Multi-mode intelligent side slope monitoring target system and side slope monitoring method

Abstract

The invention discloses a multi-mode intelligent side slope monitoring target system and a side slope monitoring method. The system comprises an intelligent target deployed on the surface of a side slope and a multi-spectral detection device for remotely observing the side slope. The active heating unit of the intelligent target is used for generating an infrared thermal signal with a specific code; the multispectral detection equipment is used for acquiring a slope infrared thermal image sequence taking the intelligent target as an observation target; identifying and positioning an intelligent target from the slope infrared thermal image sequence according to a specific code in the infrared thermal signal; performing sub-pixel-level precision tracking on the position of the intelligent target in the slope infrared thermal image sequence to generate displacement data of the surface of the slope; and analyzing temperature field distribution in the slope infrared thermal image sequence, and identifying a local temperature gradient abnormal region which is not matched with the displacement data in combination with the displacement data. According to the invention, all-weather high-precision displacement monitoring of the surface of the slope and synchronous identification of the thermal anomaly region are realized, and the timeliness of geological disaster early warning is improved.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring and early warning technology, and in particular to a multimodal intelligent slope monitoring target system and slope monitoring method. Background Technology

[0002] Slope stability monitoring is a crucial aspect of geological disaster prevention and control, but existing technologies still have significant limitations in practical applications. Traditional contact monitoring methods (such as inclinometers and strain gauges) require the deployment of sensors inside or on the surface of the slope, which is not only difficult and costly to construct, but also easily affected by construction disturbances, making large-scale deployment difficult in complex mountainous areas with dense vegetation and steep terrain. Even non-contact optical measurement methods face multiple constraints: First, monitoring efficiency is heavily dependent on lighting conditions; image acquisition quality drops significantly at night, on cloudy days, or in rainy or snowy weather, making true all-weather observation impossible. Second, dense vegetation cover can completely obscure the slope surface, making it impossible to identify optical targets and resulting in a very high rate of monitoring point loss. Third, for subtle deformations such as shallow creep and uneven settlement, existing methods lack sufficient measurement accuracy and data continuity, making it difficult to capture the spatiotemporal evolution details of the displacement field. More importantly, traditional monitoring largely focuses on recording displacement data, lacking the ability to detect precursory information such as micro-fracture initiation and localized stress concentration. It cannot identify the development process of the slip surface from microscopic features like thermodynamic anomalies and non-uniform displacement patterns, leading to delayed early warnings and hindering the technological leap from "post-event monitoring" to "pre-event prediction." Furthermore, existing monitoring systems struggle to meet the requirements for unattended operation and remote integration in terms of data transmission, energy management, and long-term stability in the field, thus restricting the construction of regional intelligent early warning networks for landslide disasters.

[0003] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a multimodal intelligent slope monitoring target system and a slope monitoring method.

[0005] In a first aspect, the present invention provides a multimodal intelligent slope monitoring target system, the technical solution of which is as follows: Includes: smart targets deployed on the slope surface, and multispectral detection equipment for remote observation of the slope; The smart target includes a housing and an active heating unit integrated inside the housing, the active heating unit being used to generate an infrared thermal signal with a specific code; The multispectral detection device is used for: Acquire a sequence of infrared thermal images of the slope with the intelligent target as the observation target; The smart target is identified and located from the slope infrared thermal image sequence based on a specific code in the infrared thermal signal. The position of the smart target in the infrared thermal image sequence of the slope is tracked with subpixel accuracy to generate displacement data of the slope surface; Analyze the temperature field distribution in the infrared thermal image sequence of the slope, and combine it with the displacement data to identify local temperature gradient anomaly areas that do not match the displacement data.

[0006] The beneficial effects of the multimodal intelligent slope monitoring target system of the present invention are as follows: The system of this invention solves the problems of monitoring blind spots caused by sunlight and vegetation blockage, insufficient accuracy of minute deformation measurement, and lack of landslide precursor information. It realizes high-precision all-weather displacement monitoring of slope surface and synchronous identification of thermal anomaly areas, thereby improving the timeliness of geological disaster early warning.

