydropower projects, and the problems of incomplete coverage, insufficient accuracy, poor adaptability to existing facilities, insufficient operational stability and high operation and maintenance costs in automated monitoring.

[0007] The above-mentioned technical objective of the present invention is achieved through the following technical solution: An automated slope monitoring method includes the following steps: Step 1: Station modification. Select an existing stable observation pier on the opposite bank of the slope as the foundation for the dual monitoring stations. Reinforce and modify the observation pier and remove surrounding obstructions. Step 2: Equipment installation. A high-precision measurement robot and an adaptive collaborative module are installed on the two measurement stations respectively. The measurement robot is fixed by a forced centering device. An inclination sensor and a wireless signal feedback unit are integrated on the grid-shaped measurement point prism. The inclination sensor collects the real-time attitude data of the prism, and the wireless signal feedback unit transmits the attitude data and measurement signal strength information to the measurement robot. Step 3: Station orientation and parameter configuration. Select stable plane monitoring points as backsight points to complete station orientation, and set basic observation parameters and adaptive adjustment thresholds. Step 4: Automated intersection measurement. The measurement robot and the measurement point work together in two directions. Based on the received attitude data and signal strength information, the robot measures according to the basic parameters when the threshold is not exceeded. When the threshold is exceeded, the observation parameters are dynamically adjusted and the horizontal and vertical displacement data of the measurement point are calculated by the intersection method. Step 5: Data transmission and verification. The observation data is transmitted to the monitoring center via fiber optic wired communication. The measurement results are then corrected and verified in conjunction with the attitude data.

[0008] In a preferred embodiment, the deployment of the dual measuring stations in step 1 satisfies the following conditions: the altitude is higher than the highest point of the monitored slope, the unobstructed line of sight to all target measuring points is ≥15°, and they are far away from vibration sources and areas with strong electromagnetic interference.

[0009] In a preferred embodiment, in step 2, the center deviation of the forced centering device is ≤ ±1 mm, the levelness of the measured robot after installation is ≤ ±0.1 mm / m, and the level bubble deviation is ≤ 1 division; the measurement accuracy of the tilt sensor is ≤ ±0.05°, the transmission delay of the wireless signal feedback unit is ≤ 100 ms, and the waterproof rating is ≥ IP67; the adaptive collaborative module is used to receive attitude data and signal strength information, dynamically adjust observation parameters, and control the measuring robot to perform intersection measurement.

[0010] In a preferred embodiment, the basic observation parameters in step 3 include a basic observation cycle of once a day and a basic number of four measurements. The adaptive adjustment thresholds include a prism tilt angle deviation threshold of ≥0.3° and a signal strength threshold of ≤-60dBm.

[0011] In a preferred embodiment, the observation parameter adjustment logic in step 4 is as follows: when the prism tilt angle deviation is between 0.3° and 0.5°, the number of repetitions is increased by 2; when the prism tilt angle deviation is greater than 0.5°, the number of repetitions is increased by 4 and the intersection observation angle is adjusted to ≥30°; when the signal strength is ≤-60dBm and ≥-70dBm, the signal gain is increased by 10dB; when the signal strength is <-70dBm, the signal gain is increased by 15dB and the single observation duration is extended by 50%.

[0012] In a preferred embodiment, the data verification criteria in step 5 are: horizontal displacement deviation ≤ ±1.0 mm, vertical displacement deviation ≤ ±1.5 mm, and data missing rate ≤ 0.1%.

[0013] An automated slope monitoring system includes: The dual measurement robot unit consists of two high-precision measurement robots. Each measurement robot integrates an adaptive coordination module. The adaptive coordination module is used to receive attitude data and signal strength information from the measurement points, dynamically adjust the number of measurements, observation angle and signal gain, and control the measurement robot to perform automated intersection measurement. The dual-station deployment unit includes a first station and a second station. The first station is modified from an existing stable observation pier on the opposite bank of the slope, and the second station is modified from a second existing stable observation pier on the opposite bank of the slope. It is used to install and fix the measurement robot. A grid-like measuring point layout unit is used to lay out several surface displacement measuring points along multiple walkways on the slope. The measuring points are shared by both horizontal and vertical displacement. Each measuring point has a prism that integrates an inclination sensor and a wireless signal feedback unit. The inclination sensor is used to collect prism attitude data, and the wireless signal feedback unit is used to transmit data to the adaptive collaborative module. The data transmission unit is used to enable data communication between the measurement robot and the monitoring center, and between the measurement point and the measurement robot. The data processing unit is used to receive observation data and prism attitude data, and to perform intersection calculations, attitude deviation corrections, integrity checks, and accuracy analysis.

[0014] In a preferred embodiment, the high-precision measuring robot has an angle measurement accuracy of ≤0.5″ and a distance measurement accuracy of ≤1mm+1ppm×D, where D is the measurement distance; the adaptive collaborative module has an adjustment response time of ≤1s.

[0015] In a preferred embodiment, in the data transmission unit, the measuring robot and the monitoring center use fiber optic wired communication with a communication rate of ≥100Mbps, and the measuring point and the measuring robot use LoRa wireless communication with a transmission distance of ≥500m; the measuring robot is powered by 220V factory power and is equipped with an isolation voltage regulator module and a surge protection device.

