Dam safety monitoring system and method based on Internet of Things

Abstract

The invention belongs to the technical field of hydraulic engineering, and relates to a dam safety monitoring system and method based on the Internet of Things, and the system comprises a vibration energy supply module, a cylindrical bluff body structure and a piezoelectric ceramic sensor are arranged on the upstream face of a dam, the bluff body structure is used for guiding water flow to form a Karman vortex street, and the water flow speed of the dam is monitored in real time; analyzing a Karman vortex shedding frequency based on an FFT spectrum, and matching the Karman vortex shedding frequency with the resonant frequency of the piezoelectric ceramic sensor to obtain dual-frequency matching data; the multi-dimensional observation acquisition module is used for converting kinetic energy of dam water flow into electric energy by using the generated Karman vortex shedding frequency, supplying power to a preset multi-dimensional sensor through the converted electric energy, and acquiring dam multi-dimensional observation data based on the multi-dimensional sensor; the three-dimensional hopping communication module is used for sensing and identifying power frequency and harmonic components of the surrounding environment in real time based on the dual-frequency matching data, and generating a dynamic avoidance frequency point table to complete frequency hopping switching; and the reliability and foresight of dam safety monitoring are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects. More specifically, the present invention relates to a dam safety monitoring system and method based on the Internet of Things. Background Art

[0002] A patent with the publication number CN111221287A discloses a dam safety monitoring system and method based on the Internet of Things. The system includes a cloud-side architecture, a pipe-side architecture, and an end-side architecture. The "cloud-pipe-edge-end" system architecture is used to achieve wireless network transmission between components in the system and cloud platform data services, effectively simplifying the structural setting of the safety monitoring system, and being able to effectively reduce the data monitoring construction difficulty and maintenance investment during the construction and operation of the dam, and reducing the labor cost.

[0003] The existing dam safety monitoring systems and methods mainly have the following problems: The prior art does not consider the characteristic that the dynamic viscosity of water changes with temperature. The dynamic viscosity is sensitive to temperature, and the water temperature of the dam changes with seasons, day and night, and water depth, resulting in measurement errors introduced by a fixed viscosity value; the traditional method does not consider changing factors such as the sediment content and pollutant concentration of water. When water carries sediment or pollutants, the dynamic viscosity increases, resulting in systematic deviations in the calculation of the water flow velocity of the dam. The piezoelectric ceramic sensor will generate sensitivity attenuation under long-term water flow impact, and the prior art does not consider the dynamic compensation of the sensor state and viscosity parameters; the prior art generally uses a fixed Strouhal number as the key parameter for calculating the water flow velocity, assuming that the dynamic viscosity of water and the fluid properties are constant, and ignoring the influence of environmental variables such as water temperature, sediment content, and pollutant concentration on the dynamic viscosity of water.

[0004] The dynamic viscosity of water changes significantly with temperature, and the water temperature of the dam is affected by seasonal changes, day-night temperature differences, and water depth, resulting in dynamic fluctuations in viscosity. In addition, when the sediment and pollutant content in water increases, the dynamic viscosity of water rises, further affecting the Reynolds number of the fluid, making the Strouhal number no longer maintain a fixed value. The piezoelectric ceramic sensor will generate sensitivity attenuation under long-term water flow impact, further exacerbating the measurement error. The Strouhal number actually changes with the Reynolds number, and the Reynolds number depends on the dynamic viscosity of the fluid; when the water temperature changes or the sediment content and pollutants in water increase, the viscosity of water will change, thereby affecting the Reynolds number and further causing the Strouhal number to deviate from the originally set value; In the existing dam safety monitoring system, the Internet of Things technology is generally used for the acquisition and transmission of multi-dimensional observation data. However, the following prominent technical problems still exist: Existing technologies mostly use fixed time windows to align the collected data in terms of time, which cannot fully adapt to the uneven distribution of timestamps of observation points in multi-dimensional data, easily leading to alignment errors and thus affecting the reliability of data comprehensive analysis. Existing alignment methods are mainly based on time proximity and lack comprehensive consideration of the spatial position information of observation points, resulting in physically unreasonable alignment results and possibly masking key monitoring signals. When the distribution of observation points is sparse, it is difficult for the existing system to achieve complete alignment, causing monitoring blanks in certain areas and affecting the overall safety assessment. For dense areas of observation points, the existing random numbers fail to dynamically adjust the alignment strategy according to data density, resulting in low alignment efficiency or over-alignment, thus wasting computing resources or reducing monitoring quality.

[0005] In view of this, the present invention proposes a dam safety monitoring system and method based on the Internet of Things to solve the above problems. Summary of the Invention

[0006] To overcome the above defects of the prior art and to achieve the above object, the present invention provides the following technical solutions: A dam safety monitoring system based on the Internet of Things, comprising: A vibration energy supply module, which sets a cylinder blunt body structure and a piezoelectric ceramic sensor on the upstream face of the dam, uses the blunt body structure to guide the water flow to form a Karman vortex street, and monitors the water flow velocity of the dam in real time; based on FFT spectrum analysis of the Karman vortex street shedding frequency, it matches with the resonance frequency of the piezoelectric ceramic sensor to obtain dual-frequency matching data; A multi-dimensional observation acquisition module, which uses the generated Karman vortex street shedding frequency to convert the kinetic energy of the dam water flow into electric energy, supplies power to a preset multi-dimensional sensor through the converted electric energy, and acquires multi-dimensional observation data of the dam based on the multi-dimensional sensor; A three-dimensional hopping communication module, based on the dual-frequency matching data, perceives and identifies the power frequency and harmonic components of the surrounding environment in real time, generates a dynamic avoidance frequency point table to complete frequency hopping switching; adopts a seepage path prediction mechanism, according to the water flow velocity of the dam, predicts the seepage active areas within the next n time periods, preferentially selects the nodes in the seepage active areas as relay paths, and obtains the node spatial position information; A multi-source asynchronous alignment module, which uses the node spatial position information to control each node to generate synchronous pulses based on a rubidium atomic clock and a GPS signal; adopts an elastic time window to dynamically align the multi-dimensional observation data of the dam to obtain the aligned multi-dimensional observation data of the dam; A seepage stress warning module, which calculates the principal stress direction gradient change rate of the aligned multi-dimensional observation data of the dam, and when it exceeds the preset gradient change rate threshold, automatically identifies the migration of the stress concentration area and generates a warning signal.