[0007] Based on the above scheme, the multimodal intelligent slope monitoring target system of the present invention can be further improved as follows.

[0008] In one alternative embodiment, the smart target further includes a temperature sensing unit disposed on the housing for collecting temperature data at the installation location of the smart target.

[0009] The advantages of adopting the above-mentioned optional method are as follows: further collecting temperature data at the installation location of the smart target through the temperature sensing unit, providing a reference temperature for infrared thermal image sequence analysis, improving the accuracy of temperature field distribution monitoring, and simultaneously calibrating thermal imaging data to ensure the reliability of thermal anomaly identification.

[0010] In one alternative embodiment, the active heating unit includes a miniature heating element and an encoding control circuit, the encoding control circuit being used to control the miniature heating element to operate according to a predetermined encoding mode in order to generate the specifically encoded infrared thermal signal.

[0011] The advantages of adopting the above-mentioned optional method are as follows: it further clarifies that the active heating unit is composed of a miniature heating element and an encoding control circuit, realizes precise encoding control of infrared thermal signals, ensures stable output of specific encoding modes, and enhances the target's identifiability and anti-interference ability in complex environments.

[0012] In one alternative approach, the predetermined encoding mode is to cause the micro heating element to undergo periodic on / off changes or stepped power changes.

[0013] The advantages of adopting the above-mentioned optional methods are: further limiting the predetermined encoding mode to periodic on-off changes or stepped power changes, providing a simple and reliable encoding implementation method, facilitating rapid decoding and recognition by multispectral detection equipment, and reducing system implementation complexity and overall power consumption.

[0014] In one alternative embodiment, the multispectral detection device further includes a visible light imaging unit for acquiring visible light image data of the slope.

[0015] The advantages of adopting the above-mentioned optional methods are: further integrating a visible light imaging unit into the multispectral detection equipment, simultaneously acquiring visible light image data of the slope, realizing the fusion verification of infrared thermal imaging and visual information, and improving the intuitiveness and data credibility of the monitoring results.

[0016] In one alternative approach, the multispectral detection device is deployed at a fixed observation point and continuously scans and images the slope area where multiple smart targets are deployed to form a temporally continuous monitoring data stream.

[0017] The advantages of adopting the above-mentioned optional methods are: further deploying multispectral detection equipment at fixed observation points to continuously scan and image the slope area with multiple smart targets, forming a continuous monitoring data stream in time, expanding the monitoring coverage and ensuring data integrity.

[0018] In one alternative approach, the multispectral detection device is specifically used for: Based on the infrared thermal image sequence of the slope, the position change of the smart target in consecutive image frames is calculated using a digital image phase correlation algorithm to generate the displacement data of the slope surface.

[0019] The advantages of adopting the above-mentioned optional method are as follows: further employing digital image phase correlation algorithm to calculate the position change of the smart target in continuous image frames, realizing displacement measurement with sub-pixel accuracy, enhancing the ability to capture small creep deformation of slope, and ensuring the accuracy of displacement data.

[0020] In one alternative approach, the multispectral detection device is specifically used for: The temperature field distribution in the infrared thermal image sequence of the slope is analyzed, the spatiotemporal changes of the temperature field distribution are compared with the displacement data, and an artificial intelligence algorithm is used to determine the local temperature gradient anomaly areas that do not match the displacement data.

[0021] The beneficial effects of adopting the above-mentioned optional methods are: further analyzing the spatiotemporal changes of temperature field distribution through artificial intelligence algorithms and comparing them with displacement data, automatically identifying local temperature gradient anomaly areas that do not match the displacement data, and improving the intelligence level of landslide precursor identification.

[0022] In one alternative, the shell is made of a material with thermophysical parameters designed to have thermal capacity and thermal conductivity configured to characterize the local thermal boundary conditions in contact between the shell and the slope surface.

