Depth space three-dimensional deformation monitoring method and device and electronic equipment

Abstract

The invention discloses a depth space three-dimensional deformation monitoring method and device and electronic equipment, and relates to the field of data processing. In the method, the gravitational acceleration of each monitoring unit is obtained through a monitoring array; calculating the displacement variation of the second monitoring unit relative to the first monitoring unit according to the gravitational acceleration; determining a target displacement variable quantity of each monitoring unit relative to a preset reference position based on the displacement variable quantity; converting the target displacement variable quantity into a capacitor voltage signal, and amplifying the capacitor voltage signal through a signal amplifier to obtain an amplified signal; converting the amplified signal into a digital signal, receiving the digital signal by using a single-chip microcomputer, and decoding the digital signal to obtain three-dimensional displacement data of the monitoring array; and comparing the three-dimensional displacement data with a plurality of historical displacement data of the monitoring array, and judging whether the slope body to be detected has a landslide risk or not. By implementing the technical scheme provided by the invention, the landslide detection cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of data processing, and specifically to a method and device for monitoring three-dimensional deformation in deep space. Background Art

[0002] With the rapid development of geological exploration technology and the water conservancy and hydropower sectors, large-scale infrastructure construction and natural resource development are increasing, leading to a series of previously uncommon technical geological hazards. Landslides have become a common and devastating geological disaster, particularly in mountainous and hilly areas. Landslides not only cause significant damage to the ecological environment but can also cause serious casualties and property losses.

[0003] Although various geological monitoring technologies are currently used for landslide early warning, they often have drawbacks and shortcomings. For example, traditional methods use triaxial MEMS accelerometers to measure attitude changes and thereby obtain deep displacement information of the landslide body. While this method enables real-time monitoring, the displacement data obtained requires specialized host computer analysis software and dedicated data acquisition terminals, which come with high costs.

[0004] Therefore, there is an urgent need for a deep space three-dimensional deformation monitoring method, device and electronic equipment. Summary of the Invention

[0005] The present application provides a deep space three-dimensional deformation monitoring method, device and electronic equipment, which reduces the cost of landslide detection.

[0006] In a first aspect of the present application, a deep spatial three-dimensional deformation monitoring method is provided, the method comprising: obtaining the gravitational acceleration of each monitoring unit through a monitoring array, wherein the monitoring array is located in a slope to be measured, the monitoring array includes a plurality of monitoring units, each of the monitoring units is connected in series, and the monitoring unit includes a three-axis MEMS accelerometer; calculating the displacement change of a second monitoring unit relative to a first monitoring unit based on the gravitational acceleration, the first monitoring unit and the second monitoring unit being any two adjacent monitoring units among the plurality of monitoring units included in the monitoring array, and the second monitoring unit being located after the first monitoring unit; determining a target displacement change of each monitoring unit relative to a preset reference position based on the displacement change; converting the target displacement change into a capacitor voltage signal, and amplifying the capacitor voltage signal through a signal amplifier to obtain an amplified signal; converting the amplified signal into a digital signal, receiving the digital signal using a single-chip microcomputer and decoding the digital signal to obtain three-dimensional displacement data of the monitoring array; and comparing the three-dimensional displacement data with a plurality of historical displacement data of the monitoring array to determine whether the slope to be measured has a landslide risk.

[0007] By adopting the above technical solution, a monitoring array consisting of multiple monitoring units connected in series is deployed within the slope to be measured. Using the three-axis MEMS accelerometers built into each monitoring unit to acquire real-time gravitational acceleration data at different locations within the slope, the displacement and deformation state of the slope can be comprehensively monitored. By calculating the relative displacement changes between adjacent monitoring units and further determining the target displacement changes of each monitoring unit relative to a preset reference position, the three-dimensional displacement and deformation field at different depths and regions within the slope can be accurately estimated. The displacement changes are converted into capacitive voltage signals that are easy to measure and transmit. Through a series of signal processing processes such as amplification, digitization, and decoding, three-dimensional displacement data that can intuitively reflect the actual displacement and deformation within the slope is ultimately obtained, providing high-quality data support for subsequent stability analysis and risk warning. By intelligently comparing and analyzing the measured three-dimensional displacement data with historical displacement data, abnormal fluctuations in slope displacement and deformation can be automatically identified, and potential landslide risks can be promptly detected. This method utilizes a microcontroller-based three-dimensional deformation monitoring array. MEMS accelerometers collect electrical acceleration signals. Signal amplifiers and analog-to-digital converters convert these signals into digital signals that can be processed by the microcontroller. The microcontroller then receives the digital signals from the MEMS accelerometers in the same array, combines them into an array for processing, and ultimately outputs three-dimensional displacement information, communicating with a server to achieve deformation monitoring within the three-dimensional underground space during construction. This design incorporates a microcontroller to process the collected voltage data in real time, effectively reducing latency associated with server communication. This reduces costs while improving the real-time, accurate, and reliable performance of deformation data within the three-dimensional underground space.

[0008] Optionally, the calculation of the displacement change of the second monitoring unit relative to the first monitoring unit based on the gravitational acceleration specifically includes: obtaining a first gravitational acceleration component of the first monitoring unit and a second gravitational acceleration component of the second monitoring unit, the first gravitational acceleration component being the gravitational acceleration component of the three-axis MEMS accelerometer of the first monitoring unit in the X-axis, Y-axis and Z-axis directions, and the second gravitational acceleration component being the gravitational acceleration component of the three-axis MEMS accelerometer of the second monitoring unit in the X-axis, Y-axis and Z-axis directions; determining a first inclination angle of the first monitoring unit relative to the horizontal direction and the vertical direction based on the first gravitational acceleration component, and determining a second inclination angle of the second monitoring unit relative to the horizontal direction and the vertical direction based on the second gravitational acceleration component; and calculating the displacement change of the second monitoring unit relative to the first monitoring unit in the vertical direction and the horizontal direction using a trigonometric function relationship based on the first inclination angle, the second inclination angle and the distance between the first monitoring unit and the second monitoring unit.

[0009] By adopting the above technical solution, the gravitational acceleration components of the first monitoring unit and the second monitoring unit in the X, Y, and Z directions are obtained, and based on the difference in the acceleration components of the two monitoring units, the relative tilt angle between the two monitoring units is calculated using trigonometric functions, and then the relative displacement changes of the two monitoring units in the vertical and horizontal directions are converted. Utilizing the high sensitivity and high resolution characteristics of the MEMS accelerometer, it is possible to accurately measure tiny changes in the tilt angle, ensuring the reliability and accuracy of the relative displacement calculation. At the same time, by separately calculating the displacement changes in the vertical and horizontal directions, it is possible to achieve a quantitative characterization of the internal displacement deformation of the slope, more carefully depicting the actual state of slope deformation, and providing richer data dimensions for judging the deformation pattern and stability of the slope.

[0010] Optionally, the three-axis MEMS accelerometers of each monitoring unit are respectively arranged in capacitors, and the conversion of the target displacement change into a capacitor voltage signal specifically includes: applying a fixed DC bias voltage across the first capacitor and the second capacitor to obtain a capacitor voltage signal corresponding to the displacement change, the first capacitor is the capacitor corresponding to the first monitoring unit, and the second capacitor is the capacitor corresponding to the second monitoring unit; according to the correspondence between the displacement change and the capacitor voltage signal, a displacement voltage conversion model is established to realize the mapping conversion of the displacement change to the capacitor voltage signal.

