A displacement auxiliary measurement mechanism and its reservoir dam monitoring and early warning system

Abstract

The present invention discloses a displacement-assisted measurement mechanism and a reservoir dam monitoring and early warning system thereof, belonging to the field of dam monitoring technology. The reservoir dam monitoring and early warning system of the present invention includes: a data acquisition module, a posture calibration module, a data fusion judgment module, a risk classification analysis module, and a remote early warning trigger module. By converting the installation posture change of the telescopic ranging component into a quantifiable direction vector correction value, the ranging accuracy under complex field conditions is significantly improved. The current mechanical direction is fused with the reference direction to generate a correction vector pointing to the target reflection point, effectively eliminating directional drift errors. By constructing a dual-path model of main path fusion and auxiliary path comparison, non-structural anomalies caused by sensor body interference or environmental disturbances are eliminated, data offset is corrected in real time, and a stable trend curve is output. When drift or error trends occur in the main path, it is automatically guided to converge to the auxiliary path, enhancing the ability to identify and repair abnormal data.

Description

Technical Field

[0001] The present invention relates to the technical field of dam monitoring, and in particular to a displacement auxiliary measurement mechanism and a reservoir dam monitoring and early warning system thereof. Background Art

[0002] As the core structure for the safe operation of a reservoir, the stability and reliability of a dam are directly linked to the safety of life and property in downstream areas. Currently, common dam deformation monitoring methods still face significant limitations in terms of field deployment complexity, real-time performance, anti-interference capabilities, and data fusion analysis. Traditional instruments are often sensitive to the installation angle, requiring precise alignment to ensure measurement accuracy, and are unable to flexibly adapt to complex terrain or dynamically changing conditions. Furthermore, existing monitoring systems generally lack real-time compensation mechanisms for changes in equipment posture, making them susceptible to external interference (such as wind, vibration, and construction) that can cause measurement errors. Furthermore, the lack of multi-source data fusion mechanisms limits the ability to accurately identify local deformation and identify abnormalities in the overall structural linkage. Summary of the Invention

[0003] The purpose of the present invention is to provide a displacement auxiliary measurement mechanism and a reservoir dam monitoring and early warning system thereof to solve the problems raised in the above background technology.

[0004] To achieve the above objectives, the present invention provides the following technical solutions: a displacement auxiliary measurement mechanism, comprising a base assembly, a fixed bracket, a telescopic ranging assembly, and target reflection points, wherein the base assembly is fixed to the concrete structure surface of the dam monitoring point by expansion bolts, the fixed bracket is vertically arranged on the base assembly, and the fixed bracket is an adjustable column, and the target reflection points are distributedly arranged on the reservoir dam;

[0005] The telescopic distance measuring component is arranged on a fixed bracket, one end of the telescopic distance measuring component is connected to the top of the fixed bracket, and the other end of the telescopic distance measuring component can be freely telescopically aligned with the target reflection point on the dam. The telescopic distance measuring component includes a laser distance measuring sensor and an electric telescopic rod;

[0006] The telescopic distance measuring component is provided with an angle sensing component, which is installed in the middle of the telescopic distance measuring component. The angle sensing component includes a gyroscope module and a tilt angle sensor. The telescopic distance measuring component is provided with an auxiliary displacement sensor.

[0007] Furthermore, the base assembly is provided with a rotating base ring and an adjustable tilt positioning pin, and the fixed bracket and the base assembly are connected via a T-slot slide rail and a locking nut;

[0008] A universal ball head connection seat is provided on the top of the fixed bracket, which is fixedly connected to the mounting end of the telescopic ranging assembly. The universal ball head connection seat is equipped with an angle dial and a positioning locking knob, and an auxiliary guide groove is provided on the fixed bracket.

[0009] Furthermore, the maximum extension length of the electric telescopic rod is not less than 1.5 meters, and magnetic limit switches are provided at both ends of the electric telescopic rod. The laser ranging sensor is connected to the top of the electric telescopic rod through a shock-proof bracket, and the outside of the shock-proof bracket is covered with a shock-absorbing silicone ring. The laser emission direction of the laser ranging sensor and the angle between the vertical line of the bracket are set between 0°-10°, and the laser beam of the laser ranging sensor is consistent with the normal direction of the target reflection surface.

[0010] Furthermore, one end of the auxiliary displacement sensor is fixed on the electric telescopic rod, and the other end of the auxiliary displacement sensor is connected to an installation anchor point independently established on the base assembly. The auxiliary displacement sensor is a retractable pull-wire structure. The pull wire of the auxiliary displacement sensor expands and contracts when the structure is deformed, and displacement measurement data is obtained through changes in internal resistance.

[0011] Furthermore, a reservoir dam monitoring and early warning system is applied to the above-mentioned displacement auxiliary measurement mechanism, comprising:

[0012] a data acquisition module configured to receive real-time ranging data, displacement change information, and attitude angle data collected from the displacement auxiliary measurement mechanism, the data acquisition module including a multi-channel interface unit and a data buffer unit for connecting to multiple displacement auxiliary measurement mechanisms, the multi-channel interface unit automatically identifying the geographic number and spatial orientation information of each monitoring point;

[0013] The attitude calibration module is configured to perform dynamic compensation and multi-angle vector correction on the collected laser ranging data based on attitude change information fed back by the angle sensing component;

[0014] The data fusion judgment module is configured to perform multi-source fusion comparison of laser ranging data and auxiliary displacement data, eliminate abnormal interference items, and judge the displacement change trend of the local or overall structure of the dam;

[0015] The risk grading analysis module is configured to generate a risk level assessment result based on the change trend data output by the data fusion judgment module and in combination with a preset structural stability model;

[0016] The remote warning trigger module is configured to automatically trigger a multi-level remote warning mechanism based on the risk level scoring results, and simultaneously upload the current status information to the remote monitoring terminal. According to the level of the risk level scoring results, it selectively sends graphical alarm instructions to the management terminal, pushes real-time trend reports, or starts the broadcast emergency warning channel.