[0023] The beneficial effects of adopting the above-mentioned optional method are as follows: by further using materials with designed thermophysical parameters to construct the shell, the heat capacity and thermal conductivity are matched with the local thermal boundary conditions of the slope surface, thereby enhancing the target's sensitivity to the abnormal heat conduction during the development of the slip surface.

[0024] Secondly, this invention provides a slope monitoring method, employing a multimodal intelligent slope monitoring target system as described in this invention. The technical solution of this method is as follows: Acquire a sequence of infrared thermal images of the slope with the intelligent target as the observation target; The smart target is identified and located from the slope infrared thermal image sequence based on a specific code in the infrared thermal signal. The position of the smart target in the infrared thermal image sequence of the slope is tracked with subpixel accuracy to generate displacement data of the slope surface; Analyze the temperature field distribution in the infrared thermal image sequence of the slope, and combine it with the displacement data to identify local temperature gradient anomaly areas that do not match the displacement data.

[0025] The beneficial effects of the slope monitoring method of the present invention are as follows: The method of this invention solves the problems of monitoring blind spots caused by sunlight and vegetation blockage, insufficient accuracy of minute deformation measurement, and lack of landslide precursor information. It realizes high-precision all-weather displacement monitoring of slope surface and synchronous identification of thermal anomaly areas, thereby improving the timeliness of geological disaster early warning.

[0026] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0027] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of an embodiment of the multimodal intelligent slope monitoring target system of the present invention; Figure 2 This is a schematic diagram of the structure of a smart target; Figure 3This is a schematic flowchart of an embodiment of a slope monitoring method according to the present invention. Detailed Implementation

[0028] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0029] Figure 1 A schematic diagram of an embodiment of a multimodal intelligent slope monitoring target system 100 provided by the present invention is shown. Figure 1 As shown, the multimodal intelligent slope monitoring target system 100 includes: an intelligent target 110 deployed on the slope surface, and a multispectral detection device 120 for remote observation of the slope; The smart target 110 includes a housing and an active heating unit integrated inside the housing, the active heating unit being used to generate an infrared thermal signal with a specific code.

[0030] The intelligent target 110 refers to a device deployed on a slope surface that integrates an active heating unit and generates a specifically coded infrared thermal signal; for example, a circular device installed in a slope monitoring area, containing a miniature heating element that heats up according to a preset pattern, thus creating a unique alternating light and dark pattern in the infrared image. The housing refers to the component that constitutes the external physical structure of the intelligent target 110 and encapsulates the internal functional units; for example, a cylindrical protective structure made of metal that protects the internal circuitry and heating elements from environmental corrosion. The active heating unit refers to a functional module integrated within the housing of the intelligent target 110 that actively and controllably generates heat; for example, a thin-film heating element with a power of several watts and a matching drive circuit inside the intelligent target 110, which controls the heating element to switch on and off at a specific frequency, thus causing periodic changes in the target surface temperature. Specific encoding refers to the modulation of the intensity or temporal variation pattern of the infrared thermal signal according to predetermined rules; for example, controlling the active heating unit to operate in a periodic pattern of "on for a few seconds, off for a few seconds," thereby generating a unique flicker pattern in the infrared image that is easily identifiable and distinguishable. The infrared thermal signal refers to the thermal radiation information generated by the active heating unit of the intelligent target 110 that can be detected by an infrared thermal imager; for example, at night, the active heating of the intelligent target 110 causes its surface temperature to be higher than the ambient temperature, appearing as a significant bright spot in the image generated by the infrared thermal imager.

[0031] The multispectral detection device 120 is used for: Acquire a sequence of infrared thermal images of the slope with the intelligent target 110 as the observation target.

