Water conservancy project quality acceptance data acquisition system based on internet of things

Abstract

The application relates to the technical field of water conservancy engineering quality acceptance, in particular to a water conservancy engineering quality acceptance data acquisition system based on the Internet of Things; initial state arch dam data acquisition is carried out to obtain initial structure data; according to the sunlight irradiation law, arch dam surface temperature monitoring is carried out to obtain temperature distribution data; the arch dam temperature field area is divided according to the initial structure data and the temperature distribution data; according to the water-free deformation data and the temperature distribution data of the arch dam temperature field area, the deformation degree of each arch dam temperature field area under the influence of different temperature distributions is recognized to obtain water-free deformation parameters; in the water state, coupling temperature data, water pressure load data and arch dam structure data are collected to obtain water deformation data; the coupling temperature data, the water pressure load data and the water deformation data of the arch dam temperature field area are recognized through the arch dam quality acceptance model, and quality judgment is carried out in combination with the water-free deformation parameters.

Description

Technical Field

[0001] This invention relates to the field of water conservancy project quality acceptance technology, specifically to a water conservancy project quality acceptance data acquisition system based on the Internet of Things. Background Technology

[0002] In the field of large-scale water conservancy projects, especially in the design stage of complex structures such as arch dams, computer-aided design and related simulation technologies, such as finite element analysis, have become indispensable core tools. Designers use CAD systems to construct accurate three-dimensional geometric models of arch dams and combine them with FEA software to simulate and verify the structural response of the model under various design loads such as water pressure, temperature changes, and earthquakes.

[0003] However, in engineering practice, the transformation from design drawings to the completed entity is fraught with uncertainty. Unavoidable tolerances during construction, local variations in the physical properties of concrete materials, and complex interactions between the dam body and bedrock all lead to significant differences between the final "as-built model" and the initial "design model." Traditional engineering quality acceptance methods largely rely on manual on-site sampling and visual inspection. The data obtained by these methods are discrete and low-dimensional, making it difficult to effectively correlate and compare with high-dimensional CAD design models, resulting in a lack of macro-level evaluation of the overall structural integrity in the acceptance conclusions. Although modern dams are generally equipped with health monitoring systems, the large amount of data generated is usually used for long-term safety monitoring and is not systematically used in the acceptance of the completed quality.

[0004] To address this, a water conservancy project quality acceptance data acquisition system based on the Internet of Things is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a water conservancy project quality acceptance data acquisition system based on the Internet of Things. The system identifies coupled temperature data, water pressure load data and water deformation data of the arch dam temperature field area through an arch dam quality acceptance model, and makes quality judgments by combining waterless deformation parameters.

[0006] To achieve the above objectives, the present invention provides a water conservancy project quality acceptance data acquisition system based on the Internet of Things, comprising:

[0007] The temperature field segmentation module collects structural data of the arch dam in its initial state to obtain initial structural data; monitors the temperature of the arch dam surface according to the sunlight irradiation pattern to obtain temperature distribution data; constructs a temperature field model of the arch dam structure, and divides the arch dam temperature field into temperature field regions based on the initial structural data and temperature distribution data.

[0008] The waterless acceptance module periodically collects structural data of the arch dam in a waterless state and identifies the waterless deformation data. Based on the waterless deformation data and temperature distribution data of the arch dam's temperature field region, it identifies the degree of deformation of each arch dam's temperature field region under the influence of different temperature distributions and obtains the waterless deformation parameters.

[0009] The water-bearing acceptance module collects coupled temperature data and water pressure load data under the influence of water in the water-bearing state, and periodically collects structural data of the arch dam to obtain water-bearing deformation data; it constructs an arch dam quality acceptance model to identify coupled temperature data, water pressure load data and water-bearing deformation data in the temperature field region of the arch dam, and makes quality judgments in combination with waterless deformation parameters.

[0010] The structural data includes the geometric features of the arch dam, material physical properties, mechanical response benchmarks, and structural integrity parameters;

[0011] The geometric features of the arch dam include the elevation of the arch crown beam, the coordinates of the thrust piers at the arch ends, the radius of curvature of the arch axis, and the thickness distribution of the arch ring; the physical properties of the materials include the density distribution, elastic modulus, and compressive strength of the concrete in the arch dam; the mechanical response benchmarks include the initial stress state and deformation benchmark values; and the structural integrity parameters include the structural integrity data of the arch crown, arch seat, and arch ends.

[0012] The process of monitoring the surface temperature of the arch dam and obtaining temperature distribution data based on the pattern of sunlight exposure includes:

[0013] Based on the analysis of the relationship between the solar incidence angle and the geometric relationship between the arch dam structure, the surface of the arch dam is divided into a region directly exposed to sunlight and a region not directly exposed to sunlight. The region directly exposed to sunlight is the surface area of ​​the arch dam that can be directly illuminated by sunlight, while the region not directly exposed to sunlight is the surface area that cannot receive direct sunlight due to the arch dam's own structure and the surrounding terrain.

[0014] By calculating the spatial geometric relationship between the solar trajectory and the various structural components of the arch dam, and combining the temperature response differences of different geometric feature areas such as the thin-walled area of ​​the arch crown, the turning area of ​​the arch shoulder, the thick area of ​​the arch seat, and the constrained area of ​​the arch end, the frequency of temperature changes and the uniformity of temperature distribution in different areas are identified. Based on the frequency of temperature changes and the uniformity of temperature distribution, the density of temperature sensors in different areas is adjusted to identify the temperature distribution data.

