Pumped storage power station dam safety monitoring method based on Beidou positioning

Abstract

The invention relates to the technical field of geometric quantity precision measurement based on satellite positioning, in particular to a pumped storage power station dam safety monitoring method based on Beidou positioning, and the method comprises the steps: arranging a Beidou monitoring station and base station array in a dam body and a slope region, synchronously receiving multi-band satellite signals, and carrying out the edge calculation processing; and the effective frequency band data are screened and fused by using a signal-to-noise ratio threshold to generate preprocessed observation data, and the preprocessed observation data are uploaded to a cloud server in real time through a 4G network and a Beidou short message dual-channel transmission link. A cloud server constructs a local enhanced network, ionosphere and troposphere errors are corrected by adopting a multi-base-station joint error modeling method, a closed-loop mechanism dynamically optimizes monitoring point distribution density, signal processing parameters and atmospheric refraction compensation coefficients, and data integrity is guaranteed in combination with a dual-channel redundancy check and interpolation completion technology. According to the invention, full-domain millimeter-level deformation dynamic monitoring of the dam under the complex terrain is realized, and the space-time reference uniformity, the data real-time performance and the structure safety evaluation precision are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of precise geometric quantity measurement based on satellite positioning, specifically to the technical field of realizing three-dimensional deformation dynamic monitoring of the dam of a pumped-storage power station by using Beidou positioning technology, and particularly to a method for dam safety monitoring of a pumped-storage power station based on Beidou positioning. Background Art

[0002] The existing deformation monitoring of the dam of a pumped-storage power station is carried out through precise geometric quantity measurement technology to obtain the displacement response characteristics of the dam structure under the combined action of static load, hydraulic seepage and temperature change in real time. The core methods include geodetic surveying method, satellite positioning method, liquid level surveying by hydrostatic level and sensor embedded monitoring. The geodetic surveying method establishes a reference network based on total station or precise level, and observes the horizontal displacement and vertical settlement of the dam surface periodically; the satellite positioning method uses a global navigation satellite system (GNSS) receiver to continuously collect the three-dimensional coordinates of key points on the dam crest and analyze the dynamic deformation trend; the hydrostatic level system monitors the uneven settlement of the dam through the change of the liquid level in the communicating pipe, while the internal deformation depends on multi-point displacement meters, inclinometers and fiber Bragg grating sensors to measure the strain accumulation and crack expansion at different elevations of the dam. After the above data are fused with statistical models and machine learning algorithms, the stiffness degradation and stability threshold of the dam structure can be quantitatively evaluated, providing a basis for risk early warning during the operation period.

[0003] The existing dam safety monitoring of pumped-storage power stations has the following technical pain points in deformation monitoring. The existing manual monitoring means rely on total stations for surface deformation measurement. Limited by terrain complexity and signal interference, the observation frequency is insufficient, the real-time performance is poor, and it is difficult to establish a unified spatio-temporal reference, which cannot meet the demand for millimeter-level three-dimensional deformation dynamic perception of the entire dam area. For example, due to the rugged terrain and electromagnetic interference in the upper and lower reservoir dams and slopes of pumped-storage power stations, manual total station periodic measurement is relied on. The acquisition interval of horizontal displacement and vertical settlement data is long, and the dynamic deformation characteristics induced by instantaneous load changes or temperature stress cannot be captured. Moreover, there is a risk of cumulative error caused by inconsistent spatio-temporal reference in manual data calculation. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method for dam safety monitoring of a pumped-storage power station based on Beidou positioning, which solves the problems of insufficient manual observation frequency, inconsistent spatio-temporal reference and inability to obtain millimeter-level three-dimensional deformation dynamic data in real time in the existing deformation monitoring of the dam of a pumped-storage power station due to terrain complexity and signal interference.

[0005] To solve the above technical problems, the specific technical solution of the present invention is as follows: The method for dam safety monitoring of a pumped-storage power station based on Beidou positioning provided by the present invention includes: Step 1: Deploy Beidou monitoring stations and reference stations in the dam body and slope area to receive multi-band satellite signals. The multi-band satellite signals include Beidou satellite signals, GPS satellite signals, GLONASS satellite signals, and Galileo system satellite signals. Perform edge computing on the received multi-band satellite signals, screen and fuse the effective band data through the signal-to-noise ratio threshold, generate preprocessed satellite observation data, and establish a dual-channel transmission link with the preset Beidou short message through the preset 4G network to upload it to the preset cloud server in real time; Step 2: Build a local augmentation network in the cloud server, perform dynamic error correction on the uploaded preprocessed satellite observation data, generate three-dimensional coordinate solution results, and output calibrated spatio-temporal reference data streams; Step 3: Align the satellite displacement data in the calibrated spatio-temporal reference data stream with the strain measurement data collected by the static level gauge and the strain measurement data collected by the multi-point displacement meter in time and space. Extract the transient response characteristics of displacement and strain through data fusion, and build a global deformation dynamic model that integrates the coupling effects of the temperature field and seepage field; Step 4: Input the satellite displacement data of the spatio-temporal reference data stream and the strain measurement data into the global deformation dynamic model, and output the displacement, rate, and acceleration monitoring parameters of the dam structure; Step 5: Set warning thresholds corresponding to the displacement, rate, and acceleration monitoring parameters of the dam structure in the cloud server. When the displacement, rate, and acceleration monitoring parameters of the dam structure exceed the corresponding warning thresholds, generate a graded warning signal including the risk level and location coordinates according to the value of the over-limit parameter; Step 6: Construct a three-dimensional deformation thermal map based on the displacement, rate, and acceleration monitoring parameters of the dam structure, and push a risk report including the coordinate positioning of the thermal map to the pre-configured terminal device through the pre-set communication link.

[0006] Furthermore, for the method for dam safety monitoring of a pumped storage power station based on Beidou positioning according to the present invention, Step 2 includes: The Beidou monitoring stations and reference stations deployed in the dam body and slope area synchronously receive the Beidou, GPS, GLONASS, and Galileo satellite signals; Perform edge computing on the received multi-system satellite signals, screen and retain the effective band data through the signal-to-noise ratio threshold and add a signal quality identifier; Predict the communication blind area according to the signal quality identifier, and switch to the Beidou short message transmission mode in the dual-channel transmission link when the detected signal strength is lower than the preset strength threshold; Upload the preprocessed satellite observation data to the cloud server through the preset 4G network and Beidou short message dual-channel; Perform spatio-temporal alignment processing on the preprocessed data on the cloud server to generate a continuous data stream and input it into step 2 to perform dynamic error correction.

[0007] Furthermore, for the dam safety monitoring method of a pumped-storage power station based on Beidou positioning according to the present invention, step 2 includes: Construct a Beidou local augmentation network through the deployed reference stations, and use the multi-base station joint error modeling method to correct the ionospheric and tropospheric errors of the preprocessed satellite observation data in real time; Realize sub-nanosecond time synchronization based on the high-precision time synchronization device preset in the reference station, and eliminate the positioning deviation through the combined processing of baseline solution and network adjustment; Input the corrected satellite observation data into the three-dimensional coordinate solution processing flow to generate a spatio-temporal reference data stream with time stamps; Perform secondary calibration on the spatio-temporal reference data stream using the preset atmospheric refraction compensation parameters, and output the calibrated spatio-temporal reference data stream.