[0011] By adopting the above technical solution, a three-axis MEMS accelerometer is integrated into a capacitive sensor. The accelerometer's displacement output signal is used to directly modulate the sensor's capacitance. By applying a fixed DC bias voltage across the capacitor, a capacitive voltage signal output is obtained that is linearly proportional to the displacement change. This converts the mechanical displacement into an electrical signal that is easy to collect and transmit. By establishing a displacement-to-voltage conversion model between the displacement change and the capacitive voltage signal, a precise mapping of the displacement physical quantity to the electrical signal is achieved, ensuring the linearity and repeatability of the displacement measurement.

[0012] Optionally, the use of a single-chip microcomputer to receive the digital signal and decode the digital signal to obtain the three-dimensional displacement data of the monitoring array specifically includes: using the decoding algorithm built into the single-chip microcomputer to decode the digital signal to obtain the real-time gravitational acceleration component of each monitoring unit; establishing a displacement calculation model based on the real-time gravitational acceleration component and position coordinates of each monitoring unit, the displacement calculation model including kinematic equations and dynamic equations in three-dimensional space; substituting the real-time gravitational acceleration component of each monitoring unit into the kinematic equation to calculate the real-time velocity component and real-time displacement component of each monitoring unit; substituting the real-time velocity component and real-time displacement component into the dynamic equation to calculate the three-dimensional displacement data of the entire monitoring array, the three-dimensional displacement data including the spatial position change of each monitoring unit of the monitoring array.

[0013] By adopting the above technical solution and utilizing the digital signal decoding algorithm built into the single-chip microcomputer to perform real-time decoding on the acceleration signal collected by the monitoring unit, the digitally quantized acceleration data can be restored to the real-time gravity acceleration component corresponding to the physical motion state, providing accurate input parameters for subsequent displacement calculations. By integrating the real-time acceleration components and spatial position coordinate information of each monitoring unit, the displacement kinematic equations and dynamic equations in three-dimensional space are established. Under the constraint of the overall deformation of the slope, a displacement calculation model reflecting the internal real physical motion process can be deduced. The kinematic equation is used to describe the geometric relationship between the displacement, velocity, and acceleration of the monitoring unit, and then combined with the dynamic equation to describe the force equilibrium state of the monitoring unit. By solving the two types of equations simultaneously, the displacement component data corresponding to the acceleration component data can be converted.

[0014] Optionally, the three-dimensional displacement data is compared with multiple historical displacement data of the monitoring array to determine whether the slope to be measured has a landslide risk, specifically including: calculating the displacement increment between each historical displacement data in the multiple historical displacement data and the historical displacement data at the previous moment to obtain a historical displacement increment data set; calculating the displacement increment data at the current monitoring moment based on the three-dimensional displacement data, and comparing the displacement increment data with the data in the historical displacement increment data set; if the displacement increment data is greater than the maximum value in the historical displacement increment data set and exceeds a preset warning range, determining that the displacement increment data is an abnormal value, and marking the abnormal value as a potential landslide point; counting the distribution density of the potential landslide points within a preset spatial range, determining the area where the distribution density exceeds a preset density threshold as a landslide risk area, and determining that the slope to be measured has a landslide risk.

[0015] By employing this technical solution, historical displacement data is differentially processed to calculate a historical displacement increment dataset, enabling the quantification of the temporal characteristics of slope displacement. Intelligently comparing the measured three-dimensional displacement data with the historical displacement increment dataset automatically identifies monitoring points with abnormally increased displacement change rates, marking them as potential landslide points and enabling preliminary localization of landslide risk zones. By calculating the spatial distribution density of potential landslide points, high-risk areas with rapid overall deformation and concentrated local strain can be automatically identified, enabling regionalized landslide risk assessment and grading.

[0016] Optionally, after counting the distribution density of the potential landslide points within a preset spatial range and determining the area where the distribution density exceeds a preset density threshold as a landslide risk area, the method further includes: determining the shape, size and position range of the landslide risk area based on the position and distribution density of the potential landslide points; calculating the displacement rate and displacement acceleration of each monitoring point in the landslide risk area based on the three-dimensional displacement data and the historical displacement data; predicting the landslide time and scale of the landslide risk area based on the changing trends of the displacement rate and the displacement acceleration, and generating landslide warning information.

[0017] By adopting the above technical solution, after identifying the landslide risk area, the morphological characteristic parameters of the risk area can be further analyzed, including the geometric shape, spatial scale, relative position, etc. of the landslide risk area, so as to more carefully characterize the spatial distribution pattern of the risk area and provide a reference for risk classification and delineation of key prevention areas.

[0018] Optionally, predicting the landslide time and scale of the landslide risk area according to the changing trends of the displacement rate and the displacement acceleration, and generating landslide warning information, specifically includes: calculating the displacement rate and displacement acceleration of each monitoring unit within a preset time window according to the three-dimensional displacement data of each monitoring unit in the landslide risk area, and performing time series analysis on the displacement rate and displacement acceleration to extract changing trend characteristics; establishing a landslide time prediction model and a landslide scale prediction model based on the changing trend characteristics to respectively predict the landslide occurrence time and landslide volume in the landslide risk area; generating the landslide warning information according to the landslide occurrence time and the landslide volume.

[0019] By employing this technical solution, we extract displacement rate and acceleration time series data from each monitoring unit and perform statistical analysis on data subsets within a preset time window. This allows us to uncover the multi-scale evolution of displacement and deformation at different time scales, enabling a comprehensive understanding of trends from the local to the global, and from the short-term to the long-term. By constructing a landslide time prediction model and using the changing characteristics of displacement rate and acceleration as model inputs, we can quantitatively extrapolate the imminent slip time of landslide risk areas. By constructing a landslide scale prediction model and combining displacement and deformation parameters with soil physical and mechanical parameters, we can simulate and estimate the volume of the potential landslide.

[0020] In a second aspect of the present application, a deep space three-dimensional deformation monitoring device is provided, which includes: an acceleration acquisition module, a change calculation module, a change conversion module and a landslide judgment module, wherein: the acceleration acquisition module is used to obtain the gravity acceleration of each monitoring unit through a monitoring array, wherein the monitoring array is located in the slope to be measured, the monitoring array includes a plurality of monitoring units, each of the monitoring units is connected in series, and the monitoring unit includes a three-axis MEMS accelerometer; the change calculation module is used to calculate the displacement change of the second monitoring unit relative to the first monitoring unit based on the gravity acceleration, and the first monitoring unit and the second monitoring unit are any two adjacent monitoring units among the plurality of monitoring units included in the monitoring array. And the second monitoring unit is located after the first monitoring unit; the change calculation module is further used to determine the target displacement change of each monitoring unit relative to a preset reference position based on the displacement change; the change conversion module is used to convert the target displacement change into a capacitor voltage signal, and amplify the capacitor voltage signal through a signal amplifier to obtain an amplified signal; the change conversion module is also used to convert the amplified signal into a digital signal, use a single-chip microcomputer to receive the digital signal and decode the digital signal to obtain three-dimensional displacement data of the monitoring array; the landslide judgment module is used to compare the three-dimensional displacement data with multiple historical displacement data of the monitoring array to determine whether the slope to be measured has a landslide risk.

