Lightweight slope displacement intelligent monitoring system and method

Abstract

The invention relates to the technical field of slope safety monitoring, and relates to a lightweight slope displacement intelligent monitoring system and method, and the method comprises the steps: a control module receives a control instruction, and operates a preset control program based on the control instruction to set the data measurement period and data measurement angle of a slope displacement data measurement module; the slope displacement data transmission module is controlled to start and stop based on the control instruction; the slope displacement data measurement module is provided with an industrial-grade phase-type laser range finder, a microprism cooperation target, a double-shaft steering engine holder and a computer vision auxiliary aiming assembly; the slope displacement data transmission module is connected with the Internet of Things cloud platform through a communication unit, and transmits slope displacement data measured by the slope displacement data measurement module to the slope displacement state monitoring module through a pre-selected communication protocol; the side slope displacement state monitoring module is constructed based on a cloud platform and is used for receiving and storing side slope displacement data and analyzing the side slope displacement data through big data to obtain a displacement change trend.

Description

Technical Field

[0001] This invention relates to the field of slope safety monitoring technology, specifically a lightweight intelligent slope displacement monitoring system and method. Background Technology

[0002] A slope is a sloping body with a certain gradient, formed naturally or through artificial excavation and filling. It is widely found in mountainous terrains and engineering projects such as transportation, mining, water conservancy, and construction. Based on its formation, it can be divided into natural slopes and engineering slopes; based on its material composition, it can be divided into rock slopes, soil slopes, and composite slopes. Slopes are a key element in ensuring harmony between engineering construction and the natural environment. In the transportation sector, the stability of slopes along highways and railways directly determines the safety of line operation; in mining, slope instability can trigger collapses, threatening lives; in water conservancy projects, the stability of reservoir slopes is crucial to dam safety and the safety of surrounding residents. Simultaneously, the stability of natural slopes is a core element of ecological protection and geological disaster prevention; their instability can easily induce landslides, debris flows, and other disasters.

[0003] Displacement is a key precursor to slope instability, and slope displacement monitoring is a crucial means of preventing geological disasters. In transportation engineering, abnormal displacement of slopes along the route can trigger landslides and collapses, leading to route interruptions or even vehicle overturning. In mining and water conservancy projects, excessive slope displacement can threaten the safety of workers and damage engineering facilities. Timely capture of displacement data can accurately determine slope deformation trends, providing a basis for early warning and reinforcement, and preventing casualties and property losses.

[0004] However, early slope displacement monitoring relied primarily on manual observation, such as leveling and triangulation, which required on-site personnel, resulting in low efficiency and limitations imposed by weather and terrain, making it impossible to cover remote or high-risk areas. Manual readings had significant errors, making it difficult to capture millimeter-level displacements and detect early signs of deformation in a timely manner. In the nascent stage of automated monitoring, although simple sensing devices existed, data transmission relied on wired methods, leading to high deployment costs, weak anti-interference capabilities, and poor data continuity. Early data processing was lagging, preventing real-time analysis of displacement change patterns, resulting in delayed warnings and hindering effective prevention and control of slope instability risks.

[0005] Therefore, slope displacement monitoring suffers from problems such as low monitoring efficiency, poor accuracy, and inability to provide real-time early warnings. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a lightweight intelligent slope displacement monitoring system and method to solve the problems in the background art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: The present invention provides a lightweight intelligent slope displacement monitoring system, comprising: The control module is used to receive control commands issued by the slope displacement status monitoring module, run a preset control program based on the control commands to set the data measurement cycle and data measurement angle of the slope displacement data measurement module, and control the start and stop of the slope displacement data transmission module based on the control commands. The slope displacement data measurement module is equipped with an industrial-grade phase laser rangefinder, a microprism cooperative target, a dual-axis servo gimbal, and a computer vision-assisted aiming component to measure slope displacement data through laser reflection ranging principle and image recognition alignment technology. The slope displacement data transmission module establishes a connection with the Internet of Things cloud platform through a communication unit, and transmits the slope displacement data measured by the slope displacement data measurement module to the slope displacement status monitoring module through a pre-selected communication protocol. The slope displacement status monitoring module is built on a cloud platform and is used to receive and store the slope displacement data. It also uses big data analysis to obtain the displacement change trend of the slope displacement data and triggers a graded warning when the slope displacement data reaches a preset warning threshold.

[0008] In one embodiment of this application, the control module includes a main control hub, a horizontal rotation servo, a pitch rotation servo, and a lithium battery. The horizontal rotation servo and the pitch rotation servo are serial bus digital servos. They receive control signals from the main control center through serial port commands to realize the angle adjustment of the laser rangefinder in the horizontal and vertical directions, and to feed back the current actual position and rotation angle information to the main control center. The lithium battery, in conjunction with the power conversion component, converts the output voltage into the operating voltage of each electrical component adapted to the control module and the slope displacement data measurement module, so as to supply power to the control module and the slope displacement data measurement module.

[0009] In one embodiment of this application, the computer vision-assisted aiming component is a camera, which is fixed above the industrial-grade phase laser rangefinder and maintains a consistent line of sight. The camera uses image recognition alignment technology to collect the color or shape features of the microprism cooperative target. When the camera detects that the microprism cooperative target is not within the crosshair range based on the color or shape features, it sends a deviation signal to the control module. The control module then controls the dual-axis servo gimbal to perform angle calibration until the industrial-grade phase laser rangefinder is precisely aligned with the microprism cooperative target, triggering the laser ranging operation.

[0010] In one embodiment of this application, the industrial-grade phase laser rangefinder has an IP67 protection rating, a measurement accuracy of ±1.5-3mm, a ranging range of 5 meters to 300 meters, supports a measurement frequency of 20-40Hz, and adopts a wide voltage input design, directly connected to a 12V power supply. The industrial-grade phase laser rangefinder establishes communication with the main control center of the control module through a USB to RS485 converter. After the laser signal emitted by the laser rangefinder is reflected by the microprism cooperative target, the absolute distance between the industrial-grade phase laser rangefinder and the microprism cooperative target is obtained by analyzing the laser reflection time, so as to obtain the slope displacement data.

[0011] In one embodiment of this application, the microprism cooperative target is an L-shaped structure specifically for total stations. One side of the main body of the microprism cooperative target is the mounting reference surface, and the other side carries a microprism reflective array with a diameter greater than or equal to 30 mm. The light intensity reflectivity of the microprism reflective array is greater than 1000 cd / lx / m². The bottom of the microprism cooperative target is rigidly fixed by a mounting bracket, which is equipped with a horizontal adjustment knob and a pitch adjustment knob to allow for multi-angle installation.

[0012] In one embodiment of this application, the dual-axis servo gimbal includes a multi-functional bracket, a long U-shaped bracket, a short U-shaped bracket, a cup bearing, and a metal servo disk; The multi-functional bracket is used to fix the horizontal rotation servo motor and install the horizontal rotation servo motor on the slope measurement base station; The long U-shaped bracket connects the horizontal rotation servo and the pitch rotation servo; The short U-shaped bracket secures the industrial-grade phase laser rangefinder and connects it to the pitch and rotation servo motor. The cup bearing is used for auxiliary support to improve the stability of the gimbal.

[0013] In one embodiment of this application, the communication unit in the slope displacement data transmission module is connected to the main control hub of the control module via a USB interface, and an industrial-grade IoT card is inserted into the card slot of the communication unit to access the IoT cloud platform through the operator's dedicated IoT APN access point. The main control center converts the slope displacement data according to a preset data format and transmits the converted slope displacement data to the Internet of Things cloud platform through the communication protocol.

[0014] In one embodiment of this application, the slope displacement status monitoring module stores the slope displacement data in a time series database according to the time series, and generates a real-time monitoring screen through a data visualization tool. In the real-time monitoring screen, the displacement change trend is displayed in the form of a time displacement curve and a data dashboard. When the slope displacement data reaches the preset warning threshold, the slope displacement status monitoring module pushes alarm information through a mobile terminal and triggers a graded warning.

[0015] In one embodiment of this application, the intelligent slope displacement monitoring system further includes: The solar power supply module includes a solar panel, a solar charge controller, and two step-down converters. The solar panel converts light energy into electrical energy and uses the solar charging controller to charge the lithium battery at a stable voltage. The two step-down converters convert the 12V output voltage from the lithium battery to 5V and 7.4V respectively. The 5V voltage powers the main control hub of the control module, and the 7.4V voltage powers the serial bus digital servo motor.

[0016] This application also provides a lightweight intelligent slope displacement monitoring method, which includes: The dual-axis servo gimbal is controlled to rotate to the zero-position reference target coordinate position. The computer vision-assisted aiming component is used to visually lock onto the zero-position reference target and perform laser ranging to obtain the reference measured vector at the current moment. The reference measured vector includes the measured distance, the measured horizontal azimuth angle, and the measured pitch angle. Read the pre-calibrated reference theoretical vector, perform a difference operation between the reference measured vector and the reference theoretical vector to obtain the deviation vector under the current environment; Receive automatic measurement and control commands, drive the dual-axis servo gimbal to rotate to the corresponding physical pointing area according to the automatic measurement and control commands, activate the computer vision-assisted aiming component to measure the microprism cooperative target in the physical pointing area, and obtain slope displacement data; Based on the deviation vector, the slope displacement data is vector-corrected to generate initial corrected data; Obtain the echo signal intensity value corresponding to each initial correction data, remove data whose signal intensity is lower than a preset confidence threshold, use a statistical filtering algorithm to remove outliers from the remaining data, and calculate the arithmetic mean as the final slope displacement data; The final slope displacement data is uploaded to the Internet of Things (IoT) cloud platform via a communication unit, so that the IoT cloud platform can analyze the trend of the final slope displacement data, obtain the displacement change trend, and trigger a graded warning when the slope displacement data reaches a preset warning threshold.

[0017] The beneficial effects of this invention are as follows: The lightweight intelligent slope displacement monitoring system and method of this invention allows the control module to receive control commands, automatically set the measurement cycle and angle, and control the start and stop of the data transmission module. This eliminates the need for manual on-site operation, significantly reducing the cost and time spent on manual intervention and effectively improving monitoring efficiency. The slope displacement data measurement module is equipped with an industrial-grade phase-type laser rangefinder and a microprism cooperative target. Combining the laser reflection ranging principle with computer vision-assisted aiming technology, image recognition alignment technology ensures precise alignment between the laser rangefinder and the target. The microprism cooperative target enhances the laser reflection effect, and the industrial-grade phase-type laser rangefinder itself possesses high-precision measurement characteristics. Together, they achieve accurate acquisition of slope displacement data, solving the problem of poor accuracy. The slope displacement data transmission module establishes a connection with the IoT cloud platform through a communication unit, achieving real-time transmission of measurement data using a preset communication protocol. The slope displacement status monitoring module receives and stores data based on the cloud platform, obtains displacement change trends through big data analysis, and can trigger graded warnings when the data reaches a preset warning threshold. This breaks through the limitations of traditional monitoring data transmission lag and untimely warnings, achieving real-time warnings. Attached Figure Description

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the structure of a lightweight intelligent slope displacement monitoring system shown in one embodiment of this application; Figure 2 This is a closed-loop control logic diagram of a system according to an embodiment of this application; Figure 3 This is a system hardware collaborative control logic diagram in one embodiment of this application; Figure 4 This is a diagram of the system remote monitoring and data interaction architecture in one embodiment of this application; Figure 5 This is a flowchart of a system automated periodic measurement task in one embodiment of this application; Figure 6 , Figure 7 This is a schematic diagram of the system wiring in one embodiment of this application; Figure 8 This is a flowchart illustrating a lightweight intelligent slope displacement monitoring method in one embodiment of this application. Detailed Implementation

[0019] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the layers related to the present invention and are not drawn according to the actual number, shape and size of the layers in the actual implementation. In the actual implementation, the form, number and proportion of each layer can be arbitrarily changed, and the layer layout may also be more complex.