[0008] Furthermore, for the dam safety monitoring method of a pumped-storage power station based on Beidou positioning according to the present invention, step 3 includes: Extract satellite displacement data from the calibrated spatio-temporal reference data stream, and perform time stamp alignment with the strain data collected by the static level and the strain data collected by the multi-point displacement meter; Adopt the preset Kalman filtering algorithm to perform cross-dimensional fusion on the aligned displacement and strain data, and extract the horizontal displacement, vertical settlement and crack expansion characteristics; Combine the fused data with the field data collected by the temperature sensor and the seepage pressure gauge, and input it into the global deformation dynamic model; Verify the consistency of the transient response between the fused data and the output of the global deformation dynamic model through time series analysis, and generate a transient response consistency verification result for the early warning rule to call.

[0009] Furthermore, for the dam safety monitoring method of a pumped-storage power station based on Beidou positioning according to the present invention, step 4 includes: Based on the displacement parameters output by the global deformation dynamic model, establish a stiffness degradation model on the cloud server and set multi-dimensional early warning thresholds for the deformation amount, rate and acceleration; Compare the displacement monitoring value of the deformation dynamic model with the early warning threshold in real time. When the monitoring value continuously exceeds the limit for a set number of times, trigger a graded early warning signal; Map the coordinates of the over-limit area to a three-dimensional deformation thermal map through the preset digital twin model, and superimpose the transient response consistency verification result to obtain the marked high-risk area; Push the marked high-risk area to the preset inspection terminal, and update the early warning threshold parameters according to the disposal results feedback by the inspection terminal.

[0010] Further, in the dam safety monitoring method of the pumped-storage power station based on Beidou positioning according to the present invention, step 5 includes: Collect real-time displacement monitoring information according to the arranged reference station, and eliminate the systematic error caused by the structural deformation of the reference station through a preset dynamic coordinate update algorithm; Based on the historical monitoring feature distribution law generated by the spatio-temporal alignment and data fusion processing, dynamically optimize the spatial distribution density and data acquisition frequency of the initially arranged monitoring points, and the historical monitoring feature distribution law is obtained through the time series analysis of the transient response consistency verification result; According to the data quality evaluation index generated by the secondary calibration processing of the atmospheric refraction compensation parameter, dynamically optimize the noise suppression coefficient and the multi-band signal weight ratio parameter in the signal-to-noise ratio threshold screening processing; Combined with the environmental parameter change data in the hierarchical warning signal, update the atmospheric refraction compensation parameter described in step 3 according to a preset calibration period.

[0011] Further, in the dam safety monitoring method of the pumped-storage power station based on Beidou positioning according to the present invention, step 6 includes: When the 4G network signal strength is higher than the set signal strength threshold, upload the preprocessed satellite observation data generated in step 1 through a high-speed channel; When it is detected that the network delay exceeds the preset tolerance threshold or the data packet loss rate increases, switch to the Beidou short message transmission mode in the dual-channel transmission link to send the compressed data packet; Based on the signal quality identifier added in step 2 in the cloud server, perform redundant check and timestamp alignment and recombination on the data in the dual-channel transmission link; Transmit the recombined data to the spatio-temporal reference data stream update processing flow described in step 3, and perform linear interpolation and completion operations on the low-quality data segments marked by the identifier.

[0012] Advantages of the present invention; The present invention improves the reliability of data collection in complex terrains through multi-system satellite signal fusion and dynamic error correction technology. The Beidou monitoring station and the reference station array are combined with a dual-channel transmission link to ensure the data transmission continuity in communication blind areas. The construction of a local enhancement network and the joint error modeling of multiple base stations eliminate the interference of the ionosphere and troposphere, generating a spatio-temporal reference data stream with millimeter-level accuracy, and realizing the spatio-temporal reference unification of multi-source heterogeneous data. The cross-dimensional data fusion based on the Kalman filter algorithm extracts the features of horizontal displacement, vertical settlement and crack expansion, and constructs a global deformation dynamic model by coupling the temperature field and the seepage field. Through digital twin technology, the dynamic mapping and visual warning of the coordinates in high-risk areas are realized. The closed-loop adaptive mechanism dynamically optimizes the distribution density of monitoring points and signal processing parameters according to historical monitoring features, and combines the environmental-driven atmospheric refraction compensation update and dual-link redundancy check to form a full-process closed-loop management from data collection, error correction to hierarchical warning, effectively improving the real-time performance, accuracy of dam deformation monitoring and the reliability of structural safety assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can also be obtained according to the drawings without creative efforts.

[0014] Figure 1 It is a flowchart of the dam safety monitoring method for pumped storage power stations based on Beidou positioning provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments and corresponding drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention. The following will detail the technical solutions provided by each embodiment of the present invention with reference to the drawings. To better understand the objectives of the present invention, the present invention will be further described in detail below.

[0016] Please refer to Figure 1 , the dam safety monitoring method for pumped storage power stations based on Beidou positioning provided by the present invention includes: Step 1: Deploy Beidou monitoring stations and reference stations in the dam body and slope area to receive multi-band satellite signals. The multi-band satellite signals include Beidou satellite signals, GPS satellite signals, GLONASS satellite signals, and Galileo system satellite signals. Perform edge computing on the received multi-band satellite signals, screen and fuse the effective band data through the signal-to-noise ratio threshold, generate preprocessed satellite observation data, and establish a dual-channel transmission link with the preset Beidou short message through the preset 4G network to upload it to the preset cloud server in real time; Step 2: Build a local augmentation network in the cloud server, perform dynamic error correction on the uploaded preprocessed satellite observation data, generate a three-dimensional coordinate solution result, and output a calibrated spatio-temporal reference data stream; Step 3: Align the satellite displacement data in the calibrated spatio-temporal reference data stream with the strain measurement data collected by the static level gauge and the strain measurement data collected by the multi-point displacement meter in space and time. Extract the transient response characteristics of displacement and strain through data fusion, and build a global deformation dynamic model that integrates the coupling effects of the temperature field and the seepage field; Step 4: Input the satellite displacement data and the strain measurement data of the spatio-temporal reference data stream into the global deformation dynamic model, and output the displacement, rate, and acceleration monitoring parameters of the dam structure; Step 5: Set warning thresholds corresponding to the displacement, rate, and acceleration monitoring parameters of the dam structure in the cloud server. When the displacement, rate, and acceleration monitoring parameters of the dam structure exceed the corresponding warning thresholds, generate a graded warning signal including the risk level and location coordinates according to the value of the over-limit parameter; Step 6: Build a three-dimensional deformation thermal map based on the displacement, rate, and acceleration monitoring parameters of the dam structure, and push a risk report including the coordinate positioning of the thermal map to the pre-configured terminal device through the preset communication link.

