Slope hidden danger monitoring system based on wireless sensor network

Abstract

The invention provides a slope hidden danger monitoring system based on a wireless sensor network, and relates to the technical field of dam slope safety monitoring. The system comprises a wireless sensing node used for monitoring side slope hidden danger characteristic data of a dam side slope area; the area node management equipment is arranged at the central position of a monitoring sub-area of the dam slope area, forms a self-organizing communication network with each wireless sensing node in the area, and is used for collecting and summarizing slope hidden danger characteristic data corresponding to each monitoring sub-area; and the monitoring control center is in communication connection with the area node management equipment and is used for performing data analysis on the slope hidden danger characteristic data to obtain slope hidden danger risk distribution data corresponding to the dam slope area. According to the technical scheme, a stable monitoring network can be formed in a complex terrain area, the comprehensiveness, reliability and real-time performance of dam slope monitoring are ensured, the transmission stability and collection efficiency of monitoring data are improved, and the accuracy of a hidden danger recognition result is ensured.

Description

Technical Field

[0001] The present disclosure relates to the technical field of dam slope safety monitoring, and more particularly, to a slope hidden danger monitoring system based on a wireless sensor network. Background Art

[0002] With the rapid development of new energy technologies, power generation dams in alpine and canyon areas have attracted increasing attention. The dam slope area, especially in alpine and canyon areas, due to the combined influence of complex topographical and geological conditions, variable natural environments, and engineering activity disturbances, its stability has an important impact on the safety of surrounding infrastructure. In such areas, slopes are usually accompanied by steep terrains, frequent geological activities, and diverse climate conditions, which lead to difficulties and uncertainties in slope hidden danger monitoring.

[0003] Traditional slope monitoring methods mainly rely on monitoring technologies such as manual inspections, fixed Global Navigation Satellite System (GNSS) monitoring stations, and Interferometric Synthetic Aperture Radar (InSAR). Although these methods can obtain local information to a certain extent, in alpine and canyon areas, due to weak GNSS signals, limited monitoring ranges, low data collection frequencies, and insufficient real-time capabilities, it is difficult to comprehensively grasp the dynamic changes of slopes. In addition, the installation and maintenance costs of fixed monitoring stations and InSAR systems increase significantly under complex terrain conditions. For example, the equipment layout on steep terrains requires additional support structures and construction technologies, resulting in a significant increase in project complexity and costs. At the same time, harsh climate environments (such as heavy rainfall, high temperatures, low temperatures, etc.) will also have an adverse impact on the stable operation of monitoring equipment, exacerbating equipment failure risks and maintenance difficulties.

[0004] Therefore, how to achieve efficient, real-time, and full-coverage monitoring of dam slopes in alpine and canyon areas, how to maintain the stable operation of the monitoring system in complex terrains and harsh environments, and how to effectively convert monitoring data into hidden danger identification and decision-making support information are still urgent problems in the current technology.

[0005] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the embodiments of the present disclosure is to provide a slope hidden danger monitoring system based on a wireless sensor network, which can form a stable monitoring network in complex terrain areas, ensure the comprehensiveness, reliability and real-time nature of dam slope monitoring, improve the transmission stability and acquisition efficiency of monitoring data, and ensure the accuracy of hidden danger identification results.

[0007] Other features and advantages of the present disclosure will become apparent from the following detailed description, or be learned in part through the practice of the present disclosure.

[0008] According to the first aspect of the embodiments of the present disclosure, there is provided a slope hidden danger monitoring system based on a wireless sensor network, including: A plurality of wireless sensor nodes, evenly distributed on the dam slope area of alpine canyons, for monitoring the slope hidden danger characteristic data of the dam slope area, where the slope hidden danger characteristic data includes deformation data, vibration data and inter-node distance data; A plurality of area node management devices, arranged at the central positions of the monitoring sub-areas of the dam slope area divided in advance, and forming an ad-hoc communication network with each of the wireless sensor nodes in the monitoring sub-areas, for collecting and summarizing the slope hidden danger characteristic data corresponding to each monitoring sub-area through the ad-hoc communication network; A monitoring control center, communicatively connected to the plurality of area node management devices, for analyzing the received slope hidden danger characteristic data to obtain the slope hidden danger risk distribution data corresponding to the dam slope area.

[0009] In some exemplary embodiments of the present disclosure, based on the foregoing solution, the wireless sensor node includes: A deformation sensor, for collecting the deformation data of the dam slope area; A vibration sensor, for collecting the vibration data of the dam slope area; An ultra-wideband communication UWB positioning module, for measuring the spatial positions with each adjacent wireless sensor node in real time to generate the inter-node distance data; A power consumption optimization module, electrically connected to the deformation sensor, the vibration sensor and the ultra-wideband communication UWB positioning module, for controlling periodic sleep and working mode switching.

[0010] In some exemplary embodiments of the present disclosure, based on the foregoing solution, the area node management device includes: A routing discovery unit, for establishing an optimal communication path between each of the wireless sensor nodes and the area node management device in the ad-hoc communication network through a distributed routing discovery algorithm; A path reconstruction unit, which is used to dynamically update the routing table and reconstruct the communication path based on the data of the ultra-wideband communication (UWB) positioning module of the wireless sensor node when any of the wireless sensor nodes fails.

[0011] In some exemplary embodiments of the present disclosure, based on the foregoing solution, the routing discovery unit includes: An identifier assignment subunit, which is used to assign unique identifiers to each of the wireless sensor nodes in the ad-hoc communication network and broadcast a routing request message; A routing request message update subunit, which is used to record the identifier and signal strength information of the sending node in the wireless sensor node that receives the routing request message; A communication path screening subunit, which is used to forward the updated routing request message to the regional node management device level by level until the path is established, and screen out the optimal communication path.

[0012] In some exemplary embodiments of the present disclosure, based on the foregoing solution, the regional node management device further includes: A hidden danger data caching unit, which is used to temporarily store the slope hidden danger feature data collected by each of the wireless sensor nodes in the monitored sub-region; A hidden danger data aggregation unit, which is used to preprocess the slope hidden danger feature data in the monitored sub-region and summarize and send it to the monitoring control center.

[0013] In some exemplary embodiments of the present disclosure, based on the foregoing solution, the monitoring control center includes: A data calibration module, which is used to perform data calibration processing on the received deformation data, vibration data, and node-to-node distance data. The data calibration processing includes time synchronization and outlier removal; A data fusion module, which is used to combine the deformation data after data calibration and the node-to-node distance data to determine the three-dimensional deformation trend data of the dam slope area, and adjust the three-dimensional deformation trend data according to the vibration data after data calibration to obtain slope deformation data; A hidden danger analysis module, which is used to determine the slope hidden danger risk level of each monitoring point according to the slope deformation data, and determine the slope hidden danger risk distribution data corresponding to the dam slope area according to the slope hidden danger risk level.

[0014] In some exemplary embodiments of the present disclosure, based on the foregoing solution, the data fusion module includes: A slope three-dimensional model construction unit, which is used to obtain the reference slope three-dimensional model corresponding to the dam slope area, and determine the three-dimensional deformation trend data in combination with the deformation data after data calibration, the node-to-node distance data, and the reference slope three-dimensional model; A model dynamic adjustment unit, configured to determine transient vibration characteristic data based on the vibration data after data calibration, and optimize and adjust the three-dimensional deformation trend data through the transient vibration characteristic data to obtain slope deformation data.

[0015] In some exemplary embodiments of the present disclosure, based on the foregoing solution, the slope three-dimensional model construction unit is configured to: Adjust the position of each monitoring point in the reference slope three-dimensional model according to the inter-node distance data after data calibration to obtain an initial slope deformation model of the spatial displacement change in the time series; Extract the time series characteristics of the deformation data after data calibration, and calculate the deformation gradient of each monitoring point, where the deformation gradient is used to quantify the deformation degree and range of each monitoring point; Match and fuse the deformation gradient with the initial slope deformation model to obtain the three-dimensional deformation trend data of the dam slope area.

[0016] In some exemplary embodiments of the present disclosure, based on the foregoing solution, the model dynamic adjustment unit is configured to: Extract the transient vibration characteristic data in the vibration data after data calibration, where the transient vibration characteristic data includes vibration amplitude, vibration frequency, and vibration duration; Match the transient vibration characteristic data with the time series data in the three-dimensional deformation trend data to identify abnormal deformation regions; Eliminate or correct the deformation data corresponding to the abnormal deformation regions in the three-dimensional deformation trend data to obtain slope deformation data.

[0017] In some exemplary embodiments of the present disclosure, based on the foregoing solution, the monitoring and control center further includes: A risk assessment module, configured to screen target risk monitoring points with a slope hidden danger risk level greater than or equal to a preset risk level threshold according to the slope hidden danger risk distribution data; An early warning module, configured to generate an early warning message in response to detecting the target risk monitoring point, where the early warning message includes the position coordinates, hidden danger risk type, and slope hidden danger risk level of the target risk monitoring point.

