A radar device for monitoring landslides

Abstract

This invention discloses a radar device for landslide monitoring, relating to the field of geological disaster monitoring technology. It includes: an environmental sensing module for real-time acquisition of environmental status data at the radar deployment point, the environmental status data including at least one or more of temperature, humidity, and vibration intensity; a reconfigurable radar transceiver processing module, comprising a signal generation unit, a frequency conversion amplification unit, and an antenna unit, wherein the signal generation unit can adjust radar waveform parameters according to control commands, and the frequency conversion amplification unit can adjust the operating frequency band and gain; and an intelligent control and decision-making module, communicatively connected to the environmental sensing module and the reconfigurable radar transceiver processing module. Through task-driven parameter optimization, the radar can adopt a low-power, long-cycle general survey mode for wide-area areas, while automatically switching to a high-precision, short-cycle detailed survey mode for high-risk areas identified by the algorithm.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring technology, and more specifically, to a radar device for monitoring landslides. Background Technology

[0002] Synthetic Aperture Radar (SAR) and Differential Interferometry (D-InSAR) technologies have been widely used for deformation monitoring of geological hazards such as slopes and landslides. Existing ground-based landslide monitoring radars typically operate based on fixed hardware parameters and modes, which presents significant limitations when facing complex and variable outdoor environments (such as drastic temperature changes, extremely high humidity, and continuous vibration) and diverse monitoring task requirements (such as large-scale surveys versus intensive monitoring in high-risk areas).

[0003] Specifically, existing technologies have the following shortcomings: Radars with fixed parameters may experience a sharp deterioration in signal link performance (such as signal-to-noise ratio and phase stability) under extreme environments (e.g., -30°C or 50°C, 96%RH high humidity), leading to data failure or inability to guarantee accuracy (e.g., sub-millimeter level accuracy). Radars typically operate with fixed revisit periods and signal parameters, making dynamic adjustments based on landslide risk levels impossible. This results in either over-monitoring of low-risk areas, causing data and energy redundancy, or under-monitoring of high-risk areas, failing to capture precursory changes.

[0004] Therefore, there is an urgent need for an intelligent radar device that can automatically sense the environment, understand the monitoring task, and dynamically adjust its own working status, so as to perform landslide monitoring tasks stably, efficiently, and accurately for a long time under complex outdoor conditions. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, this invention provides a radar device for landslide monitoring. Through internal intelligent decision-making, it automatically fuses and optimizes radar operating parameters to achieve an optimal balance between environmental robustness, task compliance, and radar energy efficiency, thereby improving the reliability, accuracy, and efficiency of landslide monitoring in complex outdoor scenarios.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A radar device for monitoring landslides, comprising:

[0008] An environmental perception module is used to acquire environmental status data of the radar deployment point in real time. The environmental status data includes at least one or more of temperature, humidity, and vibration intensity.

[0009] A reconfigurable radar transceiver processing module includes a signal generation unit, a frequency conversion amplification unit, and an antenna unit. The signal generation unit can adjust radar waveform parameters according to control commands, and the frequency conversion amplification unit can adjust the operating frequency band and gain.

[0010] The intelligent control and decision-making module is communicatively connected to the environmental perception module and the reconfigurable radar transceiver processing module.

[0011] The intelligent control and decision-making module is configured to perform the following operations: a. receive the environmental state data and determine a first set of radar operating parameters adapted to the current environment based on a preset environment-performance mapping relationship; b. parse the spatial scale and deformation sensitivity requirements of the monitored target according to a preset or remotely issued monitoring task instruction, and determine a second set of radar operating parameters accordingly; c. fuse and optimize the first set of radar operating parameters and the second set of radar operating parameters to generate a final control instruction that drives the reconfigurable radar transceiver processing module, so that the radar can automatically adapt to a working state that balances environmental robustness and monitoring task optimization in complex outdoor environments.

[0012] In a preferred embodiment, step c of the intelligent control and decision-making module performing fusion optimization specifically includes: taking the link budget and signal-to-noise ratio of the radar signal in the current environment as the core constraints, and under the premise of meeting the deformation sensitivity requirements of the monitored target, searching in the parameter solution space through an optimization algorithm to find the parameter combination that minimizes the total power consumption of the radar or maximizes the data acquisition efficiency, as the final control command.

[0013] In a preferred embodiment, the parameter solution space is composed of at least the following adjustable parameters: the frequency modulation time and bandwidth of the linear frequency modulated signal generated by the signal generation unit, the center frequency and instantaneous bandwidth of the frequency conversion amplification unit, the gain of the receiving channel in the frequency conversion amplification unit, and the radar revisit period; wherein the frequency modulation time can be continuously adjusted between 0.00005ms and 10ms.

[0014] In a preferred embodiment, the reconfigurable radar transceiver processing module adopts a modular enclosure structure. The signal generation unit includes a digital board based on an XC7VX690T FPGA and an AD9739 DAC. The frequency conversion amplification unit includes a frequency conversion board covering the 2GHz to 18GHz frequency band and a power amplifier board with a linear output power of not less than 35dBm. Each board is connected via a side-plug method and supports hot-swappable replacement.

[0015] In a preferred embodiment, the radar further includes an interactive ruggedized terminal running monitoring software; the monitoring software provides a graphical task configuration interface, allowing users to define sub-areas with different priorities and monitoring requirements on an electronic map by dragging or drawing, and the monitoring task instructions are automatically generated based on the analysis of the sub-areas.