[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: This method utilizes a microcontroller-based three-dimensional deformation monitoring array. MEMS accelerometers collect electrical acceleration signals. Signal amplifiers and analog-to-digital converters convert these signals into digital signals that can be processed by the microcontroller. After receiving the digital signals from the MEMS accelerometers in the same array, the microcontroller processes the individual MEMS accelerometer signals into an array, ultimately outputting three-dimensional displacement information. This achieves the goal of monitoring deformation within the three-dimensional underground space during construction. This design uses a microcontroller to process the collected voltage data in real time, effectively reducing the latency associated with direct communication with a host computer and lowering the cost of direct data processing via the host computer. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a method for monitoring three-dimensional deformation in a deep space disclosed in an embodiment of the present application; Figure 2 This is an example schematic diagram of a deep space three-dimensional deformation monitoring method disclosed in an embodiment of the present application; Figure 3 This is another exemplary schematic diagram of a deep space three-dimensional deformation monitoring method disclosed in an embodiment of the present application; Figure 4 This is a module diagram of a deep space three-dimensional deformation monitoring device disclosed in an embodiment of the present application; Figure 5 This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0025] Explanation of the accompanying symbols: 401, acceleration acquisition module; 402, change calculation module; 403, change conversion module; 404, landslide judgment module; 500, electronic device; 501, processor; 502, communication bus; 503, user interface; 504, network interface; 505, memory. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0028] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] This application provides a deep space three-dimensional deformation monitoring method, referring to Figure 1 , Figure 1 This is a flow chart of a method for monitoring three-dimensional deformation in a deep space, provided in an embodiment of the present application. This method is applied to a server, which is a server that executes a program for monitoring three-dimensional deformation in a deep space. The server can be a single server, a server cluster consisting of multiple servers, or a cloud computing service center. The method includes steps S101 to S106, which are as follows: Step S101: Obtain the gravity acceleration of each monitoring unit through a monitoring array, wherein the monitoring array is located in the slope to be measured, the monitoring array includes multiple monitoring units, each monitoring unit is connected in series, and the monitoring unit includes a three-axis MEMS accelerometer.

[0030] In step S101, a monitoring array is constructed by connecting multiple monitoring units in series to form a three-dimensional spatial monitoring network. The monitoring array, or array displacement meter, is a quasi-distributed static tilt measurement instrument based solely on accelerometers. It consists of multiple fixed-length monitoring units connected in series, resembling a flexible steel cable. Each monitoring unit incorporates a triaxial MEMS accelerometer, an STM32 series microprocessor, and a DS18B20 digital temperature sensor. Flexible connections between the units allow for conformability to various curved surfaces and good contact. The integration of a triaxial MEMS accelerometer within each monitoring unit enables real-time measurement of the gravitational acceleration components in three orthogonal directions (X, Y, and Z) at the location of the monitoring unit. By deploying multiple monitoring units at different depths and locations within the slope under test, comprehensive three-dimensional spatial deformation monitoring is achieved.

[0031] The monitoring units connect to the server via wired or wireless communication and upload the collected gravity acceleration data to the server. The server periodically sends data request commands to each monitoring unit using a preset data interface protocol. Upon receiving the commands, the monitoring units package and send the current three-axis gravity acceleration data to the server.

[0032] For example, a monitoring array could consist of 20 monitoring units, buried at varying depths (e.g., 1, 3, or 5 meters) at regular intervals (e.g., 5 meters) in key locations on the slope to be measured (e.g., top, toe, and mid-slope). These units are connected in series via a bus to form a monitoring chain. Each monitoring unit is equipped with a low-power wireless communication module. Using wireless mesh networking technology, monitoring data is transmitted and aggregated to a gateway node at the top of the slope. The gateway node then uploads the data to a server via a public network like 4G / 5G. The server sends a data request to the gateway node every 10 minutes. Upon receiving the request, the gateway node broadcasts a data collection command to all monitoring units. Upon receiving the command, the monitoring units immediately sample and transmit back the gravity acceleration data. The gateway node aggregates the data and forwards it to the server.

[0033] After receiving the data from the monitoring array, the server identifies the location of the monitoring unit corresponding to each gravity acceleration data point based on the addressing information in the data frame and stores it in a database. In this way, the server can periodically obtain gravity acceleration monitoring data reflecting the three-dimensional deformation state of different locations within the slope under test, providing data support for subsequent deformation calculations and stability analysis.

[0034] Step S102: Calculating the displacement change of the second monitoring unit relative to the first monitoring unit according to gravity acceleration, where the first monitoring unit and the second monitoring unit are any two adjacent monitoring units among the plurality of monitoring units included in the monitoring array, and the second monitoring unit is located behind the first monitoring unit.

[0035] In step S102, the displacement change of the second monitoring unit relative to the first monitoring unit is calculated based on the gravitational acceleration, specifically including: obtaining a first gravitational acceleration component of the first monitoring unit and a second gravitational acceleration component of the second monitoring unit, the first gravitational acceleration component being the gravitational acceleration component of the three-axis MEMS accelerometer of the first monitoring unit in the X-axis, Y-axis and Z-axis directions, and the second gravitational acceleration component being the gravitational acceleration component of the three-axis MEMS accelerometer of the second monitoring unit in the X-axis, Y-axis and Z-axis directions; determining a first inclination angle of the first monitoring unit relative to the horizontal direction and the vertical direction based on the first gravitational acceleration component, and determining a second inclination angle of the second monitoring unit relative to the horizontal direction and the vertical direction based on the second gravitational acceleration component; and calculating the displacement change of the second monitoring unit relative to the first monitoring unit in the vertical direction and the horizontal direction using a trigonometric function relationship based on the first inclination angle, the second inclination angle and the distance between the first monitoring unit and the second monitoring unit.

[0036] Specifically, the server reads the gravity acceleration data collected simultaneously by any two adjacent monitoring units (denoted as the first and second monitoring units) from the database. The data from the first monitoring unit includes the gravity acceleration components of its three-axis MEMS accelerometer in the X, Y, and Z directions, denoted as the first gravity acceleration component. The data from the second monitoring unit includes the gravity acceleration components of its three-axis MEMS accelerometer in the X, Y, and Z directions, denoted as the second gravity acceleration component.

[0037] The server then uses trigonometric relationships based on the first gravitational acceleration component to calculate the inclination angle of the first monitoring unit relative to the horizontal and vertical directions, recording this as the first inclination angle. Specifically, the gravitational acceleration components in the three directions can be considered as spatial vectors, and the vector angle formula can be used to solve for the attitude angle of the first monitoring unit. The server then uses the same method to calculate the inclination angle of the second monitoring unit relative to the horizontal and vertical directions based on the second gravitational acceleration component, recording this as the second inclination angle.

[0038] Finally, the server calculates the vertical and horizontal displacement changes of the second monitoring unit relative to the first monitoring unit using trigonometric functions based on the first inclination angle, the second inclination angle, and the distance between the first monitoring unit and the second monitoring unit (the distance can be calculated through the buried position coordinates of the monitoring unit or manually measured and input in advance).

[0039] Step S103: determining a target displacement change of each monitoring unit relative to a preset reference position based on the displacement change.

[0040] In step S103, the server selects a position as a reference position, and its position coordinates are defined as a preset reference position, such as Figure 2 As shown, it is a distance diagram of a monitoring array provided by an embodiment of the present application. The first monitoring unit in the monitoring array uses the preset reference position as the reference unit. Then, starting from the preset reference position, the server calculates the absolute displacement change of each monitoring unit relative to the preset reference position step by step. Specifically, for the adjacent units of the preset reference position, the absolute displacement change is equal to the absolute displacement change of the reference unit plus the relative displacement change between them; for the adjacent units of the next level of the adjacent units, the absolute displacement change is equal to the absolute displacement change of the adjacent units of the previous level plus the relative displacement change between them, and so on, until the absolute displacement changes of all monitoring units in the monitoring array are calculated.