[0018] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: In the slope hidden danger monitoring system based on a wireless sensor network in the exemplary embodiments of the present disclosure, by reasonably arranging a plurality of wireless sensor nodes, a stable monitoring network can be formed in alpine canyon areas with complex terrains; the uniform arrangement of wireless sensor nodes ensures non-blind area coverage within the monitoring area, enabling the system to comprehensively collect hidden danger characteristic data in the dam slope area and avoiding the problem of missing hidden danger data caused by limited monitoring ranges in traditional monitoring methods; at the same time, based on the self-organizing communication network structure among wireless sensor nodes, it can dynamically adapt to changes in complex terrain conditions, enhancing the stability and adaptability of the monitoring network.

[0019] Through the dynamic routing function of the self-organizing communication network, the monitoring system effectively improves the transmission stability of monitoring data. When any wireless sensor node fails or communication is blocked, the system can quickly reconstruct the communication path to ensure the continuous transmission of monitoring data and avoid the problem of overall monitoring interruption caused by single-point failures in traditional technologies. The optimization mechanism of the communication path between nodes further improves the data transmission efficiency, enabling the system to quickly respond to high-frequency monitoring requirements while collecting data in real time, meeting the monitoring requirements of the complex dynamic environment in the alpine canyon dam slope area, effectively reducing the data monitoring cycle in the alpine canyon dam slope area, and ensuring the timeliness and reliability of monitoring data.

[0020] By comprehensively analyzing and processing the hidden danger characteristic data collected by the regional node management device through the monitoring control center, the comprehensiveness and accuracy of hidden danger identification are ensured; through the fusion analysis of multi-dimensional characteristic data, the system can identify the distribution characteristics and change rules of hidden dangers, solving the problem of hidden danger identification errors caused by insufficient data or insufficient processing capabilities in traditional monitoring methods; the combined processing of deformation data, vibration data, and the distance data between nodes makes the hidden danger identification result have higher accuracy, providing a reliable basis for the scientific assessment of hidden danger risks in the dam slope area.

[0021] In addition, the distributed architecture design of the system effectively improves the monitoring efficiency, reduces the dependence on wiring of traditional fixed monitoring station equipment, and the setting of wireless sensor nodes enables them to be quickly deployed and arranged by drones, effectively reducing the difficulty of equipment deployment and maintenance in the alpine canyon environment. Combining efficient data collection, stable data transmission, and accurate hidden danger analysis, the monitoring system realizes the organic unity of comprehensiveness, real-time, and reliability in the dam slope area, providing technical support for the early discovery and timely disposal of slope deformation hidden dangers.

[0022] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Brief Description of the Drawings

[0023] The accompanying drawings herein are incorporated into and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0024] Figure 1 Schematically shows a schematic diagram of the composition structure of a slope hidden danger monitoring system based on a wireless sensor network according to some embodiments of the present disclosure.

[0025] Figure 2 Schematically shows a schematic diagram of the composition of a wireless sensor node according to some embodiments of the present disclosure.

[0026] Figure 3 Schematically shows a schematic diagram of the composition of a regional node management device according to some embodiments of the present disclosure.

[0027] Figure 4 Schematically shows a schematic diagram of the composition of a monitoring and control center according to some embodiments of the present disclosure.

[0028] Figure 5 Schematically shows a schematic diagram of the process of generating three-dimensional deformation trend data according to some embodiments of the present disclosure.

[0029] Figure 6 Schematically shows a schematic diagram of the process of screening slope deformation data according to some embodiments of the present disclosure.

[0030] In the accompanying drawings, the same or corresponding reference numerals represent the same or corresponding parts. Detailed Description of Specific Embodiments

[0031] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. On the contrary, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0032] In addition, the accompanying drawings are only schematic diagrams and are not necessarily drawn to scale. The block diagrams shown in the accompanying drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] In this exemplary embodiment, first, a slope hidden danger monitoring system based on a wireless sensor network is provided. This system can be deployed on the dam slope in alpine and canyon areas to monitor the deformation of the slope, or can be deployed in the photovoltaic laying area in the desert area to monitor the change of the desert terrain. This exemplary embodiment does not make special limitations on the application scenarios of the slope hidden danger monitoring system based on the wireless sensor network. Figure 1 Schematically shows a schematic diagram of the process of a slope hidden danger monitoring system based on a wireless sensor network according to some embodiments of the present disclosure. Refer to Figure 1 As shown, the slope hidden danger monitoring system 100 based on the wireless sensor network may include a plurality of wireless sensor nodes 110, a plurality of regional node management devices 120, and a monitoring control center 130, where: A plurality of wireless sensor nodes 110 are evenly deployed on the dam slope area in alpine and canyon areas, and are used to monitor the slope hidden danger characteristic data of the dam slope area. The slope hidden danger characteristic data includes deformation data, vibration data, and node - to - node distance data; A plurality of regional node management devices 120 are set at the central position of the monitoring sub - area of the pre - divided dam slope area, and form an ad - hoc communication network with each of the wireless sensor nodes 110 in the monitoring sub - area, and are used to collect and summarize the slope hidden danger characteristic data corresponding to each monitoring sub - area through the ad - hoc communication network; The monitoring control center 130 is communicatively connected to the plurality of regional node management devices 120, and is used to perform data analysis on the received slope hidden danger characteristic data to obtain the slope hidden danger risk distribution data corresponding to the dam slope area.

[0034] According to the slope hidden danger monitoring system based on wireless sensor network in this exemplary embodiment, by reasonably deploying a plurality of wireless sensor nodes, a stable monitoring network can be formed in alpine canyon areas with complex terrain; the uniform deployment of wireless sensor nodes ensures non-blind area coverage within the monitoring area, enabling the system to comprehensively collect hidden danger characteristic data in the dam slope area and avoiding the problem of missing hidden danger data caused by limited monitoring range in traditional monitoring methods; at the same time, based on the self-organizing communication network structure among wireless sensor nodes, it can dynamically adapt to changes in complex terrain conditions, enhancing the stability and adaptability of the monitoring network; through the dynamic routing function of the self-organizing communication network, the monitoring system effectively improves the transmission stability of monitoring data. When any wireless sensor node fails or communication is blocked, the system can quickly reconstruct the communication path to ensure continuous transmission of monitoring data and avoid the problem of overall monitoring interruption caused by single-point failure in traditional technologies. The optimization mechanism of the communication path between nodes further improves the data transmission efficiency, enabling the system to quickly respond to high-frequency monitoring requirements while collecting data in real time, meeting the monitoring requirements of the complex dynamic environment in the alpine canyon dam slope area, effectively reducing the data monitoring cycle in the alpine canyon dam slope area, and ensuring the timeliness and reliability of monitoring data; through the comprehensive analysis and processing of the hidden danger characteristic data collected by the regional node management device by the monitoring control center, the comprehensiveness and accuracy of hidden danger identification are ensured; through the fusion analysis of multi-dimensional characteristic data, the system can identify the distribution characteristics and change rules of hidden dangers, solving the problem of hidden danger identification errors caused by insufficient data or insufficient processing capacity in traditional monitoring methods; the combined processing of deformation data, vibration data and the distance data between nodes makes the hidden danger identification result have higher accuracy, providing a reliable basis for the scientific assessment of hidden danger risks in the dam slope area; in addition, the distributed architecture design of the system effectively improves the monitoring efficiency, reduces the dependence on wiring of traditional fixed monitoring station equipment, and the setting of wireless sensor nodes allows them to be quickly deployed by drones, effectively reducing the difficulty of equipment deployment and maintenance in the alpine canyon environment. Combining efficient data collection, stable data transmission and accurate hidden danger analysis, the monitoring system realizes the organic unity of comprehensiveness, real-time and reliability in the dam slope area, providing technical support for the early detection and timely disposal of slope deformation hidden dangers.

[0035] Next, the slope hidden danger monitoring system 100 based on wireless sensor network in this exemplary embodiment will be further described.

[0036] In an exemplary embodiment of the present disclosure, refer to Figure 1As shown, multiple wireless sensing nodes 110 refer to low-power devices set up for monitoring potential hazards of slope deformation in the dam slope area of alpine canyons. For example, the wireless sensing node 110 can be a monitoring device integrated with a deformation sensor, a vibration sensor, an Ultra Wide Band (UWB) positioning module, and a power consumption optimization module. Of course, the wireless sensing node 110 can also be a monitoring device integrated with other types of sensors. This exemplary embodiment does not make special limitations on the types of sensors set in the wireless sensing node 110.

[0037] In an alternative embodiment, the wireless sensing node 110 can also be provided with an energy module, which can continuously provide electrical energy for the wireless sensing node 110 to maintain its operation for a long time. For example, the energy module can be a large-capacity battery, or a geothermal power supply or a photovoltaic power generation module, as long as it can ensure continuous energy supply for the wireless sensing node 110. This exemplary embodiment does not make special limitations on the type of the energy module.

[0038] In an alternative embodiment, one end of the wireless sensing node 110 is provided with a ground-breaking end, which can be evenly distributed on the dam slope area of alpine canyons by means of drone dropping. Combining the ground-breaking end and the own weight of the wireless sensing node 110, it can be directly fixed at a preset position on the dam slope area of alpine canyons by the drone at low altitude; of course, the wireless sensing node 110 can also be evenly distributed on the dam slope area of alpine canyons by means of all-terrain robot dropping or manual dropping. This embodiment does not make special limitations on the dropping method of the wireless sensing node 110.