[0021] Numerous details are explored in the following description to provide a more thorough explanation of embodiments of the invention; however, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details.

[0022] Figure 1 This is a structural schematic diagram of a lightweight intelligent slope displacement monitoring system shown in one embodiment of this application, as follows: Figure 1 As shown: This embodiment of a lightweight intelligent slope displacement monitoring system may include: The control module 110 is used to receive control commands issued by the slope displacement status monitoring module, run a preset control program based on the control commands to set the data measurement cycle and data measurement angle of the slope displacement data measurement module, and control the start and stop of the slope displacement data transmission module based on the control commands. The slope displacement data measurement module 120 is equipped with an industrial-grade phase laser rangefinder, a microprism cooperative target, a dual-axis servo gimbal, and a computer vision-assisted aiming component to measure slope displacement data through laser reflection ranging principle and image recognition alignment technology. The slope displacement data transmission module 130 establishes a connection with the Internet of Things cloud platform through a communication unit, and transmits the slope displacement data measured by the slope displacement data measurement module to the slope displacement status monitoring module through a pre-selected communication protocol. The slope displacement status monitoring module 140 is built on a cloud platform and is used to receive and store the slope displacement data. It also uses big data analysis to obtain the displacement change trend of the slope displacement data and triggers a graded warning when the slope displacement data reaches a preset warning threshold.

[0023] The control module is the core hub that coordinates the collaborative work of all components. Its general functions include receiving external control commands, running preset programs, driving and controlling the start and stop of related modules such as measurement and transmission, and configuring parameters. It typically consists of a main control device, drive components, and power supply components, and is crucial for achieving automated system operation. In this application, the control module specifically receives control commands from the slope displacement monitoring module, precisely sets the data measurement cycle and data measurement angle of the slope displacement data measurement module by running preset control programs, and controls the start and stop of the slope displacement data transmission module according to the commands. Its hardware consists of a Raspberry Pi 4B main controller, a horizontal rotation servo, a pitch rotation servo, and a lithium battery, and achieves precise control of each component through serial communication.

[0024] The data measurement module is the core unit for collecting target data in the intelligent monitoring system. Its general design must be adapted to the characteristics of the monitored object, configuring specialized measurement equipment, auxiliary aiming components, and a supporting structure, combined with corresponding measurement principles to ensure the accuracy of data acquisition. In this application, the module is specifically configured with an industrial-grade phase-type laser rangefinder, a microprism cooperative target, a dual-axis servo gimbal, and a computer vision-assisted aiming component. Through the laser reflection ranging principle and image recognition alignment technology, it ultimately completes the acquisition of slope displacement data, adapting to the measurement needs of complex field slope environments.

[0025] The data transmission module is a crucial bridge connecting the data acquisition terminal and the monitoring platform. Its general function is to stably transmit the acquired data to the target monitoring module using a communication unit and an adapted communication protocol, meeting the requirements of data real-time performance, integrity, and environmental adaptability. In this application, the module uses a 4G / 5G communication unit to establish a connection with the IoT cloud platform, inserts an industrial-grade IoT card to connect to the network through a dedicated APN access point of an operator, and selects the MQTT protocol as the pre-selected communication protocol to stably and in real-time transmit the displacement data collected by the slope displacement data measurement module to the slope displacement status monitoring module, adapting to scenarios where there is no wired network on outdoor slopes.

[0026] The status monitoring module is the core of the intelligent monitoring system for data analysis and early warning. It is typically built on a cloud or local platform and possesses data reception, storage, analysis, visualization, and early warning functions. It is used to present changes in the status of the monitored object and respond to abnormal situations. In this application, the module is built on the Alibaba Cloud IoT platform, receiving and storing slope displacement data in a time-series manner. It uses cloud-based big data analytics to mine displacement change trends, pre-sets different levels of hazard warning thresholds, and automatically triggers graded warnings when the monitored slope displacement data reaches the threshold. It also supports remote access via mobile terminals for real-time monitoring of the slope status.

[0027] For example, the control module uses a Raspberry Pi 4B as the main controller, paired with a Feite STS3215 horizontal rotation servo, a pitch rotation servo, and a 12V lithium battery. Python control code is written to enable the Raspberry Pi 4B to control the rotation of the two servos via serial communication and to control the start and stop of the data transmission module. The data measurement cycle is set to every 6 hours, with measurement angles of 0°, 15°, 30°, 45°, and 60° respectively. The slope displacement data measurement module uses a Shendawei SW-LDS100DB industrial-grade phase laser rangefinder (IP67 protection rating, measurement accuracy ±1.5-3mm, ranging range 5M-300M), paired with a total station-specific L-shaped microprism target (reflective array with a light intensity reflectivity exceeding 1000cd / lx / m²). The dual-axis servo gimbal adopts a hard aluminum alloy double-support structure. (Computer vision...) The aiming assistance component uses a Raspberry Pi V2 camera, which captures target features using OpenCV image recognition technology to achieve precise alignment between the laser rangefinder and the target. The slope displacement data transmission module uses a Quectel EC600N series 4GLTECat.1 industrial module, which is connected to a dedicated APN access point of the operator by inserting an industrial-grade IoT card and establishes communication with the Alibaba Cloud IoT platform via the MQTT protocol. The slope displacement status monitoring module is built on the Alibaba Cloud IoT platform, which stores the received displacement data in the Alibaba Cloud Time Series Database (TSDB). It generates time-displacement curves and data dashboards using the DataV data visualization tool. The preset displacement warning thresholds are 5mm (Level 1 warning) and 10mm (Level 2 warning). When the detected slope displacement reaches 7mm, the Level 1 warning is automatically triggered and the alarm information is pushed to the mobile APP.

[0028] In this embodiment, through the automated parameter configuration and component control of the control module, periodic or remote command-controlled measurements can be achieved without manual on-site operation, significantly improving the efficiency of slope displacement monitoring and reducing labor costs. Through the professional equipment configuration and dual-technology integration of the slope displacement data measurement module, combining the high precision of an industrial-grade phase laser rangefinder with the strong reflective effect of a microprism target, millimeter-level slope displacement data acquisition is achieved, solving the problem of poor accuracy in traditional monitoring. The 4G / 5G communication and dedicated protocol transmission of the slope displacement data transmission module ensure real-time and stable uploading of measurement data. Combined with the cloud-based big data analysis and hierarchical early warning functions of the slope displacement status monitoring module, accurate presentation of slope displacement trends and timely early warning of abnormal situations are achieved, breaking through the time and space limitations of traditional monitoring and effectively improving the safety and control capabilities of field slope scenarios.

[0029] In some embodiments, the control module includes a main control hub, a horizontal rotation servo, a pitch rotation servo, and a lithium battery. The horizontal rotation servo and the pitch rotation servo are serial bus digital servos. They receive control signals from the main control center through serial port commands to realize the angle adjustment of the laser rangefinder in the horizontal and vertical directions, and to feed back the current actual position and rotation angle information to the main control center. The lithium battery, in conjunction with the power conversion component, converts the output voltage into the operating voltage of each electrical component adapted to the control module and the slope displacement data measurement module, so as to supply power to the control module and the slope displacement data measurement module.

[0030] Among them, the serial bus digital servo is a core component for precise angle adjustment in the field of intelligent control. Its general characteristics include receiving digital control commands via a serial bus. Compared to traditional analog servos, it has advantages such as high control precision, strong signal anti-interference capability, and the ability to feedback its own working status, making it suitable for automated equipment requiring closed-loop control. In this application, the horizontal rotation servo and pitch rotation servo are selected from this type of servo. They receive control signals from the main control center via serial port commands, precisely achieving angle adjustment of the laser rangefinder in the horizontal and vertical directions. Simultaneously, they can provide real-time feedback of the current actual position and rotation angle information to the main control center, providing data support for the accuracy of angle adjustment and ensuring that the laser rangefinder can be aligned with cooperative targets at different positions.

[0031] Lithium batteries, as energy storage power sources, are generally characterized by high energy density, stable discharge, and long cycle life. Power conversion components (such as DC-DC step-down converters) are key components in electronic systems for voltage adaptation. Their core function is to convert electrical energy at one voltage level to the voltage level required by other devices, preventing equipment burnout or malfunction due to voltage mismatch. They are widely used in integrated systems with multiple components and multiple voltage requirements. In this application, the lithium battery, in conjunction with the power conversion component, converts its output voltage into the operating voltage of each electrical component in the adaptation control module and the slope displacement data measurement module, providing a stable and safe power supply to all electrical hardware in both modules and ensuring the coordinated operation of all system components.

[0032] In this embodiment, the horizontal rotation servo and the pitch rotation servo adopt serial bus digital servos. Their precise angle adjustment capability and real-time feedback function ensure that the laser rangefinder can quickly and accurately align with the cooperative target at different locations on the slope, reducing the impact of angle deviation on the measurement results and improving the measurement accuracy of slope displacement data. The power supply design of the lithium battery and power conversion component not only utilizes the high energy storage advantage of lithium batteries to meet the power supply needs of the field, but also achieves power supply adaptation for components with different voltage requirements through voltage conversion, avoiding equipment damage caused by voltage mismatch, ensuring the stable and continuous operation of the control module and the slope displacement data measurement module, thereby improving the reliability and long-term working capability of the entire monitoring system, and indirectly ensuring monitoring efficiency.

[0033] In some embodiments, the computer vision-assisted aiming component is a camera, which is fixed above the industrial-grade phase laser rangefinder and maintains a consistent line of sight. The camera uses image recognition alignment technology to collect the color or shape features of the microprism cooperative target. When the camera detects that the microprism cooperative target is not within the crosshair range based on the color or shape features, it sends a deviation signal to the control module. The control module then controls the dual-axis servo gimbal to perform angle calibration until the industrial-grade phase laser rangefinder is precisely aligned with the microprism cooperative target, triggering the laser ranging operation.

[0034] In automated measurement systems, computer vision-assisted aiming components are key parts for improving target alignment accuracy. Generally, devices with image acquisition capabilities should be selected, and during installation, their lines of sight must be aligned with the core measurement equipment to ensure that the acquired target image accurately reflects the relative positional relationship between the measurement equipment and the target, providing a foundation for precise alignment. In this application, the component is specifically a camera, fixed above an industrial-grade phase-detection laser rangefinder. Strict alignment of their lines of sight is maintained, ensuring that the microprism-cooperative target image acquired by the camera directly corresponds to the aiming direction of the laser rangefinder, providing accurate image data for subsequent image recognition and alignment.

[0035] Image recognition alignment technology is a technique for automatic positioning and alignment based on the unique visual characteristics of a target. The general process involves acquiring target features, comparing them with a preset standard, determining the deviation, sending an adjustment signal, and executing a calibration action until precise alignment is achieved. This technology is widely used in automated measurement, intelligent aiming, and other scenarios. In this application, the camera uses this technology to acquire the color or shape features of the microprism cooperative target. The acquired features are compared with preset target features. When the target is detected to be outside the crosshair range, a deviation signal is immediately sent to the control module. Upon receiving the signal, the control module drives the dual-axis servo gimbal to perform angle adjustment and calibration. This calibration process is repeated until the camera recognizes that the industrial-grade phase-detection laser rangefinder is precisely aligned with the microprism cooperative target. At this point, the laser rangefinder is triggered to perform laser ranging, forming a closed-loop process of acquisition, recognition, deviation feedback, calibration, alignment, and ranging.