[0032] The multispectral detection device 120 refers to a remote observation device capable of simultaneously performing infrared thermal imaging, visible light imaging, and radar imaging on a slope area. For example, it could be an observation device installed on a stable area opposite the slope, comprising an infrared thermal imager, a visible light camera, and a radar camera, capable of simultaneously capturing infrared thermal images, ordinary color photographs, and radar point clouds or interferometric data of the slope. The observation target refers to the specific object that the multispectral detection device 120 aims at during imaging monitoring. For example, the lens of the multispectral detection device 120 might be aimed at a smart target 110 deployed on the slope, continuously acquiring image data about this target. The slope infrared thermal image sequence refers to a series of infrared thermal images continuously acquired by the multispectral detection device 120 in chronological order, reflecting the temperature distribution on the slope surface. For example, over several consecutive hours, the device might capture an infrared thermal image of the slope at fixed time intervals, ultimately obtaining a series of images arranged in chronological order, showing the dynamic changes in the slope surface temperature field.

[0033] The smart target 110 is identified and located from the slope infrared thermal image sequence based on a specific code in the infrared thermal signal.

[0034] The position of the smart target 110 in the infrared thermal image sequence of the slope is tracked with subpixel accuracy to generate displacement data of the slope surface.

[0035] Subpixel-level precision tracking refers to calculating the displacement of a target within a single physical pixel between consecutive image frames using image processing algorithms. For example, algorithm analysis reveals that the center point of the smart target 110 moved by a few tenths of a pixel in two infrared images taken ten minutes apart. This distance corresponds to a millimeter-level displacement on the slope surface. Displacement data refers to the movement of measuring points on the slope surface calculated by analyzing the positional changes of the smart target 110 in the image sequence. For example, after a period of monitoring, data analysis shows that a smart target 110 has cumulatively moved several millimeters outward from the slope and sunk several millimeters vertically.

[0036] Analyze the temperature field distribution in the infrared thermal image sequence of the slope, and combine it with the displacement data to identify local temperature gradient anomaly areas that do not match the displacement data.

[0037] Temperature field distribution refers to the spatial distribution of temperature at various points within the monitoring area, as reflected by single or multiple frames of infrared thermal images. For example, from an infrared thermal image taken at a specific moment, the temperature difference between the sunny and shady sides of a slope can be analyzed, as well as potential local high-temperature or low-temperature areas. Anomaly areas of local temperature gradients refer to localized areas in the temperature field distribution that exhibit drastic temperature changes that are inconsistent with the overall temperature trend or the mechanical state reflected by displacement data. For example, on a slope with a uniform overall temperature, an area approximately one meter in diameter might be found where the temperature is significantly higher or lower than the surrounding area, and the displacement data for this area does not show corresponding deformation characteristics. This area may indicate a potential stress concentration or seepage point.

[0038] The technical solution of this embodiment solves the problems of monitoring blind spots caused by sunlight and vegetation blockage, insufficient accuracy of minute deformation measurement, and lack of landslide precursor information. It realizes all-weather high-precision displacement monitoring of slope surface and synchronous identification of thermal anomaly areas, thereby improving the timeliness of geological disaster early warning.

[0039] In one alternative embodiment, the smart target 110 further includes a temperature sensing unit disposed on the housing for collecting temperature data at the installation location of the smart target 110.

[0040] The temperature sensing unit refers to an in-situ sensor integrated into the smart target 110 for directly measuring the temperature of the contact surface at the target installation point; for example, a high-precision temperature sensor embedded in the back of the metal shell of the smart target 110, which can measure the temperature of the contact surface between the shell and the slope soil and rock in real time. The installation location refers to the specific location where the smart target 110 is deployed and fixed on the slope surface; for example, a smart target 110 is installed on the surface of the soil and rock at a specified elevation on the slope monitoring section using anchors. Temperature data refers to the raw measured values ​​or processed information about the temperature at the installation location collected by the temperature sensing unit; for example, the temperature sensing unit records the temperature at a fixed period to obtain a series of temperature values ​​that reflect the temperature fluctuations on the surface of the soil and rock at the installation point.

[0041] In the above-mentioned optional methods, temperature data of the installation location of the smart target is further collected through a temperature sensing unit to provide a reference temperature for infrared thermal image sequence analysis, improve the accuracy of temperature field distribution monitoring, and at the same time be used to calibrate thermal imaging data to ensure the reliability of thermal anomaly identification.