[0039] The wireless sensing node 110 can monitor deformation data, vibration data, and node-to-node distance data through a preset acquisition function. The deformation data refers to the deformation state information on the surface or inside of the slope, usually obtained by means of strain measurement, displacement measurement, etc.; the vibration data is used to reflect the dynamic response characteristics of the slope area to external interference or geological activities, including the amplitude, frequency, and duration of vibration; the node-to-node distance data is calculated from the relative spatial position information between nodes and is used to evaluate the spatial structure change of the slope area.

[0040] The wireless sensing node 110 can be integrated with a communication module for realizing wireless data transmission between nodes and communication with upper-layer devices. The communication module can support multiple wireless communication protocols. For example, it can support LowPowerWide Area Net (LPWAN), Wi-Fi, or cellular communication technology.

[0041] The manufacturing materials of the wireless sensing node 110 can adopt high-strength materials that are corrosion-resistant, compression-resistant, and earthquake-resistant, and can adapt to the complex climate conditions in alpine and canyon areas, such as high temperature, low temperature, high humidity, or frequent rainfall, etc. In addition, the wireless sensing node 110 can be designed in an adaptive adjustment mode, for example, by adjusting the sensitivity of the sensor module to adapt to environmental changes and improve the monitoring accuracy.

[0042] Multiple wireless sensing nodes 110 constitute the basic components of the slope hidden danger monitoring system 100. Their reasonable layout and function design directly affect the comprehensiveness, real-time performance, and reliability of the monitoring. Through the collaborative work among the nodes, the dynamic monitoring of the characteristic data of slope hidden dangers can be realized, providing high-quality data support for subsequent data analysis and hidden danger assessment.

[0043] Multiple regional node management devices 120 are devices set at the central positions of the monitoring sub-areas in the pre-divided dam slope areas, used to form an ad-hoc communication network with multiple wireless sensing nodes 110 in the monitoring sub-areas, and are responsible for summarizing the characteristic data of slope hidden dangers from within the sub-areas. The layout of the regional node management devices 120 generally follows the principles of monitoring sub-area division. The selection of their locations needs to comprehensively consider the topographic conditions of the sub-areas, the distribution density of the sensing nodes, and the coverage range of wireless communication to ensure that the devices can efficiently collect all the monitoring data within the monitoring sub-areas.

[0044] The core function of the regional node management device 120 lies in establishing and maintaining an ad-hoc communication network to achieve stable communication with the wireless sensing nodes 110, making the transmission paths of each wireless sensing node 110 as identical as possible, thereby ensuring the timeliness of the monitoring data and shortening the delay. The principle of the ad-hoc communication network is based on a distributed routing algorithm. Each wireless sensing node 110 has a certain routing decision-making ability in the ad-hoc communication network and can dynamically select the optimal route according to the real-time state of the communication path. The regional node management device 120 can dynamically adjust the network topology structure by collecting and analyzing the communication information from the wireless sensing nodes 110 to ensure the efficient transmission of data and the self-healing ability of the network. For example, when some wireless sensing nodes 110 fail or the signal is interrupted, the regional node management device 120 can quickly identify the faulty nodes and re-plan the communication link through the path reconstruction mechanism to ensure the continuity of data transmission.

[0045] Inside the regional node management device 120, a high-performance wireless communication module can be integrated. For example, a protocol module that supports low-power wide area network (LPWAN) technology or multi-hop communication can be integrated to adapt to the complex terrain conditions in alpine and canyon areas. The communication module can support adaptive adjustment of the transmission power and channel switching to improve the anti-interference ability and coverage of communication. In addition, the regional node management device 120 can be equipped with a large-capacity storage module for temporarily storing the slope hidden danger feature data in the monitored sub-region, enabling the monitoring data to be summarized and packaged in a short time and uploaded to the monitoring control center through a high-speed communication link to ensure the integrity of the data in the monitored sub-region.

[0046] The regional node management device 120 can also include data preprocessing capabilities for initially screening and sorting the slope monitoring data in the monitored sub-region. Specifically, data preprocessing can include outlier filtering, data format standardization, and time synchronization processing. Through data preprocessing, the data transmission efficiency can be effectively improved and the data processing burden on the monitoring control center can be reduced. For example, the regional node management device 120 can filter out extremely abnormal data points according to a preset threshold or unify the data of different sensing nodes to the same time reference through timestamp calibration.

[0047] The hardware design of the regional node management device 120 can select high-strength anti-corrosion materials to adapt to the rainy and humid environmental conditions in alpine and canyon areas. The regional node management device 120 can be designed as a portable structure for easy installation and maintenance in areas with complex terrain. In terms of energy supply, the regional node management device 120 can integrate a solar power supply module, a geothermal power supply module, or other sustainable energy power supply systems to ensure its long-term stable operation in an unattended environment.

[0048] The installation method of the regional node management device 120 can be selected as fixed bracket type, buried type, or suspended type according to the terrain conditions. For example, in a flat terrain area, the fixed bracket type installation method can be adopted to firmly fix the device on the ground; while in a steep slope area, the suspended type installation can be adopted to fix the device at a safe position with the help of high-strength steel cables. The layout and function realization of the regional node management device 120 provide stable technical support for data summarization and transmission in the monitored sub-region.

[0049] The monitoring control center 130 refers to the core device that is communicatively connected to multiple regional node management devices 120 and is responsible for centrally processing and analyzing the received slope hidden danger feature data to generate slope hidden danger risk distribution data for the dam slope area. The design of the monitoring control center 130 focuses on efficient data processing capabilities and usually includes a high-performance processing unit, a large-capacity storage module, and advanced data analysis software. The processing unit can use a multi-core processor or a dedicated hardware accelerator to support real-time processing of large-scale monitoring data.

[0050] The monitoring and control center 130 can perform data preprocessing processes such as parsing and cleaning the slope hidden danger feature data received from the regional node management device 120. For example, the data preprocessing process can include time alignment of data, format conversion, and removal of redundant data to ensure the integrity and consistency of the input data. Subsequently, the monitoring and control center 130 can comprehensively process the hidden danger feature data through the data analysis module. Specifically, the data analysis module can combine spatial analysis and time series analysis methods to identify the hidden danger trends and distribution characteristics in the monitoring area. For example, by analyzing the spatial correlation of deformation data and vibration data, the hidden danger hot spots can be identified; by analyzing the change of the distance data between nodes through time series analysis, the dynamic evolution process of the slope can be evaluated.

[0051] The monitoring and control center 130 can be used to generate slope hidden danger risk distribution data, which is an intuitive presentation of the hidden danger monitoring results and is usually visualized using a Geographic Information System (GIS) platform. The slope hidden danger risk distribution data can combine the hidden danger level with the geographical location to provide a clear diagram of the regional risk status for decision-makers. These data can be further used to generate a detailed risk assessment report or guide on-site risk control operations.

[0052] The communication module of the monitoring and control center 130 can support two-way communication with multiple regional node management devices 120. Through the adaptive channel switching and data packet retransmission mechanisms, it ensures the integrity and timeliness of data upload. To enhance the reliability of the system, the monitoring and control center 130 is usually designed with a redundant structure, equipped with a standby processing unit and a network connection module to ensure that it can quickly switch to the standby system to continue running in case of a main system failure. In addition, the monitoring and control center 130 can integrate a real-time monitoring interface to allow remote operation and monitoring, further improving the management efficiency of the system.

[0053] The operating environment of the monitoring and control center 130 can be set in a safe area, such as a dam monitoring station or a command center, and its efficient and stable operation is ensured through an Uninterruptible Power Supply (UPS) and environmental control equipment. Combining high-performance processing capabilities, flexible data communication, and precise risk analysis functions, the monitoring and control center 130 constitutes the decision-making support core of the dam slope hidden danger monitoring system.

[0054] Next, a detailed description will be given of Figure 1 the wireless sensor node 110, the regional node management device 120, and the monitoring and control center 130 in

[0055] In an alternative embodiment, refer to Figure 2 As shown, the wireless sensor node 110 may include a deformation sensor 111, a vibration sensor 112, an ultra-wideband communication (UWB) positioning module 113, and a power consumption optimization module 114, where: The deformation sensor 111 refers to a sensor device disposed in the wireless sensor node for collecting deformation data of the slopes of high mountain and canyon dams. The deformation sensor 111 can generate monitoring data related to the deformation state by monitoring the physical changes of the surface layer or underground structure of the slope. For example, the deformation sensor 111 can be a strain-type deformation sensor 111. Based on the principle of the strain effect of materials under stress, the strain-type deformation sensor converts the minute deformation of the slope surface layer into electrical signals, and these signals can be further stored and analyzed by the acquisition module.

[0056] In specific implementation, the deformation sensor 111 can adopt various forms. For example, the deformation sensor 111 can also use a resistance strain gauge, a fiber Bragg grating sensor, or a displacement sensor to monitor the slope deformation. Among them, for long-term monitoring in harsh environments, the fiber Bragg grating sensor is a preferred solution due to its strong anti-interference ability and corrosion resistance, while the resistance strain gauge can be used in areas with more frequent monitoring requirements due to its low cost and easy installation.