[0017] Furthermore, the posture calibration module includes:

[0018] an attitude data analysis unit configured to perform real-time analysis on the three-axis tilt data and gyroscope output from the angle sensor assembly, convert the pitch angle, roll angle, and yaw angle into attitude change vectors in the ranging reference coordinate system through a predetermined three-dimensional vector rotation conversion model, and generate an attitude change vector matrix;

[0019] a direction vector generating unit configured to construct a correction direction vector pointing to the target reflection point based on the posture change vector matrix, in combination with the current mechanical direction of the telescopic ranging component and the vertical direction information of the installation reference point, and to superimpose the correction direction vector with the current laser beam direction of the laser ranging sensor to obtain a direction correction value of the actual ranging path;

[0020] The ranging compensation unit is configured to dynamically compensate the original laser ranging data based on the ranging path direction correction value, and output the final compensated laser ranging compensation data to the data fusion judgment module.

[0021] Furthermore, the data fusion judgment module includes:

[0022] A main path fusion unit is configured to receive the laser ranging compensation data output by the attitude calibration module and construct a main decision path with the laser ranging data as the core based on the spatial positioning information at each monitoring time point; the main path fusion unit automatically screens the trend stable segment and the mutation segment according to a preset period, and extracts representative key ranging points through a set data weight matrix;

[0023] an auxiliary path comparison unit configured to receive displacement measurement data from the auxiliary displacement sensor and establish an auxiliary comparison path synchronized with the time series of the main determination path;

[0024] The auxiliary path comparison unit is provided with a displacement difference mapping model, which is configured to map the displacement measurement data into the coordinate system of the laser ranging data according to time, and compare the deviation value of the main determination path and the auxiliary comparison path at each time point to determine whether there is a sensor offset;

[0025] The auxiliary path comparison unit performs pattern recognition on the sensor offset based on multi-point data clustering, eliminates non-structural disturbances and dynamically adjusts the fitting curve of the auxiliary comparison path to the main determination path;

[0026] an abnormal path correction unit configured to determine whether there is a measurement error or a structural abnormality based on a difference value between the main determination path and the auxiliary comparison path;

[0027] The abnormal path correction unit is equipped with a deviation threshold judgment mechanism. When the difference between the main judgment path and the auxiliary comparison path of a monitoring point exceeds the set error limit within a specified time, it is marked as a potential abnormal node and an alarm information is generated;

[0028] The abnormal path correction unit corrects the data of the main determination path based on the stable trend of the auxiliary comparison path. When the error of the main determination path has a persistent deviation trend, the auxiliary comparison path fitting value is called to perform path compensation and output a corrected fusion path.

[0029] The abnormal path correction compares the trend synchronization of multiple measuring points in the same area, identifies whether there is a local structural linkage deformation phenomenon, and outputs the trend analysis results.

[0030] Furthermore, the steps for determining the set error limit in the deviation threshold judgment mechanism are as follows:

[0031] According to the location of the reservoir dam, the water storage and flood discharge record data of the reservoir dam, and the structural parameters of the dam, the corresponding finite element model representing the migration risk is retrieved from a pre-configured finite element model library representing the migration risk;

[0032] According to the finite element units in the finite element model corresponding to the installation reference points and monitoring points, the finite element units to be analyzed are retrieved;

[0033] Determine a set error limit based on the retrieved offset risk parameter of the finite element to be analyzed;

[0034] The rule for selecting the finite element units to be analyzed is as follows: the finite element units within a preset first radius around the installation reference point, the finite element units within a preset second radius around the monitoring point, and the finite element units on the shortest path from the installation reference point to the monitoring point are used as the finite element units to be analyzed;

[0035] Based on the retrieved offset risk parameters of the finite element to be analyzed, the error limits are determined, including:

[0036] The finite element elements to be analyzed are divided into three groups according to the different determination methods;

[0037] Convert the offset risk parameters of the finite element units into stable values ​​according to the preset conversion table corresponding to each group;

[0038] A stability assessment is performed based on the stability value of each finite element unit in each group to obtain a stability assessment value;

[0039] The pre-configured correspondence table is queried with the stable evaluation value to determine the error limit.

[0040] Furthermore, the risk classification analysis module includes:

[0041] The risk trend extraction unit is configured to receive the fusion path and trend analysis results output by the data fusion judgment module, and perform trend segmentation and feature vector extraction on the displacement, attitude change, and change rate of each monitoring point of the dam based on a set time window;

[0042] The risk trend extraction unit identifies trend fluctuation segments based on time series fitting, and extracts classification indicators including continuous rising trend segments, sudden transition segments, trend stable segments and reversal deformation segments, to generate a multi-dimensional risk trend factor set between multiple spatial measurement points;

[0043] The grade assessment modeling unit is configured to establish a multi-factor risk assessment model based on fuzzy logic judgment and weighted matrix scoring according to a multi-dimensional risk trend factor set, combined with a preset structural stability model and environmental sensitivity parameters, and input the trend segments and characteristic vectors extracted by the risk trend extraction unit into the multi-factor risk assessment model to generate a risk grade scoring result, which includes four levels: normal area, safety warning area, risk warning area and emergency warning area.