[0017] The specific technical solution of the dam safety monitoring method for pumped storage power stations based on Beidou positioning is as follows: Deploy Beidou monitoring stations and reference stations in the dam body and slope area to receive multi-band satellite signals from Beidou, GPS, GLONASS, and Galileo systems. Perform real-time processing on the multi-band satellite signals through edge computing, use the signal-to-noise ratio threshold screening technology to fuse the effective band data, eliminate the interference of low signal-to-noise ratio bands, and generate preprocessed satellite observation data with signal quality identifiers. The preprocessed data is uploaded to the cloud server in real time through the dual-channel transmission link of the preset 4G network and Beidou short message. The Beidou short message transmission mode is automatically enabled when the detected signal strength is lower than the preset threshold to ensure the continuity of data transmission in the communication blind area.

[0018] The cloud server constructs a Beidou local augmentation network and performs dynamic error correction on the preprocessed satellite observation data uploaded by using the multi-base station joint error modeling method. Sub-nanosecond time synchronization is achieved through the high-precision time synchronization device of the reference station group. Combining baseline solution and network adjustment joint processing to eliminate the positioning deviation caused by ionospheric delay, tropospheric refraction and multipath effect. The corrected satellite observation data is input into the three-dimensional coordinate solution process to generate a spatio-temporal reference data stream with a timestamp, and secondary calibration is performed by using the preset atmospheric refraction compensation parameters, and a spatio-temporal reference data stream with millimeter-level accuracy is output.

[0019] The satellite displacement data in the calibrated spatio-temporal reference data stream is time-stamped with the strain measurement data collected by the static level and the multi-point displacement meter, and the Kalman filter algorithm is used to perform cross-dimensional fusion on the multi-source data. During the fusion process, characteristic parameters such as horizontal displacement, vertical settlement and crack expansion are extracted, and combined with the temperature field and seepage field data collected by the temperature sensor and the seepage pressure gauge, a global deformation dynamic model integrating the coupling effect of multiple physical fields is constructed. This model verifies the transient response consistency between the fusion data and the model output through time series analysis, and generates a transient response consistency verification result.

[0020] The displacement, rate and acceleration monitoring parameters output by the global deformation dynamic model are input into the early warning analysis module of the cloud server, and multi-dimensional early warning thresholds corresponding to the structural safety level are set. When the monitoring parameters exceed the threshold, a hierarchical early warning signal is triggered according to the number and value of the over-limit parameters, and the coordinates of the over-limit area are mapped to a three-dimensional deformation thermal map through the digital twin model. The thermal map is overlaid with the transient response consistency verification result, and the coordinate positioning information of the high-risk area is marked and pushed to the inspection terminal to form a closed-loop disposal process.

[0021] The distribution density and observation frequency of the monitoring points are dynamically optimized according to the distribution characteristics of the historical monitoring data, and the noise suppression parameters and signal weight ratios are dynamically adjusted based on the quality assessment results of the spatio-temporal reference data stream. The systematic error caused by the deformation of the reference station itself is corrected in real time by the dynamic coordinate update algorithm, and the atmospheric refraction compensation parameters are updated regularly in combination with the environmental parameter change data in the early warning record to maintain the consistency of the monitoring accuracy.

[0022] The dual-channel transmission link adaptively switches the transmission mode according to the network status. When the 4G network signal strength is higher than the set threshold, the high-speed channel is used to transmit data. When the network delay or packet loss rate exceeds the limit, it switches to the Beidou short message to transmit compressed data packets. The cloud server performs redundant verification and timestamp alignment and recombination on the dual-channel data based on the signal quality identifier. The recombined data stream is updated to the spatio-temporal reference data stream processing process, and linear interpolation is performed on the low-quality data segments marked by the identifier to ensure data integrity and timeliness.

[0023] The above technical solution realizes the dynamic monitoring of millimeter-level deformation in the whole area of the dam through multi-band signal fusion, dynamic error correction, multi-source data fusion and closed-loop early warning mechanism, solves the technical problems of insufficient artificial observation frequency and inconsistent spatio-temporal reference under complex terrain, and improves the real-time performance and accuracy of structural safety early warning.

[0024] Specifically, for the method for monitoring the safety of a pumped-storage power station dam based on Beidou positioning according to the present invention, step 2 includes: The Beidou monitoring stations and reference stations arranged in the dam body and slope areas synchronously receive the Beidou, GPS, GLONASS and Galileo satellite signals. Perform edge computing processing on the received multi-system satellite signals, and retain the effective band data by screening through the signal-to-noise ratio threshold and add a signal quality identifier. Predict the communication blind area according to the signal quality identifier, and switch to the Beidou short message transmission mode in the dual-channel transmission link when it is detected that the signal strength is lower than the preset strength threshold. Upload the preprocessed satellite observation data to the cloud server through the preset 4G network and Beidou short message dual channels. Perform spatio-temporal alignment processing on the preprocessed data in the cloud server, and generate a continuous data stream to perform dynamic error correction in step 2.

[0025] The specific technical implementation process of step 2 is as follows: Arrange an array of Beidou monitoring stations and reference stations in the dam body and slope areas according to the preset spatial distribution density, and each station synchronously receives multi-band satellite signals of the Beidou, GPS, GLONASS and Galileo systems. The monitoring station is equipped with a multi-mode GNSS receiver, which parallelly captures the navigation signals of different satellite systems through multi-frequency RF channels, and uses carrier phase differential technology to improve the accuracy of the original observation data. The reference station is deployed with a high-stability base and an anti-interference shielding cover, and realizes sub-millisecond clock synchronization with the monitoring station through optical fiber transmission to ensure the unity of the spatio-temporal reference for multi-source satellite signal acquisition.

[0026] The received multi-system satellite signals are transmitted to the edge computing node for real-time processing. The edge computing node integrates a signal quality evaluation module and a band fusion algorithm. The signal quality evaluation module dynamically calculates the carrier-to-noise ratio parameters of each band signal based on the signal-to-noise ratio threshold screening mechanism, and eliminates the low-quality band data below the set threshold. The effective band data is integrated through a weighted fusion algorithm, and the fusion weight is dynamically allocated according to the geometric dilution of precision of the constellation distribution of each satellite system to generate preprocessed satellite observation data with a signal quality identifier. The signal quality identifier includes the carrier-to-noise ratio, the multipath effect index and the data integrity check code, which are used for the quality control of the subsequent transmission link.

[0027] The cloud server constructs a signal quality monitoring module to parse the signal quality identifiers in the preprocessed data in real time, and predict communication blind spots based on the carrier-to-noise ratio attenuation trend and the multipath effect index. When the detected signal strength is lower than the preset strength threshold or the data integrity check fails, a transmission mode switching instruction is automatically triggered. During the switching process, the data transmission continuity is maintained, and the compressed data packet is preferentially sent through the Beidou short message transmission mode. The compression algorithm uses entropy coding technology to retain the phase and pseudorange information of the original observation data to avoid loss of key data.

[0028] The preprocessed satellite observation data is uploaded in parallel through a preset 4G network and Beidou short message dual-channel transmission link. The dual-channel adopts a differential quality of service strategy. The 4G network channel carries high-frequency original observation data streams, and the Beidou short message channel transmits compressed data packets as redundant backups. The data receiving module of the cloud server performs timestamp alignment and data packet recombination on the dual-channel data to eliminate the timing misalignment problem caused by transmission delay.