[0007] Preferably, the method for real-time monitoring of the water flow velocity of the dam includes: A cylinder blunt body structure is arranged on the water-facing side of the dam. The cylinder blunt body structure is used to guide the water flow to flow around and form a Karman vortex street. A piezoelectric ceramic sensor is arranged on the cylinder blunt body structure to detect in real time the periodic vibration signal generated due to the action of the water flow on the cylinder blunt body structure. The diameter and length of the cylinder blunt body structure are designed according to the preset target water flow velocity and scale, and it is made of water erosion-resistant materials; The piezoelectric ceramic sensor converts the monitored periodic vibration signal into an electrical signal, and acquires in real time the vibration frequency signal caused by the shedding of the Karman vortex street. According to the relationship between the Karman vortex street shedding frequency, the diameter of the cylinder blunt body structure, and the Strouhal number, the water flow velocity of the dam is calculated in real time.

[0008] Preferably, the method for obtaining the dual-frequency matching data includes: Based on the electrical signal monitored by the piezoelectric ceramic sensor, spectrum analysis is carried out using the fast Fourier transform to convert the electrical signal into a frequency-domain signal, and the vortex shedding frequency characteristic signal is extracted. The piezoelectric ceramic sensor has a preset natural resonance frequency; The vortex shedding frequency obtained by spectrum analysis is matched and judged with the preset natural resonance frequency. When the matching rate between the vortex shedding frequency and the preset natural resonance frequency reaches the preset matching rate threshold, the amplitude of the sensor signal is enhanced, and a frequency resonance enhancement effect appears, thereby obtaining the dual-frequency matching data. The dual-frequency matching data includes the vortex shedding frequency, the resonance frequency of the piezoelectric ceramic sensor, the frequency matching rate, the signal amplitude, and the resonance enhancement effect characteristics.

[0009] Preferably, the multi-dimensional observation data of the dam includes dam water flow parameter data, structural vibration parameter data, stress and strain data, seepage parameter data, and environmental monitoring parameter data.

[0010] Preferably, the method for generating a dynamic avoidance frequency point table to complete frequency hopping switching includes; Through spectrum scanning technology, monitor the electromagnetic environment of the current communication frequency band and the adjacent communication frequency bands of the current communication frequency band, and collect the amplitude and frequency characteristics of the harmonic components of the power frequency and integer multiples of the power frequency. Through the preset power frequency and harmonic frequency templates, compare with the real-time spectrum data to determine the positions and intensities of various power frequency and harmonic signals in the environment, and screen out the frequency bands that interfere with the communication signal; According to the recognition result, automatically generate an avoidance frequency point table. The avoidance frequency point table lists the frequency points and the bandwidth ranges of the frequency points that need to be avoided. According to the avoidance frequency point table, adjust the preset frequency hopping algorithm, and intelligently select a non-interfering frequency band for signal transmission to achieve frequency hopping switching; continuously monitor the spectrum environment, dynamically update the avoidance frequency point table, and adjust the frequency hopping strategy.

[0011] Preferably, the method for obtaining the node spatial position information includes: Training and constructing a seepage active area prediction model, including an input layer, an LSTM layer, and an output layer; the input layer of the model is used to input the historical dam water flow velocity; the output layer of the model is used to output the seepage active area within the next n time periods, and the seepage active area prediction model is an LSTM model; Marking the nodes corresponding to the seepage active area, defined as key nodes, and preferentially selecting the key nodes in the seepage active area as the data relay path in the communication protocol; by using the connection relationship between nodes and the known node position information, the multi-dimensional scaling analysis algorithm is used to obtain the spatial position of the inferred unknown nodes, and the node spatial position information is obtained.

[0012] Preferably, the method for controlling each node to generate synchronous pulses based on a rubidium atomic clock and a GPS signal includes: By using the multi-dimensional scaling analysis algorithm, the spatial position information of each node is obtained. Each node obtains a globally unified time reference by receiving the GPS signal; the time information transmitted by the GPS signal enables each node to achieve preliminary coarse synchronization, thereby establishing a unified time reference; Using the rubidium atomic clock as the local stability clock source of the node to generate a stable oscillation frequency and the corresponding time signal; using the time signal generated by the rubidium atomic clock, in cooperation with the GPS reference time, to correct and compensate the local time; Based on the spatial position information of the node, calculate the physical distance from each node to the reference synchronization anchor node, and calculate the propagation delay compensation amount according to the propagation speed of electromagnetic waves in air or water; the node adjusts the clock signal output by the rubidium atomic clock according to its spatial position information; Introduce dynamic correction based on environmental monitoring data to adjust the propagation delay compensation amount to cope with the change in the propagation speed of electromagnetic waves in different environmental media; each node generates synchronous pulses with a unified time reference and compensation through its own spatial position information, GPS signal, and the output of the rubidium atomic clock.

[0013] Preferably, the method for obtaining the aligned multi-dimensional dam observation data includes: Presetting the time alignment reference point of the multi-dimensional dam observation data as the time alignment reference point for the multi-dimensional observation data; for each dimension of the multi-dimensional dam observation data, set the initial time window width , the initial time window is centered on and search for the observation data point closest to the reference time in the observation data of this dimension near ; And dynamically adjust the range of the time window according to the deviation of the observation data points in the observation data of each dimension relative to the time alignment reference point; during the time alignment process, calculate in real time the time stamps of the observation data points in each observation data dimension relative to the reference point of the deviation amount ; where represents the time stamp of the th observation data point; represents the index of the observation data point; when the deviation amount is greater than the preset deviation amount threshold, dynamically expand the time window and set the expansion ratio according to the expansion function; Within the dynamically adjusted time window range, for each dimension of observation data, screen the observation data points whose time difference from the time point of the reference point is less than the preset time difference threshold as the observed value of this dimension at the time alignment reference point; adjust the time window width according to the density of the aligned observation data points, count the number of observation data points, if in any dimension of observation data near the reference point there is an observation data point number greater than or equal to the preset number threshold, then calculate the observed value of the time alignment reference point by means of weighted average; the weights of the weighted average are jointly determined according to the time difference and the spatial distance difference between the observation data points and the reference point; If in any dimension of observation data near the reference point there is an observation data point number less than the preset number threshold, then estimate the observed value of the time alignment reference point by means of linear interpolation; align the observed values of each dimension in the multi-dimensional observation data under the time alignment reference point to form an aligned multi-dimensional observation data set.

[0014] Preferably, the method for automatically identifying the migration of the stress concentration area and generating a warning signal includes: Based on the aligned multi-dimensional observation data of the dam, extract the stress tensor data at each observation data point; for each observation data point, through eigenvalue decomposition, obtain the eigenvector and eigenvalue of its stress tensor data, and determine the maximum principal stress direction vector and the maximum principal stress value therefrom; select each observation data point and compare it with the adjacent observation data points. For each pair of adjacent observation data points, calculate the included angle between the two maximum principal stress direction vectors to obtain the direction change angle; Calculate the ratio of the direction change angle to the physical distance between the two points to obtain the local change rate of the maximum principal stress direction gradient; calculate and take the average value of all adjacent points within the neighborhood of each observation data point to obtain the principal stress direction gradient change rate of this observation data point; preset the gradient change rate threshold. For each observation data point, if the principal stress direction gradient change rate exceeds the preset gradient change rate threshold, mark this observation data point as a stress concentration abnormal point; When continuous observed data points in adjacent areas are detected to meet the condition of exceeding the preset gradient change rate threshold, identify this area as a potential stress concentration area; by analyzing the position changes of the stress concentration area at different time points, judge whether there is a migration trend in the stress concentration area; after identifying the stress concentration area and judging its migration trend, automatically generate a warning signal.