[0044] Furthermore, the reservoir dam monitoring and early warning system also includes: a monitoring point and installation reference point analysis and determination module for analyzing the reservoir dam and determining the monitoring points and their corresponding installation reference points;

[0045] The analysis steps of the monitoring point and installation reference point analysis and determination module are as follows:

[0046] According to the location of the reservoir dam, the water storage and flood discharge record data of the reservoir dam and the structural parameters of the dam, the corresponding finite element model representing the migration risk is retrieved from the pre-configured finite element model library representing the migration risk;

[0047] The finite element unit with the largest offset risk parameter is used as the monitoring point, and the installation reference point is determined based on the pre-configured association rule between the installation reference point and the monitoring point;

[0048] The area within a preset radius around the finite element unit determined as the monitoring point is set as an unselectable area;

[0049] An area can be selected in the finite element model, and the finite element unit with the largest offset risk parameter is continuously selected as the monitoring point; until the selected offset risk parameter is less than or equal to the preset risk threshold.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. This invention significantly improves ranging accuracy under complex field conditions by converting the installation posture changes of the telescopic ranging assembly into quantifiable directional vector correction values. Three-dimensional vector modeling is performed on the tilt changes of the laser ranging device in the three spatial axes. The pitch, yaw, and roll angles are analytically converted into a posture change vector matrix, enabling the ranging system to perceive and compensate for posture disturbances. The current mechanical direction is integrated with the reference direction to generate a correction vector pointing to the target reflection point, effectively eliminating directional drift errors caused by wind, installation deviation, or platform vibration. The path direction correction value is used to perform vector compensation on the original laser ranging data, ensuring the spatial consistency and temporal stability of the output data and improving the system's ability to recognize high-frequency micro-deformations.

[0052] 2. The present invention constructs a dual-path model of main path fusion and auxiliary path comparison, establishes a continuous time series judgment path, automatically identifies trend segments and mutation segments, extracts key nodes to realize the main line interpretation of the overall trend, and uses time synchronization and spatial mapping methods to establish a consistent comparison benchmark with the main path, forming a redundant verification mechanism for ranging data, eliminating non-structural anomalies caused by sensor body interference or environmental disturbances, correcting data offsets in real time and outputting a stable trend curve. When drift or error trends appear in the main path, it automatically guides it to converge to the auxiliary path, significantly enhancing the ability to identify and repair abnormal data. By comparing the trends of multiple measuring points in a linked manner, it can determine whether there are regional structural anomalies, realizing the transition from single-point anomaly judgment to spatial linkage analysis, and enhancing the system's comprehensive perception of overall structural changes.

[0053] 3. By combining time window sliding analysis with feature extraction, the present invention can segment trends of small structural deformation changes, not only identifying the intensity and direction of the changes, but also revealing the potential causes of trend fluctuations. Through the detailed classification of different forms such as continuous trends, sudden trends, and trend reversals, it can provide comprehensive data support for subsequent risk modeling. Fuzzy logic rules are constructed based on multidimensional trend factors, and multi-factor fusion judgment is realized in conjunction with a weighted scoring matrix model. Combined with the structural stability model and external environmental sensitivity parameters, a comprehensive assessment of different types of risks is conducted to ensure the timeliness of early warning actions and the rationality of classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Schematic diagram of the displacement auxiliary measurement mechanism of the present invention;

[0055] Figure 2 This is a schematic diagram of the target reflection point of the reservoir dam of the present invention;

[0056] Figure 3 This is a schematic diagram of the reservoir dam monitoring and early warning system module of the present invention.

[0057] In the figure: 1. Base assembly; 11. Rotating base ring; 12. Adjustable tilt positioning pin; 2. Fixed bracket; 21. Universal ball joint connector; 3. Telescopic ranging assembly; 31. Laser ranging sensor; 32. Electric telescopic rod; 33. Angle sensing assembly; 34. Auxiliary displacement sensor; 4. Target reflection point. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] See also Figure 1-3 , the present invention provides the following technical solutions:

[0060] A displacement auxiliary measurement mechanism includes a base assembly 1, a fixed bracket 2, a telescopic ranging assembly 3, and a target reflection point 4. The base assembly 1 is fixed to the concrete structure surface of the dam monitoring point by expansion bolts. The base assembly 1 is provided with a rotating base ring 11 and an adjustable tilt positioning pin 12. The fixed bracket 2 and the base assembly 1 are connected by a T-slot slide rail and a locking nut. The fixed bracket 2 is vertically arranged on the base assembly 1. The fixed bracket 2 is an adjustable column. The target reflection points 4 are distributed on the reservoir dam. A universal ball head connector 21 is provided on the top of the fixed bracket 2. The universal ball head connector 21 is fixedly connected to the mounting end of the telescopic ranging assembly 3. The universal ball head connector 21 is equipped with an angle dial and a positioning locking knob. The fixed bracket 2 is provided with an auxiliary guide groove.