[0029] In the data processing engine of the cloud server, the preprocessed data is input into the spatio-temporal alignment module for refined processing. The spatio-temporal alignment module uses a sliding window mechanism to match satellite displacement data with the reference station coordinate framework, and eliminates the spatial deviation caused by receiver clock error and orbit error through the least squares adjustment algorithm. The aligned data stream is interpolated and resampled according to a unified time reference to generate a standardized data set with continuous time series, and is input into the dynamic error correction process for ionospheric and tropospheric error modeling. The dynamic error correction process uses the local augmentation network constructed by the reference station group to jointly solve the real-time separation of common mode errors and random noise through multi-base station, and outputs the three-dimensional coordinate solution result with millimeter-level accuracy.

[0030] The above technical solution forms a complete chain of satellite observation data preprocessing and error correction through multi-system signal fusion, edge quality screening, dual-channel redundant transmission, and spatio-temporal reference standardization processing, providing high-precision spatio-temporal reference data support for subsequent deformation dynamic modeling.

[0031] Specifically, for the dam safety monitoring method based on Beidou positioning described in the present invention, step 2 includes: Construct a Beidou local augmentation network through the deployed reference stations, and use the multi-base station joint error modeling method to correct the ionospheric and tropospheric errors of the preprocessed satellite observation data in real time; Realize sub-nanosecond-level time synchronization based on the high-precision time synchronization device preset in the reference station, and eliminate the positioning deviation through the combined processing of baseline solution and network adjustment; Input the corrected satellite observation data into the three-dimensional coordinate solution processing flow to generate a spatio-temporal reference data stream with timestamps; Perform secondary calibration on the spatio-temporal reference data stream using preset atmospheric refraction compensation parameters, and output the calibrated spatio-temporal reference data stream.

[0032] The technical implementation process of Step 2 is as follows: Arrange a reference station group around the dam body according to a preset topological structure to form a Beidou local augmentation network. The reference stations are equipped with multi-band GNSS receivers and meteorological sensors to collect satellite raw observation data and atmospheric parameters in real time. The network adopts a distributed data processing architecture, and each reference station is interconnected through a fiber-optic private network to achieve millisecond-level synchronous transmission of observation data. The multi-base station joint error modeling method constructs a regional differential model of ionospheric delay and tropospheric refraction error based on the spatial distribution characteristics of the reference station group, and eliminates systematic errors on the satellite signal propagation path through real-time differential correction technology. During the error correction process, a sliding window adaptive filtering algorithm is introduced to dynamically separate the common mode error and site-specific noise in the observation data.

[0033] The reference station is built with a rubidium atomic clock and a satellite common view module to form a high-precision time synchronization system, achieving sub-nanosecond-level time synchronization between stations. The time synchronization data is embedded in the observation data stream and used as the reference input for baseline solution. The baseline solution uses a double-difference carrier phase observation model and combines network adjustment algorithms to eliminate positioning deviations caused by receiver clock errors, orbit errors, and multipath effects. The network adjustment process introduces robust estimation theory, and optimizes the site coordinate residuals through iterative weighted least squares to output a relative positioning result that meets the millimeter-level accuracy requirements.

[0034] The corrected satellite observation data is input into a three-dimensional coordinate solution module, and the solution module integrates precise point positioning algorithms and dynamic filtering models. The data processing process uses an extended Kalman filter to fuse pseudorange and carrier phase observations to calculate the three-dimensional geocentric coordinates of the monitoring station in real time. The solution result is superimposed with the reference station coordinate frame transformation parameters to generate a displacement data stream under a unified spatio-temporal reference. The data stream is embedded with Coordinated Universal Time (UTC) timestamps to form a time-space associated standardized data set for subsequent deformation modeling calls.

[0035] The spatio-temporal reference data stream is input into an atmospheric refraction compensation module for secondary calibration. The compensation module calls a preset atmospheric refraction parameter library, which is constructed based on historical meteorological data and machine learning algorithms and contains signal propagation correction coefficients under different pressure, temperature, and humidity conditions. During the calibration process, an adaptive weighted interpolation technique is used to dynamically match the optimal compensation parameters according to real-time meteorological observation data, and non-linearly correct the elevation component in the spatio-temporal reference data stream. The calibrated data stream is output after integrity verification to eliminate systematic measurement deviations caused by changes in the atmospheric environment.

[0036] The above solution forms a complete correction chain from raw observations to high-precision spatio-temporal reference data streams through multi-level processing including local enhancement network construction, time synchronization optimization, coordinate calculation process, and atmospheric compensation calibration. The collaborative error modeling of the reference station group improves the regional differential accuracy. The time synchronization system ensures the spatio-temporal consistency of multi-source data. The dynamic matching mechanism of atmospheric refraction parameters enhances the measurement robustness in complex environments, providing basic data support for the dynamic modeling of global deformation.

[0037] Specifically, for the method for dam safety monitoring of a pumped-storage power station based on Beidou positioning described in the present invention, step 3 includes: extracting satellite displacement data from the calibrated spatio-temporal reference data stream, and performing timestamp alignment with the strain data collected by the static level gauge and the strain data collected by the multi-point displacement meter; Using a preset Kalman filtering algorithm to perform cross-dimensional fusion on the aligned displacement and strain data, and extracting horizontal displacement, vertical settlement, and crack expansion characteristics; Combining the fused data with the field data collected by the temperature sensor and the seepage pressure gauge, and inputting it into the global deformation dynamic model; Verifying the transient response consistency between the fused data and the output of the global deformation dynamic model through time series analysis, and generating a transient response consistency verification result for the early warning rule to call.

[0038] The specific technical implementation process of step 3 is as follows: Parsing satellite displacement data from the calibrated spatio-temporal reference data stream output in step 2, and using a timestamp parser to extract the satellite positioning coordinate sequence. The static level gauge and the multi-point displacement meter upload the strain measurement data through the industrial bus protocol. The data acquisition module is built-in with a high-precision clock source, which is synchronized with the Beidou timing signal to generate nanosecond-level timestamps. The spatio-temporal alignment module uses a sliding window matching algorithm to align the time bases of multi-source data, and performs cubic spline interpolation processing on the non-equidistant sampled data to eliminate the timing deviation caused by the difference in the sensor acquisition frequency.

[0039] The aligned displacement and strain data are input into the cross-dimensional data fusion engine. The preset Kalman filtering algorithm constructs a state space model, using the satellite displacement data as the system observation variable and the strain data as the process noise compensation term. During the filtering iteration process, multi-dimensional observation values such as horizontal displacement, vertical displacement, and crack opening are fused, and the fusion weights of each dimension data are dynamically adjusted through the covariance matrix to extract the characteristic parameters of the horizontal displacement component, vertical settlement gradient, and crack expansion rate representing the dam deformation.

[0040] The preprocessing module of the global deformation dynamic model for the fused multi-source data inputs the temperature gradient field and seepage pressure field data collected by the temperature sensor array and seepage piezometer network deployed inside the dam body. The temperature field data is mapped to the equivalent deformation correction amount through the thermodynamic expansion coefficient conversion module, and the seepage field data calculates the influence weight of the seepage force on the structural strain according to Darcy's law to form a multi-physical field coupling input parameter set.