[0041] During the calculation process, the server determines the sign of the displacement change based on the spatial layout of the monitoring units. For example, if monitoring unit A is located above monitoring unit B and A is displaced upward relative to B, the relative displacement change between them is recorded as a positive value; if A is displaced downward relative to B, the relative displacement change is recorded as a negative value.

[0042] Through the above calculations, the server can obtain a data set containing the absolute displacement changes of all monitoring units, which reflects the distribution of displacement changes of the entire monitoring array relative to the preset reference position. Next, the server filters out the target displacement changes of each monitoring unit from the absolute displacement change data set. The target displacement change refers to the key data point that can represent the real displacement change trend of the monitoring unit. The specific method of screening the target displacement change can be determined according to actual needs and data characteristics. One method is to perform statistical analysis on the absolute displacement change data, calculate the mean, variance, peak and other characteristic parameters of the displacement change of each monitoring unit, eliminate abnormal values ​​that exceed the normal range, and use the representative value that can reflect the overall deformation trend as the target displacement change.

[0043] For example, suppose the absolute displacement change data for monitoring unit A over a certain time period is [1.2, 1.5, 1.8, 1.3, 200.0, 1.6, 1.4] (unit: mm). 200.0 is significantly higher, likely due to an outlier caused by a data acquisition or transmission error. By calculating the mean and variance of the data, we find that 200.0 is outside the normal range (e.g., exceeding three standard deviations from the mean), so we remove it. After removing the outlier, we average the remaining normal data to obtain 1.47 mm, which is used as the target displacement change for monitoring unit A over that time period.

[0044] Step S104: converting the target displacement variation into a capacitance voltage signal, and amplifying the capacitance voltage signal through a signal amplifier to obtain an amplified signal.

[0045] In step S104, the three-axis MEMS accelerometers of each monitoring unit are respectively set in the capacitor to convert the target displacement change into a capacitor voltage signal, specifically including: applying a fixed DC bias voltage across the first capacitor and the second capacitor to obtain a capacitor voltage signal corresponding to the displacement change, the first capacitor is the capacitor corresponding to the first monitoring unit, and the second capacitor is the capacitor corresponding to the second monitoring unit; according to the correspondence between the displacement change and the capacitor voltage signal, a displacement voltage conversion model is established to realize the mapping conversion of the displacement change to the capacitor voltage signal.

[0046] Specifically, the three-axis MEMS accelerometer of each monitoring unit is integrated into a capacitor. The displacement change of the accelerometer will cause the capacitance value of the capacitor to change, thereby causing the voltage signal across the capacitor to change. By measuring the voltage signal across the capacitor, the displacement change information of the accelerometer can be indirectly obtained. Inside the monitoring unit, the capacitor where the accelerometer is located adopts a differential capacitor structure, that is, it is composed of two identical capacitors, respectively referred to as the first capacitor and the second capacitor. In the initial state, the capacitance values ​​of the two capacitors are equal, and the voltage signal across the capacitor is zero. When the accelerometer is displaced, the capacitance values ​​of the two capacitors change in opposite directions, resulting in a voltage signal proportional to the displacement across the capacitor.

[0047] To measure the capacitor-voltage signal, a fixed DC bias voltage is applied across the first and second capacitors. This bias voltage is provided by the power supply circuit within the monitoring unit and typically ranges from a few volts to tens of volts, depending on the capacitor's design parameters and sensitivity requirements. When the monitoring unit is displaced, the capacitance of the first and second capacitors changes. Under the action of the bias voltage, a voltage signal proportional to the displacement change is generated across the capacitors. This voltage signal is typically in the millivolt range and requires amplification to be effectively detected and converted by the subsequent data acquisition circuitry. Based on the target displacement change data, the server establishes a displacement-voltage conversion model to map the displacement change to a capacitor-voltage signal. This conversion model can take the form of a linear function, assuming a linear relationship between the displacement change and the capacitor-voltage signal. For each unit change in displacement, the capacitor-voltage signal changes by a fixed proportional coefficient. The proportional coefficient is determined by factors such as the capacitor's sensitivity and the bias voltage, and can be determined through experimental calibration.

[0048] For example, suppose experimentally measured sensitivity of a certain MEMS accelerometer is 1.5mV / g, where g represents the acceleration due to gravity (1g ≈ 9.8m / s²). Converting 1g of acceleration to a displacement of 1μm is straightforward. Using the linear conversion model, we can derive the relationship between displacement change and capacitor voltage signal as follows: 1μm ≈ 1.5mV.

[0049] Therefore, when the server obtains a target displacement change of 10μm for a monitoring unit in a certain direction, it can estimate the corresponding capacitor voltage signal to be 10×1.5=15mV. The server sends this voltage signal value to the monitoring unit's data acquisition circuit, instructing it to amplify the actual voltage signal output by the capacitor, amplifying the weak signal at the millivolt level to the volt level for analog-to-digital conversion. The amplified voltage signal is converted from analog to digital using an analog-to-digital converter. The converted digital signal can be transmitted to the server via a communication interface. The server decodes and converts the digital signal to obtain the actual measurement value corresponding to the target displacement change, which is used for subsequent stability analysis and early warning.

[0050] Step S105: converting the amplified signal into a digital signal, using a single chip microcomputer to receive and decode the digital signal to obtain the three-dimensional displacement data of the monitoring array.

[0051] In step S105, a single-chip microcomputer is used to receive and decode the digital signal to obtain three-dimensional displacement data of the monitoring array, specifically including: using a decoding algorithm built into the single-chip microcomputer to decode the digital signal to obtain the real-time gravity acceleration component of each monitoring unit; establishing a displacement calculation model based on the real-time gravity acceleration component and position coordinates of each monitoring unit, the displacement calculation model including kinematic equations and dynamic equations in three-dimensional space; substituting the real-time gravity acceleration component of each monitoring unit into the kinematic equation to calculate the real-time velocity component and real-time displacement component of each monitoring unit; substituting the real-time velocity component and real-time displacement component into the dynamic equation to calculate the three-dimensional displacement data of the entire monitoring array, the three-dimensional displacement data including the spatial position change of each monitoring unit in the monitoring array.

[0052] Specifically, the server feeds the amplified capacitor voltage signal into the microcontroller's analog-to-digital converter (ADC) for digital-to-analog conversion. The ADC converts the continuously varying analog voltage signal into discrete digital codes, which are then output as binary codes, with each code value corresponding to a specific voltage amplitude. The ADC's conversion accuracy depends on its number of bits. The higher the number of bits, the higher the resolution of the converted digital signal and the finer the voltage amplitude that can be represented. The microcontroller then decodes the digital signal converted by the ADC to obtain real-time gravitational acceleration component data for each monitoring unit. The microcontroller has a pre-stored decoding algorithm. Based on parameters such as the digital signal's code value, the ADC's reference voltage, and the capacitor's sensitivity, the algorithm calculates the actual voltage value corresponding to the digital signal and converts this voltage value into a gravitational acceleration component.

[0053] The decoding algorithm can be implemented using either a lookup table or a calculation method. The lookup table pre-stores the digital signal's code value and the corresponding gravity acceleration component value in a lookup table. The microcontroller then retrieves the corresponding gravity acceleration component value based on the received code value. The calculation method calculates the corresponding gravity acceleration component value based on the code value and conversion parameters.