[0016] In a preferred embodiment, in the grid-like measuring point arrangement unit, the surface displacement measuring points are uniformly covered in a grid pattern across the entire monitored slope. The tilt sensor integrated in the prism at each measuring point has a measurement range of ±5°, and the power consumption of the wireless signal feedback unit is ≤3mW. The measuring points achieve attitude data acquisition and signal strength feedback through the prism-integrated components, and work with the adaptive collaborative module to dynamically adjust the observation parameters.

[0017] Compared with existing technologies, this invention reuses existing stable observation piers on the opposite bank of the slope and transforms them into dual monitoring stations. This reduces the cost and difficulty of the transformation, while relying on the dual monitoring station layout and the intersection measurement logic of a high-precision measurement robot to achieve full-range coverage monitoring of grid-like monitoring points. By integrating tilt sensors and wireless signal feedback units into the prisms at the monitoring points, the invention captures prism attitude drift and changes in measurement signal intensity in real time. The adaptive collaborative module integrated into the measurement robot then completes data reception and threshold judgment. When the threshold is not exceeded, the measurement is performed efficiently according to the basic parameters. When the threshold is exceeded, the measurement is dynamically adjusted according to parameters such as the number of repetitions, observation angle, and signal gain. This avoids the impact of attitude deviation and environmental interference on the measurement from the source. Finally, the observation data is transmitted to the monitoring center via fiber optic wired communication, and deviation correction is performed in combination with attitude data, forming a closed-loop monitoring logic of "monitoring point perception - station adaptation - data correction". Its advantages are reflected in the following aspects: it replaces the traditional manual monitoring mode, greatly improves monitoring efficiency, and eliminates human error and on-site operation safety risks; the combination of dual-station intersection measurement and dynamic parameter adjustment not only solves the problem of incomplete coverage by a single station, but also controls horizontal and vertical displacement deviations within a higher precision range, effectively making up for the accuracy shortcomings of conventional automated monitoring; the design of the renovation based on existing observation piers adapts to the upgrade needs of existing projects, significantly reducing construction costs; the adaptive collaborative mechanism and fiber optic transmission ensure the stable operation of the system in complex environments, reducing data loss rate, and at the same time, targeted operation and maintenance are achieved through attitude data and signal strength feedback, further reducing long-term operation and maintenance costs. Attached Figure Description

[0018] Figure 1 It is a diagram showing the layout of the measuring points.

[0019] Figure 2 This is a schematic diagram of the measurement robot's station location and observation scheme. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the accompanying drawings.

[0021] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.

[0022] Reference Figure 1 and Figure 2 As shown, Figure 1 In the diagram, each dot represents a measurement point. Figure 2 In the diagram, the measuring point is located on the slope in the upper half of the diagram, and the two measuring stations are located in the lower half of the diagram, specifically at the positions indicated by the letters WQX01 and WQX02.

[0023] An automated slope monitoring method includes the following steps: Step 1: Station modification. Select an existing stable observation pier on the opposite bank of the slope as the foundation for the dual monitoring stations. Reinforce and modify the observation pier and remove surrounding obstructions. Step 2: Equipment installation. A high-precision measurement robot and an adaptive collaborative module are installed on the two measurement stations respectively. The measurement robot is fixed by a forced centering device. An inclination sensor and a wireless signal feedback unit are integrated on the grid-shaped measurement point prism. The inclination sensor collects the real-time attitude data of the prism, and the wireless signal feedback unit transmits the attitude data and measurement signal strength information to the measurement robot. Step 3: Station orientation and parameter configuration. Select stable plane monitoring points as backsight points to complete station orientation, and set basic observation parameters and adaptive adjustment thresholds. Step 4: Automated intersection measurement. The measurement robot and the measurement point work together in two directions. Based on the received attitude data and signal strength information, the robot measures according to the basic parameters when the threshold is not exceeded. When the threshold is exceeded, the observation parameters are dynamically adjusted and the horizontal and vertical displacement data of the measurement point are calculated by the intersection method. Step 5: Data transmission and verification. The observation data is transmitted to the monitoring center via fiber optic wired communication. The measurement results are then corrected and verified in conjunction with the attitude data.

[0024] This embodiment of an automated slope monitoring method utilizes existing stable observation piers on the opposite bank of the slope, transforming them into dual monitoring stations. This reduces modification costs and construction difficulty while leveraging the dual-station layout and the intersection measurement logic of a high-precision measurement robot to achieve full-range coverage monitoring of grid-like monitoring points. By integrating tilt sensors and wireless signal feedback units into the prisms at the monitoring points, the method captures prism attitude drift and changes in measurement signal intensity in real time. The adaptive collaborative module integrated into the measurement robot then completes data reception and threshold judgment. When the threshold is not exceeded, measurements are performed efficiently according to basic parameters. When the threshold is exceeded, parameters such as the number of repetitions, observation angle, and signal gain are dynamically adjusted to avoid the impact of attitude deviation and environmental interference on the measurement from the source. Finally, the observation data is transmitted to the monitoring center via fiber optic wired communication, and deviation correction is performed in conjunction with attitude data, forming a closed-loop monitoring logic of "monitoring point perception - station adaptation - data correction". Its advantages are reflected in the following aspects: it replaces the traditional manual monitoring mode, greatly improves monitoring efficiency, and eliminates human error and on-site operation safety risks; the combination of dual-station intersection measurement and dynamic parameter adjustment not only solves the problem of incomplete coverage by a single station, but also controls horizontal and vertical displacement deviations within a higher precision range, effectively making up for the accuracy shortcomings of conventional automated monitoring; the design of the renovation based on existing observation piers adapts to the upgrade needs of existing projects, significantly reducing construction costs; the adaptive collaborative mechanism and fiber optic transmission ensure the stable operation of the system in complex environments, reducing data loss rate, and at the same time, targeted operation and maintenance are achieved through attitude data and signal strength feedback, further reducing long-term operation and maintenance costs.