[0015] The process of dividing the temperature field of the arch dam structure into its temperature field, as described in the arch dam structure temperature field model, includes:

[0016] Clustering analysis algorithm is used to extract spatiotemporal features from the temperature distribution data and initial structure data. Furthermore, by combining the geometric orientation of the arch dam structure, the daily temperature variation amplitude, and the temperature response phase delay, the point set with similar thermal behavior is divided into the same arch dam temperature field region.

[0017] Furthermore, based on the rate of change of temperature gradient and structural stress sensitivity, the temperature field region of the arch dam is divided into a high-temperature sensitive region, a medium-temperature sensitive region, and a low-temperature sensitive region; the structural stress sensitivity is identified based on structural parameters.

[0018] The anhydrous deformation data includes: geometric changes, stress changes, and displacement changes of the arch dam;

[0019] The geometric changes are obtained by periodically and repeatedly measuring the elevation of the arch crown beam, the coordinates of the thrust piers at the arch ends, the radius of curvature of the arch axis, and the thickness distribution of the arch rings using high-precision measuring equipment. The stress changes are obtained by continuously monitoring the stress state of each arch ring of the arch dam using a strain monitoring system, and identifying stress redistribution and stress concentration areas by comparing with the initial stress reference value. The displacement changes are obtained by measuring the spatial displacement of key parts of the arch dam using a displacement monitoring network, including the radial displacement of the arch crown region, the tangential displacement of the arch seat region, and the constraint displacement of the arch end region.

[0020] The process of obtaining the anhydrous deformation parameters includes: anhydrous linear expansion coefficient, anhydrous temperature stress coefficient, anhydrous deformation compatibility coefficient, and anhydrous structural stability index.

[0021] The anhydrous linear expansion coefficient reflects the differences in the degree of thermal expansion and contraction of different regions under temperature change conditions;

[0022] The anhydrous temperature stress coefficient numerically characterizes the difference in efficiency of temperature load conversion into structural stress in different regions.

[0023] The waterless deformation compatibility coefficient describes the difference in deformation compatibility between each region and its adjacent regions.

[0024] Numerical evaluation of the stability index of waterless structures is used to assess the differences in the ability of different regions to maintain structural integrity under temperature conditions.

[0025] The process of acquiring the coupled temperature data and water pressure load data includes:

[0026] Water pressure load data: The hydrostatic pressure distribution data acting on the upstream face of the arch dam is collected using an underwater pressure sensor network to obtain the spatial distribution characteristics of the water load on each arch ring of the arch dam.

[0027] Coupled temperature data are collected simultaneously from the reservoir water temperature monitoring system and the arch dam internal temperature monitoring network to form water-arch dam coupled temperature field data.

[0028] The process of judging the quality of arch dams using the arch dam quality acceptance model includes:

[0029] The coupled temperature data, water pressure load data, and water deformation data of the arch dam temperature field region are identified to obtain water deformation parameters; the water deformation parameters include the water linear expansion coefficient, the water temperature stress coefficient, the water deformation compatibility coefficient, and the water structural stability index.

[0030] By comparing and analyzing the degree of change of the water-bearing deformation parameters and the corresponding waterless deformation parameters in the temperature field regions of each arch dam, and evaluating the numerical rationality of the water-bearing deformation parameters themselves, regional anomaly indicators are identified.

[0031] The safety contribution weights are obtained by considering the contributions of deformation control in high-temperature sensitive areas, force transmission coordination in medium-temperature sensitive areas, and stable support in low-temperature sensitive areas to the overall safety of the arch dam. Based on the safety contribution weights, the regional anomaly indicators are weighted and fused to obtain the arch dam quality anomaly coefficient.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] 1. This invention achieves intelligent and differentiated deployment of temperature sensors through detailed analysis of solar radiation patterns and arch dam geometry. Compared with the traditional uniform deployment scheme, it can significantly reduce the procurement and deployment costs of sensors without sacrificing the monitoring accuracy of key areas, and reduce data redundancy and improve data processing efficiency. This refined data acquisition method provides high-quality input for constructing a high-fidelity temperature field model of the arch dam structure.

[0034] 2. This scheme discretizes the complex and continuous temperature field into a finite number of temperature field regions with clear physical meaning. By introducing "structural stress sensitivity", the region division not only considers temperature behavior but also incorporates mechanical importance, allowing the system to focus on the key areas that have the greatest impact on the overall safety of the dam. This provides a scientific and reasonable zoning basis for subsequent differentiated deformation analysis and weighted quality assessment.

[0035] 3. By comparing the response parameters of arch dams under water and dry conditions, this invention establishes a health benchmark for the dam, effectively filtering out environmental interference and improving the sensitivity of identifying minor construction defects exposed under water load. At the same time, the model introduces safety contribution weights, giving greater attention to anomalies in key areas such as the arch crown, so that the evaluation results can truly reflect the actual risks to structural safety, making the judgment more accurate and significantly improving the efficiency and reliability of acceptance. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a water conservancy project quality acceptance data acquisition system based on the Internet of Things according to the present invention.

[0037] Figure 2This is a logical diagram of a water conservancy project quality acceptance data acquisition system based on the Internet of Things according to the present invention;

[0038] Figure 3 This is a schematic diagram of the arch dam quality acceptance model of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1:

[0041] This invention proposes a data acquisition system for water conservancy project quality acceptance based on the Internet of Things. The system process is as follows: Figure 1 As shown, the logic of the system is as follows: Figure 2 As shown, it includes: temperature field division module, waterless acceptance module, and water-containing acceptance module;

[0042] The temperature field division module collects structural data of the arch dam in its initial state to obtain initial structural data; monitors the temperature of the arch dam surface according to the sunlight irradiation pattern to obtain temperature distribution data; constructs a temperature field model of the arch dam structure, and divides the temperature field of the arch dam into temperature field regions based on the initial structural data and temperature distribution data.