[0016] In a preferred embodiment, the monitoring software includes a real-time imaging engine and a sequence interferometry analysis engine; the real-time imaging engine is configured to complete imaging of echo data with a 2-meter synthetic aperture within 120 seconds; the sequence interferometry analysis engine is configured to perform differential interferometry processing on the time-series radar image, and when the identified deformation anomaly region spatially overlaps with a user-defined high-priority sub-region, it automatically triggers an alarm and generates a suggested instruction containing optimized monitoring parameters, which is then pushed to the intelligent control and decision-making module.

[0017] In a preferred embodiment, the vibration sensor data integrated in the environmental perception module is used to trigger radar self-test; when the vibration intensity exceeds a preset threshold, the intelligent control and decision module initiates link diagnosis of each subunit of the reconfigurable radar transceiver processing module, and when a fault is diagnosed, uploads the fault location information and the corresponding replaceable board identifier to the remote operation and maintenance platform.

[0018] In a preferred embodiment, the device adopts a distributed power supply and management architecture, including a main power supply and a backup power supply module. The intelligent control and decision module also monitors the radar power consumption in real time and can dynamically adjust some parameters in the final control command to reduce radar power consumption when the main power supply power is lower than the threshold. At the same time, it seamlessly switches to backup power supply to extend the continuous monitoring time.

[0019] The technical effects and advantages of the radar device for landslide monitoring proposed in this invention are as follows: Through task-driven parameter optimization, the radar can adopt a low-power, long-cycle general survey mode for wide-area coverage, while automatically switching to a high-precision, short-cycle detailed survey mode for high-risk areas identified by the algorithm. This intelligent scheduling that integrates reconnaissance and strike capabilities effectively focuses monitoring resources on the most dangerous areas with the same energy consumption, thus improving the timeliness of early warning.

[0020] The optimization algorithm in the decision-making module not only considers a single monitoring task but also incorporates the quality feedback from historical monitoring data into the evaluation. For example, if the coherence of the actual monitoring data remains lower than the predicted value, the radar will determine that the current parameters are not optimal and automatically initiate a re-optimization process. This continuous self-adjustment based on performance feedback allows the radar to continuously learn and adapt to the micro-environment of a specific installation point, which is impossible for traditional radars.

[0021] Intelligent fault location and modular design enable on-site maintenance personnel to quickly replace faulty boards without requiring in-depth professional knowledge, greatly shortening the radar mean time to recovery (MTTR) and ensuring the continuity of the safe task of landslide monitoring. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall architecture and module connection of a radar device for monitoring landslides according to the present invention.

[0023] Figure 2 This is an adaptive decision-making loop flowchart of the intelligent control and decision-making module of a landslide monitoring method according to the present invention.

[0024] Figure 3 This is a schematic diagram of the graphical task configuration interface of an interactive hardening terminal for a radar device used for landslide monitoring according to the present invention.

[0025] Figure 4 This is a schematic diagram of the distributed power supply and power consumption management logic of a radar device for landslide monitoring according to the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Example 1, Figure 1 , Figure 3 and Figure 4 This invention discloses a radar device for landslide monitoring, comprising: an environmental sensing module for real-time acquisition of environmental status data at the radar deployment point, the environmental status data including at least one or more of temperature, humidity, and vibration intensity; a reconfigurable radar transceiver processing module, comprising a signal generation unit, a frequency conversion amplification unit, and an antenna unit, wherein the signal generation unit can adjust radar waveform parameters according to control commands, and the frequency conversion amplification unit can adjust the operating frequency band and gain; and an intelligent control and decision-making module, communicatively connected to the environmental sensing module and the reconfigurable radar transceiver processing module.

[0028] The intelligent control and decision-making module is configured to perform the following operations: a. Receive environmental status data and determine the first set of radar operating parameters adapted to the current environment based on the preset environment-performance mapping relationship; b. Analyze the spatial scale and deformation sensitivity requirements of the monitored target according to the preset or remotely issued monitoring task instructions, and determine the second set of radar operating parameters accordingly; c. Fusion and optimization of the first and second sets of radar operating parameters to generate the final control instructions that drive the reconfigurable radar transceiver processing module, so that the radar can automatically adapt to a working state that balances environmental robustness and monitoring task optimization in complex outdoor environments.

[0029] It should be noted that the environmental perception module collects real-time environmental status data at the radar deployment site, providing environmental input for subsequent parameter adjustments. This input must include at least one or more of the following: temperature, humidity, and vibration intensity. This breaks away from the traditional static operating mode of radar that ignores environmental changes, allowing the radar to sense its environment and provide a basis for adaptive adjustments (for example, high temperatures can affect the performance of electronic components, high humidity can attenuate radar signals, and vibration may indicate equipment loosening or geological activity).

[0030] Reconfigurable radar transceiver processing module: the core unit for generating, amplifying, transmitting and receiving radar signals, with the key feature being reconfigurability (i.e., parameters can be dynamically adjusted).