[0025] Furthermore, the deployment of the dual measuring stations in step 1 meets the following requirements: the altitude is higher than the highest point of the monitored slope, the unobstructed line-of-sight angle for all target measuring points is ≥15°, and they are far away from vibration sources and areas with strong electromagnetic interference. The altitude advantage and unobstructed line-of-sight design ensure that the dual measuring stations can fully cover all grid-like measuring points, completely eliminating monitoring blind spots caused by terrain obstruction, providing a stable and effective observation angle basis for intersection measurements, and ensuring the continuity and integrity of measurement signal transmission; being far away from vibration sources avoids the impact of external vibrations on equipment operation, preventing mechanical structure deviation or posture fluctuations of the measuring robot, ensuring the stability of angle and distance measurements, and reducing measurement errors caused by vibration from the source; being far away from areas with strong electromagnetic interference avoids interference from electromagnetic signals to the electronic components of the measuring equipment and data transmission links, avoiding problems such as measurement data distortion and communication interruptions, and ensuring the accuracy and consistency of observation data.

[0026] Furthermore, in step 2, the center deviation of the forced centering device is ≤ ±1mm, the levelness of the measured robot after installation is ≤ ±0.1mm / m, and the deviation of the level bubble is ≤ 1 division; the measurement accuracy of the tilt sensor is ≤ ±0.05°, the transmission delay of the wireless signal feedback unit is ≤ 100ms, and the waterproof rating is ≥ IP67; the adaptive collaborative module is used to receive attitude data and signal strength information, dynamically adjust observation parameters, and control the measuring robot to perform intersection measurement. The tilt sensor, with a measurement accuracy of ≤±0.05°, accurately captures minute attitude drift of the prism, providing high-precision data support for measurement result correction and avoiding measurement distortion caused by attitude deviation. The wireless signal feedback unit, with a transmission delay of ≤100ms, ensures real-time transmission of attitude data and signal strength information, enabling the adaptive coordination module to respond promptly to changes in the measurement point's status and providing a time window for dynamic adjustment of observation parameters. Its waterproof rating of ≥IP67 allows the measurement equipment to adapt to complex outdoor weather conditions on slopes, resisting erosion from rain and dust, extending equipment lifespan, and reducing downtime. The adaptive coordination module, as the core control unit, receives data, dynamically adjusts observation parameters, and controls measurement execution. It links measurement point perception with station depth measurement, achieving closed-loop control of "status perception - parameter optimization - precise measurement," effectively integrating the accuracy advantages and environmental adaptability of each hardware component.

[0027] Furthermore, in step 3, the basic observation parameters include a basic observation cycle of once per day and a basic number of measurements of 4. The adaptive adjustment thresholds include a prism tilt angle deviation threshold of ≥0.3° and a signal strength threshold of ≤-60dBm. The basic observation cycle of once per day satisfies the continuous requirements of long-term slope deformation monitoring, promptly capturing slope deformation trends, while avoiding excessive energy consumption and data redundancy due to overly frequent observations. The basic number of measurements of 4 ensures measurement accuracy under normal conditions while also considering monitoring efficiency and avoiding unnecessary time consumption. The prism tilt angle deviation threshold of ≥0.3° in the adaptive adjustment thresholds can identify significant attitude drift that could affect measurement accuracy, avoiding frequent and ineffective adjustments due to an excessively small threshold, and also prevents key deviations from being missed due to an excessively large threshold. The signal strength threshold of ≤-60dBm accurately determines the effective transmission status of the measurement signal and promptly captures signal attenuation caused by environmental interference. The coordinated setting of these parameters and thresholds provides a clear and reliable basis for judgment for the adaptive coordination module, enabling the monitoring system to operate efficiently according to basic parameters in normal scenarios, and to accurately activate the parameter adjustment mechanism when the measurement point attitude is abnormal or there is environmental signal interference, thus achieving a dynamic balance of "efficient monitoring in normal scenarios + accurate adaptation to abnormal scenarios".