[0041] The global deformation dynamic model is constructed based on the finite element theory and data-driven hybrid architecture, and adopts the offline training and online update mechanism. The input end of the model integrates the fused multi-source data, and the output end generates the displacement field, stress field and crack evolution prediction results of the dam structure. The time series analysis module performs moving average and residual analysis on the transient response data output by the model, calculates the correlation coefficient and root mean square error between the measured data and the prediction results, and generates the transient response consistency verification index. The verification results are transmitted to the early warning rule library through the standardized interface, triggering the threshold comparison and risk assessment logic to support the generation and optimization of the hierarchical early warning signal.

[0042] The above solution establishes a complete analysis chain from the original data to the deformation assessment through progressive processing of space-time reference alignment, multi-dimensional data fusion, multi-physical field coupling modeling and response consistency verification. The Kalman filter algorithm improves the fusion accuracy of multi-source heterogeneous data, the multi-physical field coupling mechanism enhances the environmental adaptability of the model, and the time series analysis ensures the reliability of the model output, providing a high-confidence decision-making basis for structural safety early warning.

[0043] Specifically, for the dam safety monitoring method of the pumped storage power station based on Beidou positioning described in the present invention, step 4 includes: establishing a stiffness degradation model on the cloud server based on the displacement parameters output by the global deformation dynamic model and setting multi-dimensional early warning thresholds for the deformation amount, rate and acceleration; Comparing the displacement monitoring value of the deformation dynamic model with the early warning threshold in real time, and triggering a hierarchical early warning signal when the monitoring value continuously exceeds the limit for a set number of times; Mapping the coordinates of the over-limit area to a three-dimensional deformation thermal map through a preset digital twin model, and superimposing the transient response consistency verification results to obtain the marked high-risk area; Pushing the marked high-risk area to the preset inspection terminal, and updating the early warning threshold parameters according to the disposal results feedback by the inspection terminal.

[0044] The specific technical implementation process of Step 4 is as follows: In the structural safety assessment module of the cloud server, a stiffness degradation model is constructed based on the displacement parameters output by the global deformation dynamic model. The stiffness degradation model integrates the constitutive relation database of the dam structure materials, and through machine learning algorithms, the mapping relationship between the structural stiffness and displacement parameters is obtained. Combining historical load spectrum data, multi-dimensional warning thresholds for deformation, deformation rate, and acceleration are established. The threshold setting adopts a dynamic adjustment mechanism, and based on the dam design parameters, real-time environmental loads, and material aging coefficients, a hierarchical safety interval is generated to form a warning threshold system associated with the structural health status.

[0045] The real-time comparison between the displacement monitoring values of the deformation dynamic model and the warning thresholds is achieved through the sliding window statistical method. The monitoring data is input into the adaptive filtering module for noise suppression, and after extracting the effective deformation features, it is input into the threshold comparison engine. The comparison engine adopts an event-driven architecture. When the number of times the monitoring value exceeds the corresponding dimension threshold within consecutive sampling periods reaches the preset threshold, an instruction for generating a hierarchical warning signal is triggered. The warning signal includes the over-limit dimension identifier, over-limit amplitude, and duration parameters, and is transmitted to the risk analysis module for priority ranking.

[0046] The digital twin model is constructed based on the three-dimensional geometric model of the dam and the finite element mesh. Through the physical field coupling engine, the coordinates of the over-limit area are mapped to a three-dimensional deformation thermal map. The thermal map rendering module uses color gradient encoding technology to dynamically adjust the regional color temperature according to the displacement over-limit amplitude and rate, and synchronously superimposes the transient response consistency verification results generated in Step 3. The superimposed data realizes the visualization of risk levels through the transparency fusion algorithm. The coordinates of the marked high-risk areas are matched with the engineering drawing coordinate system through the spatial indexing algorithm to generate a risk positioning report containing geographic information encoding.

[0047] The data of the marked high-risk areas is pushed to the inspection terminal through a low-latency communication protocol. The terminal device integrates an augmented reality navigation module, and based on the coordinate information of the risk positioning report, an inspection path is planned. The disposal results are transmitted back to the cloud server through the terminal feedback interface. The feedback data includes the deformation features verified on-site, disposal measures, and corrected measurement values. The warning threshold parameter update module adopts an incremental learning algorithm to dynamically optimize the weight coefficients of the threshold generation model according to the feedback data, improving the matching accuracy between the threshold setting and the actual damage state of the structure.

[0048] The above solution forms a closed-loop management chain from deformation monitoring to risk disposal through stiffness degradation modeling, dynamic threshold comparison, digital twin visualization, and feedback optimization mechanisms. The stiffness degradation model establishes a quantitative correlation between structural performance and deformation parameters. The digital twin technology realizes risk spatial positioning and visual expression. The feedback mechanism ensures the adaptive optimization of the warning system, providing technical support for the full-life cycle safety management of the pumped-storage power station dam.

[0049] Specifically, for the method for dam safety monitoring of pumped - storage power stations based on Beidou positioning according to the present invention, step 5 includes: Collect real - time displacement monitoring information according to the deployed reference stations, and eliminate the systematic errors caused by the structural deformation of the reference stations through a preset dynamic coordinate update algorithm; Dynamically optimize the spatial distribution density and data acquisition frequency of the initially deployed monitoring points based on the historical monitoring feature distribution law generated by the spatio - temporal alignment and data fusion processing, and the historical monitoring feature distribution law is obtained through the time - series analysis of the transient response consistency verification results; Dynamically optimize the noise suppression coefficient and multi - band signal weight ratio parameters in the signal - to - noise ratio threshold screening process according to the data quality evaluation index generated by the secondary calibration process of the atmospheric refraction compensation parameters; Combined with the environmental parameter change data in the hierarchical early - warning signal, update the atmospheric refraction compensation parameters described in step 3 according to a preset calibration period.

[0050] The specific technical implementation process of step 5 is as follows: The reference station is equipped with a multi - mode GNSS receiver to collect displacement monitoring data in real - time, and the systematic errors caused by the deformation of the reference station itself are eliminated through a preset dynamic coordinate update algorithm. The dynamic coordinate update algorithm adopts a robust Kalman filter model, fuses the data of the reference station structural health monitoring sensors and satellite positioning observations, and constructs a three - dimensional deformation compensation matrix for the reference station. The compensation matrix is dynamically updated through a sliding window mechanism to correct the coordinate frame deviation of the reference station in real - time and improve the reference stability of the local augmentation network.

[0051] The historical monitoring data generated by the spatio - temporal alignment and data fusion processing is input into the feature analysis engine. The engine extracts the distribution law of deformation hot - spot areas based on the time - series clustering algorithm. The distribution law is subjected to spatial correlation analysis with the transient response consistency verification results output in step 4 to identify highly sensitive monitoring areas. The monitoring point optimization module dynamically adjusts the monitoring point density according to the analysis results, adds redundant sensor nodes in highly sensitive areas, and reduces the data acquisition frequency in low - change areas to achieve adaptive allocation of monitoring resources.