[0015] A dam safety monitoring method based on the Internet of Things, including: S1. Set a cylinder blunt body structure and a piezoelectric ceramic sensor on the upstream face of the dam. Use the blunt body structure to guide the water flow to form a Karman vortex street, and monitor the water flow velocity of the dam in real time; based on FFT spectrum analysis of the Karman vortex street shedding frequency, match it with the resonance frequency of the piezoelectric ceramic sensor to obtain dual-frequency matching data; S2. Use the generated vortex street shedding frequency to convert the kinetic energy of the dam water flow into electrical energy, and use the converted electrical energy to supply power to a preset multi-dimensional sensor. Collect multi-dimensional observation data of the dam based on the multi-dimensional sensor; S3. Based on the dual-frequency matching data, perceive and identify the power frequency and harmonic components of the surrounding environment in real time, generate a dynamic avoidance frequency point table to complete frequency hopping switching; adopt a seepage path prediction mechanism, according to the dam water flow velocity, predict the seepage active area in the next n time periods, and preferentially select the nodes in the seepage active area as relay paths to obtain the node spatial position information; S4. Use the node spatial position information to control each node to generate a synchronization pulse based on a rubidium atomic clock and a GPS signal; use an elastic time window to dynamically align the multi-dimensional observation data of the dam to obtain the aligned multi-dimensional observation data of the dam; S5. Calculate the principal stress direction gradient change rate of the aligned multi-dimensional observation data of the dam. When it exceeds the preset gradient change rate threshold, automatically identify the migration of the stress concentration area and generate a warning signal.

[0016] Compared with the prior art, the present invention has the following beneficial effects: By introducing a dynamic correction model for water body viscosity, temperature, and concentration, and combining the Reynolds number to correct the Strouhal number, the limitation of the constant viscosity assumption in the traditional Karman vortex street water flow velocity calculation is overcome, significantly improving the flow velocity monitoring accuracy in actual complex hydrological environments. By combining water body viscosity correction and sensor status monitoring, dynamic compensation for the sensitivity attenuation of piezoelectric ceramic sensors during long-term operation is achieved, further enhancing the stability and reliability of flow velocity monitoring during long-term operation. The Strouhal number correction mechanism based on the Reynolds number enables the flow velocity calculation to accurately reflect the current physical state of the water body, significantly reducing the deviation caused by environmental changes. It responds to environmental changes in real time, ensuring that the monitoring system continuously outputs high-precision water flow velocity monitoring results under variable working conditions, providing more reliable real-time data support for dam safety monitoring. By obtaining water temperature and pollutant concentration sensor data in real time, the water flow velocity calculation results are dynamically corrected, reducing system upgrade and deployment costs, and enhancing water resource management and risk warning capabilities.

[0017] By dynamically adjusting the time window width, the time window can automatically expand or contract according to the observation point deviation during the alignment process. It effectively adapts to the differences in time distributions in different observation dimensions, significantly improving the time alignment accuracy of multi-dimensional observation data and reducing analysis deviations caused by alignment errors. By introducing an expansion factor and an expansion function based on the observation point deviation amount, this solution can achieve adaptive adjustment of the time window. Whether facing dense or sparse distributions of observation points, it can flexibly adjust the time window according to the characteristics of the actual observation data, avoiding the deficiencies of a fixed time window and ensuring that the system can maintain efficient and accurate data alignment under different monitoring conditions. When the time window expands, an observation point density feedback adjustment mechanism is added to avoid a decrease in alignment accuracy or waste of computing resources due to excessive expansion in dense observation point areas. By dynamically adjusting the expansion factor, while maintaining high-precision alignment, the system's computational complexity and resource consumption can be reduced, and the overall operating efficiency of the system can be improved. Through the multi-dimensional observation data alignment and fusion mechanism based on the time alignment reference point, it can naturally adapt to the access and fusion of multi-dimensional and multi-source observation data, ensuring that the system still has excellent adaptability and processing capabilities when facing a complex multi-dimensional data environment. Brief Description of the Drawings

[0018] Figure 1 It is a schematic structural diagram of the dam safety monitoring system based on the Internet of Things according to the present invention; Figure 2 It is a schematic flowchart of the dam safety monitoring method based on the Internet of Things according to the present invention; Figure 3 It is a flowchart of the method for real-time monitoring of the water flow velocity of a dam provided by the present invention. Detailed Embodiments

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Embodiment 1

[0021] Please refer to Figure 1 and Figure 3 shown in the figure. Embodiment 1 further illustrates the dam safety monitoring system based on the Internet of Things proposed by the present invention, including: With the wide application of Internet of Things technology, the dam safety monitoring system has gradually realized the real-time collection and transmission of multi-dimensional observation data, providing important support for the evaluation and early warning of the dam operation status. However, there are still many technical deficiencies in the existing technology, which restrict the accuracy and reliability of dam safety monitoring.

[0022] In the existing dam water flow velocity monitoring method, a cylindrical bluff body structure is used to guide the water flow to form a vortex street, and a piezoelectric ceramic sensor is combined to detect the periodic vibration signal generated by the water flow in real time, and the water flow velocity is calculated through the Strouhal number. The Strouhal number is usually assumed to be a fixed value without considering the influence of the change of water body dynamic viscosity and fluid characteristics. However, the viscosity of the dam water body is highly sensitive to temperature, and the water temperature will fluctuate dynamically with seasons, day-night temperature differences and water depths, resulting in significant changes in the water body viscosity. In addition, the increase in the sediment content and pollutant concentration in the water body will further increase the dynamic viscosity of the water body, thereby affecting the Reynolds number of the fluid, making the Strouhal number no longer remain a fixed value, easily introducing systematic errors and reducing the accuracy of flow velocity calculation. The existing technology does not introduce real-time correction for the change of water body viscosity, resulting in insufficient reliability of the water flow velocity monitoring results under complex hydrological conditions.

[0023] The piezoelectric ceramic sensor will generate sensitivity attenuation under long-term water flow impact, and the existing systems generally lack a compensation mechanism for the dynamic changes of the sensor state and environmental factors (such as temperature, sediment content, pollutant concentration), further exacerbating the error accumulation of monitoring data.