[0042] In one alternative embodiment, the active heating unit includes a miniature heating element and an encoding control circuit, the encoding control circuit being used to control the miniature heating element to operate according to a predetermined encoding mode in order to generate the specifically encoded infrared thermal signal.

[0043] Among them, the miniature heating element refers to the specific physical component in the active heating unit that performs the conversion of electrical energy into thermal energy; for example, a resistive heating film several centimeters square, encapsulated on an insulating substrate, which generates heat when current passes through it. The encoding control circuit refers to the electronic circuit used to receive instructions and control the miniature heating element to operate according to a predetermined encoding mode; for example, a circuit board containing a microprocessor and a power switch, where the microprocessor stores the on / off time sequence and controls the power switch to drive the heating element to operate cyclically according to the preset mode. The predetermined encoding mode refers to the set time and power control rules used to modulate the infrared thermal signal; for example, a preset mode is: operating at full power, half power, or zero power in different time periods with a cycle of several minutes.

[0044] In the above-mentioned optional methods, it is further clarified that the active heating unit consists of a miniature heating element and an encoding control circuit, which realizes precise encoding control of infrared thermal signals, ensures stable output of specific encoding modes, and enhances the target's identifiability and anti-interference ability in complex environments.

[0045] In one alternative approach, the predetermined encoding mode is to cause the micro heating element to undergo periodic on / off changes or stepped power changes.

[0046] Periodic on / off changes refer to a coding pattern that causes the heating element to repeatedly turn on and off within fixed time intervals; for example, controlling the heating element to cycle every few seconds, turning it on for a portion of the time and turning it off for the remaining time, repeating this process. Stepped power changes refer to a coding pattern that causes the output power of the heating element to vary in segments according to predetermined power levels during different time periods; for example, controlling the heating element to operate at full power in the first period, at 50% power in the second period, and at 25% power in the third period.

[0047] Among the above-mentioned optional methods, the predetermined encoding mode is further limited to periodic on-off changes or stepped power changes, providing a simple and reliable encoding implementation method, which facilitates rapid decoding and recognition by multispectral detection equipment, and reduces system implementation complexity and overall power consumption.

[0048] In an alternative embodiment, the multispectral detection device 120 further includes a visible light imaging unit for acquiring visible light image data of the slope.

[0049] The visible light imaging unit refers to the component in the multispectral detection device 120 used to acquire images in the visible light band; for example, a color camera equipped with a high-pixel image sensor and optical lens in the observation device. Visible light image data refers to image information acquired by the visible light imaging unit that reflects the texture, color, and morphological characteristics of the monitored area in the visible light band; for example, in a color photograph taken under daylight conditions, the vegetation cover on the slope, the morphology of rock fissures, and the visual appearance of the smart target 110 can be clearly seen.

[0050] In the above-mentioned optional methods, a visible light imaging unit is further integrated into the multispectral detection equipment to simultaneously acquire visible light image data of the slope, realize the fusion verification of infrared thermal imaging and visual information, and improve the intuitiveness and data credibility of the monitoring results.

[0051] In one alternative approach, the multispectral detection device 120 is deployed at a fixed observation point and continuously scans and images the slope area where multiple smart targets 110 are deployed to form a time-continuous monitoring data stream.

[0052] Fixed observation points refer to stable locations where the multispectral detection equipment 120 is permanently or semi-permanently installed and used for observation. For example, on a geologically stable bedrock or concrete observation pier several hundred meters away opposite a slope, the equipment is firmly installed with supports, and its spatial position and observation angle remain unchanged throughout the monitoring period. Monitoring data streams refer to the collection of image data and other relevant data continuously and sequentially generated from the multispectral detection equipment 120. For example, time-stamped infrared thermal images, visible light images, and equipment status parameters continuously transmitted back to the data processing center from the equipment at the fixed observation point via a communication network, forming a continuous and uninterrupted monitoring data stream.