[0036] In this embodiment, by embodying the computer vision-assisted aiming component as a camera and installing it in line with the rangefinder's line of sight, the basic accuracy of image recognition alignment is ensured. Utilizing the closed-loop control logic of image recognition alignment technology, automated and precise alignment of the laser rangefinder and the microprism target is achieved, avoiding the errors and inefficiencies of manual alignment and significantly improving alignment accuracy and monitoring efficiency. Even in the complex environment of field slopes, this closed-loop alignment process can respond and calibrate quickly, ensuring the effectiveness and accuracy of laser ranging, providing crucial support for millimeter-level measurement of slope displacement data, while reducing manual intervention and further adapting to the needs of unattended monitoring scenarios in the field.

[0037] In some embodiments, the industrial-grade phase laser rangefinder has an IP67 protection rating, a measurement accuracy of ±1.5-3mm, a ranging range of 5 meters to 300 meters, supports a measurement frequency of 20-40Hz, and adopts a wide voltage input design, directly connected to a 12V power supply. The industrial-grade phase laser rangefinder establishes communication with the main control center of the control module through a USB to RS485 converter. After the laser signal emitted by the laser rangefinder is reflected by the microprism cooperative target, the absolute distance between the industrial-grade phase laser rangefinder and the microprism cooperative target is obtained by analyzing the laser reflection time, so as to obtain the slope displacement data.

[0038] In the field of slope monitoring, laser rangefinders must simultaneously meet requirements for environmental adaptability, measurement accuracy, coverage, and power supply compatibility. IP67 is a high protection rating commonly used in industrial equipment, providing complete dustproof protection and short-term immersion in water, resisting erosion from harsh environments such as rain and dust. Millimeter-level measurement accuracy is a core prerequisite for capturing minute slope displacements, avoiding the risk of missing early deformation. A ranging range of 5 to 300 meters must be adaptable to different monitoring scenarios for small to medium-sized slopes and high slopes, ensuring no measurement blind spots. A measurement frequency of 20-40Hz can balance data acquisition efficiency and device power consumption, ensuring data timeliness while avoiding excessive energy consumption. The wide voltage input and direct 12V power supply design is designed to adapt to commonly used lithium battery power systems in the field, eliminating the need for additional complex voltage conversion components and simplifying the power supply chain. In this application, the above parameters of the rangefinder are precisely matched to the environmental characteristics and monitoring needs of field slopes, solving the problems of measurement failure and insufficient accuracy caused by parameter mismatch in traditional rangefinders.

[0039] Among them, the RS485 serial interface is a commonly used interface for long-distance, interference-resistant communication in industrial scenarios. However, it has protocol incompatibility issues with the USB interface of host devices such as Raspberry Pi. A USB-to-RS485 converter is needed to achieve signal conversion and link establishment to ensure communication stability in harsh environments. Laser reflection ranging is a general principle for high-precision ranging. By calculating the time difference between laser signal emission and reflection, combined with the speed of light, the absolute distance between the transmitter and the target can be derived. In this application, the rangefinder establishes communication with the main control center of the control module through a USB-to-RS485 converter, receives control commands, and feeds back measurement data. Simultaneously, after the laser signal emitted by the rangefinder is reflected by the microprism cooperative target, the system analyzes the laser reflection time difference to accurately calculate the absolute distance between the rangefinder and the target. Through comparative analysis of multiple measurement data, the slope displacement data is finally obtained, forming a complete workflow of command reception, laser emission, reflection reception, distance calculation, and displacement acquisition.

[0040] In this embodiment, the IP67 protection rating of the industrial-grade phase laser rangefinder enables it to stably adapt to the harsh environment of field slopes, avoiding equipment failures caused by rain and dust, and ensuring the reliability of long-term continuous monitoring. Its millimeter-level measurement accuracy of ±1.5-3mm, wide ranging range of 5m to 300m, and measurement frequency of 20-40Hz ensure accurate capture of minute slope displacements, full coverage monitoring of different slope types, and high efficiency in data acquisition, solving the problems of insufficient accuracy and limited coverage of traditional monitoring methods. The wide-voltage 12V power supply design is compatible with the system's lithium battery power supply, simplifying the process. The power supply link enhances the compatibility and convenience of power supply in the field; the communication connection established through the USB to RS485 converter enhances the anti-interference capability of communication in harsh environments, ensuring stable transmission of control commands and measurement data and avoiding data loss or transmission delays; combining the laser reflection ranging principle with the strong reflection characteristics of the microprism cooperative target, the absolute distance is accurately calculated to obtain displacement data, further improving the accuracy of measurement results and providing high-quality data support for subsequent cloud data analysis and early warning, thus improving the overall environmental adaptability, measurement accuracy and operational stability of the entire monitoring system.

[0041] In some embodiments, the microprism cooperative target is an L-shaped structure specifically for total stations. One side of the main body of the microprism cooperative target is the mounting reference surface, and the other side carries a microprism reflective array with a diameter greater than or equal to 30 mm. The light intensity reflectivity of the microprism reflective array is greater than 1000 cd / lx / m². The bottom of the microprism cooperative target is rigidly fixed by a mounting bracket, which is equipped with a horizontal adjustment knob and a pitch adjustment knob to allow for multi-angle installation.

[0042] In high-precision laser ranging scenarios, the structure and reflectivity of the target directly affect the stability of the echo signal and the measurement accuracy. Total station-specific targets must possess structural compatibility with professional measuring equipment. The L-shaped structure is a commonly used stable load-bearing structure in industrial measurement. One side of the target serves as the mounting reference surface to ensure positioning accuracy during installation, while the reflective array on the other side must meet sufficient size and reflectivity to ensure that the laser signal can be effectively received by the rangefinder after reflection, avoiding measurement failure due to weak echoes. In this application, the target adopts a total station-specific L-shaped structure. One side of the main body serves as the mounting reference surface, while the other side carries a microprism reflective array with a diameter ≥30mm and a reflectivity >1000cd / lx / m². Through this structural design and high reflectivity parameters, the laser signal is accurately returned along the original incident direction after three reflections by the microprism array, significantly enhancing the intensity and stability of the echo signal and adapting to the long-distance ranging requirements in complex outdoor lighting environments.

[0043] In complex installation scenarios such as outdoor slopes, the stability and alignment accuracy of the target are prerequisites for ensuring reliable measurement data. Rigid fixation prevents the target from shifting due to environmental vibrations, wind, and other factors. The horizontal and vertical adjustment knobs are common structures used in industrial installations for fine-tuning component orientation, allowing for quick calibration of the relative position between the target and the rangefinder. In this application, the bottom of the target is rigidly fixed via a mounting bracket, ensuring the relative position between the target and the slope remains unchanged, providing a stable benchmark for displacement measurement. The horizontal and vertical adjustment knobs on the mounting bracket allow for flexible adjustment of the target's horizontal and vertical angles during installation, ensuring the reflector's normal direction is precisely aligned with the laser rangefinder's intended position, solving the problem of difficult precise alignment in outdoor installation environments and guaranteeing alignment accuracy.

[0044] In this embodiment, the total station-specific L-shaped structure and high-reflectivity microprism array of the microprism cooperative target significantly enhance the reflection intensity and stability of the laser signal, effectively solving the problems of weak laser echo and measurement failure in complex outdoor lighting and long-distance scenarios, providing reliable signal assurance for millimeter-level precision ranging. The target is rigidly fixed by the mounting bracket, avoiding target displacement caused by environmental factors, ensuring the stability of the displacement measurement benchmark, and reducing measurement errors caused by target displacement. The horizontal and vertical adjustment knobs of the mounting bracket allow the target to be quickly and accurately calibrated in complex outdoor installation environments, reducing installation difficulty, ensuring the alignment accuracy between the target and the laser rangefinder, and thus improving the measurement reliability and data accuracy of the entire monitoring system, adapting to diverse installation scenarios and monitoring needs of outdoor slopes.

[0045] In some embodiments, the dual-axis servo gimbal includes a multi-functional bracket, a long U-shaped bracket, a short U-shaped bracket, a cup bearing, and a metal servo disc; The multi-functional bracket is used to fix the horizontal rotation servo motor and install the horizontal rotation servo motor on the slope measurement base station; The long U-shaped bracket connects the horizontal rotation servo and the pitch rotation servo; The short U-shaped bracket secures the industrial-grade phase laser rangefinder and connects it to the pitch and rotation servo motor. The cup bearing is used for auxiliary support to improve the stability of the gimbal.

[0046] In the dual-axis servo gimbal system, the multi-functional bracket is a fundamental structural component responsible for fixing the core drive components and mounting the gimbal as a whole. Its general characteristics include sufficient structural strength and compatible mounting holes, enabling a rigid connection between the servo and the mounting base. This prevents loose installation from affecting angle adjustment accuracy and is a prerequisite for ensuring stable gimbal operation. In this application, the bracket is specifically used to fix the horizontal rotation servo, and simultaneously uses bolts and other connecting components to firmly install the horizontal rotation servo onto the slope measurement base station, providing solid installation support for the entire dual-axis servo gimbal and ensuring the stability of the servo and the gimbal as a whole during subsequent angle adjustments.

[0047] In the multi-dimensional angle adjustment gimbal, the long U-shaped bracket is a key intermediate connector linking the horizontal and pitch servos. The universal design must be compatible with the installation dimensions of both types of servos, possessing good rigidity to transmit rotational power and achieve coordinated operation between the two servos, providing a structural transmission path for multi-dimensional angle adjustment. In this application, the core function of the long U-shaped bracket is to connect the horizontal rotation servo and the pitch rotation servo, enabling the coordinated transmission of the horizontal rotational power of the horizontal servo and the vertical rotational power of the pitch servo. This ensures that the laser rangefinder can follow the coordinated operation of the two types of servos to achieve multi-directional angle adjustment.

[0048] In the adaptation of the measuring equipment and the gimbal servo, the short U-shaped bracket is a special structural component used to fix the core measuring equipment and connect it to the pitch servo. Generally, it needs to be designed according to the external dimensions of the measuring equipment to ensure that the equipment is firmly fixed and can rotate accurately and synchronously with the servo, avoiding measurement direction deviation caused by loose connection between the equipment and the servo. In this application, the short U-shaped bracket is used to fix an industrial-grade phase-type laser rangefinder and simultaneously achieves a rigid connection with the pitch rotation servo, ensuring that the laser rangefinder can accurately follow the rotation of the pitch rotation servo to achieve vertical angle adjustment, guaranteeing consistency between the measurement direction and the servo adjustment action.

[0049] In mechanical rotating structures, cup bearings are auxiliary components used to support rotating parts and reduce rotational friction and vibration. They are generally applicable to scenarios requiring high-precision, low-vibration rotation. By reducing radial runout during rotation, they improve the overall stability and rotational accuracy of the structure and are widely used in gimbals, robotic arms, and other equipment. In this application, cup bearings are used as auxiliary supports for a dual-axis servo gimbal. Addressing potential interference from wind and minor vibrations in outdoor slope environments, they suppress wobbling and friction during servo rotation, further enhancing the structural stability of the gimbal and ensuring precise angle adjustment of the servo.