[0024] Most existing Internet of Things (IoT) - based dam safety monitoring systems use a fixed - time - window method to align the multi - dimensional observation data collected. However, due to the often uneven distribution of timestamps at observation points, the fixed - time - window alignment method is difficult to fully adapt to the temporal characteristics of the observation data, easily leading to alignment errors, which in turn affect the effectiveness of data comprehensive analysis. At the same time, the existing alignment methods are mainly based on time proximity, ignoring the spatial location information of the observation points, which may cause physical unreasonableness in the alignment results, even masking key monitoring signals and reducing the accuracy of global safety assessment. In sparse areas of observation point distribution, it is difficult for existing systems to achieve complete data alignment, resulting in monitoring blanks; while in dense areas of observation points, without using a dynamic alignment strategy based on data density, it is easy to cause low alignment efficiency or over - alignment, wasting computing resources or reducing monitoring quality.

[0025] Therefore, to effectively solve the above problems, the present invention proposes an IoT - based dam safety monitoring system, including: A vibration energy supply module. A cylinder bluff - body structure and a piezoelectric ceramic sensor are arranged on the upstream face of the dam. The bluff - body structure is used to guide the water flow to form a Karman vortex street, and the water flow velocity of the dam is monitored in real - time. Based on the FFT spectrum analysis of the Karman vortex street shedding frequency and matching it with the resonant frequency of the piezoelectric ceramic sensor, dual - frequency matching data is obtained. A multi - dimensional observation acquisition module. Using the generated Karman vortex street shedding frequency, the kinetic energy of the dam water flow is converted into electrical energy, and the converted electrical energy is used to power a preset multi - dimensional sensor. Based on the multi - dimensional sensor, multi - dimensional observation data of the dam is collected. A three - dimensional hopping communication module. Based on the dual - frequency matching data, the power frequency and harmonic components of the surrounding environment are sensed and identified in real - time to generate a dynamic avoidance frequency - point table for hopping frequency switching. An infiltration path prediction mechanism is adopted. According to the dam water flow velocity, the infiltration - active areas within the next n time periods are predicted, and the nodes within the infiltration - active areas are preferentially selected as relay paths to obtain the node spatial location information. A multi - source asynchronous alignment module. Using the node spatial location information, each node is controlled to generate a synchronization pulse based on a rubidium atomic clock and a GPS signal. An elastic time - window is used to dynamically align the multi - dimensional observation data of the dam to obtain the aligned multi - dimensional observation data of the dam. An infiltration stress warning module. Calculate the principal stress direction gradient change rate of the aligned multi - dimensional observation data of the dam. When it exceeds the preset gradient change rate threshold, the migration of the stress - concentration area is automatically identified and a warning signal is generated.

[0026] The method for real - time monitoring of the dam water flow velocity includes: A cylindrical bluff body structure is arranged on the water-facing side of the dam. The cylindrical bluff body structure is used to guide the water flow to flow around and form a Karman vortex street, and a piezoelectric ceramic sensor is arranged on the cylindrical bluff body structure to detect in real time the periodic vibration signals generated by the action of the water flow on the cylindrical bluff body structure. The diameter and length of the cylindrical bluff body structure are designed according to the preset target water flow velocity and scale, and are made of water erosion-resistant materials. The piezoelectric ceramic sensor converts the monitored periodic vibration signals into electrical signals and collects in real time the vibration frequency signals caused by the shedding of the Karman vortex street. According to the relationship between the Karman vortex street shedding frequency, the diameter of the cylindrical bluff body structure, and the Strouhal number, the water flow velocity of the dam is calculated in real time.

[0027] The water flow velocity of the dam is ; where represents the Karman vortex street shedding frequency; represents the Strouhal number; represents the water flow velocity of the dam; represents the diameter of the cylindrical bluff body structure; However, the prior art does not consider the characteristic that the dynamic viscosity of water changes with temperature. The dynamic viscosity is sensitive to temperature, and the water temperature of the dam will change with seasons, day and night, and water depth, resulting in measurement errors introduced by fixed viscosity values. Traditional methods do not consider changing factors such as sediment content and pollutant concentration in water. When water carries sediment or pollutants, the dynamic viscosity increases, resulting in systematic deviations in the calculation of the water flow velocity of the dam. The piezoelectric ceramic sensor will have a sensitivity attenuation under long-term water flow impact, and the prior art does not consider the dynamic compensation of the sensor state and viscosity parameters. The prior art generally uses a fixed Strouhal number as the key parameter for calculating the water flow velocity, assuming that the dynamic viscosity of water and fluid properties are constant, and ignoring the influence of environmental variables such as water temperature, sediment content, and pollutant concentration on the dynamic viscosity of water.

[0028] The dynamic viscosity of water changes significantly with temperature, and the water temperature of the dam is affected by seasonal changes, day-night temperature differences, and water depth, resulting in dynamic fluctuations in viscosity. In addition, when the sediment and pollutant content in water increases, the dynamic viscosity of water rises, further affecting the Reynolds number of the fluid and causing the Strouhal number not to remain a fixed value. Therefore, the traditional Karman vortex street flow velocity calculation method based on a fixed Strouhal number is difficult to ensure high precision under actual working conditions, is prone to introducing systematic measurement errors, and affects the reliability of dam safety monitoring. At the same time, the piezoelectric ceramic sensor will have a sensitivity attenuation under long-term water flow impact, and traditional methods lack a dynamic compensation mechanism for sensor state and environmental changes, further exacerbating measurement errors. The Strouhal number actually changes with the Reynolds number, and the Reynolds number depends on the dynamic viscosity of the fluid; when the water temperature changes or the sediment content and pollutants in water increase, the viscosity of water will change, thus affecting the Reynolds number and further causing the Strouhal number to deviate from the originally set value; To this end, the Reynolds number and water body viscosity correction are introduced to improve the calculation accuracy and reliability of the dam water flow velocity based on the Karman vortex street. The Reynolds number is calculated as follows: ; where represents the water body density; represents the water body dynamic viscosity function, which changes with temperature and varies; The water body dynamic viscosity function is: ; where represents the preset reference benchmark water body dynamic viscosity; represents the concentration influence coefficient; represents the pollutant concentration; represents the temperature influence coefficient; represents the water temperature; The beneficial effects compared with the prior art are as follows: By introducing a dynamic correction model of water body viscosity with temperature and concentration, and combining the Reynolds number to correct the Strouhal number, the limitation of the constant viscosity assumption in the traditional Karman vortex street water flow velocity calculation is overcome, and the flow velocity monitoring accuracy in the actual complex hydrological environment is significantly improved; By combining water body viscosity correction and sensor status monitoring, dynamic compensation for the sensitivity attenuation during the long-term operation of the piezoelectric ceramic sensor is realized, further enhancing the stability and reliability of the flow velocity monitoring during long-term operation. Based on the Strouhal number correction mechanism of the Reynolds number, the flow velocity calculation can accurately reflect the current physical state of the water body, significantly reducing the deviation caused by environmental changes; Responding to environmental changes in real time, ensuring that the monitoring system continuously outputs high-precision water flow velocity monitoring results under changing working conditions, providing more reliable real-time data support for dam safety monitoring; By obtaining the water temperature and pollutant concentration sensor data in real time, dynamically correcting the water flow velocity calculation results, reducing the system upgrade and deployment costs, and enhancing the water resource management and risk warning capabilities.