[0061] The telescopic ranging assembly 3 is arranged on the fixed bracket 2, one end of the telescopic ranging assembly 3 is connected to the top of the fixed bracket 2, and the other end of the telescopic ranging assembly 3 can be freely telescopically aligned with the target reflection point 4 on the dam. The telescopic ranging assembly 3 includes a laser ranging sensor 31 and an electric telescopic rod 32. The maximum extension length of the electric telescopic rod 32 is not less than 1.5 meters. Magnetic limit switches are provided at both ends of the electric telescopic rod 32. The laser ranging sensor 31 is connected to the top of the electric telescopic rod 32 through a shockproof bracket. The outside of the shockproof bracket is covered with a shock-absorbing silicone ring. The angle between the laser emission direction of the laser ranging sensor 31 and the vertical line of the bracket is set between 0° and 10°. The laser beam of the laser ranging sensor 31 is consistent with the normal direction of the target reflection surface.

[0062] An angle sensing assembly 33 is provided on the telescopic ranging assembly 3, and the angle sensing assembly 33 is installed in the middle of the telescopic ranging assembly 3. The angle sensing assembly 33 includes a gyroscope module and a tilt angle sensor. An auxiliary displacement sensor 34 is provided on the telescopic ranging assembly 3. One end of the auxiliary displacement sensor 34 is fixed on the electric telescopic rod 32, and the other end of the auxiliary displacement sensor 34 is connected to an installation anchor point independently established on the base assembly 1. The auxiliary displacement sensor 34 is a retractable pull-wire structure. The pull wire of the auxiliary displacement sensor 34 changes in extension and contraction when the structure is deformed, and the displacement measurement data is obtained through changes in internal resistance.

[0063] In the above embodiment, through the structural optimization and functional integration of the displacement auxiliary measurement mechanism, multi-dimensional real-time monitoring of the deformation state of large structures such as reservoir dams is achieved. Through the combination of the electric telescopic rod 32 and the laser ranging sensor 31, high-precision, long-distance target reflection point positioning and ranging functions are achieved, which can effectively adapt to the complex spatial distribution and structural curvature of the dam surface. Through the angle sensing component 33 and the auxiliary displacement sensor 34 provided on the telescopic ranging component 3, real-time acquisition of posture changes and redundant monitoring of local structural strain can be achieved respectively, avoiding errors or monitoring blind spots caused by a single measurement method. It has good portability and deployment flexibility, is suitable for various complex terrains and harsh environments, and is an important improvement in the accuracy, reliability and on-site adaptability of traditional displacement monitoring devices.

[0064] A reservoir dam monitoring and early warning system, applied to the above-mentioned displacement auxiliary measurement mechanism, comprises:

[0065] A data acquisition module configured to receive real-time ranging data, displacement change information, and attitude angle data collected from the displacement auxiliary measurement mechanism. The data acquisition module includes a multi-channel interface unit and a data buffer unit for connecting to multiple displacement auxiliary measurement mechanisms. The multi-channel interface unit automatically identifies the geographic number and spatial orientation information of each monitoring point.

[0066] The attitude calibration module is configured to perform dynamic compensation and multi-angle vector correction on the collected laser ranging data based on attitude change information fed back by the angle sensing component;

[0067] The data fusion judgment module is configured to perform multi-source fusion comparison of laser ranging data and auxiliary displacement data, eliminate abnormal interference items, and judge the displacement change trend of the local or overall structure of the dam;

[0068] The risk grading analysis module is configured to generate a risk level assessment result based on the change trend data output by the data fusion judgment module and in combination with a preset structural stability model;

[0069] The remote warning trigger module is configured to automatically trigger a multi-level remote warning mechanism based on the risk level scoring results, and simultaneously upload the current status information to the remote monitoring terminal. According to the level of the risk level scoring results, it selectively sends graphical alarm instructions to the management terminal, pushes real-time trend reports, or starts the broadcast emergency warning channel.

[0070] In the above embodiment, a complete closed loop from front-end data perception to back-end risk response is effectively constructed through multi-module collaborative design. The system uses a highly integrated data acquisition module as the front-end core, which can realize parallel data acquisition and unified management of multiple monitoring points. With the automatic identification function, it can quickly complete the archiving and classification of deployment points, reducing the workload of manual intervention. Through the collaborative processing of the posture calibration module and the data fusion judgment module, the system can overcome the ranging error caused by the angular disturbance caused by equipment placement error and foundation displacement in actual application, thereby greatly improving the accuracy and credibility of the original data. At the same time, the system has a hierarchical response mechanism. Combined with the real-time risk trend assessment results, it can trigger the remote alarm process in time when the risk level reaches the set threshold, effectively improving the response efficiency to sudden structural deformation events.

[0071] Attitude calibration module, including:

[0072] an attitude data analysis unit configured to perform real-time analysis on the three-axis tilt data and gyroscope output from the angle sensor assembly, convert the pitch angle, roll angle, and yaw angle into attitude change vectors in the ranging reference coordinate system through a predetermined three-dimensional vector rotation conversion model, and generate an attitude change vector matrix;

[0073] a direction vector generating unit configured to construct a correction direction vector pointing to the target reflection point based on the posture change vector matrix, in combination with the current mechanical direction of the telescopic ranging component and the vertical direction information of the installation reference point, and to superimpose the correction direction vector with the current laser beam direction of the laser ranging sensor to obtain a direction correction value of the actual ranging path;

[0074] The ranging compensation unit is configured to dynamically compensate the original laser ranging data based on the ranging path direction correction value, and output the final compensated laser ranging compensation data to the data fusion judgment module.