[0053] In the above-mentioned optional methods, multispectral detection equipment is further deployed at fixed observation points to continuously scan and image the slope area with multiple smart targets, forming a continuous monitoring data stream in time, expanding the monitoring coverage and ensuring data integrity.

[0054] In one alternative embodiment, the multispectral detection device 120 is specifically used for: Based on the infrared thermal image sequence of the slope, the position change of the smart target 110 in consecutive image frames is calculated by a digital image phase correlation algorithm to generate the displacement data of the slope surface.

[0055] The digital image phase correlation algorithm refers to an algorithm that obtains sub-pixel-level displacement by calculating the phase information of the cross power spectrum of two images in the frequency domain. For example, by performing a Fourier transform on the sub-region image of the target in two consecutive infrared images, the phase correlation of the sub-region image is analyzed, thereby accurately calculating the sub-pixel displacement components of the target in two vertical directions. Position change refers to the change in the coordinates of the image representation center point of the intelligent target 110 in a spatiotemporal coordinate system composed of a continuous image sequence. For example, through algorithm analysis, if the image coordinates of a target undergo a slight numerical change in two adjacent frames, this change represents a position change.

[0056] In the above-mentioned optional methods, a digital image phase correlation algorithm is further used to calculate the positional change of the smart target in continuous image frames, so as to achieve sub-pixel level displacement measurement, enhance the ability to capture small creep deformations of the slope, and ensure the accuracy of displacement data.

[0057] In one alternative embodiment, the multispectral detection device 120 is specifically used for: The temperature field distribution in the infrared thermal image sequence of the slope is analyzed, the spatiotemporal changes of the temperature field distribution are compared with the displacement data, and an artificial intelligence algorithm is used to determine the local temperature gradient anomaly areas that do not match the displacement data.

[0058] Artificial intelligence algorithms refer to computer algorithms that can learn patterns from data and make predictions or judgments, such as machine learning or deep learning models. For example, a neural network model trained with a large amount of historical slope monitoring data can automatically analyze the input temperature field change sequence and displacement vector map, and output a probability map that identifies areas with potential slippage risks.

[0059] In the above-mentioned optional methods, artificial intelligence algorithms are further used to analyze the spatiotemporal changes of temperature field distribution and compare them with displacement data to automatically identify local temperature gradient anomaly areas that do not match the displacement data, thereby improving the intelligence level of landslide precursor identification.

[0060] In one alternative, the shell is made of a material with thermophysical parameters designed to have thermal capacity and thermal conductivity configured to characterize the local thermal boundary conditions in contact between the shell and the slope surface.

[0061] Thermophysical parameters refer to characteristic parameters describing the thermal properties of materials. For example, specific heat capacity and thermal conductivity are important thermophysical parameters, determining a material's ability to store and conduct heat, respectively. Designed materials refer to materials whose composition, structure, or manufacturing process is specifically adjusted or selected according to specific functional requirements. For example, to make the target shell more sensitive to the thermal contact state of the slope surface, a polymer-based material with specific thermal properties is specifically selected or composited. Heat capacity and thermal conductivity refer to physical quantities that characterize a material's ability to store and conduct heat, respectively. For example, the shell material has specific heat capacity and thermal conductivity values ​​that match the shell's response speed to changes in ambient temperature with the adhered slope medium. Local thermal boundary conditions refer to the sum of surface physical conditions that affect heat exchange in the small area where the smart target 110 contacts the slope surface. For example, factors such as the tightness of the contact between the target shell and the soil, the presence of filling material between the contact surfaces, the thermal conductivity and water content of the soil itself, etc., together constitute the local thermal boundary conditions, which dominate the heat transfer process between the target and the slope.

[0062] In the above-mentioned alternative methods, the shell is further constructed using a material with designed thermophysical parameters, so that the heat capacity and thermal conductivity match the local thermal boundary conditions of the slope surface, thereby enhancing the target's sensitivity to the abnormal heat conduction during the development of the slip surface.