[0029] The method for obtaining dual-frequency matching data includes: Based on the electrical signals monitored by the electro-ceramic sensor, using fast Fourier transform for spectrum analysis, converting the electrical signals into frequency-domain signals, and extracting the vortex shedding frequency characteristic signals; The piezoelectric ceramic sensor has a preset inherent resonance frequency; It should be noted that the time-domain signal refers to the electrical signal directly collected by the piezoelectric ceramic sensor, which reflects the vibration of the bluff body structure caused by the water flow changing with time. Specifically, when the water flow bypasses the bluff body and the Karman vortex street is formed, periodic vibrations will occur in the bluff body of the cylinder. These mechanical vibrations are converted into corresponding voltage signals through the electromechanical coupling effect of the piezoelectric ceramic sensor. This voltage signal is a waveform that continuously changes with time, that is, the time-domain signal, which describes the change of vibration intensity with time. By performing a fast Fourier transform on this time-domain electrical signal, the frequency-domain signal can be obtained to identify the main frequency components, such as the Karman vortex street shedding frequency. Therefore, the time-domain signal is derived from the vibration electrical signal collected by the sensor in real time and is the basic data for the entire spectrum analysis.

[0030] The vortex shedding frequency obtained from the spectrum analysis is matched and judged with the preset natural resonance frequency. When the matching rate of the vortex shedding frequency and the preset natural resonance frequency reaches the preset matching rate threshold, the amplitude of the sensor signal increases, and a frequency resonance enhancement effect appears, thereby obtaining dual-frequency matching data. The dual-frequency matching data includes the vortex shedding frequency, the resonance frequency of the piezoelectric ceramic sensor, the frequency matching rate, the signal amplitude, and the resonance enhancement effect characteristics.

[0031] The multi-dimensional observation data of the dam includes dam water flow parameter data, structural vibration parameter data, stress and strain data, seepage parameter data, and environmental monitoring parameter data.

[0032] The water flow parameter data includes the dam water flow velocity, dam water flow pressure, dam water level height, dam water flow turbulence, and dam water temperature; the structural vibration parameter data includes the dam structural vibration frequency, vibration acceleration, and vibration displacement; the stress and strain data includes the stress and strain change conditions of each part of the dam; the seepage parameter data includes the seepage rate and seepage pressure; The environmental monitoring parameter data includes atmospheric temperature, humidity, rainfall, wind speed, and wind direction; the multi-dimensional sensors include flow velocity sensors, pressure sensors, water level sensors, strain sensors, temperature and humidity sensors, rain gauges, and wind speed and wind direction sensors.

[0033] The method for generating a dynamic avoidance frequency point table to complete frequency hopping switching includes; Through spectrum scanning technology, monitor the electromagnetic environment of the current communication frequency band and the adjacent communication frequency bands of the current communication frequency band, and collect the amplitude and frequency characteristics of the harmonic components of the power frequency and integer multiples of the power frequency. Compare with the real-time spectrum data through the preset power frequency and harmonic frequency templates to determine the positions and intensities of various power frequency and harmonic signals in the environment, and screen out the frequency bands that interfere with the communication signals; According to the recognition result, an avoidance frequency point table is automatically generated. The avoidance frequency point table lists the frequency points that need to be avoided and the bandwidth ranges of the frequency points. According to the avoidance frequency point table, the preset frequency hopping algorithm is adjusted, and a non-interference frequency band is intelligently selected for signal transmission to achieve frequency hopping switching; continuously monitor the spectrum environment, dynamically update the avoidance frequency point table, and adjust the frequency hopping strategy.

[0034] The methods for obtaining the spatial position information of nodes include: Train and construct a seepage active area prediction model, including an input layer, an LSTM layer, and an output layer; the input layer of the model is used to input the historical dam water flow velocity; the output layer of the model is used to output the seepage active area within the next n time periods, and the seepage active area prediction model is an LSTM model; Mark the nodes corresponding to the seepage active area, defined as key nodes. In the communication protocol, preferentially select the key nodes in the seepage active area as the data relay path; through the connection relationship between nodes and the known node position information, use the multi-dimensional scaling analysis algorithm to obtain the spatial position of the inferred unknown nodes and obtain the node spatial position information.

[0035] The methods for controlling each node to generate synchronous pulses based on a rubidium atomic clock and a GPS signal include: Through the multi-dimensional scaling analysis algorithm, obtain the spatial position information of each node. Each node obtains a globally unified time reference by receiving the GPS signal; the time information transmitted by the GPS signal enables each node to initially achieve coarse synchronization, thereby establishing a unified time reference; Use the rubidium atomic clock as the local stability clock source of the node to generate a stable oscillation frequency and the corresponding time signal; use the time signal generated by the rubidium atomic clock to correct and compensate the local time in cooperation with the GPS reference time; Based on the spatial position information of the nodes, calculate the physical distance from each node to the reference synchronization anchor node, and calculate the propagation delay compensation amount according to the propagation speed of electromagnetic waves in air or water; the node adjusts the clock signal output by the rubidium atomic clock according to its spatial position information; Introduce dynamic correction based on environmental monitoring data to adjust the propagation delay compensation amount to cope with the change in the propagation speed of electromagnetic waves in different environmental media; each node generates synchronous pulses with a unified time reference and compensation through its own spatial position information, GPS signal, and the output of the rubidium atomic clock.

[0036] The methods for obtaining the aligned multi-dimensional observation data of the dam include: Preset the time alignment reference point of the dam multi-dimensional observation data , as the time alignment reference point for the multi-dimensional observation data; for each dimension of the dam multi-dimensional observation data, set the initial time window width , and the initial time window is based on Centered around, search for the observation data point closest to the reference time in the observation data of this dimension near ; and dynamically adjust the range of the time window according to the deviation of the observation data points in the observation data of each dimension relative to the time alignment reference point; during the time alignment process, calculate in real time the time stamp of the observation data point in each observation data dimension relative to the reference point ; The deviation amount ; where represents the time stamp of the th observation data point ; represents the index of the observation data point; when the deviation amount is greater than the preset deviation amount threshold, dynamically expand the time window and set the expansion ratio according to the expansion function The expansion function is: ; where represents the width of the time window after dynamic expansion represents the expansion factor, which is used to control the expansion degree of the time window represents the th deviation amount of the observation data point Within the range of the dynamically adjusted time window, for the observation data of each dimension, screen out the observation data points whose time difference from the time point of the reference point is less than the preset time difference threshold as the observed value of this dimension at the time alignment reference point; adjust the width of the time window according to the density of the aligned observation data points, count the number of observation data points, if in any dimension of the observation data near the reference point , there are observation data points whose number is greater than or equal to the preset number threshold, then calculate the observed value of the time alignment reference point by weighted average; the weights of the weighted average are jointly determined according to the time difference and the spatial distance difference between the observation data point and the reference point If in any dimension of the observation data near the reference point , there are observation data points whose number is less than the preset number threshold, then estimate the observed value of the time alignment reference point by linear interpolation; align the observed values of each dimension in the multi-dimensional observation data at the time alignment reference point to form an aligned multi-dimensional observation data set