[0043] The structural data includes the geometric features of the arch dam, material physical properties, mechanical response benchmarks, and structural integrity parameters;

[0044] The geometric features of the arch dam include the elevation of the arch crown beam, the coordinates of the thrust piers at the arch ends, the radius of curvature of the arch axis, and the thickness distribution of the arch ring; the physical properties of the materials include the density distribution, elastic modulus, and compressive strength of the concrete in the arch dam; the mechanical response benchmarks include the initial stress state and deformation benchmark values; and the structural integrity parameters include the structural integrity data of the arch crown, arch abutment, and arch ends.

[0045] The arch dam in its initial state is a completed arch dam that has not yet impounded water;

[0046] Geometric features of the arch dam: The entire arch dam was scanned and mapped using a 3D laser scanner and a high-precision total station to obtain point cloud data of the dam surface; by fitting the point cloud data, the elevation of the top of the arch crown beam, the 3D coordinates of the dam abutments and thrust piers, the radius of curvature of the arch axis of each dam section, and the distribution of the arch ring thickness at different elevations were accurately obtained.

[0047] Material physical properties: During the construction of the arch dam, concrete test blocks are reserved and standard curing is carried out. The elastic modulus and compressive strength of the concrete are obtained through laboratory pressure tests. At the same time, at different locations in the dam body, non-destructive testing technology (such as ultrasonic testing) is used to measure the actual density and wave velocity of the concrete to evaluate its uniformity.

[0048] Mechanical response benchmark: After the arch dam is demolded and the temperature stress is basically stable, the initial readings of the strain gauges and stress gauges embedded in the dam body are read and defined as the initial stress state and deformation benchmark value.

[0049] Structural integrity parameters: The key parts such as the arch crown, arch seat and arch end are scanned by shock elastic wave method or ground-penetrating radar to detect whether there are initial microcracks or voids within the allowable range of the acceptance specifications, and these data are used as the initial parameters of structural integrity.

[0050] This invention comprehensively collects parameters from four dimensions of arch dams: geometry, materials, mechanics, and integrity. This provides high-precision, high-dimensional initial conditions for subsequent temperature field division and quality acceptance models. It ensures that the starting point of the analysis is accurate and complete, improves the reliability and accuracy of subsequent identification results, avoids misjudgments caused by insufficient initial data, and lays a solid data foundation for the entire acceptance process.

[0051] The process of monitoring the surface temperature of the arch dam and obtaining temperature distribution data based on the pattern of sunlight exposure includes:

[0052] Based on the analysis of the relationship between the solar incidence angle and the geometric relationship between the arch dam structure, the surface of the arch dam is divided into a region directly exposed to sunlight and a region not directly exposed to sunlight. The region directly exposed to sunlight is the surface area of ​​the arch dam that can be directly illuminated by sunlight, while the region not directly exposed to sunlight is the surface area that cannot receive direct sunlight due to the arch dam's own structure and the surrounding terrain.

[0053] By calculating the spatial geometric relationship between the solar trajectory and the various structural components of the arch dam, and combining the temperature response differences of different geometric feature areas such as the thin-walled area of ​​the arch crown, the turning area of ​​the arch shoulder, the thick area of ​​the arch seat, and the constrained area of ​​the arch end, the frequency of temperature change and the uniformity of temperature distribution in different areas are identified; based on the frequency of temperature change and the uniformity of temperature distribution, the density of temperature sensors in different areas is adjusted to identify the temperature distribution data.

[0054] As a huge three-dimensional structure, the thermal environment and thermal response of different points on the surface of an arch dam are completely different. It is uneconomical and inefficient to use a sensor deployment scheme with uniform density. This scheme is based on the principles of thermodynamics and heat transfer to predict the temperature dynamic characteristics of different regions and prioritize the limited monitoring resources to the regions with the largest amount of information and the most drastic changes, thereby achieving the highest accuracy temperature field distribution data at the most optimized cost.

[0055] Preliminary regional division: Using a solar geometry algorithm, input the geographical latitude, longitude, altitude, and orientation of the arch dam; the algorithm will calculate the solar altitude angle and azimuth angle at any time of year; combined with the digital model (BIM or CAD model) of the arch dam, through light projection analysis, the dam surface is automatically divided into "direct sunlight area" and "non-direct sunlight area" (i.e., permanent shadow area or shadow area formed by its own structure).

[0056] Response characteristic analysis: By performing Fourier transform on the preliminary monitored temperature data, the frequency of temperature changes and the amplitude of daily temperature difference in different regions were analyzed. For example, the thin-walled area of ​​the arch crown is directly affected by sunlight, and its temperature change frequency is high and the amplitude is large. In contrast, the temperature response in the thick area of ​​the arch seat and inside the dam body shows obvious phase delay and smaller amplitude.

[0057] Dynamic sensor deployment: Based on the above response characteristics, differentiated sensor deployment is carried out; in areas with drastic temperature changes and direct sunlight, especially in thin-walled structures such as arched canopies, high-density distributed fiber optic temperature sensors or thermal imaging cameras are used for area monitoring; in areas with gentle temperature changes and no direct sunlight, lower-density point-type platinum resistance thermometers are used to save costs; this deployment strategy ensures the efficiency and accuracy of data acquisition.