[0031] The three main sub-units and their functions are as follows: Signal Generation Unit: Adjusts radar waveform parameters according to control commands, rather than outputting a fixed single waveform, adapting to different monitoring accuracy requirements. Frequency Conversion Amplification Unit: Adjustable operating frequency band and gain (refined to 2GHz~18GHz band, adjustable receiving gain), adapting to signal transmission needs in different environments and matching the distance and deformation sensitivity requirements of the monitored target. Antenna Unit: Responsible for transmitting radar signals and receiving echoes reflected from landslide bodies. Reconfigurability is key, distinguishing it from traditional radar designs with fixed waveforms, frequency bands, and gains, providing the hardware foundation for subsequent dynamic adjustments.

[0032] The intelligent control and decision-making module communicates bidirectionally with the environmental perception module and the reconfigurable radar transceiver processing module. It can receive input data (environmental data and equipment status data) from the former two and send control commands downwards. It undertakes the core functions of data fusion, decision analysis, and command generation, and is key to achieving self-adaptation.

[0033] The intelligent control and decision-making module comprises three operations (a, b, c) that form the core decision-making link driven by both environment and task. Logically, these operations are progressive and mutually constraining: Operation a: Receives real-time environmental data from the environmental perception module. Through pre-defined environment-performance mapping relationships (e.g., high humidity environment → increased radar signal attenuation → need to increase transmission power or switch to a specific frequency band; low temperature environment → decreased electronic device gain → need to adjust amplification unit parameters), it determines the first set of radar operating parameters. This ensures stable transmission and reception of the radar signal in the current environment, guarantees uninterrupted link operation, and maintains a sufficient signal-to-noise ratio, addressing the problem of traditional radar failure in harsh environments. Operation b: Receives pre-defined or remotely issued monitoring task instructions, analyzes the core requirements, and determines the spatial scale of the monitored target (e.g., whether the monitoring area is a wide-area mountain or a local slope) and deformation sensitivity (e.g., whether it needs to detect millimeter-level micro-deformation). Based on this, it determines the second set of radar operating parameters. This ensures that the radar parameters match the task requirements (e.g., wide-area survey → low power consumption, long revisit period; local detailed survey → high precision, short revisit period), avoiding overkill or insufficient capability. Operation c: The first set of parameters for environment adaptation and the second set of parameters for task adaptation are fused and optimized to generate the final control command driving the reconfigurable module. The essence of fusion is balance: it must satisfy both signal stability in the environment (without sacrificing link quality due to task requirements) and monitoring accuracy for the task (without reducing monitoring effectiveness due to environment adaptation). This allows the radar to enter a working state that balances environmental robustness and optimal monitoring task performance.

[0034] In this embodiment, step c of the intelligent control and decision-making module for fusion optimization specifically includes: taking the link budget and signal-to-noise ratio of the radar signal in the current environment as the core constraints, and under the premise of meeting the sensitivity requirements of the monitored target deformation, searching in the parameter solution space through optimization algorithms to find the parameter combination that minimizes the total power consumption of the radar or maximizes the data acquisition efficiency, as the final control command.

[0035] It's important to note that: link budget refers to the balance between loss and gain across the entire radar signal transmission and reception chain (e.g., the comprehensive matching of transmit power, transmission attenuation, and receive gain); signal-to-noise ratio (SNR) refers to the ratio of effective echo signal to noise, directly determining the accuracy of the monitoring data. This is a hard constraint for fusion optimization and has the highest priority. Regardless of parameter optimization, it's crucial to ensure that the radar signal can transmit stably in the current environment (uninterrupted link) and that the echo signal is sufficiently clear (noise does not obscure effective information). For example, in high-humidity environments, signal attenuation is significant. If transmit power is excessively reduced to lower power consumption, it will lead to insufficient SNR and invalid monitoring data; such parameter combinations will be excluded from the constraints.

[0036] While meeting core constraints, priority must be given to ensuring the core requirements of the monitoring mission, namely deformation sensitivity (i.e., the minimum deformation of the landslide that radar can detect). Fusion optimization should not be done for the sake of optimization itself, but must be based on completing the mission. For example, if the monitoring mission requires detecting millimeter-level deformation, the optimized parameters must support high-precision configurations such as narrow bandwidth and high sampling rate; deformation detection capability should not be sacrificed to reduce power consumption or adapt to the environment.

[0037] Fusion optimization is not about subjectively selecting parameters, but rather a quantitative search based on algorithms. The parameter solution space refers to the set of all parameter combinations that meet the hardware capabilities (the adjustable range of reconfigurable modules) and technical requirements. By replacing manual decision-making with algorithmic search, the scientific nature of parameter combinations is ensured, parameter imbalances caused by insufficient human experience are avoided, and the efficiency of parameter adjustment is improved to adapt to the dynamic changes in outdoor environments and mission requirements.

[0038] The final parameter combination locked by the optimization algorithm must satisfy the core constraints and task prerequisites, and on this basis, achieve one of the following two objectives (or a balance between the two): Applicable to outdoor scenarios without mains power and relying on mobile power supplies, extending the continuous monitoring time of the radar (e.g., in the wide-area survey phase, power consumption can be reduced without affecting basic monitoring by lowering transmission power and extending the revisit period). Applicable to detailed survey scenarios in high-risk areas, acquiring more effective data per unit time by optimizing parameters such as frequency modulation time and bandwidth, improving the real-time tracking capability of landslide deformation (e.g., shortening data processing latency and increasing sampling frequency). No single objective is forcibly limited; the selection can be dynamically based on the priority of the monitoring task to adapt to different scenario requirements.