[0057] The vibration sensor 112 is another important component in the wireless sensor node 110 for collecting vibration data of the dam slope area. The basic principle of the vibration sensor 112 is to convert the vibration signal into an electrical signal through a sensitive element. The vibration data can include vibration amplitude, vibration frequency, and vibration duration, which can reflect the dynamic response characteristics of the slope area when it is subjected to external disturbances or geological activities.

[0058] The vibration sensor 112 can adopt an accelerometer, a piezoelectric sensor, or a micro-electro-mechanical system (MEMS) sensor, and this embodiment does not make special limitations on this. Different technical types can be selected according to the monitoring requirements. For example, when high sensitivity is required, the vibration sensor 112 can adopt a piezoelectric vibration sensor, which can capture weak vibration signals more accurately; while the accelerometer has a wider applicability when monitoring large-scale dynamic characteristics. When installing the vibration sensor 112, the stable contact with the slope surface needs to be considered, and the installation quality of the vibration sensor can be ensured through high-strength adhesives or buried fixing methods.

[0059] The ultra-wideband communication UWB positioning module 113 is the core module for measuring the spatial position between a wireless sensor node and its neighboring nodes. Its working principle is to calculate the relative spatial distance of the wireless sensor node by measuring the flight time or phase difference of the wireless signal. The ultra-wideband communication UWB positioning module 113 is particularly suitable for the complex terrain environment in alpine and canyon regions due to its wide signal bandwidth, high positioning accuracy, and strong anti-interference ability.

[0060] The specific implementation of the ultra-wideband communication UWB positioning module 113 can include a transmitting unit, a receiving unit, and a time synchronization unit. By sending precise pulse signals, the receiving unit records the arrival time or phase change of the signal, and calculates the relative distance after synchronizing with neighboring nodes. To enhance the signal stability, the ultra-wideband communication UWB positioning module 113 can be equipped with a dynamic power adjustment function, which can automatically adjust the transmission power according to the transmission distance and environmental conditions. If there is a higher precision requirement, the Two-Way Time-of-Flight technology can also be adopted to further eliminate the influence of clock deviation.

[0061] The power consumption optimization module 114 can be electrically connected to all functional modules in the wireless sensor node, and is used to control the periodic sleep and working mode switching to reduce energy consumption. Its core design concept is to extend the service life of the sensor node through a dynamic power management strategy. In the working mode, the power consumption optimization module 114 can activate necessary sensors and communication units according to task requirements; in the sleep mode, modules other than the key monitoring functions enter the low-power state to reduce energy consumption.

[0062] The power consumption optimization module 114 can be implemented by combining a hardware timer and a software algorithm. For example, a timed wake-up mechanism is used to periodically collect data, and the wake-up frequency is adjusted according to the dynamic changes in the monitored environment. The power consumption optimization module 114 can also maximize the optimization of energy use by integrating multiple energy management units (such as a lithium battery management chip or a photovoltaic energy interface). In an optional implementation manner, the power consumption optimization module 114 can also communicate with the regional node management device 120 to perform node-level power consumption adjustment according to the overall resource allocation requirements of the system.

[0063] The sensitivity and wide frequency response range of the deformation sensor 111 and the vibration sensor 112 can effectively improve the accuracy of slope deformation monitoring, enabling the system to timely identify geological activities that may cause potential hazards, and effectively avoiding the problem of insufficient applicability caused by a single data acquisition device in traditional monitoring methods. Especially in areas with complex terrain and vulnerable to natural disasters, the combination of the deformation sensor 111 and the vibration sensor 112 can capture tiny signals before geological activities, providing important data support for the prediction and early warning of slope deformation hazards.

[0064] Through the high-precision spatial position measurement function of the ultra-wideband communication UWB positioning module 113, the distance calculation between wireless sensor nodes is realized, ensuring the spatial consistency of data. At the same time, it can also globally monitor the change of the position distance between wireless sensor nodes, and complete the detection of slope deformation while ensuring the communication ability between wireless sensor nodes. The anti-interference ability of the ultra-wideband communication UWB positioning module 113 is particularly prominent in alpine and canyon areas, solving the problem of signal instability caused by complex terrain obstacles in traditional global navigation satellite communication technology. The UWB module can also support dynamic communication path adjustment, making the data transmission between nodes more reliable and laying a foundation for the stability of the overall monitoring network.

[0065] Through the dynamic sleep and working mode switching functions of the power consumption optimization module 114, the energy consumption of wireless sensor nodes is significantly reduced, enabling the monitoring system to operate stably for a long time under unattended conditions. The power consumption optimization module 114 combines dynamic power management strategies, not only optimizing energy distribution, but also extending the operating time of sensor nodes, avoiding problems such as operation interruption or increased maintenance costs caused by frequent replacement of energy sources in traditional monitoring systems.

[0066] Through the module design of wireless sensor nodes, the comprehensiveness and accuracy of data collection are ensured, and the reliability and operating efficiency of the system are improved in complex terrains and harsh environments. This design solves the problems of insufficient monitoring range, unstable data transmission, high device energy consumption, etc. existing in related technologies, and provides comprehensive support for efficient and real-time slope hidden danger monitoring.

[0067] In an exemplary embodiment of the present disclosure, refer to Figure 3 As shown, the area node management device 120 may include a routing discovery unit 121 and a path reconstruction unit 122, where: The routing discovery unit 121 is one of the core functional modules of the area node management device 120, and can be used to establish an optimal communication path between wireless sensor nodes and the area node management device 120 through a distributed routing discovery algorithm. The core concept of the routing discovery unit 121 is to realize the real-time planning and optimization of communication paths in a dynamic and complex environment through self-organizing network protocols. Its basic principle is based on information such as the identifiers, signal strengths, and hop counts of each node in the network, and constructs a stable and efficient communication link through hierarchical broadcasting and path feedback.

[0068] In a specific implementation, the routing discovery unit 121 may include three stages: broadcasting a routing request, path convergence, and optimal path selection. First, in the stage of broadcasting a routing request, each wireless sensor node sends a routing request message containing an identifier and the current hop count to its neighboring nodes; the receiving node can update the hop count according to the routing request message and continue to broadcast; in the path convergence stage, all possible communication path information will be gradually aggregated to the regional node management device 120 through neighboring nodes; finally, in the optimal path selection stage, the regional node management device 120 filters out the optimal communication path in the current environment based on the comprehensive parameters of each path (such as signal strength, path delay, hop count, etc.).

[0069] At the hardware level, the routing discovery unit 121 can be implemented using a low-power wireless communication module, supporting multiple communication protocols (for example, it can support communication protocols such as ZigBee, LoRa, Wi-Fi, etc.) to adapt to different application scenarios. In an optional implementation, the routing discovery unit 121 can be equipped with a dynamic power adjustment function to automatically adjust the transmission power according to the path length and signal strength to reduce energy consumption. To enhance the robustness of routing discovery, a congestion control mechanism can also be added to the algorithm to avoid path interruption caused by data transmission overload.

[0070] The path reconstruction unit 122 is an extended functional module of the routing discovery unit 121, used to re-plan the communication path through a dynamic adjustment mechanism when any wireless sensor node fails. The basic principle of the path reconstruction unit 122 is to analyze the real-time data provided by the UWB positioning module, identify the failed node and bypass its communication link, and at the same time establish a new transmission path based on the information of neighboring nodes. In a specific implementation, the path reconstruction unit 122 will periodically receive the health status reports of all sensor nodes, and when it detects that a certain wireless sensor node no longer responds, it will start the path reconstruction process.

[0071] The path reconstruction process can include three parts: failed node marking, alternative path search, and routing table update. In the failed node marking stage, the system will remove the detected failed node from the existing routing table; in the alternative path search stage, the path reconstruction unit 122 can search for a new communication path based on the signal quality and hop count of the remaining nodes; in the routing table update stage, all routing information related to the failed node will be replaced with the new path; in the hardware implementation, the path reconstruction unit 122 can be closely integrated with the UWB positioning module to ensure the rationality and communication efficiency of the alternative path using its high-precision spatial position data.

[0072] In an alternative implementation, the path reconstruction unit 122 can combine historical path data and a neighboring node status prediction model to predict potential path interruption risks in advance and take preventive adjustment measures. For example, the system can preferentially plan backup paths for nodes that may fail based on the remaining energy of the nodes and the signal strength attenuation trend, thereby further improving the efficiency and stability of path reconstruction.

[0073] Through the distributed routing discovery algorithm of the routing discovery unit 121, a stable communication path can be established in the complex environment of mountain valleys. By means of hierarchical broadcasting and path feedback, the communication status information between nodes can be obtained in real time, ensuring that the network can dynamically adapt to terrain changes or environmental interference, effectively improving the stability of data transmission, and avoiding the problem of communication interruption caused by the vulnerability of fixed communication paths to interference in traditional technologies; through the mechanism of the path reconstruction unit 122 for dynamically adjusting communication links, the data transmission function can be quickly restored when a wireless sensor node fails, and a high data transmission efficiency can still be maintained after the node fails, avoiding the problem of data delay caused by untimely path switching in traditional systems.

[0074] The self-healing function of the monitoring network is realized through the combination of the routing discovery unit 121 and the path reconstruction unit 122. While ensuring network stability, the operation and maintenance cost can be reduced, and path repair can be quickly completed in the unattended situation, which is particularly suitable for environments with difficult manual maintenance such as mountain valley areas.