[0050] In this embodiment, the dual-axis servo gimbal achieves a stable assembly of the horizontal rotation servo, pitch rotation servo, and industrial-grade phase laser rangefinder through a scientific layout and rigid connection of multi-functional brackets, long U-shaped brackets, and short U-shaped brackets. This ensures smooth linkage between components and guarantees that the laser rangefinder can accurately follow the servo to achieve multi-angle adjustments in the horizontal and vertical directions, meeting the comprehensive measurement needs of different positions and angles on the slope. The auxiliary support of the bearings effectively reduces friction loss and vibration interference during servo rotation, avoiding gimbal swaying caused by wind, minor collisions, and other factors in the field, significantly improving the rotation accuracy and overall stability of the gimbal. The hard aluminum alloy brackets, combined with a compact connection structure, give the gimbal excellent corrosion resistance and deformation resistance, enabling it to adapt to harsh environments such as rain, dust, and temperature differences in the field. This ensures structural reliability during long-term continuous use and provides a stable structural foundation for the precise alignment and millimeter-level measurement of the laser rangefinder, further improving the measurement accuracy and operational stability of the entire monitoring system.

[0051] In some implementations, the communication unit in the slope displacement data transmission module is connected to the main control hub of the control module via a USB interface, and an industrial-grade IoT card is inserted into the card slot of the communication unit to access the IoT cloud platform through a dedicated IoT APN access point of the operator. The main control center converts the slope displacement data according to a preset data format and transmits the converted slope displacement data to the Internet of Things cloud platform through the communication protocol.

[0052] In the communication architecture of IoT devices, the USB interface is a universal interface for rapid integration of the main control device and the communication module. It features plug-and-play functionality, stable communication, and strong anti-interference capabilities, making it suitable for data transmission and command interaction between short-distance devices. An industrial-grade IoT card paired with a carrier-dedicated IoT APN access point is a dedicated solution for IoT device networking. The APN access point enables a dedicated connection between the device and the core network, isolating it from public network services, significantly improving the security and stability of data transmission and avoiding transmission interruptions caused by public network interference. In this application, the communication unit is directly connected to the main control hub of the control module via the USB interface, simplifying the connection link and ensuring smooth transmission of control commands and data. After the industrial-grade IoT card is inserted into the card slot of the communication unit, it accesses the IoT cloud platform through the carrier-allocated dedicated IoT APN access point, providing a secure and stable network channel for the remote transmission of slope displacement data.

[0053] In cross-device data interaction, data format conversion is a crucial step to ensure the receiver can correctly parse the data. Different devices and platforms have different data storage and parsing rules, necessitating the conversion of raw data into a pre-defined, unified format to avoid parsing failures due to incompatible formats. Communication protocols, which define the rules for data transmission, specify the encoding method, transmission rate, and verification mechanism, are core elements for ensuring the integrity and orderly transmission of data. In this application, the main control center first standardizes the collected raw slope displacement data according to a pre-defined data format to ensure the data structure meets the parsing requirements of the IoT cloud platform. Then, through a pre-determined communication protocol (such as MQTT), the format-converted displacement data is stably transmitted to the IoT cloud platform, forming a complete process of data conversion, protocol encapsulation, and remote transmission.

[0054] In this embodiment, the communication unit connects to the main control center via a USB interface, simplifying device integration and ensuring the stability of control commands and data transmission, avoiding communication failures caused by complex wiring. An industrial-grade IoT card paired with a carrier-dedicated APN access point achieves data transmission isolation from the public network, effectively resisting network attacks and signal interference, and improving the security and reliability of slope displacement data transmission. The main control center's data format conversion processing solves the compatibility problem between the original data and the cloud platform's format, ensuring that the data can be correctly parsed and stored by the platform. The preset communication protocol ensures the integrity and orderliness of data transmission, preventing data loss or corruption. The overall design enables real-time, stable, and secure uploading of slope displacement data, providing timely and high-quality data support for big data analysis, trend judgment, and tiered early warning in the remote monitoring module. This overcomes the deployment limitations and anti-interference shortcomings of traditional wired transmission, adapting to the long-term monitoring needs of unattended slopes in the field.

[0055] In some implementations, the slope displacement monitoring module stores the slope displacement data in a time-series database according to the time series, and generates a real-time monitoring screen through a data visualization tool. In the real-time monitoring screen, the displacement change trend is displayed in the form of a time displacement curve and a data dashboard. When the slope displacement data reaches the preset warning threshold, the slope displacement status monitoring module pushes alarm information through a mobile terminal and triggers a graded warning.

[0056] In long-term monitoring systems, time-series databases are dedicated databases for storing time-stamped data. Their general advantages include high write performance, low storage cost, efficient support for historical data tracing and trend analysis, and broad adaptability to the time-series characteristics of monitoring data. Data visualization tools are technical means of transforming abstract data into intuitive graphics. Time-displacement curves and data dashboards are commonly used display formats in the monitoring field, clearly presenting the data's changing patterns and real-time status over time. In this application, the slope displacement status monitoring module stores the collected slope displacement data in a time-series database, ensuring data orderliness and traceability. Simultaneously, it generates real-time monitoring screens through data visualization tools, visually presenting displacement change trends with time-displacement curves and displaying key information such as real-time displacement values ​​on data dashboards, allowing maintenance personnel to quickly grasp the slope's status.

[0057] Among them, the graded early warning mechanism is a pre-set early warning mechanism with multiple thresholds based on the risk level of the monitored object. Generally, the threshold levels need to be divided according to the safety range of the monitored data to achieve a gradient response to risks. Mobile terminal push is a method of sending alarm information to users' mobile phones, computers, and other devices in real time through network communication. The core is to break spatial limitations and ensure that users receive early warning notifications in a timely manner. In this application, the system pre-sets different levels of slope displacement early warning thresholds. When the monitored slope displacement data reaches the corresponding threshold, the slope displacement status monitoring module automatically triggers the graded early warning and pushes alarm information to maintenance personnel through mobile terminals, realizing a closed-loop response of threshold triggering, early warning initiation, and information push.

[0058] In this embodiment, slope displacement data is stored in a time-series database according to time series, ensuring data integrity and traceability. Combined with time displacement curves and data dashboards displayed by data visualization tools, maintenance personnel can quickly grasp the real-time status and changing trends of slope displacement without complex analysis, solving the problems of messy and difficult-to-judge trends in traditional monitoring data. The hierarchical early warning mechanism enables precise response to different levels of risk, avoiding the limitations of single-threshold early warning. The method of pushing alarm information through mobile terminals breaks the spatial limitations of traditional monitoring and early warning, ensuring that maintenance personnel can receive early warning notifications in real time and take timely measures such as reinforcement and inspection. This effectively avoids the risk of slope instability caused by delayed early warnings and greatly improves the timeliness and pertinence of slope safety control.

[0059] In some embodiments, the intelligent slope displacement monitoring system further includes: The solar power supply module includes a solar panel, a solar charge controller, and two step-down converters. The solar panel converts light energy into electrical energy and uses the solar charging controller to charge the lithium battery at a stable voltage. The two step-down converters convert the 12V output voltage from the lithium battery to 5V and 7.4V respectively. The 5V voltage powers the main control hub of the control module, and the 7.4V voltage powers the serial bus digital servo motor.

[0060] In monitoring scenarios without mains power supply in the field, solar power modules are the core energy solution for achieving long-term autonomous operation of the system. Their general design requires energy conversion components, energy storage components, and charging protection components. The core logic is that the solar panel converts light energy into electrical energy, which is then processed by the charging controller to charge the lithium battery, which in turn powers the system. This design is widely adaptable to the energy needs of long-term unattended outdoor equipment. In this application, the solar power module specifically includes a solar panel, a solar charging controller, and two step-down converters. The solar panel, as the energy acquisition end, converts light energy into electrical energy. The solar charging controller performs voltage stabilization, overcharge protection, and over-discharge protection functions, providing safe charging protection for the lithium battery and ensuring long-term stable energy storage, thereby providing continuous energy support for the entire monitoring system.

[0061] In integrated systems with multiple components and voltage requirements, step-down converters (DC and DC) are key components for achieving precise voltage matching. Their general function is to convert a uniform energy storage voltage into the rated operating voltage required by each piece of hardware, avoiding hardware damage or malfunction due to voltage mismatch. In this application, two step-down converters specifically divide and convert the uniform 12V output voltage from the lithium battery. One is converted to 5V to power the main control hub of the control module, and the other is converted to 7.4V to power the serial bus digital servo. Through precise voltage distribution, it is ensured that both the core control components and the execution components receive appropriate power, guaranteeing the stable and coordinated operation of all hardware.

[0062] In this embodiment, the solar power supply module completely solves the energy problem of no mains power supply in the field slope monitoring scenario, realizing the autonomous collection, storage and supply of system energy, and adapting to the monitoring needs of long-term unattended operation; the voltage stabilization, overcharge protection and over-discharge protection functions of the solar charging controller effectively protect the lithium battery, extend the battery cycle life, and reduce the frequency and cost of battery replacement in system operation and maintenance; the targeted voltage conversion design of the two step-down transformers accurately matches the rated operating voltage of the main control center and the serial bus digital servo motor, avoiding problems such as hardware burnout and operation lag caused by excessively high or low voltage, and ensuring the stable operation of the core components of the control module; the overall power supply solution is both environmentally friendly and sustainable, without relying on external power supply, which greatly improves the flexibility of the system's field deployment and long-term working capability, and provides a reliable energy guarantee for the continuous realization of the entire monitoring system's automated measurement, real-time data transmission, and hierarchical early warning functions.

[0063] In some implementations, during intelligent slope displacement monitoring, the user inputs the target angle and triggers a measurement command through the visual control panel of the slope displacement status monitoring module. The IoT cloud platform sends a service call command to the main control center of the control module. The main control center responds to the service call command and controls the dual-axis servo gimbal to adjust to the target angle to complete laser ranging; or... During intelligent slope displacement monitoring, the measurement cycle is set through the power management expansion board of the main control center. After the main control center is powered on, it connects to the IoT cloud platform, retrieves the preset angle measurement cycle, and completes the measurement, data storage and uploading of each angle in sequence. After the task is completed, the power is turned off to reduce power consumption.

[0064] In the remote monitoring system, the visual control panel is the core entry point for user interaction with the system. Its general functions include supporting parameter input and command triggering, enabling remote control of the equipment. Service call commands are a standardized communication method used by the cloud platform to send control commands to terminal devices, ensuring the accuracy of command transmission and timely response. In this application, the user inputs the target measurement angle and triggers a measurement command through the visual control panel of the slope displacement monitoring module. The IoT cloud platform then sends a service call command to the main control center of the control module. After receiving and responding to the command, the main control center drives the dual-axis servo gimbal to adjust to the target angle, thereby controlling the laser rangefinder to complete the distance measurement, forming a remote control process of user operation, cloud command, terminal execution, and measurement completion.

[0065] The power management expansion board is a dedicated component with timed start / stop functionality, commonly used in field equipment requiring low-power operation. It controls the power-on and power-off of terminal devices by setting a cycle, reducing energy consumption during non-operational states. The cloud-based preset angle measurement cycle is the foundation for automated measurement. Synchronizing the device and cloud configurations ensures the standardization and consistency of measured angles. In this application, a fixed measurement cycle is set via the power management expansion board in the main control center. After powering on, the main control center automatically connects to the IoT cloud platform, retrieves the preset angle measurement cycle list from the cloud, and sequentially adjusts the dual-axis servo gimbal to each preset angle, completing the distance measurement, data storage, and upload for each angle. After all tasks are completed, it automatically shuts down, achieving low-power automated monitoring.