[0063] Figure 2 A schematic diagram of the structure of the smart target 110 is shown. Figure 2 As shown, the smart target 110, as an integrated micro-intelligent node, embodies the overall concept of multi-functional integration in its design. The smart target 110 has an outer shell, which is a compact physical structure. Figure 2 Corner reflectors are arranged on the upper part of the inner shell. The number of corner reflectors is not limited to two. The corner reflectors are used to enhance the reflected radar wave signal. The shell integrates an active heating unit, a temperature sensing unit, and a potential stress sensing unit. The active heating unit includes a miniature heating element and an encoded control circuit. The encoded control circuit controls the miniature heating element to operate according to a predetermined encoded pattern, such as generating periodic on / off changes or stepped power changes, thereby generating an infrared thermal signal with a specific code. This design makes the smart target 110 no longer a traditional passive marker that reflects or emits thermal radiation, but a "smart heating" target that can actively emit a unique identification signal.

[0064] A temperature sensing unit is mounted on the housing to collect temperature data at the installation location of the smart target 110 on the slope surface. The housing material is specially designed, and its thermophysical parameters, such as heat capacity and thermal conductivity, are configured to characterize the local thermal boundary conditions in contact with the slope surface. This thermal characteristic simulation design allows for the inversion of the local thermal state at the target adhesion point by analyzing the dynamic thermal response of the smart target 110.

[0065] The intelligent targets 110 are deployed on the slope surface, within the monitoring range of the multispectral detection device 120. The multispectral detection device 120 is deployed at a fixed observation point and includes an infrared thermal imaging unit, a visible light imaging unit, and a radar camera, forming a multispectral remote observation device. The multispectral detection device 120 continuously scans and images the slope area where multiple intelligent targets 110 are deployed, acquiring infrared thermal image sequences, visible light image data, and radar data of the slope with the intelligent targets 110 as the observation targets. The corner reflector enhances the echo signal received by the radar camera, thereby forming a higher-quality radar monitoring data stream. All data together constitute a temporally continuous monitoring data stream.

[0066] In displacement monitoring, the multispectral detection device 120 uses a specific coded infrared thermal signal emitted by a smart target 110 as a stable tracking feature based on the acquired infrared thermal image sequence of the slope. The multispectral detection device 120 employs a digital image phase correlation algorithm to track the infrared signal center of the smart target 110 in each frame of the image with sub-pixel precision, calculating the minute positional changes of the smart target 110 in the continuous time series, thereby generating high-precision displacement data of the slope surface. This method achieves non-contact, long-distance, all-weather multi-point synchronous displacement monitoring.

[0067] In terms of crack monitoring and precursor identification, the temperature sensing unit integrated in the intelligent target 110 works in conjunction with the temperature field distribution information acquired by the multispectral detection device 120. When stress concentration or microcrack initiation occurs on the slope, it may lead to local temperature anomalies. By analyzing the spatiotemporal changes of the temperature field distribution in the slope's infrared thermal image sequence and combining it with the generated displacement data, an artificial intelligence algorithm is used for data fusion analysis. The artificial intelligence algorithm automatically identifies local temperature gradient anomaly areas that do not match the displacement data change patterns. These areas indicate potential stress concentration zones or early microcrack locations, achieving a leap from displacement monitoring to prediction of precursors of failure.

[0068] The Smart Target 110 is compact and lightweight, making it easy to deploy on various structural surfaces such as slopes, bridges, and tunnels. All monitoring data, including displacement data, temperature data, temperature field distribution, radar data, and information on identified abnormal areas, are ultimately converged into a unified data processing platform for fusion and visualization, forming intuitive heat maps, displacement cloud maps, etc. The data from each monitoring function corroborate each other, improving the accuracy and reliability of the monitoring results.