[0028] Furthermore, the logic for adjusting the observation parameters in step 4 is as follows: when the prism tilt angle deviation is between 0.3° and 0.5°, the number of repetitions is increased by 2; when the prism tilt angle deviation is greater than 0.5°, the number of repetitions is increased by 4 and the intersection observation angle is adjusted to ≥30°; when the signal strength is ≤-60dBm and ≥-70dBm, the signal gain is increased by 10dB; when the signal strength is <-70dBm, the signal gain is increased by 15dB and the duration of a single observation is extended by 50%. This hierarchical and progressive observation parameter adjustment logic, based on a precise adaptation and optimization strategy for different degrees of measurement point attitude deviation and signal strength attenuation, enables dynamic on-demand adjustment of observation parameters: For slight deviations of 0.3° to 0.5° in prism tilt angle, only two additional measurements are needed to offset the deviation's impact through multiple observations, ensuring accuracy while avoiding excessive consumption of equipment resources; when the tilt angle deviation is >0.5°, four additional measurements are added and the intersection observation angle is adjusted to the optimal range of ≥30°, which not only strengthens the data redundancy verification capability but also further reduces the systematic error caused by attitude drift by optimizing the observation geometry, ensuring measurement accuracy under severe attitude anomalies. For signal strength attenuation scenarios, slight attenuation of -60dBm to -70dBm only requires a 10dB increase in signal gain to restore transmission stability without additionally extending the observation time, balancing signal optimization and monitoring efficiency; when the signal strength is <-70dBm, a combination strategy of a 15dB gain increase and a 50% extension of the observation time effectively compensates for the transmission quality degradation caused by severe signal attenuation, avoiding data acquisition interruptions or distortion. The entire adjustment logic avoids the waste of resources or insufficient optimization caused by "one-size-fits-all" adjustments, and ensures that the measurement robot can maintain its optimal operating state under different working conditions by accurately matching the degree of anomaly with the adjustment intensity.

[0029] Furthermore, the data verification standards in step 5 are: horizontal displacement deviation ≤ ±1.0mm, vertical displacement deviation ≤ ±1.5mm, and data missing rate ≤ 0.1%. The high-precision verification standards of ≤ ±1.0mm horizontal displacement deviation and ≤ ±1.5mm vertical displacement deviation match the early deformation warning requirements of high slopes in large hydropower projects. This can capture minute deformation trends of the slope, effectively avoiding the omission of early hidden dangers due to insufficient measurement accuracy, and providing reliable data support for slope stability assessment. The stringent requirement of a data missing rate ≤ 0.1% ensures the reliability of the monitoring system from the perspective of data continuity, avoiding the impact of data gaps on the integrity of deformation trend analysis, and ensuring comprehensive tracing of the entire slope deformation process. The verification system, composed of these three elements, not only defines the data quality baseline through high-precision standards but also ensures monitoring continuity with a low missing rate. This forms a closed loop with the previously mentioned adaptive adjustment logic and optimized deployment design, ultimately ensuring that the monitoring data accurately reflects the true deformation state of the slope and continuously provides effective evidence for engineering safety management.

[0030] In this embodiment, a power supply voltage monitoring function can be added to the wireless signal feedback unit of the measurement point to synchronously transmit the device power supply status data to the adaptive coordination module. At the same time, a personalized calibration factor for the measurement point is embedded in the adaptive coordination module. This factor is dynamically updated based on the historical attitude drift pattern and signal transmission stability data of each measurement point, so that the parameter adjustment logic is accurately matched with the individual characteristics of each measurement point. In addition, the collaborative response logic between the measurement point and the station is optimized. When attitude data, signal strength data and power supply status data show cross anomalies (such as signal strength attenuation accompanied by power supply voltage below the threshold), the adaptive coordination module will prioritize triggering power supply warning and related adjustments, rather than simply adjusting the observation parameters. Through the above settings, firstly, the personalized calibration factor upgrades the adjustment of observation parameters from "unified logic adapting to the entire domain" to "precisely adapting to a single measurement point," avoiding adjustment deviations caused by differences in environment and location among different measurement points. This further reduces horizontal displacement deviation to ≤±0.7mm and vertical displacement deviation to ≤±1.0mm, achieving a step-by-step improvement in measurement accuracy. Secondly, the cross-linking of power supply status with attitude and signal data effectively avoids false signal feedback and invalid parameter adjustments caused by insufficient power supply, reducing the false trigger rate of coordinated response and significantly improving the accuracy of the adaptive mechanism and the operating efficiency of the equipment. Thirdly, the personalized calibration factor, by dynamically adapting to the individual characteristics of the measurement point, reduces the wear and tear on the equipment hardware caused by over-adjustment. Combined with power supply status warnings, it further extends the stable operating cycle of the measurement point equipment and the measurement robot, reducing the risk of monitoring interruption due to equipment malfunction.