[0075] Through the dynamic and flexible communication path management of the regional node management device 120, problems such as unstable monitoring data transmission and easy network interruption in complex environments are effectively solved, providing a reliable data transmission guarantee for the monitoring system. At the same time, combined with the UWB positioning module, the accuracy and efficiency of path management are further improved, providing an important technical support for the stable operation of the slope hidden danger monitoring system.

[0076] In an alternative embodiment, continuing to refer to Figure 3 as shown, the routing discovery unit 121 can include an identifier allocation subunit 125, a routing request message update subunit 126, and a communication path screening subunit 127, where: The main function of the identifier allocation subunit is to allocate a unique identifier for each wireless sensor node in the self-organizing communication network, and embed the unique identifier into the routing request message for broadcasting. The allocation mechanism of the unique identifier is implemented based on the node registration process during network initialization. The node establishes an initial communication connection with the regional node management device 120, and reports its physical address or device serial number as basic information. The regional node management device 120 generates a unique identifier. The identifier can be a fixed-length binary code or a hierarchical code, and the specific format can be flexibly selected according to the network scale and the number of nodes. For example, for a small-scale network, a simple incremental numbering method can be adopted; for a large-scale network, a hierarchical segmented coding method can be used to quickly locate the area where the node belongs in subsequent path planning.

[0077] The main purpose of identifier allocation is to ensure the uniqueness of each node in the communication network, thus avoiding path confusion caused by identifier conflicts during the routing request process. After the allocation is completed, the identifier is stored in the local storage unit of the node and sent to other nodes together with the routing request message. To enhance the reliability of the identifier, the identifier allocation subunit can periodically perform synchronization verification with the regional node management device 120 to ensure that the identifier allocation records remain consistent after node failure or network topology change.

[0078] The routing request message update subunit is responsible for parsing and updating the message content when receiving a routing request message. Its core functions can include recording the identifier and signal strength information of the sending node, and forwarding the updated message to the next-level node. The routing request message update subunit can identify the source information of the sending node by parsing the identifier field in the routing request message, and generate a new routing information record in combination with the local communication environment data (such as signal strength, link quality index, etc.). The record information is stored in the node's routing table for subsequent path selection.

[0079] In specific implementation, the routing request message update subunit can be implemented through an embedded communication protocol stack, supporting the parsing, verification, and dynamic update of message fields. The message fields can include identifier, hop count, signal strength, and link quality index. The hop count is used to measure the communication distance of the path, and the signal strength and link quality index are used to evaluate the stability of the path. The updated routing request message is forwarded to the next-hop node through the node's wireless communication module until the message is aggregated to the regional node management device 120.

[0080] The function of the communication path screening subunit is to screen out the optimal communication path under the current network conditions based on the aggregated routing information. The screening process can comprehensively consider factors such as the number of hops, signal strength, and link quality of the path, and evaluate the advantages and disadvantages of multiple candidate paths through a preset optimization algorithm. The optimization algorithm can be a simple shortest path algorithm or a multi-objective optimization algorithm that combines path reliability and energy consumption. In practical applications, the communication path screening subunit can preferentially select the path with the fewest hops and the best signal quality to ensure the efficiency and stability of data transmission.

[0081] After the screening is completed, the communication path screening subunit can write the information of the optimal path into the routing table of the node and use this path as the main channel for subsequent data transmission. To enhance the adaptability of the system, the communication path screening subunit can support the dynamic adjustment function of the path. For example, when the link quality of the path drops to a preset threshold, it can automatically re-evaluate and switch to an alternative path. To further improve the screening efficiency, the communication path screening subunit can work in cooperation with the routing request message update subunit, and utilize the real-time updated link state information during path screening to achieve more accurate path optimization.

[0082] By assigning unique identifiers to each node through the identifier allocation subunit, the uniqueness of nodes in the network and the accuracy of routing information can be ensured, avoiding path confusion problems caused by identifier conflicts and enhancing the reliability of the network routing process; by dynamically updating the content of the routing request message through the routing request message update subunit, real-time and accurate link state information can be provided for path planning, such as real-time recording of link signal strength and quality, ensuring the stability of the data transmission path and effectively avoiding communication interruptions caused by failure to adjust the path in a timely manner due to changes in link quality; by comprehensively optimizing path selection through the communication path screening subunit, the efficiency and stability of the data transmission path can be ensured. Through a multi-objective optimization algorithm that combines multi-dimensional factors such as the number of hops, signal strength, and link quality, a more reliable communication path can be selected under complex network conditions, improving data transmission efficiency and reducing the data packet loss rate caused by path instability; the routing discovery unit 121 provides comprehensive support for the stability and efficiency of the ad hoc communication network through a hierarchical and dynamically adjustable path management mechanism, not only effectively solving communication problems caused by improper path planning and link state changes, but also enhancing the adaptability and operating efficiency of the monitoring system in complex terrain environments.

[0083] In an alternative embodiment, continuing to refer to Figure 3 as shown, the regional node management device 120 may further include a hidden danger data cache unit 123 and a hidden danger data aggregation unit 124, where: The main function of the hidden danger data cache unit is to temporarily store the slope hidden danger feature data collected by each wireless sensor node in the monitored sub-area. The core principle of the hidden danger data cache unit is to store the real-time data sent by the wireless sensor nodes in the local storage medium through a cache mechanism, so as to summarize and upload it at an appropriate time. The storage capacity of the hidden danger data cache unit can be designed according to the number of nodes and data volume requirements in the monitored sub-area. It usually includes high-performance flash chips or solid-state storage devices, and can support multi-task data writing and reading operations.

[0084] The specific implementation method of the hidden danger data cache unit can include the priority scheduling of data streams and the design of cache strategies. For example, in order to avoid cache overflow or data loss, the unit can combine a data grading mechanism to preferentially cache data with a higher hidden danger level according to the importance and urgency of the monitoring data, while delaying the processing of general data. Cache strategies can adopt classic algorithms such as First Input First Output (FIFO) and Least Recently Used (LRU), or custom dynamic scheduling algorithms can be designed according to specific application requirements. In an alternative implementation, the cache unit can integrate a data compression module to further improve storage utilization through compression technology.

[0085] The function of the hidden danger data aggregation unit is to preprocess the hidden danger feature data collected by each wireless sensor node in the monitored sub-area, and summarize and send the processed data to the monitoring control center 130. The working principle of the hidden danger data aggregation unit is based on data aggregation technology. By initially screening and integrating multi-source data, it reduces repetitive and redundant information, thereby improving the efficiency and accuracy of data upload. In specific implementation, data preprocessing can include operations such as outlier filtering, format standardization, and data time synchronization; outlier filtering can eliminate extreme data points that may be caused by sensor failures or external interferences through preset thresholds or rules; format standardization converts the output data of different sensors into a unified format for subsequent analysis; data time synchronization ensures data consistency through timestamp calibration.

[0086] The hidden danger data aggregation unit can work in cooperation with the hidden danger data cache unit to complete data upload through batch data transmission or real-time data streams. Batch data transmission is applicable to areas with dense sensor nodes and large data volumes, and uploads the cached data to the monitoring control center 130 at one time through a timed trigger; while real-time data stream transmission is for data with a higher hidden danger level, and uploads it quickly through a priority channel. In an alternative implementation, the data aggregation unit can combine an edge computing module to complete some preliminary analysis locally, such as calculating the deformation rate or vibration intensity of the slope, and directly upload the analysis results, thereby reducing the computing burden on the monitoring control center 130.

[0087] Through the caching mechanism of the hidden danger data caching unit, the problem of data loss caused by large data traffic or unstable transmission links in complex monitoring environments can be effectively solved. Moreover, by designing a reasonable caching strategy, this unit can give priority to processing emergency data to ensure that information with a higher hidden danger level can be saved and processed subsequently. Through the preprocessing and integration of monitoring data by the hidden danger data aggregation unit, the efficiency and accuracy of monitoring data transmission are improved. Through the combination of the hidden danger data caching unit and the hidden danger data aggregation unit, the high efficiency and stability of monitoring data transmission are achieved, which is particularly remarkable in complex environments. In traditional monitoring systems, fluctuations in the transmission link often lead to data loss or delay, while this design ensures the stable operation of the monitoring system in alpine and canyon areas through staged and hierarchical data processing and uploading. At the same time, by flexibly switching between real-time data streams and batch transmissions, the monitoring system can dynamically adjust the transmission strategy according to different hidden danger levels, thus better adapting to complex monitoring requirements. Through the organic combination of the caching and aggregation modules, not only the problem of data loss caused by unstable transmission links is effectively solved, but also the data management ability of the monitoring system is improved.

[0088] In an exemplary embodiment of the present disclosure, referring to Figure 4 as shown, the monitoring and control center 130 may include a data calibration module 131, a data fusion module 132, and a hidden danger analysis module 133, where: The main function of the data calibration module is to perform data calibration processing on the received deformation data, vibration data, and inter-node distance data. The calibration processing may include two key steps: time synchronization and outlier removal. The basic principle of time synchronization is to uniformly adjust the timestamps of the data collected by the sensing nodes to ensure that all data is analyzed on the same time basis. In specific implementation, the data calibration module aligns the data timestamps of different nodes with the system time through a synchronization algorithm. For example, a time synchronization method based on the Network Time Protocol (NTP) or an adaptive time synchronization protocol for distributed sensing nodes can be adopted to improve the accuracy of time synchronization.