[0058] This invention achieves intelligent and differentiated deployment of temperature sensors through detailed analysis of solar radiation patterns and arch dam geometry. Compared with the traditional uniform deployment scheme, it can significantly reduce the procurement and deployment costs of sensors, reduce data redundancy, and improve data processing efficiency without sacrificing the monitoring accuracy of key areas. This refined data acquisition method provides high-quality input for constructing a high-fidelity temperature field model of the arch dam structure.

[0059] The process of dividing the temperature field of the arch dam structure into its temperature field, as described in the arch dam structure temperature field model, includes:

[0060] Clustering analysis algorithm is used to extract spatiotemporal features from the temperature distribution data and initial structure data. Furthermore, by combining the geometric orientation of the arch dam structure, the daily temperature variation amplitude, and the temperature response phase delay, the point set with similar thermal behavior is divided into the same arch dam temperature field region.

[0061] Furthermore, based on the rate of change of temperature gradient and structural stress sensitivity, the temperature field region of the arch dam is divided into a high-temperature sensitive zone, a medium-temperature sensitive zone, and a low-temperature sensitive zone; the structural stress sensitivity is identified based on structural parameters; and the rate of change of temperature gradient is identified based on temperature distribution data.

[0062] For each temperature sensor measuring point, extract its time-series temperature data over a period of time (e.g., 7 consecutive days); construct a feature vector for each measuring point, which may include: the three-dimensional coordinates of the measuring point, the geometric orientation of the arch dam structure, the daily average temperature, the daily temperature variation amplitude, the phase delay time of the temperature response relative to the peak solar radiation, etc.

[0063] The geometric orientation of an arch dam structure refers to the direction of the normal to a certain measuring point on the dam surface, that is, the direction perpendicular to the tangent plane at that point; it directly determines the angle and intensity of solar radiation received at that point throughout the day, and is a spatial factor affecting its temperature change;

[0064] The daily temperature variation amplitude refers to the difference between the highest and lowest temperatures experienced by a measuring point within 24 hours; it reflects the sensitivity or thermal fluctuation of that point to changes in sunshine and ambient temperature; generally, the amplitude on the dam crest and the sunny side is much greater than that on the dam foundation and the shaded side.

[0065] Temperature response phase delay refers to the time it takes for the temperature at a measuring point to reach its peak value, relative to the time it takes for the solar radiation intensity to reach its peak value that day (usually between 12 noon and 2 pm). This delay reflects the thermal inertia of the structural part where the point is located. The thicker the concrete and the farther away from the surface, the greater the thermal inertia and the longer the phase delay. The acquisition process includes: collecting temperature sequence data and local solar radiation intensity sequence data at the measuring point; determining the radiation peak time and temperature peak time, and thus calculating the phase delay.

[0066] The temperature gradient describes the speed and direction of temperature change in space, while the rate of change of the temperature gradient describes the drastic change of this gradient over time. At sunrise and sunset, the dam surface changes from cold to hot or from hot to cold, at which time the temperature gradient changes most drastically, which may cause thermal shock effects on the surface concrete. The acquisition process includes: calculating the spatial temperature gradient using temperature data from adjacent measuring points; then differentiating the gradient value over time to obtain its rate of change; this indicator can serve as a supplementary basis for assessing the thermal stability of the region.

[0067] Clustering algorithm application: K-Means++ or DBSCAN clustering analysis algorithms are used to perform unsupervised learning on the feature vectors of all measurement points; the algorithm will automatically divide measurement points with similar thermal behavior (i.e., feature vectors that are close in distance in multidimensional space) into the same cluster; each cluster constitutes a "temperature field region of the arch dam"; for example, the sunny and windward area of ​​the dam top may be clustered into one class, while the shady and water-near area may be clustered into another class;

[0068] Sensitivity-based secondary partitioning: After clustering, a mechanical model of the arch dam is established using finite element analysis (FEA) software; a unit temperature load (e.g., an overall temperature increase of 1°C) is applied to the model, and the resulting stress field is analyzed; based on the magnitude of the stress increment, each temperature field region is further labeled as a "high-temperature sensitive area" (maximum stress change), a "medium-temperature sensitive area," and a "low-temperature sensitive area" (minimum stress change); in this embodiment, the arch dam mechanical model is established using general-purpose finite element software such as ANSYS or ABAQUS; the model is meshed using three-dimensional eight-node solid elements (such as SOLID185 elements), and the mesh is refined for high-stress areas such as the arch crown and arch abutment to ensure convergence of calculation results; the contact surface between the dam and the bedrock is defined as a fixed constraint, i.e., constraining the translational and rotational degrees of freedom in all directions; the concrete material adopts a linear elastic constitutive model, and its elastic modulus and Poisson's ratio are set according to the collected material physical property parameters.

[0069] Taking K-Means++ as an example, its goal is to minimize the sum of squared distances from each point in a cluster to its centroid. The process is as follows: randomly select a centroid, select the next centroid so that its distance from the selected centroid is proportional to the probability, repeat until K centroids are selected, assign each point to the nearest centroid, recalculate the centroid of each cluster, and repeat the assignment and calculation until the centroid no longer changes. The value of K (number of regions) can be determined by the "elbow rule" or the contour coefficient.

[0070] The process of classifying high-temperature sensitive areas, medium-temperature sensitive areas, and low-temperature sensitive areas includes:

[0071] The temperature gradient change rate and structural stress sensitivity of all arch dam temperature field regions were normalized and mapped to the [0, 1] interval. A comprehensive sensitivity score was calculated for each region. Based on the comprehensive sensitivity score, two thresholds were set to divide the regions.