[0039] In this embodiment, the parameter solution space is composed of at least the following adjustable parameters: the frequency modulation time and bandwidth of the linear frequency modulation signal generated by the signal generation unit, the center frequency and instantaneous bandwidth of the frequency conversion amplifier unit, the gain of the receiving channel in the frequency conversion amplifier unit, and the radar revisit period; wherein, the frequency modulation time can be continuously adjusted between 0.00005ms and 10ms.

[0040] It should be noted that the listed parameters all correspond to the hardware capabilities of the reconfigurable radar transceiver processing module and directly affect monitoring performance, power consumption, and environmental adaptability. These are core adjustment dimensions in landslide monitoring scenarios: Frequency modulation time and bandwidth of the linear frequency modulated signal: These are core parameters of the signal generation unit. Frequency modulation time determines the signal's temporal resolution (affecting the response speed of deformation detection), while bandwidth determines the signal's distance resolution (affecting monitoring accuracy). Both are crucial for balancing response speed and monitoring accuracy. Center frequency and instantaneous bandwidth of the frequency conversion amplifier unit: These correspond to the frequency band adjustment capability of the frequency conversion amplifier unit. The center frequency determines the transmission characteristics of the radar signal (e.g., high-frequency bands have strong anti-interference but fast attenuation, while low-frequency bands have good penetration). Instantaneous bandwidth is related to the parallel processing capability of the signal, both adapting to different environments (e.g., switching to low-frequency bands in high-humidity environments) and monitoring range requirements (wide-area / local). Gain of the receiving channel in the frequency conversion amplifier unit: This corresponds to the signal amplification capability of the frequency conversion amplifier unit. Gain adjustment directly affects the signal-to-noise ratio of the echo signal (too low a gain will mask the signal with noise, while too high a gain will cause signal saturation and distortion), and is a core parameter for adapting to environmental attenuation (such as high humidity or long distances leading to signal attenuation). Radar revisit cycle: refers to the time interval between repeated radar monitoring of the same area, directly related to monitoring efficiency and power consumption. Short cycles are suitable for detailed surveys of high-risk areas (real-time deformation tracking), while long cycles are suitable for wide-area surveys (reducing power consumption), and are key to balancing mission requirements and power supply capabilities.

[0041] The practicality of the adjustable range: 0.00005ms (ultra-short frequency tuning time) is suitable for fast-response scenarios (such as when a landslide deforms rapidly, requiring high-frequency data acquisition); 10ms (long frequency tuning time) is suitable for high-precision monitoring scenarios (such as monitoring minute deformations, requiring long-term signal accumulation to improve accuracy). Unlike segmented adjustable ranges, continuous adjustable ranges allow parameters to precisely match the dynamic changes of the environment and task (such as fine-tuning to the optimal value based on humidity attenuation and deformation sensitivity requirements), avoiding performance gaps caused by segmented adjustments.

[0042] In this embodiment, the reconfigurable radar transceiver processing module adopts a modular box structure. The signal generation unit includes a digital board based on an XC7VX690T FPGA and an AD9739 DAC. The frequency conversion amplification unit includes a frequency conversion board covering the 2GHz to 18GHz frequency band and a power amplifier board with a linear output power of not less than 35dBm. Each board is connected by a side-plugging method and supports hot-swapping replacement.

[0043] It should be noted that the reconfigurable radar transceiver processing module is integrated into a unified modular enclosure, rather than being separate independent components. On the one hand, the enclosure structure provides physical protection (adapting to the vibration and collision environment of mountainous outdoor environments), while facilitating the integration of auxiliary functions such as heat dissipation and shielding; on the other hand, the modular design echoes the reconfigurable function mentioned earlier, with the functional units inside the enclosure using circuit boards as carriers, providing a physical basis for subsequent parameter adjustments and module replacements.

[0044] XC7VX690T FPGA: Belonging to the high-performance Virtex-7 series FPGA, it possesses abundant logic resources, high-speed interfaces, and parallel processing capabilities. It can support complex signal generation algorithms (such as the CORDIC algorithm and linear frequency modulation signal generation mentioned earlier) and meet the computing power requirements for adjusting parameters such as adjustable frequency modulation time from 0.00005ms to 10ms and a high sampling rate of 2.4Gsps. AD9739 DAC: A 14-bit high refresh rate digital-to-analog converter with a sampling rate up to 2500MSPS, supporting wide-band signal output (covering 1.25G~3GHz in mixer mode). It can accurately convert digital signals generated by the FPGA into analog intermediate frequency signals, providing a high-quality signal source for the subsequent frequency conversion amplification unit's 2GHz~18GHz frequency band coverage.

[0045] 2GHz~18GHz frequency conversion board: Directly matches the adjustable center frequency parameter requirements, and its wide frequency band coverage can adapt to different environments (such as switching to the low frequency band to reduce attenuation in high humidity environments) and monitoring distances (high frequency bands are suitable for short-range high-precision monitoring, and low frequency bands are suitable for long-range wide-area monitoring). Power amplifier board with linear output power ≥35dBm: Ensures that the radar signal has sufficient transmit power reserve to support the core constraints of link budget and signal-to-noise ratio (such as long-range monitoring and high attenuation environments, where high power is required to ensure signal transmission), while providing reverse support for adjustable receive gain (coordinated optimization of transmit power and receive gain to avoid signal saturation or noise interference).