[0054] After obtaining the real-time gravitational acceleration component of each monitoring unit, the server builds a displacement calculation model in three-dimensional space based on the spatial coordinates of each monitoring unit. The displacement calculation model includes kinematic and dynamic equations, which are used to describe the motion state and force conditions of the monitoring unit in three-dimensional space.

[0055] The kinematic equations describe the geometric relationship between the displacement, velocity, and acceleration of a monitoring unit, expressed using differential and integral mathematical forms. For example, given the real-time gravitational acceleration component of a monitoring unit, the real-time velocity component can be obtained by integrating the acceleration function; further, integrating the velocity function yields the real-time displacement component of the monitoring unit.

[0056] Kinematic equation: Let the displacement of the i-th monitoring unit at time t be s i (t), with a velocity of v i (t), acceleration is a i (t), then the following kinematic equations are obtained: The relationship between velocity and displacement: v i (t) = ds i (t) / dt The relationship between acceleration and speed: a i (t) = dv i (t) / dt Integrating the relationship between acceleration and displacement, we can get: a i (t) = d2 s i (t) / dt 2 Therefore, through the above formula, the acceleration function is known, and the velocity function and displacement function can be obtained by integration.

[0057] The dynamic equation describes the force balance relationship of the monitoring unit under the action of external forces and is expressed in the mathematical form of Newton's laws of motion. For example, if the real-time velocity and displacement components of the monitoring unit are known, the force balance equation of the monitoring unit under external forces such as gravity and support reaction forces can be calculated based on parameters such as the monitoring unit's mass and damping coefficient. This can then solve for the monitoring unit's actual motion trajectory and displacement change.

[0058] The server substitutes the real-time gravitational acceleration component of each monitoring unit into the kinematic equation to calculate the real-time velocity component and displacement component of each monitoring unit; then substitutes the real-time velocity component and displacement component into the dynamic equation to calculate the three-dimensional displacement data of the entire monitoring array, including the spatial position change of each monitoring unit in the monitoring array relative to the initial position.

[0059] Dynamic equation: Assume the mass of the i-th monitoring unit is m i , the net external force at time t is F i (t), then according to Newton’s second law: F i (t)=m i *a i (t); If the influence of factors such as damping is considered, the damping coefficient c can be introduced i , then the dynamic equation can be expressed as: F i (t)=m i *a i (t)+c i *v i (t); replace the velocity v in the kinematic equation i (t) and displacement s i Substituting (t) into the dynamics equation, we can establish the relationship between acceleration, velocity, displacement and external force: F i (t)=m*d 2 s i (t) / dt 2 +c i *ds i (t) / dt. Solving this differential equation, we can get the displacement s i (t) is a function of the change over time, thereby obtaining the actual motion trajectory and displacement change of the monitoring unit.

[0060] In one possible implementation, to compensate for temperature effects, the hardware uses low-temperature drift components, and the software uses the following temperature compensation formula when processing data locally using a single-chip microcomputer: x comp (t)=x raw (t)-k(T-T0) where x comp (t) is the target displacement change after temperature compensation; x raw (t) is the original target displacement change; k is the temperature sensitivity coefficient, which can be measured in the laboratory in advance based on the component module used; T is the current actual temperature; T0 is the calibration temperature.

[0061] Step S106: Compare the three-dimensional displacement data with a plurality of historical displacement data of the monitoring array to determine whether the slope to be measured has a landslide risk.

[0062] In step S106, the three-dimensional displacement data is compared with multiple historical displacement data of the monitoring array to determine whether the slope to be measured has a landslide risk. Specifically, the following steps are performed: calculating the displacement increment between each historical displacement data in the multiple historical displacement data and the historical displacement data at the previous moment to obtain a historical displacement increment data set; calculating the displacement increment data at the current monitoring moment based on the three-dimensional displacement data, and comparing the displacement increment data with the data in the historical displacement increment data set; if the displacement increment data is greater than the maximum value in the historical displacement increment data set and exceeds a preset warning range, the displacement increment data is determined to be an abnormal value, and the abnormal value is marked as a potential landslide point; and the distribution density of the potential landslide points within a preset spatial range is calculated, and an area where the distribution density exceeds a preset density threshold is determined as a landslide risk area, and the slope to be measured is determined to have a landslide risk.

[0063] Specifically, the server reads the historical displacement data of the monitoring array in the past period from the database and constructs a time series dataset, such as Figure 3 As shown, Figure 3 This is a schematic diagram illustrating an example of historical displacement data provided in an embodiment of the present application. The time series dataset contains the three-dimensional displacement data of each monitoring unit in the monitoring array at different historical moments, reflecting the deformation history of the slope under test over a period of time. The server then preprocesses the historical displacement dataset, calculating the displacement increment between each two adjacent historical moments to generate a historical displacement increment dataset. The displacement increment reflects the slope's deformation rate per unit time and is an important indicator for assessing slope stability.

[0064] The displacement increment is calculated as follows: for each monitoring unit in the historical displacement dataset, find its three-dimensional displacement vectors (d1 and d2) at two adjacent historical moments (t1 and t2), calculate the Euclidean distance difference between the two displacement vectors, and obtain the displacement increment value of the monitoring unit in the time period from t1 to t2; repeat the above calculation for all monitoring units to obtain a historical displacement increment dataset containing the displacement increment values ​​of all monitoring units.

[0065] Next, the server calculates the displacement increment value of each monitoring unit at the current moment based on the three-dimensional displacement data at the current moment, compares it with the data in the historical displacement increment data set, and determines whether the current displacement increment is abnormal. The judgment criteria for abnormal displacement increment include two aspects: first, whether the current displacement increment value exceeds the maximum value in the historical displacement increment data set; second, whether the current displacement increment value exceeds the preset warning range. The preset warning range is a displacement increment threshold range pre-set based on the geological conditions, environmental factors, historical deformation data of the slope, etc., combined with expert experience and theoretical models. Displacement increment values ​​outside this range are considered abnormal, indicating that the deformation rate of the slope exceeds the normal level and there is a potential risk of instability. After judging the displacement increment values ​​of all monitoring units for abnormalities, the server obtains a set of abnormal displacement increment values ​​and marks them as potential landslide points. Potential landslide points reflect the deformation anomalies in local areas within the slope and are an important basis for evaluating the overall stability of the slope.

[0066] To further assess the hazard level of potential landslide sites, the server performs a spatial cluster analysis on these sites. Specifically, the slope area covered by the monitoring array is divided into several grid cells of a preset size. The number and distribution density of potential landslide sites within each grid cell are counted. The distribution density is measured as the ratio of potential landslide sites to the total number of monitoring sites in the grid cell.

[0067] If the density of potential landslide points within a grid cell exceeds a preset density threshold (e.g., 50%), the grid cell is marked as a landslide risk zone. The greater the number of monitoring units within a landslide risk zone and the higher the distribution density, the more severe the slope deformation within that area and the greater the likelihood of a landslide. When the number and extent of monitored landslide risk zones exceed the preset density threshold, the server determines that the entire slope under test is at risk of landslide, and initiates early warning and prevention measures.