[0069] Figure 3 A flowchart illustrating an embodiment of a slope monitoring method provided by the present invention is shown. This method employs the multimodal intelligent slope monitoring target system 100 provided by the present invention, such as... Figure 3 As shown, the method includes the following steps: S1. Obtain a sequence of infrared thermal images of the slope with the smart target 110 as the observation target; S2. Identify and locate the smart target 110 from the slope infrared thermal image sequence based on the specific code in the infrared thermal signal; S3. Track the position of the smart target 110 in the infrared thermal image sequence of the slope with subpixel accuracy to generate displacement data of the slope surface; S4. Analyze the temperature field distribution in the infrared thermal image sequence of the slope, and in combination with the displacement data, identify local temperature gradient anomaly areas that do not match the displacement data.

[0070] It should be noted that the methods and system embodiments provided above belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0071] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0072] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0073] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A multimodal intelligent slope monitoring target system, characterized in that, include: Smart targets deployed on the slope surface, and multispectral detection equipment for remote observation of the slope; The smart target includes a housing and an active heating unit integrated inside the housing, the active heating unit being used to generate an infrared thermal signal with a specific code; The multispectral detection device is used for: Acquire a sequence of infrared thermal images of the slope with the intelligent target as the observation target; The smart target is identified and located from the slope infrared thermal image sequence based on a specific code in the infrared thermal signal. The position of the smart target in the infrared thermal image sequence of the slope is tracked with subpixel accuracy to generate displacement data of the slope surface; Analyze the temperature field distribution in the infrared thermal image sequence of the slope, and combine it with the displacement data to identify local temperature gradient anomaly areas that do not match the displacement data.

2. The multimodal intelligent slope monitoring target system according to claim 1, characterized in that, The smart target also includes a temperature sensing unit, which is disposed on the housing and is used to collect temperature data at the installation location of the smart target.

3. The multimodal intelligent slope monitoring target system according to claim 1, characterized in that, The active heating unit includes a miniature heating element and an encoding control circuit. The encoding control circuit controls the miniature heating element to operate according to a predetermined encoding mode to generate the infrared thermal signal with the specific encoding.

4. The multimodal intelligent slope monitoring target system according to claim 3, characterized in that, The predetermined encoding mode is to cause the micro heating element to produce periodic on / off changes or step-like power changes.

5. The multimodal intelligent slope monitoring target system according to claim 1, characterized in that, The multispectral detection device further includes a visible light imaging unit, which is used to acquire visible light image data of the slope.

6. The multimodal intelligent slope monitoring target system according to claim 1, characterized in that, The multispectral detection equipment is deployed at fixed observation points and continuously scans and images the slope area where multiple smart targets are set up, so as to form a continuous monitoring data stream in time.

7. The multimodal intelligent slope monitoring target system according to any one of claims 1 to 6, characterized in that, The multispectral detection equipment is specifically used for: Based on the infrared thermal image sequence of the slope, the position change of the smart target in consecutive image frames is calculated using a digital image phase correlation algorithm to generate the displacement data of the slope surface.

8. The multimodal intelligent slope monitoring target system according to claim 7, characterized in that, The multispectral detection equipment is specifically used for: The temperature field distribution in the infrared thermal image sequence of the slope is analyzed, the spatiotemporal changes of the temperature field distribution are compared with the displacement data, and an artificial intelligence algorithm is used to determine the local temperature gradient anomaly areas that do not match the displacement data.

9. The multimodal intelligent slope monitoring target system according to claim 1, characterized in that, The shell is made of a material with designed thermophysical parameters, the heat capacity and thermal conductivity of which are configured to characterize the local thermal boundary conditions in contact between the shell and the slope surface.

10. A slope monitoring method, employing the multimodal intelligent slope monitoring target system as described in any one of claims 1 to 9, characterized in that, include: Acquire a sequence of infrared thermal images of the slope with the intelligent target as the observation target; The smart target is identified and located from the slope infrared thermal image sequence based on a specific code in the infrared thermal signal. The position of the smart target in the infrared thermal image sequence of the slope is tracked with subpixel accuracy to generate displacement data of the slope surface; Analyze the temperature field distribution in the infrared thermal image sequence of the slope, and combine it with the displacement data to identify local temperature gradient anomaly areas that do not match the displacement data.

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