[0046] Side-mounted connection: Compared to traditional rear-mounted plug-in or cable connections, side-mounted connections facilitate module layout and maintenance, while also improving the stability of connections between boards (adapting to the vibration environment of mountainous outdoor environments and reducing the risk of loose connections). Hot-swappable replacement: This refers to the ability to directly plug in and replace faulty boards without shutting down the radar or interrupting monitoring. For the 24-hour continuous operation requirement of landslide monitoring, the hot-swappable design avoids monitoring interruptions due to module failures, significantly reducing the time cost of outdoor maintenance (repairs can be completed without power outages).

[0047] In this embodiment, the radar also includes an interactive ruggedized terminal that runs monitoring software. The monitoring software provides a graphical task configuration interface, allowing users to define sub-regions with different priorities and monitoring requirements on an electronic map by dragging or drawing. Monitoring task instructions are automatically generated based on the analysis of the sub-regions.

[0048] It should be noted that this is not an ordinary computer, but a specialized interactive terminal adapted to the harsh outdoor environment of mountains. As the platform for the monitoring software, it performs three core functions: task configuration, input of monitoring data, display of alarm information, and reception of data. It is the sole entry point for user interaction with the system. Its ruggedized features ensure stable operation in the outdoor environment of landslide monitoring, preventing the failure of ordinary terminals due to vibration, high temperatures, rain, etc., and guaranteeing the continuity of task configuration.

[0049] This feature allows users to define monitoring sub-regions on an electronic map through drag-and-drop or drawing visual operations, rather than the traditional input parameter command-based configuration. It supports defining sub-regions with different priorities and monitoring requirements. For example: priority classification: setting landslide hazard points (such as cracked areas) as high priority and surrounding stable areas as low priority; monitoring requirement classification: high-priority areas require millimeter-level deformation sensitivity and short revisit cycles, while low-priority areas require centimeter-level accuracy and long revisit cycles. Monitoring task commands are not manually entered by the user, but are automatically generated by the terminal software based on the analysis of the sub-regions. The software extracts information such as the spatial range, priority, and monitoring accuracy requirements of the sub-regions, converting them into standardized commands that can be recognized by the intelligent control and decision-making module (e.g., high-priority sub-region A: deformation sensitivity ≤ 2mm, revisit cycle ≤ 10 minutes).

[0050] Lowering the barrier to task configuration and enhancing usability: Graphical, drag-and-drop operation replaces traditional professional parameter input, allowing non-technical personnel (such as field monitoring personnel) to quickly complete task configuration. This solves the pain point of powerful but complex adaptive systems, expanding the system's applicability. Achieving refined and differentiated configuration of monitoring tasks: Through the combination of sub-region, priority, and monitoring requirements, the limitations of the traditional single-domain radar monitoring mode are broken. The system can allocate different monitoring resources to different risk areas (such as high-precision detailed surveys in high-risk areas and low-power general surveys in low-risk areas), providing specific task input basis for subsequent dynamic switching between general and detailed surveys and parameter fusion optimization. Connecting user needs with system decisions: The generation path of monitoring task instructions is clearly defined (user configuration → software parsing → standardized instructions), avoiding the vague descriptions of preset or remotely issued instructions. This provides a clear source for task-driven parameter optimization, making the system's environment-task dual-driven logic more complete and the closed loop more rigorous. Adapted to outdoor field operation scenarios: The ruggedized features of the interactive terminal and the intuitiveness of the graphical interface meet the needs of rapid configuration and no complicated operation required in mountain monitoring sites. This avoids the cumbersome process of users having to carry a computer and log in remotely for configuration, and improves the efficiency and flexibility of task configuration.

[0051] In this embodiment, the monitoring software includes a real-time imaging engine and a sequence interferometry analysis engine. The real-time imaging engine is configured to complete imaging of echo data with a 2-meter synthetic aperture within 120 seconds. The sequence interferometry analysis engine is configured to perform differential interferometry processing on the time-series radar image, and when the identified deformation anomaly area coincides spatially with a user-defined high-priority sub-region, it automatically triggers an alarm and generates a suggested instruction containing optimized monitoring parameters, which is then pushed to the intelligent control and decision-making module.

[0052] It should be noted that rapid imaging processing is performed on echo data with a 2-meter synthetic aperture. The requirement to complete imaging within 120 seconds is highly technically specific: The 2-meter synthetic aperture corresponds to high-precision localized monitoring scenarios for landslides (such as detailed imaging of key hazard areas). The length of the synthetic aperture directly affects the imaging resolution; a 2-meter aperture can meet the imaging requirements for millimeter-level deformation monitoring. Completing within 120 seconds addresses the high latency of traditional radar data processing. Landslides may involve sudden deformation; rapid imaging allows users to promptly grasp the dynamics of the landslide body, buying time for emergency response and providing real-time data support for subsequent interferometric analysis. This engine is a crucial link in the data acquisition and usability process; its performance directly determines the system's real-time monitoring capabilities, avoiding delayed early warnings due to processing delays.