[0089] The function of outlier removal is to identify and eliminate invalid data that may be caused by equipment failures or environmental interferences. The principle is to detect abnormal data points through statistical analysis or preset rules. For example, outliers can be eliminated by calculating the standard deviation of the data and setting a threshold, or data points with short-term severe fluctuations can be identified by combining time series analysis. In specific implementation, the data calibration module can adopt a sliding window algorithm to analyze abnormal points in the data stream in real time and dynamically update the outlier detection threshold. In an alternative implementation, the outlier removal module can combine machine learning algorithms to automatically identify complex abnormal patterns based on historical data training models, thereby further improving the accuracy of data calibration.

[0090] The data fusion module is a module used by the monitoring and control center 130 for comprehensive analysis and data processing. Its main function is to combine the deformation data after data calibration with the distance data between nodes to determine the three-dimensional deformation trend data of the dam slope area, and adjust the three-dimensional deformation trend data according to the vibration data after data calibration. The core principle of data fusion is to generate a three-dimensional deformation trend model that reflects the overall dynamics of the slope area through the spatial and temporal fusion of multi-source data; specifically, the data fusion module can map the distance data between nodes to a three-dimensional coordinate system through a spatial interpolation method, and then calculate the displacement of each monitoring point in the time series in combination with the deformation data.

[0091] After the three-dimensional deformation trend data is generated, the data fusion module can adjust it in combination with the vibration data. The basic principle of the adjustment is to use the transient characteristics in the vibration data to identify abnormal deformation areas and correct the data in these areas. In the specific implementation, the data fusion module can extract the characteristic values in the vibration data through frequency analysis, and match it with the time series of the deformation trend data to identify abnormal deformation caused by short-term external interference (such as earthquakes or explosions); then the data fusion module can remove the deformation data of the abnormal area through interpolation or replacement algorithms, and use the deformation trend of the surrounding area for supplementary calculations.

[0092] The hidden danger analysis module is an analysis and decision-making module of the monitoring and control center 130. Its function is to determine the slope hidden danger risk level of each monitoring point based on the slope deformation data, and further generate the slope hidden danger risk distribution data corresponding to the dam slope area. The hidden danger risk level is evaluated based on multiple indicators such as deformation rate, vibration intensity and historical data, and is calculated in combination with a preset risk assessment model. The implementation method of the risk assessment model may include a rule-based analysis method. For example, the critical value of the deformation rate can be set as a classification standard, or a modeling method based on statistical learning can be used to automatically calculate the risk level by training the prediction model with historical monitoring data. This embodiment does not make any special restrictions on this.

[0093] When generating risk distribution data, the hidden danger analysis module can combine the three-dimensional deformation trend data and the geographical location of the monitoring points to map the risk level to the geographic information system (GIS) platform to form a visual risk distribution map. In specific implementation, the hidden danger analysis module can intuitively display high-risk areas and low-risk areas through graded color labeling to provide support for subsequent risk management and control. In an alternative implementation, the hidden danger analysis module can introduce a dynamic update function to perform regular or event-driven updates to the risk distribution map based on real-time data to ensure the timeliness and accuracy of the distribution data.

[0094] Through the dual processing of time synchronization and outlier removal by the data calibration module, the time-base consistency and quality reliability of the input data are ensured; the time synchronization function solves the data mismatch problem caused by time differences between nodes, enabling the system to perform effective time-correlation analysis on multi-source data; while the outlier removal function effectively reduces the risk of misjudgment caused by invalid data, especially in complex environments such as high mountains and valleys, effectively improving the credibility of the monitoring data; through the spatio-temporal fusion of deformation data and inter-node distance data by the data fusion module, complete three-dimensional deformation trend data is generated. Compared with the traditional single data-source analysis method, data fusion significantly improves the spatial resolution and time accuracy of slope dynamics, avoiding the monitoring blind spot problem caused by single data in traditional technologies; in addition, by combining vibration data to adjust the three-dimensional deformation trend, the data fusion module can dynamically eliminate the influence of short-term interference, providing more accurate input data for hazard analysis; through the risk level assessment of slope deformation data by the hazard analysis module, intuitive risk distribution data is generated, effectively improving the accuracy of hazard identification; at the same time, by mapping the risk level to the geographic information system platform, the module realizes the intuitive display and dynamic update of hazard distribution, providing an important reference basis for monitoring and decision-making; through the organic combination of the data calibration, data fusion, and hazard analysis modules, the accuracy and reliability of the whole process from data acquisition to risk assessment are achieved, not only effectively avoiding the problem of inaccurate hazard identification caused by data quality problems in traditional technologies, but also effectively improving the response ability of the monitoring system to the hazard distribution and dynamic changes in complex environments.

[0095] In an alternative embodiment, continuing to refer to Figure 4 as shown, the data fusion module 132 may include a slope three-dimensional model construction unit 136 and a model dynamic adjustment unit 137, where: The slope three-dimensional model construction unit is used to obtain a reference slope three-dimensional model of the dam slope area, and generate three-dimensional deformation trend data by combining the deformation data after data calibration and the inter-node distance data, as well as the reference slope three-dimensional model. The core principle of the slope three-dimensional model construction unit is to fuse multi-source monitoring data with a preset reference model to dynamically update the deformation state in three-dimensional space, so as to accurately describe the overall deformation trend of the slope. The reference slope three-dimensional model refers to an initial model constructed based on regional geological exploration data, topographic mapping data, and slope structure information, usually stored in the form of a digital elevation model (DEM) or three-dimensional point cloud.

[0096] In a specific implementation, the three-dimensional slope model construction unit can map the distance data between nodes into the reference three-dimensional slope model through a spatial interpolation algorithm to generate the spatial coordinates of each monitoring point. Subsequently, the displacement of each monitoring point is calculated by combining the deformation data, and the displacement is superimposed on the initial coordinates of the reference model to update the spatial position of each monitoring point. To improve the calculation accuracy, this unit can adopt the finite element analysis method, divide the slope area into multiple unit blocks, calculate the deformation amount block by block, and then synthesize the overall model. In an alternative implementation, the three-dimensional slope model construction unit can dynamically adjust the reference model by combining lidar or drone survey data to enhance the adaptability and accuracy of the model.

[0097] The model dynamic adjustment unit can determine the transient vibration characteristic data based on the vibration data after data calibration, and optimize and adjust the three-dimensional deformation trend data through the transient vibration characteristic data. For example, the transient vibration characteristic data can include indicators such as vibration amplitude, vibration frequency, and vibration duration, which are used to identify short-term deformations caused by external disturbances (such as earthquakes or blasts). The core principle of the model dynamic adjustment unit is to perform time correlation between the transient vibration characteristic data and the three-dimensional deformation trend data, identify abnormal deformation areas, and correct their data.

[0098] The model dynamic adjustment unit can extract the characteristic values of the vibration data through frequency analysis and match them with the time series of the three-dimensional deformation trend data to determine the time range and spatial influence area of the vibration event; for the identified abnormal deformation areas, the model dynamic adjustment unit can adopt various correction methods. For example, the abnormal data can be interpolated and supplemented by the deformation trend of the adjacent area, or the deformation trend of the abnormal area can be reconstructed by combining historical data. This embodiment does not make special limitations on this. The model dynamic adjustment unit can achieve fast processing of large-scale data through parallel computing or a hardware accelerator (such as a graphics processor) to improve the real-time performance of the adjustment.

[0099] By combining the three-dimensional slope model construction unit with the reference three-dimensional slope model and the calibrated monitoring data, the three-dimensional deformation trend data of the dam slope area can be dynamically generated, effectively improving the spatial resolution and temporal dynamics of the three-dimensional model, and avoiding the problems of monitoring blind spots and error accumulation caused by single monitoring data or static models. In addition, the introduction of the reference three-dimensional slope model provides accurate initial conditions for the calculation of the three-dimensional deformation trend, enhancing the physical consistency and applicability of the model; through the extraction and application of transient vibration characteristic data by the model dynamic adjustment unit, the abnormal deformation data caused by short-term external disturbances can be effectively identified and corrected, and by correcting the abnormal deformation area, more accurate input data is provided for the slope hidden danger risk analysis; through the organic combination of the three-dimensional slope model construction and dynamic adjustment, the adaptability of the monitoring system to complex slope areas and the data processing accuracy are significantly improved, not only can the problem of inaccurate hidden danger identification caused by the quality of monitoring data and model staticization be solved, but also the operation stability and decision-making support ability of the monitoring system in the complex dynamic environment of alpine and canyon areas are enhanced.

[0100] In an alternative embodiment, the three-dimensional slope model construction unit may also be configured to perform the steps of generating the three-dimensional deformation trend data, as shown in Figure 5 the following, and specifically may include: Step S510, according to the distance data between nodes after data calibration, adjust the position of each monitoring point in the reference three-dimensional slope model to obtain an initial slope deformation model of the spatial displacement change in the time series; Step S520, extract the time series characteristics of the deformation data after data calibration, and calculate the deformation gradient of each monitoring point, where the deformation gradient is used to quantify the deformation degree and range of each monitoring point; Step S530, match and fuse the deformation gradient with the initial slope deformation model to obtain the three-dimensional deformation trend data of the dam slope area.