[0068] In one possible implementation, after counting the distribution density of potential landslide points within a preset spatial range and determining an area where the distribution density exceeds a preset density threshold as a landslide risk area, the method further includes: determining the shape, size, and location range of the landslide risk area based on the location and distribution density of the potential landslide points; calculating the displacement rate and displacement acceleration of each monitoring point in the landslide risk area based on three-dimensional displacement data and historical displacement data; and predicting the landslide time and scale of the landslide risk area based on the changing trends of the displacement rate and displacement acceleration, thereby generating landslide warning information.

[0069] Specifically, the server determines the geometry, size, and location range of landslide risk zones based on the spatial distribution of potential landslide points. Specifically, the server performs a cluster analysis on the coordinates of potential landslide points, identifying areas with dense and contiguous spatial distribution and dividing them into independent landslide risk zones. For each landslide risk zone, the server calculates its spatial boundary coordinates, fits the risk zone's geometry (e.g., ellipse, polygon, etc.), and estimates the risk zone's size (e.g., area, length, etc.). The server also calculates the geometric center coordinates of the risk zone to determine its location range on the slope (e.g., upper, middle, lower, etc.).

[0070] For example, the server identified a landslide risk zone in the lower middle section of the slope. This zone contained 20 potential landslide points, distributed in an elliptical pattern with a major axis of approximately 50 meters and a minor axis of approximately 20 meters, with the geometric center located 50 meters from the slope toe. Next, the server calculated the displacement rate and acceleration for each monitoring point within the landslide risk zone based on the 3D displacement data and historical displacement data. The displacement rate reflects the deformation velocity of the monitoring point, while the displacement acceleration reflects the changing trend of the deformation velocity.

[0071] The displacement rate is calculated by taking the current displacement value and the previous displacement value at each monitoring point, calculating the difference between the two, and dividing it by the time interval between the two moments to obtain the current displacement rate at that monitoring point. The displacement acceleration is calculated by taking the current displacement rate and the previous displacement rate at each monitoring point, calculating the difference between the two, and dividing it by the time interval between the two moments to obtain the current displacement acceleration at that monitoring point. Through these calculations, the server obtains the displacement rate and displacement acceleration data for each monitoring point in the landslide risk area at different times, forming a time series dataset.

[0072] Finally, the server performs trend analysis and prediction on the time series data of displacement rate and acceleration, estimating the time and scale of a landslide in the landslide risk zone and generating landslide warning information. The landslide time prediction method involves performing time series decomposition on the displacement rate and acceleration data, extracting trend terms, periodic terms, and random terms. Using time series prediction models (such as ARIMA and gray prediction models), the trend terms are extrapolated to determine the future trends of displacement rate and acceleration. When the predicted displacement rate or acceleration exceeds a preset critical threshold, a landslide is considered likely, and the time point at which the threshold is exceeded is marked as the estimated landslide time.

[0073] Landslide scale prediction methods estimate the degree of soil damage and sliding range within the landslide risk zone based on displacement rate and acceleration. Generally speaking, greater displacement rate and acceleration indicate more severe soil damage and a larger sliding range. The server can establish a quantitative relationship between displacement rate, acceleration, and landslide scale using empirical formulas or finite element numerical simulations, thereby predicting the hazard level of a potential landslide event.

[0074] In one possible implementation, based on the changing trends of displacement rate and displacement acceleration, the landslide time and scale in the landslide risk area are predicted, and landslide warning information is generated. Specifically, the method includes: calculating the displacement rate and displacement acceleration of each monitoring unit within a preset time window based on the three-dimensional displacement data of each monitoring unit in the landslide risk area, and performing time series analysis on the displacement rate and displacement acceleration to extract the changing trend characteristics; establishing a landslide time prediction model and a landslide scale prediction model based on the changing trend characteristics to predict the landslide occurrence time and landslide volume in the landslide risk area respectively; and generating landslide warning information based on the landslide occurrence time and landslide volume.

[0075] Specifically, the server performs time-series analysis and trend prediction on the displacement rate and acceleration data of monitoring units within the landslide risk zone, constructing landslide time prediction models and landslide scale prediction models to generate quantitative landslide warning information. First, based on the three-dimensional displacement data of each monitoring unit within the landslide risk zone, the server calculates the displacement rate and displacement acceleration time series for each monitoring unit within a preset time window. This preset time window can be set based on the actual situation and data characteristics of the landslide risk zone, typically ranging from a few hours to a few days.

[0076] The calculation method of displacement rate time series is as follows: for each monitoring unit, take its displacement value sequence within the preset time window, perform first-order difference on the sequence, and obtain the displacement rate sequence. Specifically, suppose the displacement value sequence is {x(t1), x(t2), ..., x(t n )}, where t1 to t nis n moments in the preset time window, then the displacement rate sequence is {v(t2), v(t3), ..., v(t n )}, where v(t i )=[x(t i )-x(t i -1)] / [t i -t i-1 ], i=2, 3, ..., n.

[0077] The calculation method of displacement acceleration time series is as follows: for each monitoring unit, take its displacement rate sequence within the preset time window, and then perform first-order difference on the sequence to obtain the displacement acceleration sequence. Specifically, let the displacement rate sequence be {v(t2), v(t3), ..., v(t n )}, then the displacement acceleration sequence is {a(t3), a(t4), ..., a(t n )}, where a(t i )=[v(t i )-v(t i-1 )] / [t i -t i-1 ], i=3, 4, ..., n.

[0078] After obtaining the displacement rate and acceleration time series of each monitoring unit, the server performs feature extraction and trend analysis on these time series data. Time series features can be statistical quantities such as mean, variance, slope, and volatility, reflecting the overall level and trend of the time series data. Frequency domain features such as period, frequency, and autocorrelation coefficient can reflect the periodicity and long-term trends of the time series data.

[0079] The server uses a sliding time window segmented fitting method to extract trend characteristics of displacement rate and acceleration time series at different time scales. Based on these trend characteristics, the server constructs a landslide time prediction model and a landslide scale prediction model. The landslide time prediction model is used to predict the likely time of landslide occurrence in landslide-risk areas within a certain period of time; the landslide scale prediction model is used to estimate the volume and impact range of potential landslide events.

[0080] The landslide time prediction model can adopt the following methods: trend extrapolation method based on time series decomposition, such as decomposing the displacement rate series into trend terms, periodic terms and random terms, extrapolating the trend terms to obtain the future displacement rate change curve, and estimating the time of landslide occurrence based on this; time series prediction method based on machine learning, such as training long short-term memory neural network (LSTM), temporal convolutional network (TCN) and other deep learning models to predict the trend of displacement rate and acceleration series in several future time steps, and then determine the time of landslide occurrence.

[0081] The landslide scale prediction model can adopt the following methods: regression prediction method based on empirical formula, such as establishing empirical regression formulas between displacement rate, acceleration and other characteristics and landslide volume and impact range based on existing landslide case data, and interpolating prediction of new monitoring data; numerical simulation method based on physical model, such as using three-dimensional numerical simulation software to perform parametric modeling of the geological structure and physical and mechanical properties of soil in the landslide risk area, inputting measured three-dimensional displacement data, simulating the start-up, movement and accumulation process of the landslide body, and predicting the volume of the sliding body.