[0053] Differential interferometry (DI) is applied to time-series radar images. By comparing radar images from different time points, the deformation displacement and deformation rate of the landslide body are calculated, accurately identifying areas of abnormal deformation (such as areas with sudden increases in deformation rate or cumulative deformation exceeding a threshold). DI is a core technology for landslide deformation monitoring, effectively eliminating irrelevant factors such as terrain undulations and atmospheric interference, focusing on the true deformation of the landslide body, and ensuring the accuracy of anomaly identification. The engine analyzes echo data collected by the reconfigurable module, and the analysis results directly serve task-driven parameter optimization, serving as a key link between data processing and decision adjustment.

[0054] Triggering condition: When the abnormal deformation region identified by the sequence interferometry analysis engine coincides spatially with the user-defined high-priority sub-region (i.e., abnormal deformation occurs in the high-risk region).

[0055] Core Actions: Timely alerts users (such as on-site monitoring personnel and remote control centers) via sound, light, and push notifications to prevent high-risk anomalies from being missed and ensure timely warnings. Based on anomalies (such as deformation rate and environmental conditions), automatically calculates appropriate optimized monitoring parameters (such as shortening the revisit cycle, increasing deformation sensitivity, and adjusting transmission power) and pushes them to the intelligent control and decision-making module. This mechanism transforms the software from passively processing data to proactively providing optimization suggestions. This allows the intelligent control and decision-making module to dynamically optimize parameters based on real-time monitoring results, rather than solely relying on initial task configuration and environmental data, further enhancing the system's monitoring targeting.

[0056] Quantifying software performance and improving solution verifiability: Clearly defining specific indicators for imaging within 120 seconds using a 2-meter synthetic aperture lens avoids vague descriptions such as rapid imaging and high-precision analysis, allowing software functions to be verified through actual testing (e.g., testing imaging time, deformation recognition accuracy), reducing the risk of technical solution implementation. Improving the monitoring-early warning-optimization closed loop: Through the linkage of two engines and anomaly response mechanisms, a complete link is achieved: data acquisition → real-time imaging → time-series analysis → anomaly identification → alarm → parameter suggestion → decision adjustment, solving the pain points of traditional monitoring systems where data and decision-making are disconnected and there is no optimization action after an alarm. Enhancing the targeting of high-risk area monitoring: Binding deformation anomalies to sub-region priorities ensures that system resources are prioritized for high-risk areas. When anomalies occur, not only can alarms be triggered promptly, but optimization parameters can also be proactively pushed, upgrading the system from balanced monitoring across the entire domain to precise focused monitoring of high-risk areas, improving the effectiveness of early warnings. Providing dynamic feedback for intelligent decision-making: The generated parameter optimization suggestion instructions provide direct input for parameter optimization in intelligent control and decision-making modules, making the system's adaptive adjustment no longer a one-way derivation, but a two-way optimization based on real-time monitoring results, further improving parameter matching.

[0057] In this embodiment, the vibration sensor data integrated in the environmental perception module is used to trigger radar self-test; when the vibration intensity exceeds the preset threshold, the intelligent control and decision module initiates link diagnosis of each subunit of the reconfigurable radar transceiver processing module, and when a fault is diagnosed, uploads the fault location information and the corresponding replaceable board identifier to the remote operation and maintenance platform.

[0058] It should be noted that the vibration sensor data integrated into the environmental sensing module is no longer merely used as parameter input for environmental adaptation, but also includes a new fault warning trigger function. When the vibration intensity exceeds a preset threshold, the system determines that there is a risk of equipment malfunction. This threshold is not the deformation threshold for geological monitoring, but rather the threshold for the safe operation of the equipment itself (e.g., a minor landslide causing the equipment mounting base to loosen, an outdoor collision causing the circuit board connection to loosen, or abnormal vibration caused by a fault in an internal component of the module). Through the multi-functional reuse of environmental sensing data, real-time monitoring of equipment status can be achieved without adding additional fault detection hardware, aligning with the system's lightweight and modular design philosophy.

[0059] Once the vibration exceeds the threshold, the intelligent control and decision-making module automatically initiates link diagnostics, rather than waiting for manual triggering by the user. For each sub-unit of the reconfigurable radar transceiver processing module (signal generation unit, frequency conversion amplification unit, antenna unit, etc.), the focus is on detecting the signal transmission link, module connection status, and whether the operating parameters of core components are normal (such as the signal generation accuracy of the FPGA, the output power of the power amplifier board, and the conductivity of the board connections). The implementation of the diagnostic function relies on the modular design described above; each sub-unit is an independent board with clear detection interfaces and status feedback signals, enabling the intelligent decision-making module to accurately locate the sub-unit where the fault occurred, rather than simply judging a system fault.

[0060] Reported Content: After diagnosis, the system uploads two types of key information to the remote maintenance platform: a clear description of the specific location of the fault (e.g., the digital board link of the power amplifier board signal generation unit in the frequency converter amplifier unit), rather than a vague description of the equipment malfunction; and direct association with the corresponding board model or identifier (e.g., the AD9739 model DAC digital board 2GHz~18GHz frequency converter board), echoing the modular board + hot-swappable design. This allows remote maintenance personnel to quickly grasp the core fault information without going to the site, prepare suitable replacement boards in advance, and avoid the cumbersome process of on-site troubleshooting → returning for parts retrieval → secondary on-site repair.