[0037] Considering the influence of the density of the observation data points and avoiding excessive expansion in the dense area of the observation points, adjust the expansion factor through the expansion adjustment function, and the expansion adjustment function is: ; represents the adjusted expansion factor; where represents the density of the observation data points within the current time window represents the coefficient for adjusting the density feedback intensity

[0038] A method for automatically identifying the migration of stress concentration areas and generating warning signals includes: Based on the aligned multi-dimensional observation data of the dam, extract the stress tensor data at each observation data point; for each observation data point, through eigenvalue decomposition, obtain the eigenvectors and eigenvalues of its stress tensor data, and determine the maximum principal stress direction vector and the maximum principal stress value therefrom; select each observation data point and compare it with adjacent observation data points. For each pair of adjacent observation data points, calculate the angle between the maximum principal stress direction vectors of the two points to obtain the direction change angle; Calculate the ratio of the direction change angle to the physical distance between the two points to obtain the local change rate of the maximum principal stress direction gradient; calculate the average value for all adjacent points within the neighborhood of each observation data point to obtain the change rate of the principal stress direction gradient of this observation data point; preset a gradient change rate threshold. For each observation data point, if the change rate of the principal stress direction gradient exceeds the preset gradient change rate threshold, mark this observation data point as a stress concentration anomaly point; When it is detected that there are continuous observation data points in the adjacent area that meet the condition of exceeding the preset gradient change rate threshold, identify this area as a potential stress concentration area; by analyzing the position changes of the stress concentration area at different time points, judge whether there is a migration trend of the stress concentration area; after identifying the stress concentration area and judging its migration trend, automatically generate a warning signal.

[0039] The following problems existing in the prior art are solved: In the existing dam safety monitoring system, the Internet of Things technology is generally used for the acquisition and transmission of multi-dimensional observation data, but there are still the following prominent technical problems: The prior art mostly uses a fixed time window for time alignment of the acquired data, which cannot fully adapt to the uneven distribution of the timestamps of the observation points in the multi-dimensional data, easily leads to alignment errors, and further affects the reliability of data comprehensive analysis. The existing alignment methods are mainly based on time proximity and lack comprehensive consideration of the spatial position information of the observation points, resulting in physically unreasonable alignment results and possibly masking key monitoring signals. When the distribution of the observation points is sparse, it is difficult for the existing system to achieve complete alignment, resulting in monitoring blanks in some areas and affecting the global safety assessment. For the area with dense observation points, the existing random numbers fail to dynamically adjust the alignment strategy according to the data density, resulting in low alignment efficiency or over-alignment, thus wasting computing resources or reducing the monitoring quality.

[0040] The beneficial effects compared with the prior art are as follows: By dynamically adjusting the width of the time window, the time window can automatically expand or contract according to the deviation amount of the observation point during the alignment process; it can effectively adapt to the differences in time distribution in different observation dimensions, significantly improve the time alignment accuracy of multi-dimensional observation data, and reduce the analysis deviation caused by alignment errors. By introducing an expansion factor and an expansion function based on the deviation amount of the observation point, this solution can achieve the adaptive adjustment of the time window; whether facing a dense or sparse distribution of observation points, it can flexibly adjust the time window according to the characteristics of the actual observation data, avoid the deficiencies brought by a fixed time window, and ensure that the system can maintain efficient and accurate data alignment under different monitoring conditions; When the time window expands, an observation point density feedback adjustment mechanism is added to avoid the decline in alignment accuracy or waste of computing resources caused by excessive expansion in the area with dense observation points. By dynamically adjusting the expansion factor, while maintaining high-precision alignment, the computing complexity and resource consumption of the system can be reduced, and the overall operation efficiency of the system can be improved. Through the multi-dimensional observation data alignment and fusion mechanism based on the time alignment reference point, it can naturally adapt to the access and fusion of multi-dimensional and multi-source observation data, and ensure that the system still has excellent adaptability and processing capabilities when facing a complex multi-dimensional data environment.

[0041] The preset gradient change rate threshold is set by the staff, and the average value of multiple preset gradient changes is taken as the preset gradient change rate threshold; similarly, the preset matching rate threshold, preset deviation amount threshold, preset time difference threshold, and preset quantity threshold are set.

[0042] In this embodiment, by introducing a dynamic correction model of water body viscosity, temperature, and concentration, and combining the Reynolds number to correct the Strouhal number, the limitation of the constant viscosity assumption in the traditional Karman vortex street water flow velocity calculation is overcome, and the flow velocity monitoring accuracy in the actual complex hydrological environment is significantly improved; by combining water body viscosity correction and sensor status monitoring, dynamic compensation for the sensitivity attenuation during the long-term operation of the piezoelectric ceramic sensor is achieved, further improving the stability and reliability of the flow velocity monitoring during long-term operation. The Strouhal number correction mechanism based on the Reynolds number enables the flow velocity calculation to accurately reflect the current physical state of the water body, significantly reducing the deviation caused by environmental changes; it responds to environmental changes in real time, ensuring that the monitoring system continuously outputs high-precision water flow velocity monitoring results under changing working conditions, providing more reliable real-time data support for dam safety monitoring; by obtaining the data of water temperature and pollutant concentration sensors in real time, the calculation result of the water flow velocity is dynamically corrected, reducing the system upgrade and deployment costs, and enhancing the water resource management and risk warning capabilities.

[0043] By dynamically adjusting the time window width, the time window can automatically expand or contract according to the deviation amount of the observation point during the alignment process; effectively adapting to the differences in time distribution in different observation dimensions, significantly improving the time alignment accuracy of multi-dimensional observation data, and reducing the analysis deviation caused by alignment errors. By introducing an expansion factor and an expansion function based on the deviation amount of the observation point, this solution can achieve the adaptive adjustment of the time window; whether facing a dense or sparse distribution of observation points, it can flexibly adjust the time window according to the characteristics of the actual observation data, avoid the deficiencies brought by a fixed time window, and ensure that the system can maintain efficient and accurate data alignment under different monitoring conditions; When the time window expands, a feedback adjustment mechanism for the observation point density is added to avoid a decrease in alignment accuracy or waste of computing resources due to excessive expansion in the dense area of observation points. By dynamically adjusting the expansion factor, while maintaining high-precision alignment, the system computing complexity and resource consumption can be reduced, and the overall operation efficiency of the system can be improved. Through the multi-dimensional observation data alignment and fusion mechanism based on the time alignment reference point, it can naturally adapt to the access and fusion of multi-dimensional and multi-source observation data, ensuring that the system still has excellent adaptability and processing capabilities when facing a complex multi-dimensional data environment.