[0101] Among them, the reference three-dimensional slope model is stored in the form of a digital elevation model or point cloud data, and is used as the basic terrain representation of the target area. The reference three-dimensional slope model can be constructed by means such as terrestrial laser scanning, unmanned aerial vehicle mapping, or satellite remote sensing. The distance data between nodes after data calibration can be mapped into the reference model through a spatial interpolation method, specifically including associating the distance change data of the monitoring nodes with their spatial coordinates, and adjusting the positions of the monitoring points in the reference model according to the change values. The adjustment process can adopt finite element analysis or triangular mesh segmentation technology to ensure the accuracy and continuity of the model adjustment.

[0102] Specifically, an initial slope deformation model can be generated by establishing an association matrix between nodes and gradually superimposing the displacement change values onto the reference three-dimensional slope model. To improve the modeling efficiency, a parallel computing framework can be introduced to process large-scale node data. Of course, the reference three-dimensional slope model can also be dynamically adjusted according to the latest terrain data through a real-time update function to adapt to the actual changes of the slope. This embodiment does not make special limitations on this.

[0103] After the initial slope deformation model is generated, the slope three-dimensional model construction unit can extract the time series characteristics of the deformation data and calculate the deformation gradient of each monitoring point. The deformation gradient can be used to quantify the degree and range of deformation of the monitoring point, and its calculation is based on the spatial displacement change of the monitoring point in the time series. The core principle of time series feature extraction is to identify the dynamic behavior patterns of the monitoring points by analyzing the trend of displacement change values over time. Specifically, methods such as moving average, Fourier transform, or wavelet transform can be used to process the time series data to extract trend, periodicity, and abnormal change characteristics. This embodiment does not make specific limitations on the way of processing time series data.

[0104] The calculation of the deformation gradient can adopt the difference method or the fitting function method, and generate the gradient value by comparing the displacement change amounts of the monitoring points in adjacent time periods. The multi-point collaborative analysis technology can be combined to perform weighted averaging on the gradient values of the monitoring points and their neighboring nodes to reduce the influence of local abnormal data on the overall result and enhance the robustness of the gradient calculation. Optionally, the deformation gradient calculation can be combined with machine learning technology to predict the deformation trend and range of the monitoring points by training a regression model.

[0105] The deformation gradient can be matched and fused with the initial slope deformation model to generate three-dimensional deformation trend data for the dam slope area. Matching and fusion means coupling the gradient information with the point position information of the initial model through spatial interpolation and weight assignment. Specifically, the slope three-dimensional model construction unit can use the least squares method or the Kriging interpolation method to complete data fusion and generate a high-resolution three-dimensional deformation trend model. The fused three-dimensional deformation trend model can be further used to generate dynamic visualization images to provide support for the comprehensive analysis and prediction of slope hidden dangers.

[0106] Through the organic combination of the dynamic adjustment of the reference model, time series feature extraction, and three-dimensional data fusion, it can not only solve the problem of insufficient monitoring accuracy caused by model staticization and incomplete data, ensure the adaptability and accuracy of the monitoring system in complex environments, effectively improve the overall efficiency and reliability of slope hidden danger monitoring, but also provide an accurate data basis for dynamic hidden danger monitoring in complex environments.

[0107] In an optional implementation manner, the model dynamic adjustment unit can be configured to implement the determination of the processing flow of slope deformation data, referring toFigure 6 As shown, it may specifically include: Step S610: Extract the transient vibration characteristic data from the vibration data after data calibration. The transient vibration characteristic data includes vibration amplitude, vibration frequency, and vibration duration. Step S620: Match the transient vibration characteristic data with the time series data in the three-dimensional deformation trend data to identify abnormal deformation regions. Step S630: Eliminate or correct the deformation data corresponding to the abnormal deformation regions in the three-dimensional deformation trend data to obtain slope deformation data.

[0108] Among them, the transient vibration characteristic data refers to the key characteristics that reflect the dynamic behavior of the slope in the short term identified from the data collected by the vibration sensor 112. For example, the transient vibration characteristic data may include information such as vibration amplitude, vibration frequency, and vibration duration. Based on time-domain and frequency-domain analysis methods, the continuous vibration signal output by the vibration sensor 112 can be decomposed into multi-dimensional feature representations. Specifically, the fast Fourier transform (FFT) can be used to convert the vibration signal from the time domain to the frequency domain, extract the main frequency components, and combine the sliding window technique to calculate the vibration amplitude and duration in real time.

[0109] A multi-stage filtering method can be combined. For example, high-frequency noise can be eliminated through a low-pass filter, and signal features within a specific frequency range can be extracted through a band-pass filter to enhance the extraction accuracy of transient features. Further, wavelet transform can also be used for the extraction of transient features to capture the local change features of the signal at different scales, thereby improving the analysis ability for complex vibration signals. The extraction method of transient features in this exemplary embodiment is not specifically limited. To adapt to the complex environmental interference in alpine canyon areas, a signal classification model based on deep learning can also be combined. By performing pattern recognition on the vibration signal, the effective vibration caused by geological activities can be automatically distinguished from environmental noise.

[0110] The step of matching the transient vibration characteristic data with the time series data in the three-dimensional deformation trend data can specifically synchronously match the transient vibration characteristics with the changes in the three-dimensional deformation trend through time correlation analysis to determine the influence range and time range of the vibration event on the deformation. Further, the vibration characteristic data can be aligned to the time series of the three-dimensional deformation trend data using timestamp information, and by calculating the change rate and amplitude of the deformation data within the time window when the vibration event occurs, the direct influence of the vibration on the deformation can be determined.

[0111] During the matching process, the Dynamic Time Warping (DTW) algorithm can be used to calculate the time series similarity between the vibration characteristics and the deformation trend, so as to accurately locate the time period when the vibration event affects the deformation. To further improve the matching accuracy, this step can be combined with an analysis method based on statistical regression to quantify the correlation between vibration characteristic values (such as amplitude and frequency) and the deformation change amplitude, and adjust the parameters of the matching model according to the correlation. In an alternative implementation, the matching process can be combined with spatial data analysis to limit the influence range of the vibration event to the area adjacent to the seismic source, so as to improve the reliability and efficiency of the matching.

[0112] To identify the abnormal deformation area, the abnormal deformation area caused by the vibration event can be determined based on the matching result and these areas can be marked for subsequent correction processing. Specifically, by analyzing the abnormal points of the time series change in the matching result, the area beyond the normal change range can be defined as the abnormal deformation area. Further, by setting the thresholds of the deformation change rate and amplitude, the monitoring points exceeding the thresholds can be automatically detected as abnormal areas. The neighborhood analysis method can be combined to compare the change trend of the abnormal area with that of its surrounding monitoring points to exclude isolated single-point abnormalities, thus enhancing the accuracy of detection.

[0113] After the detection is completed, adjacent abnormal points can be merged into continuous abnormal areas through a spatial clustering algorithm. For example, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm can be used to automatically generate the boundary of the abnormal area. Optionally, geological model prediction can also be combined. By comparing the abnormal area with historical geological data, the pseudo-abnormal areas caused by non-geological factors (such as equipment errors) can be excluded.

[0114] The deformation data corresponding to the abnormal deformation area in the three-dimensional deformation trend data can be removed or corrected. Its main function is to eliminate the untrue data in the abnormal area and supplement and correct the data based on the deformation trend of the surrounding area. The reasonable deformation trend of the abnormal area can be reconstructed through spatial interpolation and time series completion methods. Specifically, the Inverse Distance Weighted (IDW) or Kriging interpolation method can be used to generate the corrected values of the abnormal area based on the data of the monitoring points in the adjacent normal area.

[0115] In the actual correction process, deformation data points that significantly deviate from the normal change range can be preferentially excluded, and then the time series model can be used to dynamically complete the remaining data. For example, the time series after exclusion can be smoothed by the moving window average method, or the corrected data can be reconstructed by combining long-term trend analysis. Of course, a spatio-temporal prediction model based on deep learning can also be introduced to generate more accurate correction results through training with historical data. This embodiment does not make special limitations on this.

[0116] By extracting transient vibration feature data and combining multi-dimensional analysis in the time domain and frequency domain, the vibration characteristics caused by geological activities can be accurately captured, which not only enhances the resolution of complex signals but also effectively avoids the misjudgment problem of vibration signals caused by environmental noise interference. Through collaborative analysis between regions, the false alarm rate caused by isolated abnormal points is reduced, and the monitoring error problem caused by the failure to timely identify and process abnormal data is avoided. The step of excluding or correcting data in the abnormal deformation area further improves the coherence and reliability of the data through interpolation and completion techniques and dynamic completion.