[0031] In this embodiment, the "Adaptive Coordination Module" is a hardware and software integrated functional module. Its core consists of a processor, data storage unit, and communication interface. The logic for receiving data, threshold judgment, parameter adjustment, and control measurement execution can be implemented using conventional embedded programming. Specifically, an STM32 series processor with a real-time operating system can be used, combined with a control program written in C language. Those skilled in the art can complete the programming implementation according to the functional requirements described in this solution. The dynamic update logic of the "Personalized Calibration Factor for Measurement Points" is as follows: after accumulating 10 valid observation data, the attitude drift amplitude and signal fluctuation range of the measurement point are statistically analyzed using a weighted average algorithm, and the parameter adjustment coefficient is automatically corrected. The weighting weight can be configured according to the validity of the observation data (e.g., the weight of observation data without abnormal interference is set to 1, and the weight of observation data with slight interference is set to 0.8). Those skilled in the art can implement this using conventional data statistical algorithms. The threshold for judging the power supply voltage in "Cross Anomaly" is set to ≤3.0V (for the adapted measurement point). The equipment is commonly powered by a 3.6V lithium battery. This threshold can be adaptively adjusted according to the power supply parameters of the selected measuring point equipment, which falls within the scope of conventional parameter optimization for those skilled in the art. "Power supply early warning correlation adjustment" specifically refers to the adaptive collaborative module pausing the current observation parameter adjustment and sending a low voltage warning message to the monitoring center via LoRa wireless communication, while retaining the current status data of the measuring point. Observation will resume after the maintenance personnel have investigated the power supply problem. Its communication protocol adopts the conventional LoRa communication protocol in this field (such as LoRaWAN). The reinforcement and renovation method of the existing observation pier is as follows: after roughening the top of the observation pier, HRB400 grade steel bars are inserted, C30 fine stone concrete cushion is poured and leveled to ensure that the foundation flatness is ≤±0.2mm / m. This reinforcement method is a conventional technical means for the renovation of observation piers in surveying and mapping engineering. The installation method of the forced centering device is to fix it to the pre-embedded steel plate on the top of the observation pier with expansion bolts. After fixing, a level is used for calibration to ensure that the center deviation and levelness meet the requirements. Example

[0032] Reference Figure 1 and Figure 2 As shown, Figure 1 In the diagram, each dot represents a measurement point. Figure 2 In the diagram, the measuring point is located on the slope in the upper half of the diagram, and the two measuring stations are located in the lower half of the diagram, specifically at the positions indicated by the letters WQX01 and WQX02.

[0033] An automated slope monitoring system includes: The dual measurement robot unit consists of two high-precision measurement robots. Each measurement robot integrates an adaptive coordination module. The adaptive coordination module is used to receive attitude data and signal strength information from the measurement points, dynamically adjust the number of measurements, observation angle and signal gain, and control the measurement robot to perform automated intersection measurement. The dual-station deployment unit includes a first station and a second station. The first station is modified from an existing stable observation pier on the opposite bank of the slope, and the second station is modified from a second existing stable observation pier on the opposite bank of the slope. It is used to install and fix the measurement robot. A grid-like measuring point layout unit is used to lay out several surface displacement measuring points along multiple walkways on the slope. The measuring points are shared by both horizontal and vertical displacement. Each measuring point has a prism that integrates an inclination sensor and a wireless signal feedback unit. The inclination sensor is used to collect prism attitude data, and the wireless signal feedback unit is used to transmit data to the adaptive collaborative module. The data transmission unit is used to enable data communication between the measurement robot and the monitoring center, and between the measurement point and the measurement robot. The data processing unit is used to receive observation data and prism attitude data, and to perform intersection calculations, attitude deviation corrections, integrity checks, and accuracy analysis.

[0034] The automated slope monitoring system in this embodiment uses a dual-station deployment unit, formed by modifying existing observation piers on the opposite bank of the slope, as the installation basis. By reusing existing facilities, a stable and reliable monitoring benchmark is constructed, avoiding the cumbersome process of constructing large-scale civil engineering facilities. The dual-measurement robot unit serves as the measurement carrier, and its integrated adaptive collaborative module establishes a linkage hub between measurement points and stations. The grid-like measurement point layout unit deploys shared horizontal / vertical displacement benchmarks along multiple walkways on the slope. Through the tilt sensor integrated into the prism and the wireless signal feedback unit, the prism attitude data is collected in real time and transmitted to the adaptive collaborative module. The data transmission unit establishes a two-way communication link between the measurement points and the measurement robot, and between the measurement robot and the monitoring center. Finally, the data processing unit receives the observation data and attitude data, completes the intersection calculation, attitude deviation correction, integrity verification, and accuracy analysis, forming a closed-loop monitoring logic of "deployment benchmark - measurement point perception - data transmission - collaborative control - precise processing". The adaptive collaborative module dynamically adjusts the number of measurements, observation angle, and signal gain by receiving feedback data from the measurement points, achieving precise adaptation between the measurement point status and measurement parameters. Its technical benefits are reflected in the following aspects: the dual-station retrofit design significantly improves the system's adaptability to existing projects, and significantly reduces construction costs and difficulties; the grid-like measurement point layout combined with the intersection measurement of dual measurement robots achieves full coverage of the slope without blind spots, solving the pain point of incomplete coverage by a single measurement station; the deep linkage between the adaptive collaborative module and the measurement point sensing unit can respond promptly to changes in prism attitude and signal intensity fluctuations, dynamically optimize measurement parameters, and, together with the deviation correction function of the data processing unit, greatly improve the accuracy and reliability of monitoring data; the collaborative operation of each unit completely replaces the traditional manual monitoring mode, significantly improving monitoring efficiency, eliminating manual operation errors and on-site operational safety risks, and, relying on existing facility deployment, modular design, and dynamic adaptation mechanisms, effectively enhances operational stability in complex slope environments, reduces equipment failures and data loss, lowers long-term operation and maintenance costs, and provides high-precision, continuous, and automated technical support for early warning of slope deformation.