[0066] In this embodiment, the custom angle measurement method enables users to measure specific angle displacements of slopes on demand through a visual control panel and remote service call commands. Measurement parameters can be flexibly adjusted without on-site operation, breaking through spatial limitations and improving the flexibility and targeting of monitoring. The automated periodic measurement method, with the timed start-stop function of the power management expansion board and the cloud-preset angle cycle, realizes periodic monitoring in an unattended state. Automatic shutdown after task completion significantly reduces system power consumption and is suitable for energy-constrained scenarios with solar power in the field. The two measurement methods complement each other, meeting the needs of accurate measurement in emergency situations and ensuring the stability of long-term continuous monitoring. This further reduces the cost of manual intervention and improves monitoring efficiency. At the same time, the low power consumption design extends the system's working time in the field and enhances the system's practicality and reliability in field slope scenarios.

[0067] In some implementations, see Figure 2 , Figure 2 This is a closed-loop control logic diagram of a system in one embodiment of this application. Figure 2 The system's closed-loop control process based on IoT technology is presented intuitively, unfolding around the cyclical logic of command issuance, data acquisition, transmission and analysis, and feedback control: The remote monitoring module (Alibaba Cloud IoT platform) acts as the control hub, issuing control commands such as measurement parameter configuration and start / stop to the control module (Raspberry Pi main controller); after receiving the commands, the control module coordinates the data measurement modules (laser rangefinder, camera, etc.) to perform slope displacement data acquisition; the acquired data is uploaded to the remote monitoring module in real time through the data transmission module (4G / 5G module, MQTT protocol); the cloud analyzes and processes the data and displays it visually; if the displacement exceeds the warning threshold, a new control command (such as adjusting the measurement frequency or triggering an alarm) is immediately generated and fed back to the control module, forming a complete closed-loop control to ensure the system's dynamic response and intelligent regulation of the slope condition.

[0068] In some implementations, see Figure 3 , Figure 3 This is a system hardware collaborative control logic diagram in one embodiment of this application. Figure 3The collaborative working relationship between the various hardware components of the system is clearly outlined. With the Raspberry Pi 4B master controller as the core hub, it achieves coordinated control of multiple hardware components: The Raspberry Pi communicates via Python code and serial port to control the horizontal and vertical servo motors, driving the dual-axis servo gimbal to rotate, thereby adjusting the measurement angle of the laser rangefinder; the Raspberry Pi camera connects to the master controller via a CSI cable, using image recognition technology to capture the characteristics of the microprism cooperative target, feeding back alignment deviation signals to the Raspberry Pi to assist the laser rangefinder in accurate aiming; the laser rangefinder communicates with the Raspberry Pi via a USB-to-RS485 converter, receiving ranging commands and uploading measurement data; the lithium battery, after voltage conversion via DC and DC step-down converters, powers the Raspberry Pi, servo motors, and other hardware. All hardware components achieve efficient collaboration through rigid connections and signal adaptation, jointly completing the accurate acquisition of slope displacement data.

[0069] For example, the control module specifically consists of a Raspberry Pi 4B main controller, a Feite STS3215 horizontal rotation servo, a pitch rotation servo, and a lithium battery. The Raspberry Pi 4B has a power supply voltage of 5V, while the optimal operating voltage for the Feite STS3215 servos is 7.4V. Directly connecting them to the 12V supply from the lithium battery will burn out the devices. To provide matching voltages for each piece of hardware, two DC-DC step-down converters are introduced as step-down modules to achieve voltage conversion. The lithium battery outputs 5V through step-down module A to power the Raspberry Pi 4B, and outputs 7.4V through step-down module B to power the two Feite STS3215 servos.

[0070] The embedded Raspberry Pi 4B controller is equipped with a quad-core ARM Cortex-A72 processor and features a rich array of external communication interfaces, including four high-speed, high-frequency USB ports and a Type-C power connector, perfectly meeting the integration needs of any system. Its USB ports provide stable connections to 4G / 5G modules and the SW-LDS100DB laser rangefinder; its Type-C port can receive stable power from a switching power supply. To cope with potentially harsh outdoor conditions, the controller is housed in an aluminum alloy casing equipped with a cooling fan, ensuring long-term stable operation in ambient temperatures ranging from -20°C to 70°C.

[0071] The control module employs an intelligent servo motor based on a serial bus communication protocol, specifically the Feite STS3215 model. This servo motor connects in parallel via a three-wire bus and uses serial port commands to control the target angle, rather than relying on unstable PWM pulse widths. This type of servo motor can feed back information such as the current actual position, temperature, and load to the Raspberry Pi, achieving closed-loop control and facilitating the determination of whether the servo motor has reached the designated position. Its digital control circuitry and superior mechanical structure result in a repeatability accuracy far exceeding that of analog servos. These advantages enable high-precision, low-vibration, and stable movement of the laser scanning gimbal.

[0072] Users can write the control flow to be executed by the system based on the Python language. The Raspberry Pi 4B master controller compiles and runs the Python code to realize the horizontal and vertical rotation of the Feite STS3215 servo motor, thereby achieving the purpose of measuring the displacement of the slope at different angles and positions.

[0073] The data measurement module consists of a Deepwise SW-LDS100DB laser rangefinder, a total station-specific L-shaped microprism cooperative target, a dual-axis servo gimbal, and a Raspberry Pi V2 camera.

[0074] The SW-LDS100DB supports wide voltage input and can be powered directly by a 12V lithium battery, making it an industrial-grade phase-based laser rangefinder. It features a 100-meter measurement range and ±1.5-3mm measurement accuracy, supports a measurement frequency of 20-40Hz, and utilizes phase-based ranging, which is generally more accurate than Time-of-Flight (ToF) methods, achieving millimeter-level precision more easily, and is better suited for slope ranging applications.

[0075] The SW-LDS100DB provides an industry-standard RS485 serial interface, which connects to the embedded host controller (Raspberry Pi) via a USB to RS485 converter. This connection method has strong anti-interference capabilities and high reliability.

[0076] The dual-axis servo gimbal includes: 1 multi-functional bracket for securing the bottom STS3215 horizontal servo; 1 long U-shaped bracket for connecting the arms of the horizontal and pitch servos; 1 short U-shaped bracket for connecting the STS3215 pitch servo and the SW-LDS100DB laser rangefinder; 1 cup bearing mounted on the opposite side of the U-shaped bracket for auxiliary support; 2 metal servo discs: circular metal plates connecting the servo gears and the bracket; and several nuts.

[0077] The assembly steps for the dual-axis servo platform and servo motors are as follows: The STS3215 horizontal rotation servo motor is mounted on a multi-functional bracket, which is then fixed to the slope measurement base station. A metal servo disc is mounted on the shaft of the STS3215 horizontal rotation servo motor, and the bottom of the long U-shaped bracket is locked onto the metal servo disc. At this point, the U-shaped bracket can rotate left and right with the horizontal servo motor. The STS3215 pitch rotation servo motor is installed in the opening of the long U-shaped bracket. A bush bearing is used during installation to secure the STS3215 pitch rotation servo motor and the U-shaped bracket, forming a closed frame. A short U-shaped bracket is mounted on the shaft of the STS3215 pitch rotation servo motor, and the SW-LDS100DS laser rangefinder is screwed onto the short U-shaped bracket.

[0078] The Raspberry Pi Camera V2 is used to assist aiming at the laser rangefinder and the cooperative target. It is connected to the Raspberry Pi 4B via a CSI cable. To ensure that the Raspberry Pi Camera V2 and the laser rangefinder maintain the same line of sight, the Raspberry Pi Camera V2 is fixed above the rangefinder.

[0079] The Raspberry Pi Camera V2 connects directly to the Raspberry Pi 4B via a dedicated 15-pin CSI high-speed interface. Its highly integrated design and 8-megapixel imaging capability ensure accurate laser-target alignment. The system utilizes OpenCV for automatic aiming based on image recognition, capturing the vibrant colors or unique shapes of the target. The code logic searches for the specified color or shape in the camera's viewfinder. If the target is not detected by the crosshair, automatic calibration is performed. Once the target is locked, the Raspberry Pi 4B controls the SW-LDS100DB rangefinder to emit a laser for measurement.

[0080] In long-distance, complex lighting conditions, the echo signal from the laser directly emitted by the SW-LDS100DB laser rangefinder is very weak and unstable, leading to measurement failure. To ensure stable echo signals, the system incorporates a total station-specific L-shaped microprism target to improve the accuracy and effectiveness of the echo.

[0081] The L-shaped microprism cooperative target for total stations features a high-strength aluminum alloy cast L-shaped structure. One side serves as the mounting reference surface, while the other side supports a high-precision microprism reflective array with a diameter of no less than 30mm. This microprism array has a light intensity reflectivity exceeding 1000cd / lx / m², allowing light to enter the surface composed of countless miniature glass or plastic prisms and undergo three reflections before returning precisely in a direction parallel to the original incident light. This ensures that the laser rangefinder can still achieve stable millimeter-level accurate distance measurement at long distances even in adverse outdoor environments.

[0082] The L-shaped microprism cooperative target for total stations features standard 5 / 8-inch connecting threads on its bottom. Using a custom-designed stainless steel mounting bracket, it can be rigidly fixed to the steel mesh of a slope or deeply buried ground anchors. This bracket is equipped with level and pitch adjustment knobs, allowing adjustment of the reflector's orientation during installation to ensure its normal direction is precisely aligned with the laser rangefinder's intended position, guaranteeing accurate measurement data. To match the angle measurement program of the Raspberry Pi 4B, the cooperative target is installed at preset angle intervals, such as 0°, 15°, 30°, 45°, and 60°, facilitating calibration.

[0083] The data transmission module consists of a 4G / 5G module, an industrial-grade IoT card, and an IoT cloud platform.

[0084] The communication module adopts Quectel's EC600N series 4GLTEC Cat.1 industrial module, specifically designed for the Internet of Things. Considering the characteristics of the system—small data volume, long-term online operation, and reliance on outdoor power sources—the Cat.1 standard is chosen. Compared to high-speed standards such as Cat.4, it offers significant low-power advantages while meeting data transmission requirements, making it particularly suitable for long-term monitoring and unattended operation scenarios in slope environments.

[0085] The EC600N module connects to the embedded host controller (Raspberry Pi) via a dedicated USB interface adapter board. This module supports an extended AT command set and the standard PPP dial-up protocol, and is easy to integrate. Its industrial-grade design ensures stable operation from -35°C to +75°C, guaranteeing network reliability.

[0086] The industrial-grade IoT SIM card can be selected from any of the three major telecom operators and inserted into the SMI card slot of the Quectel EC600N series 4GCat.1 communication module. This card utilizes advanced chip packaging technology, is resistant to high temperatures and corrosion, and its physical lifespan and connection stability far exceed those of ordinary commercial SIM cards. The card accesses the core network through a dedicated IoT APN access point allocated by the operator, isolating it from public network services and effectively improving the security and reliability of data transmission.

[0087] The Raspberry Pi 4B controller sends slope data to the EC600N module via USB interface. The module then connects to the 4G network through authentication via an industrial-grade IoT card, ultimately transmitting the data stably to the cloud database.

[0088] The cloud database uses Alibaba Cloud's IoT platform, which serves as the access layer for Raspberry Pi 4B. It interacts with Raspberry Pi 4B via the MQTT protocol and reports slope monitoring data according to the defined object model format.