[0061] No additional fault detection hardware is needed; a fault early warning mechanism can be built simply by expanding the functionality of vibration sensor data. This reduces system hardware complexity and cost, while upgrading the environmental perception module from an environmental adaptation tool to a dual environmental and equipment monitoring tool. Mountain monitoring points are often located in remote areas with inconvenient transportation and a shortage of professional maintenance personnel. Automatic fault location and board identification reporting allow on-site maintenance personnel, without requiring in-depth professional knowledge, to quickly replace faulty boards based on remote commands, significantly shortening fault repair time and reducing maintenance time and labor costs. Landslide monitoring requires 24-hour uninterrupted operation; traditional systems may fail to detect faults for extended periods, resulting in monitoring blind spots.

[0062] In this embodiment, the device adopts a distributed power supply and management architecture, including a main power supply and a backup power supply module. The intelligent control and decision module also monitors the radar power consumption in real time and can dynamically adjust some parameters in the final control command to reduce radar power consumption when the main power supply power is lower than the threshold. At the same time, it seamlessly switches to backup power supply to extend the continuous monitoring time.

[0063] It's important to note that a distributed architecture with a main power supply and backup power modules is used, rather than a single power source. In the event of a main power supply failure or insufficient power, the backup power supply can seamlessly take over, preventing system downtime due to a single point of failure. The main and backup power supplies can be selected according to the scenario (e.g., a 3000Wh AC power bank as the main power source and a high-capacity lithium battery as the backup power source), adapting to the power supply conditions of different monitoring points (e.g., some points can be connected to solar panels as the main power source, with lithium batteries as backup). This distributed design overcomes the vulnerability of traditional single power supplies, providing hardware-level power supply assurance for 24-hour continuous monitoring, meeting the core requirement of uninterrupted landslide monitoring.

[0064] The intelligent control and decision-making module adds a real-time monitoring function for system power consumption, collecting power consumption data from each module (digital module, frequency converter amplifier unit, ruggedized terminal, etc.) in real time to understand the system's energy consumption status. When the main power supply is below a preset threshold, instead of directly switching the power supply, it first reduces power consumption by adjusting some parameters in the final control command. The adjusted parameters are all from the parameter solution space defined above (such as extending the system revisit period, reducing transmit power, and reducing instantaneous bandwidth). This reduces energy consumption without significantly sacrificing core monitoring accuracy (such as maintaining basic accuracy in high-priority areas). While reducing power consumption, it automatically initiates the main / backup power supply switching process, and there is no monitoring interruption during the switching process (such as ensuring link continuity through cached data during switching), avoiding data flow interruption caused by power switching.

[0065] Power reduction does not sacrifice critical monitoring requirements. The adjusted parameters only target non-core optimization objectives (such as revisit cycles in low-priority areas and unnecessary high-power output). Core indicators such as deformation sensitivity and signal-to-noise ratio in high-priority areas still meet the requirements. The parameter adjustment for dynamic power reduction is an extension of parameter fusion optimization (adding a power supply constraint dimension), upgrading parameter optimization from being driven by both environment and task to being driven by a combination of environment, task, and power supply, making the logic more complete.

[0066] The distributed primary / backup power architecture with seamless switching completely avoids monitoring interruptions caused by single power supply failures or power depletion, solving the industry pain points of power supply difficulties and short battery life in remote mountain locations and ensuring the integrity of monitoring data. Through a combination of dynamic power reduction and power switching strategies, battery life can be extended without relying on additional power supply equipment. For example, in wide-area survey scenarios, extending the revisit cycle and reducing transmission power when the battery is low can increase battery life by more than 30%, adapting to extreme scenarios without mains power or solar supplementation. By incorporating power supply status into the parameter fusion optimization constraints, adaptive decision-making not only considers environmental adaptability and task requirements but also power supply capacity, avoiding an imbalance where excessive power consumption is consumed in pursuit of accuracy, achieving a dynamic balance between monitoring accuracy, power consumption, and battery life. Power adjustment and power switching can be completed without manual intervention, fully adapting to the practical application scenarios of unattended and remote management of landslide monitoring, reducing the frequency and cost of manual inspections and power replenishment, and improving the system's practicality and ease of maintenance.

[0067] Example 2, Figure 2 The present invention provides a method for monitoring landslides, comprising the following steps: acquiring real-time environmental status data through an environmental sensing module;

[0068] Receive or parse monitoring task instructions through an interactive hardened terminal;

[0069] The intelligent control and decision-making module executes the following adaptive decision-making loop: S1: Integrate environmental status data and monitoring task instructions to generate and issue final control instructions to the reconfigurable radar transceiver processing module; S2: Control the reconfigurable radar transceiver processing module to transmit signals, receive echoes, and process data according to the final control instructions to acquire monitoring data; S3: Perform real-time analysis and evaluation of the monitoring data, specifically: calculate the quality index of the current monitoring data and compare the quality index with the theoretical quality index predicted based on the current environmental status data and radar operating parameters; if the actual value is significantly lower than the predicted value, it is determined that the parameter re-optimization condition is met; S4: Based on the evaluation results and the latest acquired environmental status data, determine whether the parameter re-optimization condition is met; if it is met, return to step S1 and start a new round of adaptive decision-making; if it is not met, maintain the current parameters and continue monitoring.