[0075] In the above-mentioned embodiment, by converting the installation posture changes of the telescopic ranging assembly into quantifiable directional vector correction values, ranging accuracy under complex field conditions is significantly improved. A posture data analysis unit generates three-dimensional vector modeling of the tilt changes of the laser ranging device in three spatial axes. This is then converted into a posture change vector matrix through analysis of the pitch, yaw, and roll angles, enabling the ranging system to perceive and compensate for posture disturbances. A directional vector generation unit further fuses the current mechanical direction with the reference direction to generate a correction vector pointing to the target reflection point, effectively eliminating directional drift errors caused by wind, installation deviations, or platform vibration. The ranging compensation unit then uses the path direction correction value to perform vector compensation on the original laser ranging data, ensuring the spatial consistency and temporal stability of the output data. This improves the system's ability to recognize high-frequency, subtle deformations, making it particularly suitable for dynamic structural deformation monitoring and analysis scenarios.

[0076] Data fusion judgment module, including:

[0077] The main path fusion unit is configured to receive the laser ranging compensation data output by the attitude calibration module and construct a main decision path with the laser ranging data as the core based on the spatial positioning information at each monitoring time point. The main path fusion unit automatically selects trend stable segments and mutation segments according to a preset period and extracts representative key ranging points through a set data weight matrix.

[0078] an auxiliary path comparison unit configured to receive displacement measurement data from the auxiliary displacement sensor and establish an auxiliary comparison path synchronized with the time series of the main determination path;

[0079] The auxiliary path comparison unit is provided with a displacement difference mapping model, which is configured to map the displacement measurement data into the coordinate system of the laser ranging data according to time, and compare the deviation value of the main determination path and the auxiliary comparison path at each time point to determine whether there is a sensor offset;

[0080] The auxiliary path comparison unit performs pattern recognition on the sensor offset based on multi-point data clustering, eliminates non-structural disturbances and dynamically adjusts the fitting curve of the auxiliary comparison path to the main decision path;

[0081] an abnormal path correction unit configured to determine whether there is a measurement error or a structural abnormality based on a difference value between the main determination path and the auxiliary comparison path;

[0082] The abnormal path correction unit is equipped with a deviation threshold judgment mechanism. When the difference between the main judgment path and the auxiliary comparison path of a monitoring point exceeds the set error limit within a specified time, it is marked as a potential abnormal node and an alarm information is generated;

[0083] The abnormal path correction unit corrects the data of the main judgment path based on the stable trend of the auxiliary comparison path. When the error of the main judgment path has a persistent deviation trend, the auxiliary comparison path fitting value is called to perform path compensation and output the corrected fusion path.

[0084] Abnormal path correction compares the trend synchronization of multiple measuring points in the same area, identifies whether there is local structural linkage deformation, and outputs trend analysis results.

[0085] In the above-mentioned embodiment, a dual-path model consisting of primary path fusion and auxiliary path comparison is constructed. The primary path fusion unit establishes a continuous time series judgment path based on laser ranging compensation data, automatically identifies trend segments and mutation segments, and extracts key nodes to interpret the overall trend. The auxiliary path comparison unit incorporates auxiliary displacement sensor data and uses time synchronization and spatial mapping methods to establish a consistent comparison benchmark with the primary path, forming a redundant verification mechanism for ranging data. The accompanying displacement difference mapping model and cluster recognition mechanism not only eliminate non-structural anomalies caused by sensor interference or environmental disturbances, but also corrects data offsets in real time and outputs a stable trend curve. The abnormal path correction unit further incorporates threshold judgment and fitting correction mechanisms. When the primary path exhibits drift or error trends, it automatically guides it to converge toward the auxiliary path, significantly enhancing the ability to identify and repair abnormal data. Furthermore, by comparing the trends of multiple measurement points, regional structural anomalies can be determined, achieving a transition from single-point anomaly detection to spatial linkage analysis and enhancing the system's comprehensive perception of overall structural changes.

[0086] The steps for determining the set error limit in the deviation threshold judgment mechanism are as follows:

[0087] According to the location of the reservoir dam, the water storage and flood discharge record data of the reservoir dam and the structural parameters of the dam, the corresponding finite element model representing the offset risk is retrieved from the pre-configured finite element model library representing the offset risk; the finite element model library representing the offset risk is pre-configured, and multiple finite element models are stored in the finite element model library, the finite element model representing the offset risk, that is, the parameters of each finite element in the finite element model are the offset risk parameters; the offset risk parameters include: direction, offset and risk probability; the construction of the finite element model is obtained by analyzing a large amount of actual reservoir dam test data or model simulation experiment data. First, the reservoir dam is segmented by finite elements. The established grid segmentation method can be used for segmentation to divide the model into finite element units of the same size; then through the professional Statistical analysis is performed to configure offset risk parameters for each finite element unit. During the statistical analysis, the offset risk parameters are obtained based on the offset data of the actual position of the reservoir dam corresponding to each finite element unit. In addition, the steps for retrieving the finite element model library are as follows: feature extraction is performed on the setting position of the reservoir dam, the water storage and flood discharge record data of the reservoir dam, and the structural parameters of the dam to obtain multiple feature parameters; then the finite element model corresponding to the feature parameters is retrieved; wherein, the feature parameters include: first-class parameters representing the positioning data of the setting position of the reservoir dam, second-class parameters representing the number of water storage times, the water storage volume each time, the number of flood discharges, the flood discharge volume each time, the flood discharge time, etc., and third-class parameters representing the various types, various dimensional data, the weight data of various parts, and the type of materials used for various parts of the structural parameters of the dam, etc.