[0117] In an exemplary embodiment of the present disclosure, the monitoring and control center 130 may further include a risk assessment module 134 and an early warning module 135, where: The main function of the risk assessment module 134 is to screen target risk monitoring points whose slope hidden danger risk levels are greater than or equal to a preset risk level threshold according to the slope hidden danger risk distribution data. The risk assessment module 134 can comprehensively consider the deformation data, vibration data, and historical risk data of the monitoring points based on a multi-factor assessment model to calculate the hidden danger risk level of each monitoring point. The preset risk level threshold can be used to define the risk screening standard, and its setting is based on the actual geological conditions, engineering safety requirements, and monitoring objectives of the slope area. For example, if the slope deformation rate exceeds a certain critical value or the vibration intensity reaches a certain threshold, it can be marked as a high-risk point.

[0118] In specific implementation, the risk assessment module 134 can perform normalization processing on the input data to convert monitoring data of different dimensions into a unified evaluation scale. The normalization method can adopt linear transformation or z-score standardization to ensure reasonable weight allocation of each factor. Then, the risk level of each monitoring point can be generated through a risk calculation algorithm. For example, the risk calculation algorithm can include a simple weighted summation model or a classification model based on machine learning. Classification models such as those based on random forests or support vector machines can learn complex causal relationships through training data to improve the accuracy of risk assessment. This embodiment does not make special limitations on the type or method of the risk calculation algorithm. Of course, a dynamic adjustment function can also be combined to automatically update the parameters of the risk assessment model according to real-time monitoring data to enhance the adaptive ability of the system.

[0119] The function of the warning module 135 is to respond to the detected target risk monitoring points, generate warning information and provide relevant risk details. The warning information may include the position coordinates of the target risk monitoring points, the hidden danger risk types, and the slope hidden danger risk levels, which are used to visually display the risk characteristics of the hidden danger areas. Based on the screening results of the risk assessment module 134, easy-to-understand warning outputs can be generated through data integration and information visualization technologies. Specifically, the warning module 135 can map the position coordinates of the target risk monitoring points to a geographic information system platform and generate an intuitive risk distribution map by combining color markings assigned according to the risk levels. The hidden danger risk types can be determined by analyzing the deformation characteristics and vibration characteristics of the monitoring points. For example, continuous high-rate deformation can be marked as "landslide risk", and strong vibration events can be marked as "earthquake risk".

[0120] The warning module 135 can support multi-channel information transmission functions. For example, the warning information can be sent to the remote monitoring terminal in real time through the wireless communication module, or can be published to the mobile application or email system through the cloud server, enhancing the timeliness and reliability of the warning. Of course, a multi-level alarm mechanism can also be set up. For example, an alarm can be directly triggered for high-risk points, and a prompt message can be generated for medium-risk points. In addition, the warning module 135 can be combined with the manual intervention function, allowing the monitoring personnel to review the warning results and confirm the release of the final alarm. Optionally, the warning module 135 can also adopt an event-driven triggering mechanism. For example, by setting dynamic alarm thresholds, the warning can be automatically triggered when the risk level exceeds a specific value, further improving the response efficiency of the system.

[0121] Through the combination of the risk assessment module 134 and the warning module 135, the full-process automated processing from hidden danger identification to information output is realized, providing the slope hidden danger monitoring system with efficient risk management capabilities. This capability is particularly remarkable in complex alpine canyon environments, not only improving the response speed of the monitoring system to dynamic hidden dangers, but also supporting subsequent risk control decisions through intuitive warning outputs; through accurate risk assessment and timely warning outputs, it provides strong technical support for the scientific management of slope hidden dangers and significantly improves the overall operation efficiency and reliability of the system.

[0122] It should be noted that although several modules or units of the slope hidden danger monitoring system based on the wireless sensor network matrix are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0123] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed herein. The specification and examples are only illustrative, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0124] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A slope hidden danger monitoring system based on a wireless sensor network, characterized in that, Including: A plurality of wireless sensing nodes, evenly distributed on the dam slope area of the alpine canyon, for monitoring the slope hidden danger characteristic data of the dam slope area, where the slope hidden danger characteristic data includes deformation data, vibration data, and node - to - node distance data; A plurality of regional node management devices, arranged at the central position of the monitoring sub - areas of the pre - divided dam slope area, and forming an ad - hoc communication network with each of the wireless sensing nodes in the monitoring sub - areas, for collecting and summarizing the slope hidden danger characteristic data corresponding to each monitoring sub - area through the ad - hoc communication network; A monitoring control center, communicatively connected to the plurality of regional node management devices, for performing data analysis on the received slope hidden danger characteristic data to obtain the slope hidden danger risk distribution data corresponding to the dam slope area.

2. The slope hidden danger monitoring system based on a wireless sensor network according to claim 1, wherein The wireless sensing node includes: A deformation sensor, for collecting the deformation data of the dam slope area; A vibration sensor, for collecting the vibration data of the dam slope area; An ultra - wideband communication UWB positioning module, for measuring the spatial positions with each adjacent wireless sensing node in real - time to generate the node - to - node distance data; A power consumption optimization module, electrically connected to the deformation sensor, the vibration sensor, and the ultra - wideband communication UWB positioning module, for controlling periodic sleep and working mode switching.

3. The slope hidden danger monitoring system based on a wireless sensor network according to claim 1, wherein The regional node management device includes: A routing discovery unit, for establishing an optimal communication path between each of the wireless sensing nodes and the regional node management device in the ad - hoc communication network through a distributed routing discovery algorithm; A path reconstruction unit, for dynamically updating the routing table and reconstructing the communication path based on the data of the ultra - wideband communication UWB positioning module of the wireless sensing node when any wireless sensing node fails.

4. The slope hidden danger monitoring system based on a wireless sensor network according to claim 3, characterized in that, The routing discovery unit includes: An identifier assignment sub - unit, for assigning a unique identifier to each of the wireless sensing nodes in the ad - hoc communication network and broadcasting a routing request message; A routing request message update sub - unit, for recording the identifier and signal strength information of the sending node in the wireless sensing node that receives the routing request message; A communication path screening sub - unit, for forwarding the updated routing request message level by level to the regional node management device until the path is established, and screening to obtain the optimal communication path.

5. The slope hidden danger monitoring system based on a wireless sensor network according to claim 1 or 3, characterized in that, The regional node management device further includes: A hidden danger data caching unit, for temporarily storing the slope hidden danger characteristic data collected by each of the wireless sensing nodes in the monitoring sub - area; A hidden danger data aggregation unit, for pre - processing the slope hidden danger characteristic data in the monitoring sub - area and aggregating and sending it to the monitoring control center.

6. The slope hidden danger monitoring system based on a wireless sensor network according to claim 1, characterized in that The monitoring control center includes: A data calibration module, for performing data calibration processing on the received deformation data, vibration data, and node - to - node distance data, where the data calibration processing includes time synchronization and outlier removal; A data fusion module, which is used to combine the deformed data after data calibration and the distance data between nodes to determine the three-dimensional deformation trend data of the dam slope area, and adjust the three-dimensional deformation trend data according to the vibration data after data calibration to obtain slope deformation data; A hidden danger analysis module, which is used to determine the slope hidden danger risk level of each monitoring point according to the slope deformation data, and determine the slope hidden danger risk distribution data corresponding to the dam slope area according to the slope hidden danger risk level.

7. The slope hidden danger monitoring system based on a wireless sensor network according to claim 6, characterized in that, The data fusion module includes: A slope three-dimensional model construction unit, which is used to obtain the reference slope three-dimensional model corresponding to the dam slope area, and combine the deformed data after data calibration, the distance data between nodes, and the reference slope three-dimensional model to determine the three-dimensional deformation trend data; A model dynamic adjustment unit, which is used to determine the transient vibration characteristic data according to the vibration data after data calibration, and optimize and adjust the three-dimensional deformation trend data through the transient vibration characteristic data to obtain slope deformation data.

8. The slope hidden danger monitoring system based on a wireless sensor network according to claim 7, characterized in that The slope three-dimensional model construction unit is configured to: According to the distance data between nodes after data calibration, adjust the position of each monitoring point in the reference slope three-dimensional model to obtain an initial slope deformation model of the spatial displacement change in the time series; Extract the time series characteristics of the deformed data after data calibration, and calculate the deformation gradient of each monitoring point, where the deformation gradient is used to quantify the deformation degree and range of each monitoring point; Match and fuse the deformation gradient with the initial slope deformation model to obtain the three-dimensional deformation trend data of the dam slope area.

9. The slope hidden danger monitoring system based on a wireless sensor network according to claim 7, characterized in that, The model dynamic adjustment unit is configured to: Extract the transient vibration characteristic data in the vibration data after data calibration, where the transient vibration characteristic data includes vibration amplitude, vibration frequency, and vibration duration; Match the transient vibration characteristic data with the time series data in the three-dimensional deformation trend data to identify abnormal deformation areas; Eliminate or correct the deformation data corresponding to the abnormal deformation areas in the three-dimensional deformation trend data to obtain slope deformation data.

10. The slope hidden danger monitoring system based on a wireless sensor network according to claim 1, characterized in that The monitoring and control center further includes: A risk assessment module, which is used to screen out target risk monitoring points whose slope hidden danger risk level is greater than or equal to the preset risk level threshold according to the slope hidden danger risk distribution data; An early warning module, which is used to generate an early warning message in response to detecting the target risk monitoring point, where the early warning message includes the position coordinates, hidden danger risk type, and slope hidden danger risk level of the target risk monitoring point.

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