[0044] Embodiment 2

[0045] Please refer to Figure 2 As shown, for the parts not described in detail in this embodiment, refer to the description content of Embodiment 1. A dam safety monitoring method based on the Internet of Things is provided, including: S1. Set a cylinder blunt body structure and a piezoelectric ceramic sensor on the upstream face of the dam. Use the blunt body structure to guide the water flow to form a Karman vortex street, and real-time monitor the water flow velocity of the dam; based on the FFT spectrum analysis of the Karman vortex street shedding frequency, match it with the resonance frequency of the piezoelectric ceramic sensor to obtain dual-frequency matching data; S2. Use the generated vortex street shedding frequency to convert the kinetic energy of the dam water flow into electrical energy, and use the converted electrical energy to supply power to a preset multi-dimensional sensor. Collect multi-dimensional observation data of the dam based on the multi-dimensional sensor; S3. Based on the dual-frequency matching data, real-time sense and identify the power frequency and harmonic components of the surrounding environment, generate a dynamic avoidance frequency point table to complete frequency hopping switching; adopt a seepage path prediction mechanism, according to the water flow velocity of the dam, predict the seepage active area in the next n time periods, and preferentially select the nodes in the seepage active area as relay paths to obtain the node spatial position information; S4. Use the node spatial position information to control each node to generate a synchronization pulse based on the rubidium atomic clock and the GPS signal; use an elastic time window to dynamically align the multi-dimensional observation data of the dam to obtain the aligned multi-dimensional observation data of the dam; S5. Calculate the principal stress direction gradient change rate of the aligned multi-dimensional dam observation data. When it exceeds the preset gradient change rate threshold, automatically identify the migration of the stress concentration area and generate a warning signal.

[0046] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0047] The above is only the preferred embodiment of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for ordinary users in the technical field, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. The dam safety monitoring system based on the Internet of Things is characterized in that, Comprising: A vibration energy supply module, which sets a cylinder blunt body structure and a piezoelectric ceramic sensor on the upstream face of the dam. The blunt body structure is used to guide the water flow to form a Karman vortex street, and the water flow velocity of the dam is monitored in real time. Based on the FFT spectrum analysis of the Karman vortex street shedding frequency, it is matched with the resonant frequency of the piezoelectric ceramic sensor to obtain double-frequency matching data; A multi-dimensional observation and acquisition module, which uses the generated Karman vortex street shedding frequency to convert the kinetic energy of the dam water flow into electric energy, powers a preset multi-dimensional sensor through the converted electric energy, and acquires multi-dimensional observation data of the dam based on the multi-dimensional sensor; A three-dimensional hopping communication module, which, based on the double-frequency matching data, senses and identifies the power frequency and harmonic components of the surrounding environment in real time, generates a dynamic avoidance frequency point table to complete frequency hopping switching; adopts a seepage path prediction mechanism, and according to the water flow velocity of the dam, predicts the seepage active area in the next n time periods, preferentially selects the nodes in the seepage active area as the relay path, and obtains the node spatial position information; A multi-source asynchronous alignment module, which uses the node spatial position information to control each node to generate a synchronization pulse based on a rubidium atomic clock and a GPS signal; adopts an elastic time window to dynamically align the multi-dimensional observation data of the dam to obtain the aligned multi-dimensional observation data of the dam; A seepage stress warning module, which calculates the principal stress direction gradient change rate of the aligned multi-dimensional observation data of the dam. When it exceeds the preset gradient change rate threshold, it automatically identifies the migration of the stress concentration area and generates a warning signal.

2. The dam safety monitoring system based on the Internet of Things according to claim 1, characterized in that The method for real-time monitoring of the water flow velocity of the dam includes: Setting a cylinder blunt body structure on the upstream face of the dam. The cylinder blunt body structure is used to guide the water flow to flow around to form a Karman vortex street, and a piezoelectric ceramic sensor is set on the cylinder blunt body structure to detect the periodic vibration signal generated by the water flow acting on the cylinder blunt body structure in real time; the diameter and length of the cylinder blunt body structure are designed according to the preset target water flow velocity and scale, and are made of water erosion-resistant materials; The piezoelectric ceramic sensor converts the monitored periodic vibration signal into an electric signal, and collects the vibration frequency signal caused by the Karman vortex street shedding in real time; according to the relationship between the Karman vortex street shedding frequency, the diameter of the cylinder blunt body structure and the Strouhal number, the water flow velocity of the dam is calculated in real time.

3. The dam safety monitoring system based on the Internet of Things according to claim 2, characterized in that The method for obtaining the double-frequency matching data includes: Based on the electric signal monitored by the piezoelectric ceramic sensor, using fast Fourier transform for spectrum analysis, converting the electric signal into a frequency domain signal, and extracting the vortex shedding frequency characteristic signal; the piezoelectric ceramic sensor has a preset inherent resonant frequency; Matching and judging the vortex shedding frequency obtained by spectrum analysis with the preset inherent resonant frequency. When the matching rate of the vortex shedding frequency and the preset inherent resonant frequency reaches the preset matching rate threshold, the sensor signal amplitude increases, and a frequency resonance enhancement effect appears, so as to obtain double-frequency matching data; the double-frequency matching data includes the vortex shedding frequency, the resonant frequency of the piezoelectric ceramic sensor, the frequency matching rate, the signal amplitude and the resonance enhancement effect characteristics.

4. The dam safety monitoring system based on the Internet of Things according to claim 3, characterized in that, The multi-dimensional observation data of the dam includes dam water flow parameter data, structural vibration parameter data, stress and strain data, seepage parameter data and environmental monitoring parameter data.

5. The dam safety monitoring system based on the Internet of Things according to claim 4, characterized in that, The method for generating a dynamic avoidance frequency point table to complete frequency hopping switching includes; By means of spectrum scanning technology, monitor the electromagnetic environment of the current communication frequency band and the adjacent communication frequency bands of the current communication frequency band, and collect the amplitude and frequency characteristics of the harmonic components of the power frequency and integer multiples of the power frequency; compare with the real-time spectrum data through the preset power frequency and harmonic frequency templates to determine the positions and intensities of various power frequency and harmonic signals in the environment, and screen out the frequency bands that interfere with communication signals; According to the recognition results, automatically generate an avoidance frequency point table. The avoidance frequency point table lists the frequency points that need to be avoided and the bandwidth ranges of the frequency points. According to the avoidance frequency point table, adjust the preset frequency hopping algorithm, and intelligently select non-interfering frequency bands for signal transmission to achieve frequency hopping switching; continuously monitor the spectrum environment, dynamically update the avoidance frequency point table, and adjust the frequency hopping strategy.