[0089] In some implementations, see Figure 4 , Figure 4 This is a diagram of the system remote monitoring and data interaction architecture in one embodiment of this application. Figure 4The system showcases a four-layer interactive architecture comprising terminal hardware, communication links, a cloud platform, and user terminals. Based on a B / S (Browser / Server) model, it enables remote monitoring and data interaction: The bottom layer consists of field terminal hardware (Raspberry Pi, laser rangefinder, 4G / 5G modules, etc.), responsible for data acquisition and command execution; the communication link utilizes 4G / 5G networks and the MQTT protocol, with industrial-grade IoT cards establishing a secure connection between the terminal and the Alibaba Cloud IoT platform via a dedicated APN access point; the cloud platform, as the core layer, handles data reception, storage (Time Series Database TSDB), analysis, visualization (DataV tool), and command issuance, achieving standardized interaction with the terminal through object model definition; the top layer comprises user terminals (mobile phones, computers), allowing users to access a cloud-based visual control panel via a browser to view displacement data and trend curves in real time, while simultaneously issuing remote control commands, achieving contactless monitoring and management of field slope equipment.

[0090] The remote monitoring module consists of a cloud database and a mobile terminal.

[0091] The cloud database is Alibaba Cloud IoT platform, and the mobile terminal is a mobile phone, computer, or other device that can access Alibaba Cloud IoT platform through a browser.

[0092] The remote monitoring module is a B / S architecture built on Alibaba Cloud public cloud infrastructure. The Raspberry Pi controller transmits slope displacement data to the Alibaba Cloud IoT platform via a 4G network and the MQTT protocol. Mobile terminals access the IoT platform through a browser to achieve remote monitoring.

[0093] Alibaba Cloud's IoT platform boasts powerful data access, processing, storage, analysis, and visualization capabilities. It can generate visual control panels, acting as the system's host computer to send commands to the Raspberry Pi 4B. Slope displacement data is stored in Alibaba Cloud's Time Series Database (TSDB) according to time series. This database perfectly meets the needs for historical storage and high-performance querying of slope displacement data, providing a solid foundation for historical data backtracking and trend analysis. Users can utilize the platform's DataV data visualization tool to build real-time monitoring dashboards, presenting complex displacement data in intuitive formats such as time-displacement curves and data dashboards, achieving a global understanding of the slope displacement situation.

[0094] Alibaba Cloud IoT cloud platform implements host computer functions. Its core mechanism is the "object model" of Alibaba Cloud IoT platform, which defines attributes (e.g., current angle, current distance, manual / automatic measurement mode), services (e.g., performing measurement), and events (e.g., measurement completion notification, danger threshold alarm). Together with the Python code on the Raspberry Pi 4B, it implements host computer functions.

[0095] In some implementations, see Figure 5 , Figure 5 This is a flowchart of a system automated periodic measurement task in one embodiment of this application. Figure 5 The complete execution steps of the system's automated periodic measurement are described in detail, following the logic of timed startup, parameter synchronization, sequential measurement, data upload, and low-power shutdown: The Raspberry Pi power management expansion board starts the Raspberry Pi main controller at a preset cycle (e.g., every 6 hours); after the Raspberry Pi powers on, it automatically runs the program, establishes a connection with the Alibaba Cloud IoT platform, and retrieves the preset angle measurement cycles (e.g., 0°, 15°, 30°, etc.) from the cloud; then, it sends angle adjustment commands to the servo motors in sequence, and after the gimbal stabilizes, it controls the laser rangefinder to perform distance measurement, storing data after each angle measurement is completed; after all preset angle measurements are completed, the Raspberry Pi uploads the batch data to the cloud via the 4G / 5G module; after the data upload is complete, the Raspberry Pi executes the automatic shutdown program, and the power management expansion board cuts off power after the soft shutdown, waiting for the next measurement cycle in low-power mode to ensure long-term stable operation of the system.

[0096] The system measures slopes in two ways, and their methods and implementation logic are as follows: 1. Custom Angle Measurement: Users can enter the predicted angle on the control panel and click the "Measure" button. The Raspberry Pi 4B will then immediately start working and control the system to perform the measurement.

[0097] The implementation logic is as follows: On the visualization interface of Alibaba Cloud, an "angle input box" and a "measurement button" are set. When the "measurement button" is clicked, the cloud platform sends a "service call" instruction to the Raspberry Pi. The Alibaba Cloud SDK needs to be introduced into the Raspberry Pi program and a "listening function" needs to be written to execute the cloud call.

[0098] 2. Automated measurement (periodic task mode for slope measurement): The initial setting is to perform periodic measurements every six hours, with angle periods set to 0°, 15°, 30°, 45°, and 60°.

[0099] In the automated measurement mode, time control (every 6 hours) is achieved by the system's timer relay, specifically a Raspberry Pi power management expansion board. The Raspberry Pi power management expansion board powers the Raspberry Pi 4B every 6 hours, performing a measurement and storing / uploading the data.

[0100] The implementation logic is as follows: Utilizing Alibaba Cloud's "Device Shadow" function, the angle measurement cycle is pre-configured in the cloud. The Python program on the Raspberry Pi is set to run automatically upon startup. When the Raspberry Pi is powered on, the program will start automatically, connect to Alibaba Cloud, retrieve the configured angle measurement cycle, read the angle list, and perform a for loop to measure. At each angle, the measurement is stopped, the distance is measured, and the data is stored. When the cycle measurement is completed, the Raspberry Pi will transmit the data to the Alibaba Cloud IoT cloud platform. After the task is completed, the Raspberry Pi power management expansion board will cut off the power.

[0101] The system is built based on the characteristics of serial communication, level signals, and voltage signals of each hardware component. The auxiliary hardware and functions required for system construction are as follows: One solar controller: prevents the solar panel voltage from overcharging and causing the battery to explode, and also prevents the battery from being overcharged.

[0102] Two DC-DC step-down modules: The lithium battery outputs 12V, and the step-down modules can adjust the voltage output to the hardware matching level (5V for Raspberry Pi and 7.4V for servos).

[0103] One serial bus servo driver board: The Feite STS3215 servo is a serial bus servo and cannot be directly plugged into the Raspberry Pi pins. Plug the driver board into the USB port on the Raspberry Pi and connect it directly to the Raspberry Pi via a USB cable.

[0104] A Raspberry Pi power management extension: acting as a timer relay for the system, it time-controlled power-off of the Raspberry Pi 4B, improving system usability.

[0105] Several Wago terminal blocks: used for system branching.

[0106] One USB to RS485 converter: used to establish a communication connection between the SW-LDS100DB laser rangefinder and the Raspberry Pi 4B.

[0107] In some implementations, see Figure 6 and Figure 7 , Figure 6 as well as Figure 7 This is a schematic diagram of the system wiring in one embodiment of this application. Figure 6 This is a schematic diagram of the servo motor connection. Figure 7 This is a schematic diagram of the laser rangefinder connection. Figure 6 and Figure 7 Together, they form the system wiring electrical schematic diagram. Figure 6 and Figure 7The wiring logic of the system's power supply unit and signal unit was clearly defined to ensure safe power supply and stable communication for all hardware components. The power supply unit adopts a solar power supply mode. The solar panel charges the 12V lithium battery through a solar charge controller. The 12V output voltage from the lithium battery is distributed through two branches: Branch 1 is converted to 5V via a DC-DC step-down converter to power the Raspberry Pi 4B; Branch 2 is converted to 7.4V via another step-down converter to power the serial bus servo motors. Meanwhile, the laser rangefinder is directly connected to the 12V main power supply. For the signal unit wiring, the Raspberry Pi connects to the 4G / 5G module and the USB to RS485 converter (for the laser rangefinder) via a USB interface, connects to the camera via a CSI cable, and connects to the horizontal and pitch servo motors via the serial bus servo drive board. The system adopts a common ground design, using the Wago terminal block as the common GND point. All hardware grounding wires are connected to the same point. The GND connection of the USB interface further ensures signal stability, avoids voltage conflicts and interference, and ensures the safety and reliability of the entire system's electrical connections.

[0108] The power supply unit connection is as follows: Connect the positive and negative terminals of the solar panel to the input terminal of the solar controller. Connect the positive and negative terminals of the 12V lithium battery to the battery terminal of the solar controller. Connect the wires from the load output terminal of the solar controller (or directly from the positive terminal of the lithium battery) to the output line of the timer relay (the system is a Raspberry Pi power management expansion board). The two unused output lines (1 positive and 1 negative) are the 12V power lines of the system.

[0109] Branch 1 (Powering the Raspberry Pi 4B): Connect the 12V power line to the input terminal of the first DC-DC step-down module, adjust the knob on the module, and use a multimeter to measure the output voltage until the multimeter displays 5V (a slightly higher value is fine). Solder the positive and negative terminals of the output terminal to a Type-C cable and plug it into the Type-C power port of the Raspberry Pi 4B. This will power the Raspberry Pi controller.

[0110] Branch 2 (Powering the STS3215 servo): Connect the 12V power line to the input terminal of the second DC-DC step-down module, adjust the knob on the module, and use a multimeter to measure the output voltage until the multimeter displays 7.4V. Then connect this voltage to the power supply interface of the serial bus servo drive board.

[0111] The signal unit connection is as follows: 1. Raspberry Pi connection with 4G / 5G module: USB connection is used. The selected EC600N series 4GLTECat.1 industrial module has a USB port on the floor. Simply plug one end of the USB cable into the USB 3.0 port of the Raspberry Pi 4B and the other end into the USB port of the EC600N. The Raspberry Pi 4B will automatically recognize it as a "USB network card".

[0112] 2. Connecting the Raspberry Pi to the Camera V2: The two communicate via a CSI ribbon cable. Simply insert the ribbon cable into the "CAMERA" interface on the Raspberry Pi 4B board to complete the connection.

[0113] 3. Connecting the Raspberry Pi to the STS3215 Serial Bus Servos: Plug the serial bus driver board into the USB port of the Raspberry Pi 4B (if it's a USB driver board), or into the GPIO pin (if it's an expansion cap board). Two STS3215 servos can be daisy-chained; simply plug the signal wire of the first servo into the servo interface on the driver board, and the signal wire of the second servo into the unused interface of the first servo.

[0114] 4. Connection between Raspberry Pi and SW-LDS100DB laser rangefinder: The serial communication method (UART) of Raspberry Pi 4B is not compatible with SW-LDS100DB (RS-485), and SW-LDS100DB usually supports a wide voltage power supply of 9V to 30V, so it cannot be powered by Raspberry Pi and needs to be connected to a 12V main power supply.

[0115] Connect the VCC wire of the SW-LDS100DB to the 12V positive terminal of the Wago terminal, and the GND wire to the 12V negative terminal of the Wago terminal (common ground). Insert the yellow wire of the SW-LDS100DB laser rangefinder into port A of the converter, insert the white wire into port B, and finally plug the converter into the USB 3.0 port of the Raspberry Pi 4B.

[0116] The system uses a Linux-based Raspberry Pi controller. Frequent power outages on the Raspberry Pi power management extension can easily lead to data loss and system damage. To address this, a script is embedded in the Raspberry Pi 4B to automatically shut down the Raspberry Pi after completing its tasks. Simultaneously, the script adjusts the power management extension's timing to ensure that the power outage occurs after the Raspberry Pi has completed its soft shutdown.

[0117] To ensure the system functions correctly and all devices operate safely, a common ground connection is used for the system.