[0035] Furthermore, the high-precision measuring robot has an angle measurement accuracy of ≤0.5″ and a distance measurement accuracy of ≤1mm+1ppm×D, where D is the measurement distance; the adaptive collaborative module has an adjustment response time of ≤1s. These parameter designs provide an ultra-high precision foundation for monitoring data at the hardware level, enabling precise capture of minute slope displacement changes. Even in long-distance measurement scenarios, it effectively controls distance measurement errors, meeting the extreme accuracy requirements for early deformation warning of high slopes in large hydropower projects, and providing reliable raw data support for slope stability assessment. The adaptive collaborative module's adjustment response time of ≤1s ensures… When the posture of the measuring point changes or the signal strength fluctuates, it can be quickly sensed and parameters can be adjusted in a timely manner, avoiding measurement data distortion or invalid acquisition caused by response delay. This allows the measuring robot to adapt to changes in the state of the measuring point and the environment in real time, and to maintain the optimal measurement state in dynamic scenarios. The two work together to ensure measurement accuracy in static scenarios through ultra-high precision hardware, while the fast-response collaborative module makes up for the accuracy loss in dynamic environments. This ensures that the entire monitoring system can capture accurate data on minute deformations in complex and ever-changing slope environments, and can quickly respond to sudden changes in the external environment and the state of the measuring point.

[0036] Furthermore, in the data transmission unit, the measuring robot and the monitoring center use fiber optic wired communication with a communication rate ≥100Mbps, while the measuring points and the measuring robot use LoRa wireless communication with a transmission distance ≥500m. The measuring robot is powered by 220V factory power and equipped with an isolation voltage regulator module and surge protection device. The data transmission unit uses fiber optic wired communication to realize data interaction between the measuring robot and the monitoring center. The design of a communication rate ≥100Mbps ensures high-speed real-time transmission of large-capacity information such as observation data and attitude data, avoiding the decrease in data timeliness due to transmission delay. At the same time, the stability of wired communication effectively resists signal interference in complex mountainous environments, ensuring the integrity and accuracy of data transmission. The measuring points and the measuring robot use LoRa wireless communication with a transmission distance ≥500m, which is suitable for scenarios where multiple measuring points are distributed along slopes. It eliminates the need for additional long-distance wired lines, reducing construction difficulty and wiring costs, while meeting the stable data interaction requirements of long-distance measuring points and stations, and flexibly adapting to the layout of measuring points in different slope terrains. The measuring robot is powered by 220V factory power, and with the help of an isolation voltage regulator module, it can effectively filter grid voltage fluctuations, providing a stable power supply environment for equipment operation and preventing damage to electronic components or drift in measurement accuracy caused by voltage instability. Surge protection devices can withstand extreme situations such as lightning strikes and sudden overvoltages, further enhancing the safety of the equipment's power supply and reducing monitoring interruptions caused by sudden failures. The coordinated design of data transmission and power supply configuration ensures high-speed and stable transmission of monitoring data from the measuring point to the monitoring center, and provides reliable energy support for the long-term continuous operation of the measuring robot. This effectively reduces data loss or equipment damage caused by transmission interruptions or power failures, significantly improving the operational stability and anti-interference capability of the entire monitoring system in complex slope environments.

[0037] Furthermore, in the grid-like measuring point arrangement unit, surface displacement measuring points uniformly cover the entire monitored slope in a grid pattern. The tilt sensor integrated into the prism at each measuring point has a measurement range of ±5°, and the power consumption of the wireless signal feedback unit is ≤3mW. The measuring points achieve attitude data acquisition and signal strength feedback through the prism-integrated components, and work with the adaptive collaborative module to dynamically adjust the observation parameters. The uniform grid-like coverage of the surface displacement measuring points across the entire monitored slope enables comprehensive, blind-spot-free monitoring of slope deformation, ensuring that even minute deformations in different areas are accurately captured. This provides comprehensive and complete data support for the overall slope stability assessment, avoiding missed detections of local deformations due to uneven distribution of measuring points. The tilt sensor integrated into the prism at each measuring point has a measurement range of ±5°, which can fully cover the common range of prism attitude drift caused by slope micro-motions, accurately capturing different degrees of attitude deviation, without causing accuracy redundancy due to an excessively large measurement range, providing reliable basic data for subsequent dynamic adjustment of observation parameters. The low-power design of the wireless signal feedback unit (≤3mW) significantly extends the measuring point equipment. Its extended battery life is particularly suitable for scenarios where slope measuring points are scattered and power supply is inconvenient in outdoor environments. This reduces the workload and cost of frequent battery replacements or wiring for power supply, and improves the long-term practicality of the system. The measuring points achieve attitude data acquisition and signal strength feedback through the integrated components of the prism, and work closely with the adaptive collaborative module to build an efficient linkage mechanism of "perception-feedback-adjustment". This ensures that the measuring point status data can be quickly transmitted and trigger the accurate adaptation of observation parameters. The monitoring system can dynamically optimize the measurement strategy according to the real-time status of each measuring point, which not only ensures the accuracy of the measurement data of each measuring point, but also further improves the adaptability and operating efficiency of the entire system to complex slope environments.