[0052] The data quality evaluation index generated by the secondary calibration of the atmospheric refraction compensation parameters is input into the parameter optimization module. The index includes the signal - to - noise ratio attenuation gradient, the multipath effect index, and the phase residual standard deviation. The optimization module uses the gradient - descent algorithm to dynamically adjust the noise suppression coefficient in the signal - to - noise ratio threshold screening process, and combines the change of the satellite constellation geometry to update the multi - band signal weight ratio parameters in real - time to improve the signal fusion accuracy of the edge computing nodes.

[0053] The change data of environmental parameters in the graded early warning signal triggers the update mechanism of the atmospheric refraction compensation parameters. The environmental data collected in real time by temperature, pressure, and humidity sensors are input into the parameter prediction model. The model predicts the trend of the atmospheric refraction effect based on the long short-term memory network (LSTM). The prediction result is combined with the preset calibration period. When the change range of environmental parameters exceeds the set threshold or reaches the calibration period, the atmospheric refraction parameter library in step 3 is automatically called to perform an online update, and the compensation parameters are synchronized to all reference stations through the differential broadcast protocol.

[0054] The above solution constructs a closed-loop adaptive monitoring system through dynamic correction of reference station errors, adaptive optimization of monitoring points, real-time adjustment of signal processing parameters, and environment-driven update of atmospheric parameters. The improvement of the stability of the reference station ensures the accuracy benchmark of the local augmentation network. The optimization of the monitoring points realizes the efficient allocation of resources. The dynamic adjustment of signal processing parameters enhances the data quality in complex environments. The environment-driven compensation mechanism maintains the long-term monitoring consistency, jointly supporting the high precision and robustness of the dam safety monitoring system.

[0055] Specifically, for the dam safety monitoring method based on Beidou positioning described in the present invention, step 6 includes: When the 4G network signal strength is higher than the set signal strength threshold, the preprocessed satellite observation data generated in step 1 is uploaded through the high-speed channel; When it is detected that the network delay exceeds the preset tolerance threshold or the data packet loss rate increases, switch to the Beidou short message transmission mode in the dual-channel transmission link to send the compressed data packet; Based on the signal quality identifier added in step 2, the cloud server performs redundant verification and timestamp alignment recombination on the data in the dual-channel transmission link; The recombined data is transmitted to the spatio-temporal reference data stream update processing flow described in step 3, and a linear interpolation completion operation is performed on the low-quality data segment marked by the identifier.

[0056] The technical implementation process of step 5 is as follows: The reference station is configured with a multi-source sensor array to collect structural deformation data in real time, and the self-deformation error is eliminated through the robust filtering model in the dynamic coordinate update algorithm. This algorithm fuses the differences between the measured values of the strain gauges and inclinometers inside the reference station and the satellite positioning coordinates, constructs a three-dimensional deformation compensation matrix for the reference station, and updates the coordinate frame parameters of the reference station through the sliding window mechanism to maintain the stability of the measurement benchmark of the local augmentation network.

[0057] After the historical monitoring data is processed by spatio-temporal alignment and data fusion, the input feature distribution analysis module extracts the deformation hotspots. The analysis module uses a time series clustering algorithm to identify high-variance time periods and spatial aggregation regions, and conducts spatial correlation modeling in combination with the transient response consistency verification results generated in step 4, outputting the monitoring point optimization strategy. The optimization strategy drives the monitoring network reconstruction module to dynamically adjust the distribution density of sensor nodes, increasing redundant monitoring points in high-risk areas and reducing the data acquisition frequency in low-frequency change areas to achieve adaptive allocation of monitoring resources.

[0058] The data quality evaluation indicators generated by the secondary calibration of the atmospheric refraction compensation parameters include carrier phase residuals, multipath effect indices, and signal-to-noise ratio attenuation rate parameters. The indicators are input into the parameter optimization engine, which uses a gradient descent algorithm to dynamically adjust the noise suppression coefficient of edge computing nodes, and optimizes the multi-band signal weight ratio in combination with the real-time satellite constellation geometry configuration to improve the anti-interference ability and data validity in the signal preprocessing link.

[0059] The environmental parameter monitoring module collects temperature, pressure, and humidity data in real time and inputs it into the atmospheric refraction prediction model. The model analyzes the non-linear relationship between environmental parameters and the atmospheric refraction effect based on a long short-term memory network, and the prediction results trigger the parameter update mechanism. When the environmental change amplitude exceeds the set threshold or reaches the preset calibration period, the atmospheric refraction parameter library in step 3 is called to perform an online update, and the updated parameters are broadcast to all reference stations through the differential correction protocol to maintain the measurement accuracy consistency under complex meteorological conditions.

[0060] The above solution forms a closed-loop adaptive monitoring accuracy control system through reference station error correction, dynamic optimization of the monitoring network, adaptive adjustment of signal processing parameters, and environment-driven compensation updates. The stability maintenance of the reference station ensures the unity of the spatial reference, the optimization of monitoring points improves the data acquisition efficiency, the dynamic adjustment of signal processing parameters enhances the anti-interference ability, and the environment-coupled parameter update mechanism ensures long-term monitoring stability, jointly supporting the high precision and reliability of the dam safety monitoring system.

[0061] The technical features of the present invention are explained as follows: BeiDou monitoring stations and reference station arrays: A group of BeiDou positioning devices arranged in the dam body and slope areas.

[0062] The monitoring stations are used to collect BeiDou satellite signals in real time, and the reference stations provide high-precision positioning references as reference points. The two cooperate to improve the regional positioning consistency. The reference stations are equipped with high-performance atomic clocks and anti-interference devices, and achieve sub-nanosecond-level time synchronization through an optical fiber synchronization network to eliminate clock deviations between multiple stations.

[0063] Multi - band Satellite Signal Fusion (Beidou / GPS / Glonass / Galileo): Synchronously receive satellite signals from multiple navigation systems, and improve the reliability of positioning data through frequency - band fusion. Fusing signals from multiple systems can enhance the satellite geometric configuration, reduce the risk of signal occlusion or interference in a single system, and ensure the availability of data especially in complex terrain environments. Edge - computing nodes dynamically eliminate low - quality frequency bands according to the signal - to - noise ratio, retain effective frequency bands and generate pre - processed data through weighted fusion.

[0064] Edge - computing Processing and Signal - to - Noise Ratio Threshold Screening: Conduct preliminary signal processing at the data acquisition end (monitoring station) instead of relying on the cloud. Calculate the carrier - to - noise ratio (C / N0) of each frequency band in real time, and only retain the data of frequency bands with a signal - to - noise ratio higher than the set threshold, filter out noise interference, and improve data quality. Reduce the amount of invalid data transmission, reduce the processing burden on the cloud, and shorten the response delay.

[0065] Dual - channel Transmission Link (4G Network and Beidou Short Message): Adopt a transmission mode where the high - speed transmission of the 4G network and the satellite communication of Beidou short message are used as backups for each other. When the 4G signal is weak or the network delay is high, automatically switch to Beidou short message to transmit compressed data packets to avoid communication interruption. The cloud reorganizes the dual - channel data through timestamp alignment and quality identifier verification to ensure the continuity of data transmission.