[0082] Reference Figure 2 The present application also provides a deep space three-dimensional deformation monitoring device, which is a server. The server includes an acceleration acquisition module 401, a change calculation module 402, a change conversion module 403 and a landslide judgment module 404, wherein: the acceleration acquisition module 401 is used to obtain the gravity acceleration of each monitoring unit through a monitoring array, wherein the monitoring array is located in the slope to be measured, the monitoring array includes multiple monitoring units, each monitoring unit is connected in series, and the monitoring unit includes a three-axis MEMS accelerometer; the change calculation module 402 is used to calculate the displacement change of the second monitoring unit relative to the first monitoring unit according to the gravity acceleration, and the first monitoring unit and the second monitoring unit are multiple monitoring units included in the monitoring array. any two adjacent monitoring units in the array and the second monitoring unit is located after the first monitoring unit; the change calculation module 402 is further used to determine the target displacement change of each monitoring unit relative to the preset reference position based on the displacement change; the change conversion module 403 is used to convert the target displacement change into a capacitor voltage signal, and amplify the capacitor voltage signal through a signal amplifier to obtain an amplified signal; the change conversion module 403 is further used to convert the amplified signal into a digital signal, use a single-chip microcomputer to receive the digital signal and decode the digital signal to obtain three-dimensional displacement data of the monitoring array; the landslide judgment module 404 is used to compare the three-dimensional displacement data with multiple historical displacement data of the monitoring array to determine whether the slope to be measured has a landslide risk.

[0083] In a possible embodiment, the change calculation module 402 calculates the displacement change of the second monitoring unit relative to the first monitoring unit based on the gravitational acceleration, specifically including: the acceleration acquisition module 401 acquires the first gravitational acceleration component of the first monitoring unit and the second gravitational acceleration component of the second monitoring unit, the first gravitational acceleration component is the gravitational acceleration component of the three-axis MEMS accelerometer of the first monitoring unit in the three directions of X-axis, Y-axis and Z-axis, and the second gravitational acceleration component is the gravitational acceleration component of the three-axis MEMS accelerometer of the second monitoring unit in the three directions of X-axis, Y-axis and Z-axis; the change calculation module 402 determines the first inclination angle of the first monitoring unit relative to the horizontal direction and the vertical direction based on the first gravitational acceleration component, and determines the second inclination angle of the second monitoring unit relative to the horizontal direction and the vertical direction based on the second gravitational acceleration component; the change calculation module 402 calculates the displacement change of the second monitoring unit relative to the first monitoring unit in the vertical direction and the horizontal direction using a trigonometric function relationship based on the first inclination angle, the second inclination angle and the distance between the first monitoring unit and the second monitoring unit.

[0084] In one possible embodiment, the three-axis MEMS accelerometers of each monitoring unit are respectively arranged in capacitors, and the variation conversion module 403 converts the target displacement variation into a capacitor voltage signal, specifically including: the variation conversion module 403 applies a fixed DC bias voltage across the first capacitor and the second capacitor to obtain a capacitor voltage signal corresponding to the displacement variation, the first capacitor is the capacitor corresponding to the first monitoring unit, and the second capacitor is the capacitor corresponding to the second monitoring unit; the variation conversion module 403 establishes a displacement voltage conversion model based on the correspondence between the displacement variation and the capacitor voltage signal to realize the mapping conversion of the displacement variation to the capacitor voltage signal.

[0085] In one possible implementation, the variation conversion module 403 uses a single-chip microcomputer to receive digital signals and decode the digital signals to obtain three-dimensional displacement data of the monitoring array, specifically including: the variation conversion module 403 uses the built-in decoding algorithm of the single-chip microcomputer to decode the digital signals to obtain the real-time gravitational acceleration component of each monitoring unit; the variation conversion module 403 establishes a displacement calculation model based on the real-time gravitational acceleration component and position coordinates of each monitoring unit, and the displacement calculation model includes kinematic equations and dynamic equations in three-dimensional space; the variation conversion module 403 substitutes the real-time gravitational acceleration component of each monitoring unit into the kinematic equation to calculate the real-time velocity component and real-time displacement component of each monitoring unit; the variation conversion module 403 substitutes the real-time velocity component and real-time displacement component into the dynamic equation to calculate the three-dimensional displacement data of the entire monitoring array, and the three-dimensional displacement data includes the spatial position change of each monitoring unit in the monitoring array.

[0086] In one possible implementation, the landslide judgment module 404 compares the three-dimensional displacement data with multiple historical displacement data of the monitoring array to determine whether the slope to be measured has a landslide risk. Specifically, the landslide judgment module 404 calculates the displacement increment between each historical displacement data in the multiple historical displacement data and the historical displacement data at the previous moment to obtain a historical displacement increment data set; the landslide judgment module 404 calculates the displacement increment data at the current monitoring moment based on the three-dimensional displacement data, and compares the displacement increment data with the data in the historical displacement increment data set; if the displacement increment data is greater than the maximum value in the historical displacement increment data set and exceeds a preset warning range, the landslide judgment module 404 determines that the displacement increment data is an outlier and marks the outlier as a potential landslide point; the landslide judgment module 404 calculates the distribution density of the potential landslide points within a preset spatial range, determines the area where the distribution density exceeds the preset density threshold as a landslide risk area, and determines that the slope to be measured has a landslide risk.

[0087] In one possible implementation, the landslide judgment module 404 counts the distribution density of potential landslide points within a preset spatial range, and after determining an area where the distribution density exceeds a preset density threshold as a landslide risk area, further includes: the landslide judgment module 404 determines the shape, size, and location range of the landslide risk area based on the location and distribution density of the potential landslide points; the landslide judgment module 404 calculates the displacement rate and displacement acceleration of each monitoring point in the landslide risk area based on the three-dimensional displacement data and the historical displacement data; the landslide judgment module 404 predicts the landslide time and scale of the landslide risk area based on the changing trend of the displacement rate and displacement acceleration, and generates landslide warning information.

[0088] In one possible implementation, the landslide judgment module 404 predicts the landslide time and scale in the landslide risk area based on the changing trends of the displacement rate and displacement acceleration, and generates landslide warning information. Specifically, the landslide judgment module 404 calculates the displacement rate and displacement acceleration of each monitoring unit in the landslide risk area within a preset time window based on the three-dimensional displacement data of each monitoring unit, and performs time series analysis on the displacement rate and displacement acceleration to extract the changing trend characteristics; the landslide judgment module 404 establishes a landslide time prediction model and a landslide scale prediction model based on the changing trend characteristics, and predicts the landslide occurrence time and landslide volume in the landslide risk area respectively; and generates landslide warning information based on the landslide occurrence time and landslide volume.

[0089] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0090] This application also provides an electronic device. Figure 5 , Figure 5 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0091] The communication bus 502 is used to implement the connection and communication between these components.

[0092] The user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0093] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0094] The processor 501 may include one or more processing cores. Using various interfaces and circuits, the processor 501 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 505, as well as accesses data stored in the memory 505, to perform various server functions and process data. Optionally, the processor 501 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 501 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may also be implemented as a separate chip, rather than integrated into the processor 501.

[0095] Among them, the memory 505 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 505 may also be optionally at least one storage device located away from the aforementioned processor 501. Reference Figure 5 , as a computer storage medium, the memory 505 may include an operating system, a network communication module, a user interface module, and an application program for a deep space three-dimensional deformation monitoring method.

[0096] exist Figure 5In the electronic device 500 shown, the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call an application program for a deep space three-dimensional deformation monitoring method stored in the memory 505. When executed by one or more processors 501, the electronic device 500 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0097] The present application further provides a computer-readable storage medium storing instructions, which, when executed by one or more processors 501 , enable the electronic device 500 to perform one or more of the methods described in the above embodiments.