[0072] Structural stress sensitivity is a key mechanical concept used to quantify the mechanical response intensity of different parts of an arch dam structure to temperature changes. Its strict definition is: the stress increment generated at a location when the overall or local temperature changes uniformly by 1 degree Celsius. Areas with high stress sensitivity mean that even a small temperature change can induce significant internal stress; these are "stress hotspots" in the structure and areas that should be the focus of quality acceptance. Identifying structural stress sensitivity relies on thermo-mechanical coupling analysis based on the finite element method. By solving the equations of heat conduction and elasticity, the stress distribution caused by temperature changes within the structure can be accurately calculated, thus providing an objective physical basis for sensitivity classification. Specifically, this includes:

[0073] First, based on historical meteorological data analysis, at least three characteristic temperature load conditions covering typical seasons and extreme weather were constructed. These conditions included a summer high-temperature sunshine condition, a winter cold wave condition, and a spring / autumn severe temperature change condition. Second, these characteristic temperature load conditions were applied to the finite element analysis model of the arch dam, and a thermo-mechanical coupled transient analysis was performed to obtain the stress response time history data of all element nodes of the arch dam under each condition. Finally, for each temperature field region of the arch dam, the weighted average of the maximum principal stress response of all element nodes within it under all characteristic load conditions was calculated. This value represents the structural stress sensitivity of the region. The weights are determined based on the historical meteorological frequency of each characteristic working condition in the local area. This scheme creatively adopts multiple probabilistic characteristic temperature load conditions to replace the traditional single unit temperature rise calculation method. This makes the assessment result of structural stress sensitivity no longer a static and isolated value, but dynamically incorporates the statistical characteristics of the local climate environment. The sensitivity index obtained in this way can better reflect the stress concentration risk that the arch dam may generate when dealing with typical or extreme weather in the actual operating environment. This makes the subsequent division of high temperature sensitive areas more targeted and predictive, and improves the accuracy of quality acceptance in identifying potential risk points.

[0074] This scheme discretizes the complex and continuous temperature field into a finite number of temperature field regions with clear physical meaning. By introducing "structural stress sensitivity", the region division not only considers temperature behavior but also incorporates mechanical importance, allowing the system to focus on the key areas that have the greatest impact on the overall safety of the dam. This provides a scientific and reasonable zoning basis for subsequent differentiated deformation analysis and weighted quality assessment.

[0075] The waterless acceptance module periodically collects structural data of the arch dam in a waterless state and identifies the waterless deformation data. Based on the waterless deformation data and temperature distribution data of the arch dam's temperature field region, it identifies the degree of deformation of each arch dam's temperature field region under the influence of different temperature distributions and obtains the waterless deformation parameters.

[0076] The anhydrous deformation data includes: geometric changes, stress changes, and displacement changes of the arch dam;

[0077] The geometric changes are obtained by periodically and repeatedly measuring the elevation of the arch crown beam, the coordinates of the thrust piers at the arch ends, the radius of curvature of the arch axis, and the thickness distribution of the arch rings using high-precision measuring equipment. The stress changes are obtained by continuously monitoring the stress state of each arch ring of the arch dam using a strain monitoring system, and identifying stress redistribution and stress concentration areas by comparing with the initial stress reference value. The displacement changes are obtained by measuring the spatial displacement of key parts of the arch dam using a displacement monitoring network, including the radial displacement of the arch crown region, the tangential displacement of the arch seat region, and the constraint displacement of the arch end region.

[0078] In the absence of water, the system periodically collects data through various IoT monitoring devices and identifies deformation data by comparing it with the "mechanical response benchmark".

[0079] Geometric Change Identification: Automated total stations and laser scanners measure preset marker points on the dam surface at regular intervals every day to form a coordinate time series; the spatial displacement of each point is obtained by subtracting the coordinates at the current moment from the initial coordinates (reference values), thereby identifying the overall geometric changes of the arch dam;

[0080] Stress change identification: The internally embedded vibrating wire strain gauge network uploads frequency data in real time. The system calculates the strain value of each measuring point according to the frequency-strain conversion formula. The strain value is multiplied by the elastic modulus of concrete to obtain the stress value. The current stress value is compared with the initial stress reference value to identify stress redistribution caused by temperature and possible stress concentration phenomena.

[0081] Displacement change identification: GPS receivers on the dam crest, tension lines and positive and negative plumb lines inside the dam body continuously monitor the absolute and relative displacements of key parts (such as the arch crown and dam abutments); for example, by analyzing the radial component changes of the GPS coordinates of the arch crown apex, its downstream displacement can be directly identified.

[0082] Deformation is the most direct manifestation of a structure's response to loads. The design of this scheme is based on the principle of multi-source information fusion for structural health monitoring. A single monitoring method (such as measuring displacement only) may not be able to fully reflect the structural state. By simultaneously monitoring physical quantities at three different levels—geometry, stress, and displacement—they can corroborate and complement each other, constructing a three-dimensional response image of the arch dam under temperature loads, thereby capturing its behavioral characteristics more accurately.

[0083] This invention comprehensively and meticulously captures the response of an arch dam to temperature loads in a waterless state through multi-source and multi-dimensional monitoring methods. This three-dimensional deformation data not only reveals the surface displacement but also delves into the stress state inside the structure, providing rich and reliable data input for subsequent calculation of "waterless deformation parameters" and ensuring that the established health benchmark can truly reflect the intrinsic thermodynamic characteristics of the arch dam.

[0084] The process of obtaining the anhydrous deformation parameters includes: anhydrous linear expansion coefficient, anhydrous temperature stress coefficient, anhydrous deformation compatibility coefficient, and anhydrous structural stability index.