[0118] Wago terminals are selected as the common GND point. The grounding wires of all devices in the system are connected to this common GND point. Specifically, the 12V power negative terminal (black wire) from the solar controller, the input negative terminal of DC-DC step-down module A, and the input negative terminal of DC-DC step-down module B are connected.

[0119] The system has two special grounding methods: one is the SW-LDS100DB, which connects to the Raspberry Pi using a USB adapter; the other is the connection between the Raspberry Pi and the servo driver board, also via a USB cable. Since the metal casing of the USB interface already contains a GND connection, automatic grounding is achieved when the USB cable is plugged in.

[0120] In one embodiment of this application, a lightweight intelligent method for monitoring slope displacement is also provided. See [link to relevant documentation]. Figure 7 , Figure 7 This is a flowchart illustrating a lightweight intelligent slope displacement monitoring method in one embodiment of this application, as shown below. Figure 7 As shown, the method includes the following steps: S710: Control the dual-axis servo gimbal to rotate to the zero-position reference target coordinate position, use the computer vision-assisted aiming component to visually lock onto the zero-position reference target and perform laser ranging, and obtain the reference measured vector at the current moment. The reference measured vector includes the measured distance, the measured horizontal azimuth angle, and the measured pitch angle. S720. Read the pre-calibrated reference theoretical vector, perform a difference operation between the reference measured vector and the reference theoretical vector to obtain the deviation vector under the current environment; S730: Receive automatic measurement and control command, drive the dual-axis servo motor gimbal to rotate to the corresponding physical pointing area according to the automatic measurement and control command, activate the computer vision-assisted aiming component to measure the microprism cooperative target in the physical pointing area, and obtain slope displacement data. S740. Based on the deviation vector, perform vector correction on the slope displacement data to generate initial correction data; S750. Obtain the echo signal intensity value corresponding to each initial correction data, remove data whose signal intensity is lower than a preset confidence threshold from the echo signal intensity values, use a statistical filtering algorithm to remove outliers from the remaining data, and calculate the arithmetic mean as the final slope displacement data. S760. The final slope displacement data is uploaded to the Internet of Things (IoT) cloud platform through the communication unit, so that the IoT cloud platform can analyze the trend of the final slope displacement data, obtain the displacement change trend, and trigger a graded warning when the slope displacement data reaches a preset warning threshold.

[0121] For example, the main control center is configured with the following multi-point cruise automation measurement logic: Dual-axis servo gimbal, slope displacement data measurement module, microprism cooperative target, zero-position reference target, main control center, solar power supply module, Internet of Things cloud platform and communication unit; The dual-axis servo gimbal serves as the physical motion carrier of the system, supporting the omnidirectional rotation of the slope displacement data measurement module. This gimbal employs a high-precision mechanical transmission structure, possessing two degrees of freedom of movement (horizontal and pitch, driven by horizontal and pitch servo motors), and is capable of covering a wide field of view of the slope under test. The slope displacement data measurement module specifically includes a computer vision-assisted aiming component (camera), an industrial-grade phase laser rangefinder, and serial bus digital servos (i.e., horizontal rotation servos and pitch rotation servos) for driving the gimbal rotation. The computer vision-assisted aiming component (camera) is responsible for acquiring real-time images of the target area and feeding back image information; the industrial-grade phase laser rangefinder is responsible for high-frequency distance measurement of the locked target; and the serial bus digital servos serve as the driving tool, receiving commands from the main control center to adjust the gimbal attitude. The microprism cooperative target is fixed at the predicted point on the slope to receive signals from the industrial-grade phase laser rangefinder and feed them back to the rangefinder, providing a measurement basis for the rangefinder. The zero-position reference target is fixed on a stable bedrock of the slope to eliminate the mechanical return error of the system, the settlement error of the installation foundation, and the thermal expansion and contraction deformation error caused by harsh outdoor environments. The communication unit is connected to the USB bus of the main control center and establishes a two-way connection with the IoT cloud platform using the MQTT IoT protocol. It is responsible for the real-time uploading of monitoring data and on-site images and the reception of cloud-based control commands. The IoT cloud platform is deployed based on the IoT architecture. It receives monitoring data and images transmitted from the front end through the MQTT protocol. It can perform multi-dimensional analysis and hazard warning of slope displacement data, and is equipped with a visual control interface to control the main control center, realizing remote real-time monitoring of the slope status. In particular, this invention features an innovative hardware integration architecture. The system adopts a hub-based, fully USB bus integrated architecture. The main control hub, as the core computing and control center of the system, integrates multi-channel USB host interfaces. The main control hub expands the communication interfaces through built-in or external industrial-grade USB hubs, constructing a bus topology of "high-speed dedicated access and low-speed aggregation".

[0122] Specifically, the computer vision-assisted aiming component (camera) is selected from models that support the UVC (USB Video Class) protocol or the dedicated USB industrial transmission protocol. It is directly connected to the USB interface of the main control center via a USB data cable, and utilizes the high bandwidth characteristics of the USB bus to transmit uncompressed raw image data streams. The industrial-grade phase laser rangefinder is selected from models that support the USB communication protocol (or modules with internally integrated TTL to USB level conversion circuits). It establishes communication with the main control center through a USB to RS485 converter to realize the issuance of ranging commands and the transmission of data. The dual-axis servo gimbal is constructed using serial bus digital servos (horizontal rotation servo and pitch rotation servo), supporting daisy-chain communication. The horizontal rotation servo and pitch rotation servo are connected in series via a single bus, and dual-degree-of-freedom control can be achieved through only a USB signal adapter module, logically establishing a half-duplex control link, which significantly simplifies the internal wiring structure of the gimbal.

[0123] This architecture not only achieves electrical isolation and unified power supply for each component, but also gives the system a "plug and play" characteristic, so that in field maintenance scenarios, replacing any damaged sensor module can be completed simply by plugging and unplugging the USB interface, without the need for complicated wiring operations.

[0124] Furthermore, the solar power supply module adopts a power management strategy based on the USB protocol, and the module integrates a power supply unit and a start / stop unit; The start / stop unit comprises a microcontroller (MCU), a real-time clock (RTC), and a power switch. The module's data input connects to a USB hub via a USB interface, while its power input is connected in series in the system's main power supply circuit. The main control unit sends a control command containing the sleep duration via the USB bus. Upon receiving this command, the module starts an independent hardware countdown and physically cuts off the system's main power supply after the countdown ends, achieving zero-power sleep mode and hardware-level anti-crash restart.

[0125] The power supply unit uses solar panels to convert light energy into electrical energy and stores it in a lithium battery pack. It outputs a bus voltage and is equipped with multiple independent DC-DC step-down conversion circuits (two step-down converters) to convert the bus voltage into operating voltages suitable for hardware such as the main control hub, USB hub, serial digital bus servo motor, and industrial-grade phase laser rangefinder (5V for powering the main control hub and 7.4V for powering the serial bus digital servo motor), thus achieving electrical isolation between different components of the system.

[0126] The closed-loop vision control logic is as follows: The main control center receives automatic measurement and control commands from the IoT cloud platform, parses the preset cruise angle sequence contained in the commands, or calls the locally stored default scanning strategy. Based on this angle sequence, the main control center sends position commands to the serial bus digital servos (horizontal rotation servo and pitch rotation servo) via the USB bus, driving the dual-axis servo gimbal to quickly rotate to the preset coarse physical pointing area.

[0127] Once the dual-axis servo gimbal stops and stabilizes, the main control center activates the slope displacement data measurement module, initiating the anti-interference closed-loop alignment process. This process includes the following detailed steps: To adapt to the complex and ever-changing lighting conditions in the field (such as rain, strong sunlight, and dappled shadows), the raw images captured by the computer vision-assisted aiming component (camera) must be enhanced. First, raw RGB image frames with a resolution of 1280×720 are acquired through the computer vision-assisted aiming component (camera) connected to a USB 3.0 interface. .

[0128] To eliminate thermal noise generated by the computer vision-assisted aiming component (camera) and high-frequency noise during transmission, a Gaussian smoothing filter is used to perform convolution processing on the image. The convolution kernel size is set to 5×5, and the standard deviation is... This method can effectively smooth image textures and prevent artifacts from being generated during subsequent edge detection.

[0129] For backlit or shadowed scenes commonly encountered in field monitoring, traditional global histogram equalization can easily lead to background overexposure or loss of detail. This embodiment preferably employs the Limiting Contrast Adaptive Histogram Equalization (CLAHE) algorithm. The system divides the image into 8×8 sub-blocks, calculates the histogram of each sub-block, and performs equalization, while limiting the contrast amplification factor (set to 2). Finally, bilinear interpolation is used to eliminate block artifacts. After this processing, the local contrast between the microprism cooperative target and the background is significantly enhanced.

[0130] Because the R, G, and B components in the RGB color space are highly dependent on light intensity, changes in light intensity can cause drastic fluctuations in color values, easily leading to misidentification. To improve the robustness of recognition, this system converts the image from the RGB color space to the HSV color space. The specific mathematical transformation formula is as follows: Let the normalized RGB values ​​be... make , , .in express The maximum value in, express The minimum value in the range. Then the hue. The calculation formula is: , Saturation The calculation formula is: , brightness The calculation formula is: .

[0131] Through the above transformations, color information is mainly concentrated in... In terms of quantity, changes in light intensity mainly affect... Quantity.

[0132] In this embodiment, the microprism collaborative target uses a distinctive engineered yellow reflective film. The system presets the hue threshold range for yellow to be [range missing]. (The range is 0-180 under the OpenCV standard), with a saturation threshold S > 40 and a brightness threshold V > 40. The image is binarized and segmented using the cv2.inRange function to generate a target mask. At this point, green vegetation (H≈60), brown soil (H≈10-20), and white highlights (S≈0) in the background are effectively filtered out, leaving only the potential target area in the mask.

[0133] A morphological opening operation (erosion followed by dilation) is performed on the mask image to remove discrete noise pixels with a diameter less than 3 pixels. Then, a closing operation (dilation followed by erosion) is performed to fill any black holes that may exist within the microprism cooperative target region. A contour-finding algorithm is used to extract all connected components, and the connected component with the largest area is selected as the target microprism cooperative target. The centroid coordinates of this connected component are calculated using first-order geometric moments. ,in , in The area of ​​the connected region. , Let be the moments of the image.

[0134] Read the optical center coordinates of the computer vision-assisted aiming component (camera). Calculate the pixel offset vector for the current frame: , in This represents the current moment. This deviation directly reflects the current pointing error of the dual-axis servo gimbal.

[0135] To address the inertial overshoot and steady-state error issues during the motion of serial bus digital servos (horizontal rotation servo and pitch rotation servo), this system employs an incremental PID control algorithm to independently control the horizontal rotation servo and the pitch rotation servo. Taking the horizontal rotation servo as an example, the discretized output formula of the PID controller is as follows:

[0136] In the visual closed-loop control of this embodiment, to prevent high-frequency oscillations from occurring when the dual-axis servo gimbal locks onto a target, a "weak proportional, strong integral" tuning strategy is adopted for the PID control parameters. As a specific implementation example (specifically for the dual-axis servo and its load inertia selected in this embodiment), the PID parameters are set as follows: , , Among them, the smaller The value is used to avoid overshoot jitter when there is a large deviation; introduced This is to eliminate steady-state errors caused by mechanical dead zones and ensure that the gimbal can be accurately aligned with the center of the target; This is used to suppress inertia during the dynamic response process. In practical applications, this set of parameters can be adaptively adjusted according to the specific torque characteristics of the gimbal motor.