[0038] Building upon the aforementioned system architecture, the "point-station bidirectional adaptive collaboration" mechanism is further refined and optimized. A power supply voltage monitoring function can be added to the wireless signal feedback unit of the measurement point, synchronously transmitting equipment power supply status data to the adaptive collaboration module. Simultaneously, a personalized calibration factor for each measurement point is embedded in the adaptive collaboration module. This factor is dynamically updated based on the historical attitude drift patterns and signal transmission stability data of each measurement point, ensuring precise matching between parameter adjustment logic and the individual characteristics of each measurement point. Furthermore, the collaborative response logic between the measurement point and the station is optimized. When attitude data, signal strength data, and power supply status data exhibit cross-abnormalities, the adaptive collaboration module prioritizes triggering a power supply warning and associated adjustment, rather than simply adjusting the observation parameters. Through this series of in-depth designs focusing on existing improvements, the personalized calibration factor upgrades the observation parameter adjustment from "unified logic adapting to the entire domain" to... "Precise adaptation to individual measurement points" avoids adjustment deviations caused by differences in environment and location between different measurement points, further reducing horizontal displacement deviation to ≤±0.7mm and vertical displacement deviation to ≤±1.0mm. Measurement accuracy is improved in a step-by-step manner on the original basis. The cross-linking of power supply status with attitude and signal data effectively avoids false signal feedback and invalid parameter adjustments caused by insufficient power supply, reducing the false trigger rate of collaborative response and significantly improving the accuracy of the adaptive mechanism and the operating efficiency of the equipment. At the same time, personalized calibration factors reduce the wear and tear on the equipment hardware by dynamically adapting to the individual characteristics of the measurement points. Combined with power supply status warning, it further extends the stable operation cycle of the measurement point equipment and the measurement robot, reduces the risk of monitoring interruption caused by equipment abnormalities, and comprehensively enhances the accuracy, reliability and operational stability of the original adaptive collaborative system.

[0039] In this embodiment, the "adaptive collaborative module" is a hardware and software combined functional module. Its core consists of a processor, a data storage unit, and a communication interface. Its logic for receiving data, judging thresholds, adjusting parameters, and controlling measurement execution can be implemented using conventional embedded programming. Specifically, an STM32 series processor with a real-time operating system can be used, combined with a control program written in C language. Those skilled in the art can complete the programming implementation according to the functional requirements described in this solution. The dynamic update logic of the "personalized calibration factor for measurement points" is as follows: after accumulating 10 valid observation data, the attitude drift amplitude and signal fluctuation range of the measurement point are statistically analyzed using a weighted average algorithm, and the parameter adjustment coefficient is automatically corrected. The weighting weight can be configured according to the validity of the observation data (e.g., the weight of observation data without abnormal interference is set to 1, and the weight of observation data with slight interference is set to 0.8). Those skilled in the art can implement this using conventional data statistical algorithms to ensure that the observation parameter adjustment accurately adapts to the characteristics of a single measurement point, thereby achieving the technical effect of horizontal displacement deviation ≤ ±0.7mm and vertical displacement deviation ≤ ±1.0mm. The judgment threshold for the power supply voltage in "crossing anomalies" is set to ≤3.0V (3V is commonly used for adapting measurement point equipment). (In a 6V lithium battery powered scenario), this threshold can be adaptively adjusted according to the power supply parameters of the selected measuring point equipment, which falls within the scope of conventional parameter optimization for those skilled in the art; "Power supply early warning correlation adjustment" specifically refers to the adaptive collaborative module pausing the current observation parameter adjustment, sending a low voltage early warning information to the monitoring center via LoRa wireless communication, while retaining the current status data of the measuring point, and resuming observation after the maintenance personnel have investigated the power supply problem. Its communication protocol adopts the conventional LoRa communication protocol in this field (such as LoRaWAN). This design can reduce the false trigger rate of the collaborative response by more than 60%; existing observation The specific reinforcement and modification method of the observation pier is as follows: after roughening the top of the observation pier, HRB400 grade steel bars are inserted, C30 fine stone concrete cushion layer is poured and leveled to ensure that the flatness of the foundation is ≤±0.2mm / m. This reinforcement method is a conventional technical means for the modification of observation piers in surveying and mapping projects, and provides a guarantee for the stable deployment of dual measurement stations. The forced centering device is installed by fixing it to the pre-embedded steel plate on the top of the observation pier with expansion bolts. After fixing, a level is used for calibration to ensure that the center deviation is ≤±1mm and the levelness of the measuring robot after installation is ≤±0.1mm / m, which is completely consistent with the hardware installation parameters set in step 2.

[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Unless otherwise specified, an element defined by the phrase "comprising..." or "including..." does not exclude the presence of additional elements in the process, method, article, or terminal device that includes said element. Additionally, in this document, "greater than," "less than," "exceeding," etc., are understood to exclude the stated number; "above," "below," "within," etc., are understood to include the stated number.

[0041] The above description of the embodiments is provided to facilitate understanding and use of the present invention by those skilled in the art. It is obvious to those skilled in the art that various modifications can be easily made to the embodiments, and the general principles described herein can be applied to other embodiments without creative effort. Therefore, the present invention is not limited to the above embodiments. Improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the protection scope of the present invention.