[0085] The anhydrous linear expansion coefficient reflects the differences in the degree of thermal expansion and contraction of different regions under temperature change conditions;

[0086] The anhydrous temperature stress coefficient numerically characterizes the difference in efficiency of temperature load conversion into structural stress in different regions.

[0087] The waterless deformation compatibility coefficient describes the difference in deformation compatibility between each region and its adjacent regions.

[0088] Numerical evaluation of the stability index of waterless structures is used to assess the differences in the ability of different regions to maintain structural integrity under temperature conditions.

[0089] After obtaining the temperature and deformation time series data of each temperature field region, the system calculates the anhydrous deformation parameters through statistical regression and correlation analysis.

[0090] Anhydrous linear expansion coefficient: For a certain temperature field region, its average temperature change sequence and average radial displacement sequence are extracted; through linear regression analysis, the anhydrous linear expansion coefficient of the region is identified, which characterizes the macroscopic ability of the structure in the region to convert temperature changes into displacement under unconstrained or specific constraint conditions.

[0091] Anhydrous temperature stress coefficient: Similarly, extract the regional average temperature change sequence and average stress change sequence; obtain the anhydrous temperature stress coefficient through linear regression, which characterizes the efficiency of temperature load being converted into structural internal stress.

[0092] Anhydrous deformation compatibility coefficient: Examine the boundary between two adjacent temperature field regions; extract their displacement sequences near the boundary, and calculate the standard deviation of the difference between the two; the smaller the standard deviation, the more compatible the deformation of the two regions.

[0093] The stability index of anhydrous structures is obtained by calculating the linear regression coefficient between the average temperature change sequence and the average displacement change sequence in a specific temperature field region. If the relationship between temperature and deformation is highly linear, it indicates that the structural response is stable and predictable. If it exhibits nonlinear or hysteretic characteristics, it indicates poor stability.

[0094] This solution creatively transforms massive amounts of dynamic raw monitoring data into a set of concise, stable, and physically meaningful characteristic parameters, forming a "digital fingerprint" of each temperature field region in a healthy state. This provides a quantifiable and comparable benchmark for quality acceptance. It also linearizes and indexes complex nonlinear problems to a certain extent, reducing the difficulty of subsequent state comparison and anomaly identification.

[0095] The water-bearing acceptance module collects coupled temperature data and water pressure load data under the influence of water in the water-bearing state, and periodically collects structural data of the arch dam to obtain water-bearing deformation data; it constructs an arch dam quality acceptance model to identify coupled temperature data, water pressure load data and water-bearing deformation data in the temperature field region of the arch dam, and makes quality judgments in combination with waterless deformation parameters.

[0096] The process of acquiring the coupled temperature data and water pressure load data includes:

[0097] Water pressure load data: The hydrostatic pressure distribution data acting on the upstream face of the arch dam is collected using an underwater pressure sensor network to obtain the spatial distribution characteristics of the water load on each arch ring of the arch dam.

[0098] Coupled temperature data are collected simultaneously from the reservoir water temperature monitoring system and the arch dam internal temperature monitoring network to form water-arch dam coupled temperature field data.

[0099] To accurately analyze deformation in the presence of water, it is essential to know precisely what loads cause the deformation. In the presence of water, the loads are the superposition of temperature and water pressure. Therefore, it is necessary to measure these two loads simultaneously and with the same precision. In particular, the coupled temperature field reflects the change of the thermal boundary conditions of the dam body by the water body and is a key input for accurate thermo-mechanical coupling analysis. Ignoring this will lead to huge model errors.

[0100] Water pressure load data acquisition: A pressure sensor array pre-installed on the upstream face of the arch dam is activated; these sensors are typically distributed in a grid pattern, covering locations at different elevations and widths; each sensor measures the water pressure at its location in real time, and the data is transmitted via underwater cable to a convergence node on the dam crest, and then uploaded via an IoT gateway; based on the readings of all sensors and their spatial coordinates, the system uses an interpolation algorithm to generate a real-time, continuous water pressure distribution map of the entire upstream face;

[0101] Coupled temperature data acquisition: In the reservoir near the upstream dam face, multiple temperature chains are deployed, which are measuring devices with multiple temperature sensors connected in series on a cable, capable of measuring water temperature at different depths from the water surface to the bottom; combined with the existing temperature sensors inside the dam, the system can obtain complete temperature field data of the "water-arch dam" interface; revealing the direct impact of reservoir water (especially surface water temperature changes) on the dam surface temperature, and how this impact penetrates into the dam interior;

[0102] This invention collects two key load data points: water pressure and water-dam coupling temperature, ensuring that the "water-loaded acceptance module" has accurate input conditions. This enables the quality acceptance model to clearly separate and identify the deformation contributed by water pressure and temperature changes, thereby accurately assessing the true response characteristics of the structure under the new water load and avoiding misjudging temperature deformation as structural anomalies caused by water pressure.

[0103] The structure of the arch dam quality acceptance model is as follows: Figure 3 As shown, it includes a data input layer, an anomaly detection layer, and a quality inspection layer;

[0104] The data input layer identifies coupled temperature data, water pressure load data, and water deformation data in the temperature field region of the arch dam to obtain water deformation parameters. The water deformation parameters include the water linear expansion coefficient, the water temperature stress coefficient, the water deformation compatibility coefficient, and the water structural stability index.

[0105] The anomaly identification layer identifies regional anomaly indicators by comparing and analyzing the degree of change of the water-bearing deformation parameters and the corresponding waterless deformation parameters in the temperature field region of each arch dam, and by evaluating the rationality of the values ​​of the water-bearing deformation parameters themselves.