[0088] According to the finite element units in the finite element model corresponding to the installation reference points and monitoring points, the finite element units to be analyzed are retrieved;

[0089] Determine a set error limit based on the retrieved offset risk parameter of the finite element to be analyzed;

[0090] The rules for selecting the finite element units to be analyzed are as follows: the finite element units within a preset first radius range (the minimum side length of 2-10 finite element units) around the installation reference point, the finite element units within a preset second radius range (the minimum side length of 2-10 finite element units) around the monitoring point, and all finite element units on the shortest path from the installation reference point to the monitoring point are used as the finite element units to be analyzed;

[0091] Based on the retrieved offset risk parameters of the finite element to be analyzed, the error limits are determined, including:

[0092] The finite element elements to be analyzed are divided into three groups according to the different determination methods;

[0093] Convert the offset risk parameters of the finite element units into stable values ​​according to the preset conversion table corresponding to each group;

[0094] The stability evaluation is performed based on the stability value of each finite element in each group to obtain the stability evaluation value. The calculation formula is as follows: Where, Indicates stable evaluation value; is the average value of each stable value of the first group; is the maximum value of each stable value of the first group; is the minimum value of each stable value of the first group; For the second group The stability value corresponding to each finite element; is the total number of finite element elements in the second group; is the average value of each stable value of the second group; is the maximum value of each stable value of the second group; is the minimum value of each stable value of the second group; 、 、 are preset weight coefficients corresponding to the first group, the second group, and the third group respectively.

[0095] The pre-configured correspondence table is queried with the stable evaluation value to determine the error limit.

[0096] This embodiment determines the accurate error limit by performing offset risk analysis statistics around the monitoring point, the installation reference point, and between the two, so as to ensure the accuracy and effectiveness of the abnormality judgment. In addition, in order to further improve the accuracy and effectiveness of the abnormality judgment and avoid the occurrence of false alarms; the importance of the location of the reservoir dam where the monitoring point and / or the installation reference point is located to the reservoir dam can also be considered. The importance can be quantified as an important value, and the important value is converted into a correction value through a pre-configured conversion table; and then the error limit is corrected with the correction value. The important value can be analyzed in advance by professionals and marked in the corresponding finite element model representing the importance, that is, the finite element model uses the important value as the parameter of each finite element unit.

[0097] Risk classification analysis module, including:

[0098] The risk trend extraction unit is configured to receive the fusion path and trend analysis results output by the data fusion judgment module, and perform trend segmentation and feature vector extraction on the displacement, attitude change, and change rate of each monitoring point of the dam based on a set time window;

[0099] The risk trend extraction unit identifies trend fluctuation segments based on time series fitting, and extracts classification indicators including continuous rising trend segments, sudden transition segments, trend stable segments, and reversal deformation segments, generating a multi-dimensional risk trend factor set between multiple spatial measurement points;

[0100] The grade assessment modeling unit is configured to establish a multi-factor risk assessment model based on fuzzy logic judgment and weighted matrix scoring according to a multi-dimensional risk trend factor set, combined with a preset structural stability model and environmental sensitivity parameters. The trend segments and characteristic vectors extracted by the risk trend extraction unit are input into the multi-factor risk assessment model to generate risk grade scoring results. The risk grade scoring results include four levels: normal area, safety warning area, risk warning area and emergency warning area.

[0101] In the above embodiment, the risk trend extraction unit is combined with time window sliding analysis and feature extraction to segment the trend of small deformation changes of the structure, which not only identifies the intensity and direction of the change, but also reveals the potential causes of trend fluctuations. Through the fine classification of different forms such as continuous trends, sudden trends, and trend reversals, comprehensive data support can be provided for subsequent risk modeling. The level assessment modeling unit constructs fuzzy logic rules based on multidimensional trend factors, and cooperates with the weighted scoring matrix model to realize multi-factor fusion judgment. Combining the structural stability model with the external environmental sensitivity parameters, a comprehensive assessment of different types of risks is conducted. The risk levels are divided into normal areas, safety warning areas, risk warning areas and emergency warning areas, and corresponding response mechanisms are set to ensure the timeliness of warning actions and the rationality of classification. The traditional manual interpretation and single-indicator alarm method are upgraded to an intelligent assessment system based on multi-dimensional information automatic decision-making, which improves the risk prediction ability and operation efficiency of the monitoring system.

[0102] The reservoir and dam monitoring and early warning system also includes: a monitoring point and installation reference point analysis and determination module, which is used to analyze the reservoir and dam and determine the monitoring points and their corresponding installation reference points;

[0103] The analysis steps of the monitoring point and installation reference point analysis and determination module are as follows:

[0104] According to the location of the reservoir dam, the water storage and flood discharge record data of the reservoir dam and the structural parameters of the dam, the corresponding finite element model representing the migration risk is retrieved from the pre-configured finite element model library representing the migration risk;

[0105] The finite element unit with the largest offset risk parameter is used as the monitoring point, and the installation reference point is determined based on the pre-configured association rules between the installation reference point and the monitoring point; for example, the association rules include: mapping the center points of each finite element unit to the same horizontal plane, with the center of the finite element unit corresponding to the monitoring point as the center of the circle, and the finite element units within the area with a preset length (determined based on the maximum horizontal distance between the point corresponding to the installation reference point of the displacement auxiliary measurement mechanism and the point where the measurement ray is emitted) as the radius as the screening target, and the center point of the finite element unit with a relatively small offset risk and a large drop is taken as the installation reference point after comprehensive consideration; in the determination link of the installation reference point, the offset risk and the drop can be converted into a first value and a second value by pre-configuring a first conversion table and a second conversion table, and the center of the finite element unit corresponding to the maximum value of the sum of the first value and the second value is taken as the installation reference point;

[0106] The area within a preset radius around the finite element unit determined as the monitoring point is set as an unselectable area;

[0107] An area can be selected in the finite element model, and the finite element unit with the largest offset risk parameter is continuously selected as the monitoring point; until the selected offset risk parameter is less than or equal to the preset risk threshold.