6. The dam safety monitoring system based on the Internet of Things according to claim 5, characterized in that, The method for obtaining the spatial position information of the nodes includes: Train and construct a seepage active area prediction model, including an input layer, an LSTM layer, and an output layer; the input layer of the model is used to input the historical dam water flow velocity; the output layer of the model is used to output the seepage active area within the next n time periods, and the seepage active area prediction model is an LSTM model; Mark the nodes corresponding to the seepage active area and define them as key nodes. In the communication protocol, preferentially select the key nodes in the seepage active area as the data relay path; through the connection relationship between nodes and the known node position information, use the multi-dimensional scaling analysis algorithm to obtain the spatial position of the inferred unknown nodes to obtain the node spatial position information.

7. The dam safety monitoring system based on the Internet of Things according to claim 6, characterized in that, The method for controlling each node to generate synchronous pulses based on a rubidium atomic clock and a GPS signal includes: Through the multi-dimensional scaling analysis algorithm, obtain the spatial position information of each node. Each node obtains a globally unified time reference by receiving a GPS signal; the time information transmitted by the GPS signal enables each node to achieve rough synchronization initially, thereby establishing a unified time reference; Use a rubidium atomic clock as the local stability clock source of the node to generate a stable oscillation frequency and the corresponding time signal; use the time signal generated by the rubidium atomic clock to correct and compensate the local time in cooperation with the GPS reference time; Based on the spatial position information of the node, calculate the physical distance from each node to the reference synchronization anchor node, and calculate the propagation delay compensation amount according to the propagation speed of electromagnetic waves in air or water; the node adjusts the clock signal output by the rubidium atomic clock according to its spatial position information; Introduce dynamic correction based on environmental monitoring data to adjust the propagation delay compensation amount to cope with the change in the propagation speed of electromagnetic waves in different environmental media; each node generates synchronous pulses with a unified time reference and compensation through its own spatial position information, GPS signal, and the output of the rubidium atomic clock.

8. The dam safety monitoring system based on the Internet of Things according to claim 7, characterized in that, The method for obtaining the aligned multi-dimensional dam observation data includes: Preset the time alignment reference point for multi-dimensional dam observation data , as the time alignment reference point for multi-dimensional observation data; for each dimension of dam multi-dimensional observation data, set the initial time window width , the initial time window is centered on and search for the observation data point closest to the reference time in the observation data of this dimension near ; And dynamically adjust the range of the time window according to the deviation of the observation data points in each dimension of the observation data relative to the time alignment reference point; during the time alignment process, calculate in real time, for each dimension of the observation data, the time stamp of the observation data point relative to the reference point of the deviation amount ; where represents the time stamp of the th observation data point; represents the index of the observation data point; when the deviation amount is greater than the preset deviation amount threshold, dynamically expand the time window and set the expansion ratio according to the expansion function; Within the dynamically adjusted time window range, for each dimension of the observed data, filter out the observed data points whose time difference from the time point of the reference point is less than the preset time difference threshold as the observed value of this dimension at the time alignment reference point; adjust the width of the time window according to the density of the aligned observed data points, and count the number of observed data points. If, among the observed data of any dimension near the reference point , there are observed data points whose number is greater than or equal to the preset number threshold, then calculate the observed value of the time alignment reference point by weighted average; the weights of the weighted average are jointly determined according to the time difference and the spatial distance difference between the observed data points and the reference point. If in any one-dimensional observation data near the reference point the number of observed data points is less than the preset number threshold, the observed value of the time-aligned reference point is estimated by linear interpolation; the observed values of each dimension in the multi-dimensional observation data under the time-aligned reference point are uniformly aligned to form an aligned multi-dimensional observation data set.

9. The dam safety monitoring system based on the Internet of Things according to claim 8, characterized in that The method for automatically identifying the migration of the stress concentration area and generating a warning signal includes: Based on the aligned multi-dimensional dam observation data, extract the stress tensor data at each observation data point; for each observation data point, through eigenvalue decomposition, obtain the eigenvectors and eigenvalues of its stress tensor data, and determine the maximum principal stress direction vector and the maximum principal stress value therefrom; select each observation data point and compare it with adjacent observation data points. For each pair of adjacent observation data points, calculate the included angle between the maximum principal stress direction vectors of the two points to obtain the direction change angle. Calculate the ratio of the direction change angle to the physical distance between the two points to obtain the local change rate of the maximum principal stress direction gradient; calculate and take the average value for all adjacent points within the neighborhood of each observation data point to obtain the principal stress direction gradient change rate of this observation data point; preset the gradient change rate threshold. For each observation data point, if the principal stress direction gradient change rate exceeds the preset gradient change rate threshold, mark this observation data point as a stress concentration anomaly point. When it is detected that there are continuous observation data points in the adjacent area that meet the condition of exceeding the preset gradient change rate threshold, identify this area as a potential stress concentration area; by analyzing the position change of the stress concentration area at different time points, judge whether there is a migration trend of the stress concentration area; after identifying the stress concentration area and judging its migration trend, automatically generate a warning signal.

10. A dam safety monitoring method based on the Internet of Things, which is used to implement the dam safety monitoring system based on the Internet of Things described in any one of claims 1 to 9, and is characterized in that, Including: S1. Set a cylindrical bluff body structure and a piezoelectric ceramic sensor on the upstream face of the dam. Use the bluff body structure to guide the water flow to form a Karman vortex street, and real-time monitor the water flow velocity of the dam; based on FFT spectrum analysis of the Karman vortex street shedding frequency, match it with the resonance frequency of the piezoelectric ceramic sensor to obtain the dual-frequency matching data. S2. Use the generated vortex street shedding frequency to convert the kinetic energy of the dam water flow into electrical energy, and use the converted electrical energy to power a preset multi-dimensional sensor, and collect multi-dimensional dam observation data based on the multi-dimensional sensor. S3. Based on the dual-frequency matching data, real-time sense and identify the power frequency and harmonic components of the surrounding environment, generate a dynamic avoidance frequency point table to complete frequency hopping switching; adopt a seepage path prediction mechanism, according to the dam water flow velocity, predict the seepage active area in the next n time periods, and preferentially select the nodes in the seepage active area as relay paths to obtain the node spatial position information. S4. Use the node spatial position information to control each node to generate synchronous pulses based on the rubidium atomic clock and GPS signals; adopt an elastic time window to dynamically align the multi-dimensional dam observation data to obtain the aligned multi-dimensional dam observation data. S5. Calculate the principal stress direction gradient change rate of the aligned multi-dimensional dam observation data. When it exceeds the preset gradient change rate threshold, automatically identify the migration of the stress concentration area and generate a warning signal.

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