[0106] The quality detection layer considers the contribution of deformation control in high-temperature sensitive areas, force transmission coordination in medium-temperature sensitive areas, and stable support in low-temperature sensitive areas to the overall safety of the arch dam, and obtains the safety contribution weight; based on the safety contribution weight, the regional anomaly indicators are weighted and fused to obtain the arch dam quality anomaly coefficient.

[0107] The data input layer receives three types of raw high-dimensional time-series data from the IoT monitoring system: coupled temperature field data characterizing water-dam heat exchange; hydrostatic pressure load distribution data describing the upstream water level; and time-series data of structural deformation in the water-bearing state reflecting the actual structural response.

[0108] Processing procedure: The core function of this layer is to extract and parameterize the physical features of the input raw data stream; run a parameter inversion algorithm based on the thermo-hydraulic-mechanical coupling mechanism to decouple and correlate the monitoring data of each "arch dam temperature field region" and quantitatively calculate the key indicators that can characterize the macroscopic mechanical response characteristics of the region under the current load.

[0109] The parameter inversion algorithm employs a least-squares objective function optimization method. Its specific steps include: establishing a finite element forward model of the arch dam considering coupled temperature fields and water pressure loads; defining the objective function as the root mean square error between the measured water deformation data from monitoring points in each temperature field region and the deformation data calculated by the model; using the water deformation parameters (such as the equivalent values ​​of elastic modulus and expansion coefficient) of each region as variables to be inverted; employing optimization algorithms such as Levenberg-Marquardt to iteratively adjust the variables to be inverted, minimizing the objective function; and obtaining the variable values ​​when the objective function converges, which are the final water deformation parameters.

[0110] The anomaly identification layer is the core diagnostic unit of the model, performing dual comparative analysis to quantify the degree of anomaly.

[0111] Benchmark deviation analysis: The input water-bearing deformation parameters are compared with the historical waterless deformation parameters item by item to calculate the relative rate of change of the structural response mode caused by water load.

[0112] Theoretical compliance assessment: The input water deformation parameters are compared with the simulation parameters under the theoretical healthy state item by item to calculate the simulation deviation between the actual structural behavior and the ideal design behavior.

[0113] Subsequently, all the original indicators generated by the above two analyses are mapped to a unified and standardized [0,1] evaluation interval through a nonlinear normalization function to eliminate the influence of dimensions.

[0114] The quality detection layer is the final decision-making unit of the model, responsible for making a comprehensive judgment on the abnormal information in each region, taking into account the structural importance.

[0115] Weighting: The model first assigns a "safety contribution weight" to the abnormal indicators of each region; this weight is pre-calculated based on the structural stress sensitivity of each region, and objectively reflects the degree of contribution of the region (such as the deformation control function of the high temperature sensitive zone) to the overall safety of the arch dam.

[0116] The process of obtaining the security contribution weight includes:

[0117] The structural stress sensitivity values ​​of all arch dam temperature field regions are normalized and mapped to the [0,1] interval. Next, a nonlinear mapping function is constructed. This function is an S-shaped function, characterized by slow output growth when the input value is low, rapid growth in the middle range, and a gradual flattening out when approaching saturation. Finally, the normalized structural stress sensitivity values ​​of each region are used as independent variables input into the S-shaped mapping function, and the resulting function output value is the safety contribution weight for that region.

[0118] This invention introduces an S-shaped nonlinear mapping function. This design can appropriately widen the weight gap between medium-sensitivity and high-sensitivity regions, while avoiding the excessive weight of extremely low-sensitivity regions and the excessive dominance of the weight of extremely high-sensitivity regions in the final result. This method makes the weight allocation more scientific and reasonable, highlighting the importance of key stress-bearing parts such as the arch crown and arch seat, while also taking into account the potential influence of other regions. This allows the final fused arch dam quality anomaly coefficient to reflect the overall safety and health status of the dam more evenly and accurately.

[0119] Weighted Fusion: The model executes a weighted fusion algorithm. First, the multi-dimensional anomaly indicators of each region are aggregated into a single comprehensive anomaly score for that region. Then, the comprehensive anomaly score of each region is multiplied by its corresponding safety contribution weight to obtain the weighted anomaly degree of that region. Finally, the weighted anomaly degrees of all regions are summed to output the arch dam quality anomaly coefficient.

[0120] This invention establishes a health benchmark for the dam by comparing the response parameters of the arch dam under water and dry conditions. It can effectively filter out environmental interference and improve the sensitivity of identifying minor construction defects exposed under water load. At the same time, the model introduces safety contribution weights and pays more attention to anomalies in key areas such as the arch crown, so that the evaluation results can truly reflect the actual risks to structural safety, making the judgment more accurate and significantly improving the efficiency and reliability of acceptance.

[0121] The water-acceptance module further includes a flood discharge vibration collaborative analysis step:

[0122] During the first flood discharge test of the arch dam, vibration monitoring sensors deployed inside the dam body and the aforementioned Internet of Things monitoring system were activated simultaneously. During the flood discharge, vibration response data of the dam body caused by the impact of high-speed water flow and atomization effect were collected, and flood discharge flow, coupled temperature field and structural deformation data were recorded simultaneously. A multi-physics coupling analysis model was constructed to perform correlation analysis between vibration response characteristics (such as dominant frequency and amplitude) and deformation parameters of each temperature field region, and to identify regions that exhibit abnormal deformation or energy dissipation under strong dynamic load.