[0108] This embodiment selects monitoring points based on risk analysis to ensure that monitoring is carried out effectively and reliably.

[0109] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A reservoir dam monitoring and early warning system of a displacement auxiliary measurement mechanism, which is applied to the displacement auxiliary measurement mechanism, wherein the displacement auxiliary measurement mechanism comprises a base assembly, a fixed bracket, a telescopic ranging assembly and a target reflection point, wherein the base assembly is fixed to the concrete structure surface of the dam monitoring point by expansion bolts, the fixed bracket is vertically arranged on the base assembly, and the target reflection points are distributed on the reservoir dam; one end of the telescopic ranging assembly is connected to the top of the fixed bracket, and the other end of the telescopic ranging assembly can be freely telescopically aligned with the target reflection point on the dam, the telescopic ranging assembly comprises a laser ranging sensor and an electric telescopic rod; an angle sensing assembly is provided on the telescopic ranging assembly, and the angle sensing assembly is installed in the middle of the telescopic ranging assembly, the angle sensing assembly comprises a gyroscope module and a tilt angle sensor, and an auxiliary displacement sensor is provided on the telescopic ranging assembly, characterized in that The reservoir dam monitoring and early warning system of the displacement auxiliary measurement mechanism includes: a data acquisition module configured to receive real-time ranging data, displacement change information, and attitude angle data collected from the displacement auxiliary measurement mechanism, the data acquisition module including a multi-channel interface unit and a data buffer unit for connecting to multiple displacement auxiliary measurement mechanisms, the multi-channel interface unit automatically identifying the geographic number and spatial orientation information of each monitoring point; The attitude calibration module is configured to perform dynamic compensation and multi-angle vector correction on the collected laser ranging data based on attitude change information fed back by the angle sensing component; The data fusion judgment module is configured to perform multi-source fusion comparison of laser ranging data and auxiliary displacement data, eliminate abnormal interference items, and judge the displacement change trend of the local or overall structure of the dam; The risk grading analysis module is configured to generate a risk level assessment result based on the change trend data output by the data fusion judgment module and in combination with a preset structural stability model; The remote warning trigger module is configured to automatically trigger a multi-level remote warning mechanism based on the risk level scoring results, and simultaneously upload the current status information to the remote monitoring terminal. According to the level of the risk level scoring results, it selectively sends graphical alarm instructions to the management terminal, pushes real-time trend reports, or starts the broadcast emergency warning channel.

2. A reservoir dam monitoring and early warning system with a displacement auxiliary measurement mechanism according to claim 1, characterized in that: The base assembly is provided with a rotating base ring and an adjustable tilt positioning pin, and the fixed bracket and the base assembly are connected via a T-slot slide rail and a locking nut; A universal ball head connection seat is provided on the top of the fixed bracket, which is fixedly connected to the mounting end of the telescopic ranging assembly. The universal ball head connection seat is equipped with an angle dial and a positioning locking knob, and an auxiliary guide groove is provided on the fixed bracket.

3. The reservoir dam monitoring and early warning system with a displacement auxiliary measurement mechanism according to claim 1, characterized in that: The maximum extension length of the electric telescopic pole is not less than 1.5 meters. Magnetic limit switches are provided at both ends of the electric telescopic pole. The laser ranging sensor is connected to the top of the electric telescopic pole through a shock-proof bracket. The outside of the shock-proof bracket is covered with a shock-absorbing silicone ring. The laser emission direction of the laser ranging sensor and the angle between the vertical line of the fixed bracket are set between 0°-10°. The laser beam of the laser ranging sensor is consistent with the normal direction of the target reflection surface.

4. The reservoir dam monitoring and early warning system with a displacement auxiliary measurement mechanism according to claim 1, characterized in that: One end of the auxiliary displacement sensor is fixed on the electric telescopic rod, and the other end of the auxiliary displacement sensor is connected to an installation anchor point independently established on the base assembly. The auxiliary displacement sensor is a retractable pull-wire structure. The pull wire of the auxiliary displacement sensor expands and contracts when the structure is deformed, and displacement measurement data is obtained through changes in internal resistance.

5. The reservoir dam monitoring and early warning system with a displacement auxiliary measurement mechanism according to claim 1, characterized in that: The posture calibration module includes: an attitude data analysis unit configured to perform real-time analysis on the three-axis tilt data and gyroscope output from the angle sensor assembly, convert the pitch angle, roll angle, and yaw angle into attitude change vectors in the ranging reference coordinate system through a predetermined three-dimensional vector rotation conversion model, and generate an attitude change vector matrix; a direction vector generating unit configured to construct a correction direction vector pointing to the target reflection point based on the posture change vector matrix, in combination with the current mechanical direction of the telescopic ranging component and the vertical direction information of the installation reference point, and to superimpose the correction direction vector with the current laser beam direction of the laser ranging sensor to obtain a direction correction value of the actual ranging path; The ranging compensation unit is configured to dynamically compensate the original laser ranging data based on the ranging path direction correction value, and output the final compensated laser ranging compensation data to the data fusion judgment module.