[0066] Local Area Augmentation Network and Joint Error Modeling of Multiple Base Stations: Use a network of reference stations to construct a regional differential network to correct satellite signal propagation errors in real time. Through joint modeling of data from multiple base stations for ionospheric delay, tropospheric refraction, and multipath effects, dynamically generate error compensation parameters. Baseline solution and network adjustment algorithms eliminate positioning residuals and achieve millimeter - level three - dimensional coordinate solution.

[0067] Space - time Reference Data Stream: A unified data sequence after error correction and time synchronization. Use high - precision timestamps to align multi - source data (such as satellite displacement, strain measurement data), and secondarily correct the elevation component deviation through atmospheric refraction compensation parameters. Provide space - time consistency guarantee for subsequent data fusion and avoid cumulative errors.

[0068] Kalman Filter Cross - dimensional Data Fusion: A filtering algorithm based on the state - space model that fuses satellite displacement and strain sensor data. Eliminate the noise of a single sensor, extract key deformation characteristic parameters such as horizontal displacement, vertical settlement, and crack propagation rate. Combine temperature field and seepage field data to enhance the adaptability of the model to the coupling effect of multiple physical fields.

[0069] Global Deformation Dynamic Model: An integrated finite - element analysis and data - driven hybrid model that simulates the deformation response of the dam structure under multiple loads. Fuse multi - source parameters such as displacement, strain, temperature, and seepage pressure after fusion. Deformation monitoring indicators such as displacement, rate, and acceleration are used to evaluate the trend of structural stiffness degradation.

[0070] 3D Deformation Thermal Map and Digital Twin Mapping: Visualize deformation data based on digital twin technology and label high-risk areas. Encode the amplitude of displacement exceeding the limit through color gradient, and overlay the verification results of transient response consistency to generate a locatable risk report. Assist the inspection terminal to quickly locate potential hazard areas and improve the disposal efficiency.

[0071] Closed-loop Adaptive Mechanism: The system dynamically optimizes key parameters according to historical data and feedback. Such as the distribution density of monitoring points, the screening threshold of signal-to-noise ratio, and the atmospheric refraction compensation parameter. Predict the change trend of environmental parameters through machine learning algorithms (such as LSTM) to achieve the stability of long-term monitoring accuracy.

[0072] The specific implementation of the present invention is as follows: Deploy Beidou monitoring stations and reference station arrays in the dam and slope areas of pumped-storage power stations, and use multi-mode GNSS receivers to synchronously capture multi-band satellite signals of Beidou, GPS, GLONASS, and Galileo systems. The monitoring stations integrate edge computing units, and use signal-to-noise ratio threshold screening technology to real-time eliminate interference from low signal-to-noise ratio frequency bands, retain the data of effective frequency bands for weighted fusion, and generate preprocessed satellite observation data with signal quality identifiers. The preprocessed data is real-time uploaded to the cloud server through a preset 4G network and Beidou short message dual-channel transmission link. The Beidou short message transmission mode is automatically enabled when the signal strength is lower than the preset threshold, solving the problem of data transmission interruption in communication blind spots caused by complex terrain, and ensuring the real-time and integrity of data.

[0073] The cloud server constructs a Beidou local area augmentation network. Based on the spatial distribution characteristics of the reference station group, use the multi-base station joint error modeling method to real-time correct the ionospheric delay and tropospheric refraction error. The reference stations are built-in rubidium atomic clocks and satellite common-view modules to achieve sub-nanosecond-level time synchronization, and combine baseline solution and robust network adjustment algorithms to eliminate positioning deviations, generating a spatio-temporal reference data stream with millimeter-level accuracy. The data stream is secondarily calibrated through an atmospheric refraction compensation module, and calls a preset meteorological parameter library to dynamically match the signal propagation correction coefficient under real-time environmental conditions, eliminating systematic measurement errors caused by changes in the atmospheric environment and improving the measurement reliability of the elevation component.

[0074] The time-stamped alignment is performed between the calibrated spatio-temporal reference data stream and the strain data collected by the static level gauges and multi-point displacement gauges. The Kalman filter algorithm is used to fuse the displacement and strain features across dimensions, and the parameters of horizontal displacement, vertical settlement and crack propagation rate are extracted. The fused data, combined with the data of the temperature field and seepage field sensors, is input into the global deformation dynamic model, and the monitoring parameters of displacement, rate and acceleration are output through the finite element and data-driven hybrid architecture. The early warning analysis module dynamically sets multi-dimensional thresholds based on the stiffness degradation model. When the monitoring values continuously exceed the limits, a hierarchical early warning signal is triggered, and the over-limit area is mapped to a 3D thermal map through the digital twin model, and the high-risk coordinates are marked by superimposing the transient response consistency verification results. The distribution density of monitoring points and the data collection frequency are dynamically optimized according to the historical feature distribution, and the early warning threshold is updated by combining the feedback data of the inspection terminal to achieve closed-loop adaptive monitoring. The dual-channel transmission link switches the transmission mode according to the network status, and the cloud server completes the low-quality data segment through redundant verification and linear interpolation to ensure the integrity and timeliness of the data, and finally realizes the millimeter-level deformation dynamic monitoring and real-time safety early warning of the entire dam area.

[0075] The present invention solves the problem of insufficient artificial observation frequency through multi-system satellite signal fusion and dynamic error correction technology. Beidou monitoring stations and reference station arrays are arranged in the dam body and slope areas to synchronously receive multi-band satellite signals and perform edge computing processing, and the effective frequency band data is screened and fused by using the signal-to-noise ratio threshold. The preprocessed data is uploaded to the cloud server in real time through the preset dual-channel transmission link, and the Beidou short message mode is automatically enabled when the signal strength is insufficient to ensure the continuity of data transmission under complex terrain. The local enhancement network is constructed based on the reference station group, and the multi-base station joint error modeling method is used to correct the ionospheric and tropospheric errors in real time. Combined with the high-precision time synchronization device, the positioning deviation is eliminated, and a spatio-temporal reference data stream with millimeter-level accuracy is generated to realize the spatio-temporal reference unification processing of multi-source data.

[0076] The problem of inconsistent spatio-temporal reference is solved through multi-physical field coupling modeling and real-time deformation analysis technology. The spatio-temporal alignment is performed between the calibrated spatio-temporal reference data stream and the strain data of the static level gauges and multi-point displacement gauges. The Kalman filter algorithm is used to fuse the displacement and strain data across dimensions, and the horizontal displacement, vertical settlement and crack expansion characteristics are extracted. The fused data, combined with the temperature field and seepage field parameters, is input into the global deformation dynamic model, and the transient response consistency is verified through time series analysis, and the monitoring parameters of displacement, rate and acceleration are output. The digital twin model maps the coordinates of the over-limit area to a 3D deformation thermal map, and the high-risk area is marked by superimposing the consistency verification results to realize the dynamic visualization expression and accurate positioning of the deformation characteristics.