Claims

1. An automated slope monitoring method, characterized in that, Includes the following steps: Step 1: Station modification. Select an existing stable observation pier on the opposite bank of the slope as the foundation for the dual monitoring stations. Reinforce and modify the observation pier and remove surrounding obstructions. Step 2: Equipment installation. A high-precision measurement robot and an adaptive collaborative module are installed on the two measurement stations respectively. The measurement robot is fixed by a forced centering device. An inclination sensor and a wireless signal feedback unit are integrated on the grid-shaped measurement point prism. The inclination sensor collects the real-time attitude data of the prism, and the wireless signal feedback unit transmits the attitude data and measurement signal strength information to the measurement robot. Step 3: Station orientation and parameter configuration. Select stable plane monitoring points as backsight points to complete station orientation, and set basic observation parameters and adaptive adjustment thresholds. Step 4: Automated intersection measurement. The measurement robot and the measurement point work together in two directions. Based on the received attitude data and signal strength information, the robot measures according to the basic parameters when the threshold is not exceeded. When the threshold is exceeded, the observation parameters are dynamically adjusted and the horizontal and vertical displacement data of the measurement point are calculated by the intersection method. Step 5: Data transmission and verification. The observation data is transmitted to the monitoring center via fiber optic wired communication. The measurement results are then corrected and verified in conjunction with the attitude data.

2. The automated slope monitoring method according to claim 1, characterized in that, The deployment of dual monitoring stations in step 1 meets the following requirements: the altitude is higher than the highest point of the monitored slope, the unobstructed line of sight to all target monitoring points is ≥15°, and they are far away from vibration sources and areas with strong electromagnetic interference.

3. The automated slope monitoring method according to claim 1, characterized in that, In step 2, the center deviation of the forced centering device is ≤ ±1mm, the levelness of the robot after installation is ≤ ±0.1mm / m, and the deviation of the level bubble is ≤ 1 division; the measurement accuracy of the tilt sensor is ≤ ±0.05°, the transmission delay of the wireless signal feedback unit is ≤ 100ms, and the waterproof rating is ≥ IP67; the adaptive collaborative module is used to receive attitude data and signal strength information, dynamically adjust the observation parameters, and control the measurement robot to perform intersection measurement.

4. The automated slope monitoring method according to claim 1, characterized in that, The basic observation parameters in step 3 include a basic observation cycle of once a day and a basic number of 4 measurements. The adaptive adjustment thresholds include a prism tilt angle deviation threshold of ≥0.3° and a signal strength threshold of ≤-60dBm.

5. The automated slope monitoring method according to claim 1, characterized in that, The logic for adjusting the observation parameters in step 4 is as follows: when the prism tilt angle deviation is between 0.3° and 0.5°, the number of repetitions is increased by 2; when the prism tilt angle deviation is greater than 0.5°, the number of repetitions is increased by 4 and the intersection observation angle is adjusted to ≥30°; when the signal strength is ≤-60dBm and ≥-70dBm, the signal gain is increased by 10dB; when the signal strength is <-70dBm, the signal gain is increased by 15dB and the duration of a single observation is extended by 50%.

6. The automated slope monitoring method according to claim 1, characterized in that, The data validation criteria in step 5 are: horizontal displacement deviation ≤ ±1.0mm, vertical displacement deviation ≤ ±1.5mm, and data missing rate ≤ 0.1%.

7. An automated slope monitoring system, characterized in that, include: The dual measurement robot unit consists of two high-precision measurement robots. Each measurement robot integrates an adaptive coordination module. The adaptive coordination module is used to receive attitude data and signal strength information from the measurement points, dynamically adjust the number of measurements, observation angle and signal gain, and control the measurement robot to perform automated intersection measurement. The dual-station deployment unit includes a first station and a second station. The first station is modified from an existing stable observation pier on the opposite bank of the slope, and the second station is modified from a second existing stable observation pier on the opposite bank of the slope. It is used to install and fix the measurement robot. A grid-like measuring point layout unit is used to lay out several surface displacement measuring points along multiple walkways on the slope. The measuring points are shared by both horizontal and vertical displacement. Each measuring point has a prism that integrates an inclination sensor and a wireless signal feedback unit. The inclination sensor is used to collect prism attitude data, and the wireless signal feedback unit is used to transmit data to the adaptive collaborative module. The data transmission unit is used to enable data communication between the measurement robot and the monitoring center, and between the measurement point and the measurement robot. The data processing unit is used to receive observation data and prism attitude data, and to perform intersection calculations, attitude deviation corrections, integrity checks, and accuracy analysis.

8. The automated slope monitoring system according to claim 7, characterized in that, The high-precision measuring robot has an angle measurement accuracy of ≤0.5″ and a distance measurement accuracy of ≤1mm+1ppm×D, where D is the measurement distance; the adaptive collaborative module has an adjustment response time of ≤1s.

9. The automated slope monitoring system according to claim 7, characterized in that, In the data transmission unit, the measuring robot and the monitoring center use fiber optic wired communication with a communication rate of ≥100Mbps, and the measuring point and the measuring robot use LoRa wireless communication with a transmission distance of ≥500m. The measuring robot is powered by 220V factory power and is equipped with an isolation voltage regulator module and surge protection device.

10. The automated slope monitoring system according to claim 7, characterized in that, In the grid-like measuring point arrangement unit, the surface displacement measuring points are uniformly covered in a grid pattern across the entire monitored slope. The tilt sensor integrated in the prism at each measuring point has a measurement range of ±5°. The power consumption of the wireless signal feedback unit is ≤3mW. The measuring points achieve attitude data acquisition and signal strength feedback through the prism-integrated components, and work with the adaptive collaborative module to dynamically adjust the observation parameters.

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