6. The reservoir dam monitoring and early warning system with a displacement auxiliary measurement mechanism according to claim 1, characterized in that: The data fusion judgment module includes: A main path fusion unit is configured to receive the laser ranging compensation data output by the attitude calibration module and construct a main decision path with the laser ranging data as the core based on the spatial positioning information at each monitoring time point; the main path fusion unit automatically screens the trend stable segment and the mutation segment according to a preset period, and extracts representative key ranging points through a set data weight matrix; an auxiliary path comparison unit configured to receive displacement measurement data from the auxiliary displacement sensor and establish an auxiliary comparison path synchronized with the time series of the main determination path; The auxiliary path comparison unit is provided with a displacement difference mapping model, which is configured to map the displacement measurement data into the coordinate system of the laser ranging data according to time, and compare the deviation value of the main determination path and the auxiliary comparison path at each time point to determine whether there is a sensor offset; The auxiliary path comparison unit performs pattern recognition on the sensor offset based on multi-point data clustering, eliminates non-structural disturbances and dynamically adjusts the fitting curve of the auxiliary comparison path to the main determination path; an abnormal path correction unit configured to determine whether there is a measurement error or a structural abnormality based on a difference value between the main determination path and the auxiliary comparison path; The abnormal path correction unit is equipped with a deviation threshold judgment mechanism. When the difference between the main judgment path and the auxiliary comparison path of a monitoring point exceeds the set error limit within a specified time, it is marked as a potential abnormal node and an alarm information is generated; The abnormal path correction unit corrects the data of the main determination path based on the stable trend of the auxiliary comparison path. When the error of the main determination path has a persistent deviation trend, the auxiliary comparison path fitting value is called to perform path compensation and output a corrected fusion path. The abnormal path correction compares the trend synchronization of multiple measuring points in the same area, identifies whether there is a local structural linkage deformation phenomenon, and outputs the trend analysis results.

7. A reservoir dam monitoring and early warning system with a displacement auxiliary measurement mechanism as claimed in claim 6, characterized in that: The steps for determining the set error limit in the deviation threshold judgment mechanism are as follows: According to the location of the reservoir dam, the water storage and flood discharge record data of the reservoir dam, and the structural parameters of the dam, the corresponding finite element model representing the migration risk is retrieved from a pre-configured finite element model library representing the migration risk; According to the finite element units in the finite element model corresponding to the installation reference points and monitoring points, the finite element units to be analyzed are retrieved; Determine a set error limit based on the retrieved offset risk parameter of the finite element to be analyzed; The rule for selecting the finite element units to be analyzed is as follows: the finite element units within a preset first radius around the installation reference point, the finite element units within a preset second radius around the monitoring point, and the finite element units on the shortest path from the installation reference point to the monitoring point are used as the finite element units to be analyzed; Based on the retrieved offset risk parameters of the finite element to be analyzed, the error limits are determined, including: The finite element elements to be analyzed are divided into three groups according to the different determination methods; Convert the offset risk parameters of the finite element units into stable values ​​according to the preset conversion table corresponding to each group; A stability assessment is performed based on the stability value of each finite element unit in each group to obtain a stability assessment value; The pre-configured correspondence table is queried with the stable evaluation value to determine the error limit.

8. The reservoir dam monitoring and early warning system with a displacement auxiliary measurement mechanism according to claim 1, characterized in that: The risk classification analysis module includes: The risk trend extraction unit is configured to receive the fusion path and trend analysis results output by the data fusion judgment module, and perform trend segmentation and feature vector extraction on the displacement, attitude change, and change rate of each monitoring point of the dam based on a set time window; The risk trend extraction unit identifies trend fluctuation segments based on time series fitting, and extracts classification indicators including continuous rising trend segments, sudden transition segments, trend stable segments and reversal deformation segments, to generate a multi-dimensional risk trend factor set between multiple spatial measurement points; The grade assessment modeling unit is configured to establish a multi-factor risk assessment model based on fuzzy logic judgment and weighted matrix scoring according to a multi-dimensional risk trend factor set, combined with a preset structural stability model and environmental sensitivity parameters, and input the trend segments and characteristic vectors extracted by the risk trend extraction unit into the multi-factor risk assessment model to generate a risk grade scoring result, which includes four levels: normal area, safety warning area, risk warning area and emergency warning area.

9. The reservoir dam monitoring and early warning system with a displacement auxiliary measurement mechanism according to claim 1, characterized in that: Also includes: Monitoring point and installation reference point analysis and determination module, used to analyze the reservoir dam and determine the monitoring points and their corresponding installation reference points; The analysis steps of the monitoring point and installation reference point analysis and determination module are as follows: According to the location of the reservoir dam, the water storage and flood discharge record data of the reservoir dam and the structural parameters of the dam, the corresponding finite element model representing the migration risk is retrieved from the pre-configured finite element model library representing the migration risk; The finite element unit with the largest offset risk parameter is used as the monitoring point, and the installation reference point is determined based on the pre-configured association rule between the installation reference point and the monitoring point; The area within a preset radius around the finite element unit determined as the monitoring point is set as an unselectable area; In the finite element model, you can select an area and continue to select the finite element with the largest offset risk parameter as the monitoring point; Until the selected offset risk parameter is less than or equal to the preset risk threshold.

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