[0077] Based on a closed-loop adaptive mechanism, the problem of being unable to obtain millimeter-level three-dimensional deformation data in real time is solved. The warning threshold is dynamically set according to the stiffness degradation model. When the monitoring values continuously exceed the limit, a hierarchical warning signal is triggered, and the threshold parameters are optimized through feedback from the inspection terminal. The distribution density of monitoring points and the data acquisition frequency are dynamically adjusted according to historical monitoring characteristics, and the signal processing parameters are optimized in real time based on data quality evaluation indicators. The atmospheric refraction compensation parameters are updated regularly in combination with environmental change data to ensure the consistency of measurement accuracy under complex meteorological conditions. The dual-channel transmission link is adaptively switched according to the network status, and redundancy check and interpolation completion technologies are used to eliminate the influence of low-quality data, forming a full-process closed-loop management from data acquisition to risk disposal, and improving the real-time performance and reliability of dam safety monitoring.

Claims

1. A pumped storage power station dam safety monitoring method based on Beidou positioning, characterized in that: include: Step 1: receiving a multi-band satellite signal, wherein the multi-band satellite signal includes a Beidou satellite signal, a GPS satellite signal, a GLONASS satellite signal, and a Galileo system satellite signal, performing edge computing processing on the received multi-band satellite signal, filtering and fusing effective frequency band data through a signal-to-noise ratio threshold, generating pre-processed satellite observation data, and establishing a dual-channel transmission link through a preset 4G network and a preset Beidou short message to upload the data to a preset cloud server in real time; Step 2: Build a local area enhancement network on the cloud server, perform dynamic error correction on the uploaded pre-processed satellite observation data, generate three-dimensional coordinate solution results, and output the calibrated spatiotemporal reference data stream; Step 3, aligning the satellite displacement data in the calibrated spatiotemporal reference data stream with the strain measurement data collected by the static level and the strain measurement data collected by the multi-point displacement meter in time and space, extracting the transient response characteristics of displacement and strain through data fusion, and constructing a global deformation dynamic model integrating the coupling effects of temperature field and seepage field; Step 4, inputting the satellite displacement data of the spatiotemporal reference data stream and the strain measurement data into the global deformation dynamic model, and outputting the displacement, velocity and acceleration monitoring parameters of the dam structure; Step 5, setting warning thresholds corresponding to the displacement, velocity and acceleration monitoring parameters of the dam structure on the cloud server, and when the displacement, velocity and acceleration monitoring parameters of the dam structure exceed the corresponding warning thresholds, generating a graded warning signal including a risk level and a position coordinate according to the value of the over-limit parameter; Step 6: construct a three-dimensional deformation thermodynamic map based on the displacement, velocity and acceleration monitoring parameters of the dam structure, and push a risk report including the coordinate positioning of the thermodynamic map to a pre-configured terminal device through a preset communication link.

2. The method for pumped storage power station dam safety monitoring based on Beidou positioning according to claim 1 is characterized in that: The step 2 comprises: The Beidou monitoring stations and base stations deployed in the dam body and slope areas synchronously receive the Beidou, GPS, GLONASS and Galileo satellite signals; Perform edge computing processing on the received multi-system satellite signals, retain valid frequency band data through signal-to-noise ratio threshold screening and add signal quality identifiers; Predicting a communication blind area according to a signal quality identifier, and switching to a Beidou short message transmission mode in the dual-channel transmission link when the signal strength is detected to be lower than a preset strength threshold; Upload the pre-processed satellite observation data to the cloud server through the preset 4G network and Beidou short message dual channel; The pre-processed data is temporally and spatially aligned on the cloud server to generate a continuous data stream for input into step 2 to perform dynamic error correction.

3. The method for pumped storage power station dam safety monitoring based on Beidou positioning according to claim 2 is characterized in that: The step 2 comprises: The BeiDou local enhancement network is built through the deployment of base stations, and the multi-base station joint error modeling method is used to correct the ionospheric and tropospheric errors of the pre-processed satellite observation data in real time; Sub-nanosecond time synchronization is achieved based on the high-precision time synchronization device preset in the base station, and positioning deviation is eliminated through the combined processing of baseline solution and network adjustment; Input the corrected satellite observation data into the three-dimensional coordinate solution processing flow to generate a spatiotemporal reference data stream with a timestamp; The space-time reference data stream is recalibrated using preset atmospheric refractometer compensation parameters, and the calibrated space-time reference data stream is output.

4. The method for pumped storage power station dam safety monitoring based on Beidou positioning according to claim 3 is characterized in that: The step 3 comprises: extracting satellite displacement data from the calibrated spatiotemporal reference data stream, and aligning the satellite displacement data with the strain data collected by the static level and the strain data collected by the multi-point displacement meter; The preset Kalman filter algorithm is used to perform cross-dimensional fusion of the aligned displacement and strain data to extract horizontal displacement, vertical settlement and crack extension characteristics; The fused data is combined with the field data collected by the temperature sensor and the seepage pressure gauge and input into the global deformation dynamic model; The consistency of transient response between fused data and the output of global deformation dynamic model is verified through time series analysis, and the transient response consistency verification results are generated for early warning rule calling.

5. The method for pumped storage power station dam safety monitoring based on Beidou positioning according to claim 4 is characterized in that: The step 4 comprises: based on the displacement parameters output by the global deformation dynamic model, establishing a stiffness degradation model on the cloud server and setting multi-dimensional warning thresholds of deformation, velocity and acceleration; Compare the displacement monitoring value of the deformation dynamic model with the warning threshold in real time, and trigger a graded warning signal when the monitoring value exceeds the limit for a set number of consecutive times; The coordinates of the over-limit area are mapped to the three-dimensional deformation thermal map through the preset digital twin model, and the transient response consistency verification results are superimposed to obtain the marked high-risk area; The marked high-risk areas are pushed to the preset inspection terminals, and the warning threshold parameters are updated according to the disposal results fed back by the inspection terminals.

6. The method for pumped storage power station dam safety monitoring based on Beidou positioning according to claim 5 is characterized in that: The step 5 comprises: Collect real-time displacement monitoring information based on the deployed base stations, and eliminate system errors caused by base station structural deformation through a preset dynamic coordinate update algorithm; Based on the historical monitoring feature distribution law generated by the spatiotemporal alignment and data fusion processing, the spatial distribution density of the initially deployed monitoring points and the data acquisition frequency are dynamically optimized, and the historical monitoring feature distribution law is obtained by the time series analysis of the transient response consistency verification result; Dynamically optimize the noise suppression coefficient and multi-band signal weight matching parameters in the signal-to-noise ratio threshold screening process according to the data quality evaluation index generated by the secondary calibration process of the atmospheric refractometer compensation parameters; Combined with the environmental parameter change data in the graded warning signal, the atmospheric refractometer compensation parameters described in step 3 are updated according to the preset calibration cycle.

7. The method for pumped storage power station dam safety monitoring based on Beidou positioning according to claim 6 is characterized in that: The step 6 comprises: When the 4G network signal strength is higher than the set signal strength threshold, the pre-processed satellite observation data generated in step 1 is uploaded through the high-speed channel; When it is detected that the network delay exceeds a preset tolerance threshold or the data packet loss rate increases, switching to the Beidou short message transmission mode in the dual-channel transmission link to send compressed data packets; Based on the signal quality identifier added in step 2, the cloud server performs redundancy check and timestamp alignment reorganization on the dual-channel transmission link data; The reorganized data is transmitted to the spatiotemporal reference data stream update processing flow described in step 3, and a linear interpolation completion operation is performed on the low-quality data segments marked by the identifier.

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