[0070] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0071] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0072] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0073] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0074] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A radar device for monitoring landslides, characterized in that, include: An environmental perception module is used to acquire environmental status data of the radar deployment point in real time. The environmental status data includes at least one or more of temperature, humidity, and vibration intensity. A reconfigurable radar transceiver processing module includes a signal generation unit, a frequency conversion amplification unit, and an antenna unit. The signal generation unit can adjust radar waveform parameters according to control commands, and the frequency conversion amplification unit can adjust the operating frequency band and gain. The intelligent control and decision-making module is communicatively connected to the environmental perception module and the reconfigurable radar transceiver processing module. The intelligent control and decision-making module is configured to perform the following operations: a. Receive the environmental status data and determine the first set of radar operating parameters adapted to the current environment based on the preset environment-performance mapping relationship; b. Based on the preset or remotely issued monitoring task instructions, analyze the spatial scale and deformation sensitivity requirements of the monitored target, and determine the second set of radar operating parameters accordingly; c. The first set of radar operating parameters and the second set of radar operating parameters are fused and optimized to generate the final control command that drives the reconfigurable radar transceiver processing module, so that the radar can automatically adapt to a working state that balances environmental robustness and monitoring task optimization in complex outdoor environments.

2. The radar device for monitoring landslides according to claim 1, characterized in that, The specific steps of the intelligent control and decision-making module in the fusion optimization include: taking the link budget and signal-to-noise ratio of the radar signal in the current environment as the core constraints, and under the premise of meeting the deformation sensitivity requirements of the monitored target, searching in the parameter solution space through an optimization algorithm to find the parameter combination that minimizes the total power consumption of the radar or maximizes the data acquisition efficiency, as the final control command.

3. A radar device for monitoring landslides according to claim 2, characterized in that, The parameter solution space is composed of at least the following adjustable parameters: the frequency modulation time and bandwidth of the linear frequency modulation signal generated by the signal generation unit, the center frequency and instantaneous bandwidth of the frequency conversion amplification unit, the gain of the receiving channel in the frequency conversion amplification unit, and the radar revisit period; wherein the frequency modulation time can be continuously adjusted between 0.00005ms and 10ms.

4. A radar device for monitoring landslides according to claim 3, characterized in that, The reconfigurable radar transceiver processing module adopts a modular cabinet structure. The signal generation unit includes a digital board based on an XC7VX690T FPGA and an AD9739 DAC. The frequency conversion amplification unit includes a frequency conversion board covering the 2GHz to 18GHz frequency band and a power amplifier board with a linear output power of not less than 35dBm. Each board is connected by a side-plugging method and supports hot-swapping replacement.

5. A radar device for monitoring landslides according to claim 4, characterized in that, The radar also includes an interactive ruggedized terminal that runs monitoring software. The monitoring software provides a graphical task configuration interface, allowing users to define sub-areas with different priorities and monitoring requirements on an electronic map by dragging or drawing. The monitoring task instructions are automatically generated based on the analysis of the sub-areas.

6. A radar device for monitoring landslides according to claim 5, characterized in that, The monitoring software includes a real-time imaging engine and a sequence interferometry analysis engine. The real-time imaging engine is configured to image echo data with a 2-meter synthetic aperture within 120 seconds. The sequence interferometry analysis engine is configured to perform differential interferometry processing on time-series radar images, and when the identified deformation anomaly area coincides spatially with a user-defined high-priority sub-region, it automatically triggers an alarm and generates a suggested instruction containing optimized monitoring parameters, which is then pushed to the intelligent control and decision-making module.

7. A radar device for monitoring landslides according to claim 6, characterized in that, The vibration sensor data integrated in the environmental perception module is used to trigger radar self-test; when the vibration intensity exceeds the preset threshold, the intelligent control and decision module initiates link diagnosis of each subunit of the reconfigurable radar transceiver processing module, and when a fault is diagnosed, uploads the fault location information and the corresponding replaceable board identifier to the remote operation and maintenance platform.

8. A radar device for monitoring landslides according to claim 7, characterized in that, The device adopts a distributed power supply and management architecture, including a main power supply and a backup power supply module. The intelligent control and decision module also monitors the radar power consumption in real time and can dynamically adjust some parameters in the final control command to reduce radar power consumption when the main power supply power is lower than the threshold. At the same time, it can seamlessly switch to backup power supply to extend the continuous monitoring time.

9. A method for monitoring landslides, applied to a radar device for landslide monitoring as described in any one of claims 1-8, characterized in that, Includes the following steps: Real-time environmental status data is obtained through the environmental sensing module; Receive or parse monitoring task instructions through an interactive hardened terminal; The intelligent control and decision-making module executes the following adaptive decision-making loop: S1: Integrate the environmental status data with the monitoring task instructions to generate and send the final control instructions to the reconfigurable radar transceiver processing module; S2: Control the reconfigurable radar transceiver processing module to perform signal transmission, echo reception and processing according to the final control command, and acquire monitoring data; S3: Perform real-time analysis and evaluation of the monitoring data, specifically: calculate the quality index of the current monitoring data, and compare the quality index with the theoretical quality index predicted based on the current environmental state data and radar operating parameters; if the actual value is significantly lower than the predicted value, it is determined that the parameter re-optimization condition is met. S4: Based on the evaluation results and the latest acquired environmental status data, determine whether the parameter re-optimization conditions are met; if they are met, return to step S1 and start a new round of adaptive decision-making; if they are not met, maintain the current parameters and continue monitoring.

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