[0098] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0100] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0101] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0103] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0104] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A deep space three-dimensional deformation monitoring method, characterized in that: The method comprises: Obtaining the gravity acceleration of each monitoring unit through a monitoring array, wherein the monitoring array is located in the slope to be measured, the monitoring array includes a plurality of monitoring units, each of the monitoring units is connected in series, and the monitoring unit includes a three-axis MEMS accelerometer; calculating a displacement change of a second monitoring unit relative to a first monitoring unit according to the gravitational acceleration, where the first monitoring unit and the second monitoring unit are any two adjacent monitoring units among a plurality of monitoring units included in the monitoring array, and the second monitoring unit is located behind the first monitoring unit; determining a target displacement change amount of each monitoring unit relative to a preset reference position based on the displacement change amount; Converting the target displacement variation into a capacitance voltage signal, and amplifying the capacitance voltage signal through a signal amplifier to obtain an amplified signal; Converting the amplified signal into a digital signal, receiving the digital signal using a single chip microcomputer and decoding the digital signal to obtain three-dimensional displacement data of the monitoring array; The three-dimensional displacement data is compared with a plurality of historical displacement data of the monitoring array to determine whether the slope to be measured has a landslide risk.

2. The method according to claim 1, characterized in that The calculating, based on the gravitational acceleration, the displacement change of the second monitoring unit relative to the first monitoring unit specifically includes: Obtain a first gravitational acceleration component of the first monitoring unit and a second gravitational acceleration component of the second monitoring unit, where the first gravitational acceleration component is the gravitational acceleration component of the three-axis MEMS accelerometer of the first monitoring unit in the X-axis, Y-axis, and Z-axis directions, and the second gravitational acceleration component is the gravitational acceleration component of the three-axis MEMS accelerometer of the second monitoring unit in the X-axis, Y-axis, and Z-axis directions; determining a first inclination angle of the first monitoring unit relative to a horizontal direction and a vertical direction according to the first gravitational acceleration component, and determining a second inclination angle of the second monitoring unit relative to the horizontal direction and the vertical direction according to the second gravitational acceleration component; The displacement changes of the second monitoring unit relative to the first monitoring unit in the vertical direction and the horizontal direction are calculated using a trigonometric function relationship according to the first inclination angle, the second inclination angle, and the distance between the first monitoring unit and the second monitoring unit.

3. The method according to claim 1, characterized in that The three-axis MEMS accelerometers of the monitoring units are respectively arranged in capacitors, and the conversion of the target displacement change into a capacitor voltage signal specifically includes: Applying a fixed DC bias voltage across a first capacitor and a second capacitor to obtain a capacitance-voltage signal corresponding to the displacement change, wherein the first capacitor is a capacitor corresponding to the first monitoring unit and the second capacitor is a capacitor corresponding to the second monitoring unit; According to the corresponding relationship between the displacement variation and the capacitor voltage signal, a displacement-voltage conversion model is established to achieve mapping conversion of the displacement variation to the capacitor voltage signal.

4. The method according to claim 1, wherein The step of receiving the digital signal by a single chip microcomputer and decoding the digital signal to obtain the three-dimensional displacement data of the monitoring array specifically includes: Decoding the digital signal using a decoding algorithm built into the single chip microcomputer to obtain a real-time gravity acceleration component of each monitoring unit; Establishing a displacement calculation model based on the real-time gravity acceleration components and position coordinates of each monitoring unit, wherein the displacement calculation model includes kinematic equations and dynamic equations in three-dimensional space; Substituting the real-time gravitational acceleration component of each monitoring unit into the kinematic equation to calculate the real-time velocity component and real-time displacement component of each monitoring unit; The real-time velocity component and the real-time displacement component are substituted into the dynamic equation to calculate the three-dimensional displacement data of the entire monitoring array, wherein the three-dimensional displacement data includes the spatial position change of each monitoring unit of the monitoring array.

5. The method according to claim 1, wherein The comparing the three-dimensional displacement data with a plurality of historical displacement data of the monitoring array to determine whether the slope to be measured has a landslide risk specifically includes: Calculating the displacement increment between each historical displacement data and the historical displacement data at the previous moment in the plurality of historical displacement data to obtain a historical displacement increment data set; Calculating displacement increment data at the current monitoring moment based on the three-dimensional displacement data, and comparing the displacement increment data with data in the historical displacement increment data set; If the displacement increment data is greater than the maximum value in the historical displacement increment data set and exceeds a preset warning range, the displacement increment data is determined to be an abnormal value and the abnormal value is marked as a potential landslide point; The distribution density of the potential landslide points within a preset spatial range is counted, and an area where the distribution density exceeds a preset density threshold is determined as a landslide risk area, and the slope to be measured is determined to have a landslide risk.

6. The method according to claim 5, characterized in that After counting the distribution density of the potential landslide points within a preset spatial range and determining an area where the distribution density exceeds a preset density threshold as a landslide risk area, the method further includes: Determine the shape, size and location range of the landslide risk area based on the location and distribution density of the potential landslide points; Calculating the displacement rate and displacement acceleration of each monitoring point in the landslide risk area based on the three-dimensional displacement data and the historical displacement data; According to the change trends of the displacement rate and the displacement acceleration, the landslide time and landslide scale of the landslide risk area are predicted, and landslide warning information is generated.

7. The method according to claim 6, characterized in that The method of predicting the landslide time and scale of the landslide risk area according to the change trends of the displacement rate and the displacement acceleration, and generating landslide warning information specifically includes: Calculating the displacement rate and displacement acceleration of each monitoring unit within a preset time window based on the three-dimensional displacement data of each monitoring unit in the landslide risk area, and performing time series analysis on the displacement rate and displacement acceleration to extract change trend characteristics; Based on the change trend characteristics, a landslide time prediction model and a landslide scale prediction model are established to respectively predict the landslide occurrence time and landslide volume in the landslide risk area; The landslide warning information is generated according to the landslide occurrence time and the landslide volume.

8. A deep space three-dimensional deformation monitoring device, characterized in that: The device comprises an acceleration acquisition module (401), a variation calculation module (402), a variation conversion module (403) and a landslide judgment module (404), wherein: The acceleration acquisition module (401) is used to acquire the gravity acceleration of each monitoring unit through a monitoring array, wherein the monitoring array is located in the slope to be measured, the monitoring array includes a plurality of monitoring units, each of the monitoring units is connected in series, and the monitoring unit includes a three-axis MEMS accelerometer; The variation calculation module (402) is used to calculate the displacement variation of the second monitoring unit relative to the first monitoring unit based on the gravitational acceleration, wherein the first monitoring unit and the second monitoring unit are any two adjacent monitoring units among the plurality of monitoring units included in the monitoring array, and the second monitoring unit is located after the first monitoring unit; The variation calculation module (402) is further configured to determine a target displacement variation of each monitoring unit relative to a preset reference position based on the displacement variation; The variation conversion module (403) is used to convert the target displacement variation into a capacitance voltage signal, and amplify the capacitance voltage signal through a signal amplifier to obtain an amplified signal; The variation conversion module (403) is further configured to convert the amplified signal into a digital signal, receive the digital signal using a single chip microcomputer, and decode the digital signal to obtain three-dimensional displacement data of the monitoring array; The landslide judgment module (404) is used to compare the three-dimensional displacement data with a plurality of historical displacement data of the monitoring array to judge whether the slope to be measured has a landslide risk.

9. An electronic device, characterized in that: The electronic device (500) comprises a processor (501), a memory (505), a user interface (503) and a network interface (504), wherein the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device (500) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

Images