[0137] The main control unit (Raspberry Pi 4B) adds the floating-point angle increment calculated by the PID controller to the current angle of the serial bus digital servos (horizontal rotation servo and pitch rotation servo) to obtain a new target angle. Subsequently, the target angle is encapsulated into a hexadecimal instruction packet conforming to the serial servo communication protocol and sent to the servos to drive the dual-axis servo gimbal to make high-precision fine-tuning of its attitude.

[0138] The system cyclically executes the above "image acquisition-processing-control" process at a frequency of 20Hz. To prevent false alignment caused by wind-induced gimbal swaying, the system sets strict time-domain stability locking criteria as follows: The system is considered to be in a "locked state" if and only if the following condition is met: 1. The absolute value of the pixel deviation of the current frame. and ( (As a preset threshold), 2. The above state is maintained continuously for more than [a certain period of time]. (For example, 500ms, i.e., 10 consecutive frames). Once a lock is detected, the program immediately pauses the PID control loop, maintains the servo torque, and triggers the laser rangefinder to start high-frequency measurement, collecting N=20 sets of distance data for subsequent processing.

[0139] In long-term field monitoring scenarios, factors leading to decreased measurement accuracy mainly fall into two categories: random errors and systematic errors. Random errors (environmental interference) can be eliminated through the aforementioned PID closed-loop and multiple averaging filters, but systematic errors are usually difficult to avoid and accumulate over time. This embodiment focuses on addressing the following three types of systematic errors: 1. Mechanical return error: The mechanical backlash generated by the internal gear set of the dual-axis servo gimbal during frequent forward and reverse rotations causes the "command angle" to be inconsistent with the "physical angle"; 2. Thermal expansion and contraction deformation: In the field environment with extreme temperature differences between day and night, the steel column and aluminum alloy gimbal bracket will expand or contract at the micron level, causing a slight pitch drift of the optical axis of the computer vision-assisted aiming component (camera).

[0140] 3. Foundation settlement: After being washed by the rainy season, the equipment base may tilt slightly, which is not visible to the naked eye.

[0141] The zero-point reference target not only possesses visual recognition characteristics, but its physical position is also considered absolutely constant. During the initial installation and commissioning of the equipment, a high-precision total station is used to calibrate the theoretical reference vector of the zero-point reference target relative to the center of the equipment. ,in: Reference distance (unit: mm). As the reference horizontal azimuth, This is the reference pitch angle. This vector is stored permanently in the Raspberry Pi 4B's memory.

[0142] Regarding the placement of the zero-point reference target, this invention preferably places it within a range of 1.5m to 3m from the slope displacement data measurement module. The selection criteria are as follows: Image clarity limitations: Limited by the minimum focusing distance (usually 0.5m-1m) and depth of field range of the computer vision-assisted aiming component (camera), if the distance is too close, the target image will be blurry, affecting the accuracy of feature point extraction; Stability requirements: This distance range is typically within the rigid base or stable bedrock of the monitoring station, ensuring that the zero-point reference target itself does not undergo relative displacement; Error sensitivity balancing: If the distance is too far (e.g., >10m), the zero-point reference target itself is easily affected by atmospheric refraction or thermal disturbance, reducing its reliability as an "absolute reference". Therefore, 1.5m-3m is the optimal range for balancing imaging quality and environmental stability.

[0143] Before each run of the automatic cruise monitoring script, the main control unit (Raspberry Pi 4B) first executes a baseline self-calibration procedure, as follows: The system controls the dual-axis servo gimbal to rotate to the area pointed to by the reference vector, activates the vision closed-loop logic to lock the zero-position reference target, and performs laser ranging. At this point, the measured reference vector in the current environment is acquired. Due to the existence of the aforementioned errors, Usually not equal to .

[0144] The central control unit calculates the difference between the two to obtain the deviation vector at the current moment. The specific formula is as follows: , For example, if the calculation yields This indicates that the column may have bent backward due to heat, causing the dual-axis servo gimbal to tilt upward by 0.05 degrees.

[0145] During the subsequent monitoring process, when the system measured the first monitoring point on the slope, it obtained the original slope displacement data. Afterwards, the program will not directly store the data, but will instead apply the difference correction formula: , Through this real-time vector subtraction operation, the system can dynamically offset the effects of mechanical clearance and environmental deformation. Even if the equipment column tilts slightly, as long as the zero-position reference target remains stationary, the system can still accurately calculate the actual displacement of the monitoring point relative to the reference target by subtracting the deviation vector, thus achieving a leap from absolute measurement to relative monitoring.

[0146] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the described solutions.

[0147] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Anyone skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the technical solution of the present invention.

Claims

1. A lightweight intelligent slope displacement monitoring system, characterized in that, include: The control module is used to receive control commands issued by the slope displacement status monitoring module, run a preset control program based on the control commands to set the data measurement cycle and data measurement angle of the slope displacement data measurement module, and control the start and stop of the slope displacement data transmission module based on the control commands. The slope displacement data measurement module is equipped with an industrial-grade phase laser rangefinder, a microprism cooperative target, a dual-axis servo gimbal, and a computer vision-assisted aiming component to measure slope displacement data through laser reflection ranging principle and image recognition alignment technology. The slope displacement data transmission module establishes a connection with the Internet of Things cloud platform through a communication unit, and transmits the slope displacement data measured by the slope displacement data measurement module to the slope displacement status monitoring module through a pre-selected communication protocol. The slope displacement status monitoring module is built on a cloud platform and is used to receive and store the slope displacement data. It also uses big data analysis to obtain the displacement change trend of the slope displacement data and triggers a graded warning when the slope displacement data reaches a preset warning threshold.

2. The lightweight intelligent slope displacement monitoring system as described in claim 1, characterized in that: The control module includes a main control center, a horizontal rotation servo, a pitch rotation servo, and a lithium battery. The horizontal rotation servo and the pitch rotation servo are serial bus digital servos. They receive control signals from the main control center through serial port commands to realize the angle adjustment of the laser rangefinder in the horizontal and vertical directions, and to feed back the current actual position and rotation angle information to the main control center. The lithium battery, in conjunction with the power conversion component, converts the output voltage into the operating voltage of each electrical component adapted to the control module and the slope displacement data measurement module, so as to supply power to the control module and the slope displacement data measurement module.

3. The lightweight intelligent slope displacement monitoring system as described in claim 1, characterized in that: The computer vision-assisted aiming component is a camera, which is fixed above the industrial-grade phase laser rangefinder and maintains a consistent line of sight. The camera uses image recognition alignment technology to collect the color or shape features of the microprism cooperative target. When the camera detects that the microprism cooperative target is not within the crosshair range based on the color or shape features, it sends a deviation signal to the control module. The control module then controls the dual-axis servo gimbal to perform angle calibration until the industrial-grade phase laser rangefinder is precisely aligned with the microprism cooperative target, triggering the laser ranging operation.

4. The lightweight intelligent slope displacement monitoring system as described in claim 1, characterized in that: The industrial-grade phase laser rangefinder has an IP67 protection rating, a measurement accuracy of 1.5-3mm, a ranging range of 5 meters to 300 meters, supports a measurement frequency of 20-40Hz, and adopts a wide voltage input design, allowing direct connection to a 12V power supply. The industrial-grade phase laser rangefinder establishes communication with the main control center of the control module through a USB to RS485 converter. After the laser signal emitted by the laser rangefinder is reflected by the microprism cooperative target, the absolute distance between the industrial-grade phase laser rangefinder and the microprism cooperative target is obtained by analyzing the laser reflection time, so as to obtain the slope displacement data.

5. The lightweight intelligent slope displacement monitoring system as described in claim 1, characterized in that: The microprism cooperative target is an L-shaped structure specifically for total stations. One side of the main body of the microprism cooperative target is the mounting reference surface, and the other side carries a microprism reflective array with a diameter greater than or equal to 30 mm. The light intensity reflectivity of the microprism reflective array is greater than 1000 cd / lx / m². The bottom of the microprism cooperative target is rigidly fixed by a mounting bracket, which is equipped with a horizontal adjustment knob and a pitch adjustment knob to allow for multi-angle installation.

6. The lightweight intelligent slope displacement monitoring system as described in claim 1, characterized in that: The dual-axis servo gimbal includes a multi-functional bracket, a long U-shaped bracket, a short U-shaped bracket, a cup bearing, and a metal servo disc; The multi-functional bracket is used to fix the horizontal rotation servo motor and install the horizontal rotation servo motor on the slope measurement base station; The long U-shaped bracket connects the horizontal rotation servo and the pitch rotation servo; The short U-shaped bracket secures the industrial-grade phase laser rangefinder and connects it to the pitch and rotation servo motor. The cup bearing is used for auxiliary support to improve the stability of the gimbal.

7. The lightweight intelligent slope displacement monitoring system as described in claim 1, characterized in that: The communication unit in the slope displacement data transmission module is connected to the main control hub of the control module via a USB interface. An industrial-grade IoT card is inserted into the card slot of the communication unit to access the IoT cloud platform through the operator's dedicated IoT APN access point. The main control center converts the slope displacement data according to a preset data format and transmits the converted slope displacement data to the Internet of Things cloud platform through the communication protocol.

8. The lightweight intelligent slope displacement monitoring system as described in claim 1, characterized in that: The slope displacement status monitoring module stores the slope displacement data in a time series database according to the time series, and generates a real-time monitoring screen through a data visualization tool. In the real-time monitoring screen, the displacement change trend is displayed in the form of time displacement curve and data dashboard. When the slope displacement data reaches the preset warning threshold, the slope displacement status monitoring module pushes alarm information through a mobile terminal and triggers a graded warning.

9. The lightweight intelligent slope displacement monitoring system as described in claim 1, characterized in that, The intelligent slope displacement monitoring system also includes: The solar power supply module includes a solar panel, a solar charge controller, and two step-down converters. The solar panel converts light energy into electrical energy and uses the solar charging controller to charge the lithium battery at a stable voltage. The two step-down converters convert the 12V output voltage from the lithium battery to 5V and 7.4V respectively. The 5V voltage powers the main control hub of the control module, and the 7.4V voltage powers the serial bus digital servo motor.

10. A lightweight intelligent monitoring method for slope displacement, characterized in that, include: The dual-axis servo gimbal is controlled to rotate to the zero-position reference target coordinate position. The computer vision-assisted aiming component is used to visually lock onto the zero-position reference target and perform laser ranging to obtain the reference measured vector at the current moment. The reference measured vector includes the measured distance, the measured horizontal azimuth angle, and the measured pitch angle. Read the pre-calibrated reference theoretical vector, perform a difference operation between the reference measured vector and the reference theoretical vector to obtain the deviation vector under the current environment; Receive automatic measurement and control commands, drive the dual-axis servo gimbal to rotate to the corresponding physical pointing area according to the automatic measurement and control commands, activate the computer vision-assisted aiming component to measure the microprism cooperative target in the physical pointing area, and obtain slope displacement data; Based on the deviation vector, the slope displacement data is vector-corrected to generate initial corrected data; Obtain the echo signal intensity value corresponding to each initial correction data, remove data whose signal intensity is lower than a preset confidence threshold, use a statistical filtering algorithm to remove outliers from the remaining data, and calculate the arithmetic mean as the final slope displacement data; The final slope displacement data is uploaded to the Internet of Things (IoT) cloud platform via a communication unit, so that the IoT cloud platform can analyze the trend of the final slope displacement data, obtain the displacement change trend, and trigger a graded warning when the slope displacement data reaches a preset warning threshold.

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