[0123] This scheme creatively utilizes the special but critical condition of flood discharge to add a dynamic load dimension to the quality acceptance process. The performance of the arch dam under hydrostatic pressure may mask certain defects, but under the violent vibration of flood discharge, these defects (such as weak structural connections and insufficient local stiffness) may be triggered and amplified. By combining vibration analysis with the original multi-field analysis of temperature, water, and force, an ultimate pressure test can be conducted on the integrity and construction quality of the arch dam from the perspective of dynamic response, identifying deep-seated problems that are difficult to detect by static monitoring, making the conclusions of the quality acceptance more comprehensive and robust.

[0124] Example 2:

[0125] This invention proposes an Internet of Things-based water conservancy project quality acceptance data acquisition system, comprising:

[0126] The temperature field division module collects structural data of the arch dam in its initial state to obtain initial structural data; monitors the temperature of the arch dam surface according to the sunlight irradiation pattern to obtain temperature distribution data; constructs a temperature field model of the arch dam structure, and divides the temperature field of the arch dam into temperature field regions based on the initial structural data and temperature distribution data.

[0127] This embodiment takes an arch dam as the object. After the arch dam is completed and before water is impounded, the initial structural data is collected first.

[0128] Geometric characteristics of the arch dam: The elevation of the top of the arch crown beam was measured to be 150.05 meters using a 3D laser scanner, which is within the allowable range of the design value; Material physical properties: Concrete test blocks reserved during construction were tested; For example, the average elastic modulus was obtained as 35 GPa, which was used as the basic material parameter for finite element analysis; Mechanical response benchmark: After the dam body temperature basically stabilized, the initial frequency readings of all embedded vibrating wire strain gauges were read, and the converted stress values ​​(average 0.1 MPa) were used as the initial stress benchmark; Structural integrity parameters: The arch crown and arch abutment were scanned using the impact elastic wave method, and the results showed that the internal wave velocity was uniform and there were no initial macroscopic cracks; Pa is the pressure unit Pascal.

[0129] Clustering: The system collected continuous temperature data from all temperature sensors over a week; a feature vector containing dimensions such as geometric orientation and daily temperature variation was constructed for each sensor; for example, a sensor on the sunny side of the dam crest has a daily temperature variation of up to 20℃, while a sensor near the bedrock at the dam heel has a daily temperature variation of only 5℃; all feature vectors were input into the K-Means++ clustering algorithm, and the optimal number of clusters K=9 was determined through the "elbow rule" analysis; accordingly, the system automatically divided the complex arch dam surface into 9 "arch dam temperature field regions" (labeled R1 to R9) with similar thermodynamic behavior.

[0130] Furthermore, based on the rate of change of temperature gradient and structural stress sensitivity, the temperature field region of the arch dam is divided into a high-temperature sensitive region, a medium-temperature sensitive region, and a low-temperature sensitive region; the structural stress sensitivity is identified based on structural parameters.

[0131] The waterless acceptance module periodically collects structural data of the arch dam in a waterless state and identifies the waterless deformation data. Based on the waterless deformation data and temperature distribution data of the arch dam's temperature field region, it identifies the degree of deformation of each arch dam's temperature field region under the influence of different temperature distributions and obtains the waterless deformation parameters.

[0132] During the window period before formal water impoundment, the system continuously collects temperature and deformation data from various temperature fields, establishing a unique "health fingerprint" for each region, i.e., waterless deformation parameters; taking the "high-temperature sensitive area" R1, which is of particular concern, as an example:

[0133] Anhydrous linear expansion coefficient: The relationship between the average temperature of the region and its radial displacement (monitored by GPS on the dam crest) was systematically analyzed, showing a clear linear positive correlation; through linear regression analysis, the anhydrous linear expansion coefficient of the region was calculated to be 0.02 mm / ℃.

[0134] Anhydrous temperature stress coefficient: Similarly, analyzing the relationship between temperature and internal stress (monitored by strain gauges), the anhydrous temperature stress coefficient is found to be 0.15 MPa / ℃;

[0135] Anhydrous deformation compatibility coefficient: Analyze the displacement data at the boundary between R1 and the adjacent medium-temperature zone R3. The standard deviation of the displacement difference between the two is 0.2 mm, and the compatibility coefficient is calculated.

[0136] Stability index of anhydrous structure: In the above temperature-displacement linear regression analysis, the coefficient of determination is 0.97; this value close to 1 indicates that the structural response of region R1 is highly stable and predictable in the anhydrous state.

[0137] The water-bearing acceptance module collects coupled temperature data and water pressure load data under the influence of water in the water-bearing state, and periodically collects structural data of the arch dam to obtain water-bearing deformation data; it constructs an arch dam quality acceptance model to identify coupled temperature data, water pressure load data and water-bearing deformation data in the temperature field region of the arch dam, and makes quality judgments in combination with waterless deformation parameters.

[0138] Once the reservoir reaches its normal high water level, the system automatically enters the water-filled acceptance mode, and the core arch dam quality acceptance model begins to execute.

[0139] Data Input Layer: The model first receives water pressure load data from the underwater pressure sensor on the upstream surface, as well as water-dam coupled temperature field data; then, it initiates the parameter inversion process based on the Levenberg-Marquardt optimization algorithm; the algorithm uses the measured dam displacement as the target and iteratively adjusts parameters such as the equivalent elastic modulus of each region in the finite element model until the error between the model's calculated displacement and the measured displacement is minimized; finally, the inversion yields a water-bearing temperature stress coefficient of 0.24 MPa / ℃ for region R1 under water conditions;

[0140] Anomaly Identification Layer: The second layer of the model diagnoses parameter changes; the temperature stress coefficient in region R1 increases from 0.15 in the anhydrous state to 0.24 in the aquatic state, a change rate of up to 60%; this change rate is input into the Sigmoid normalization function; since the 60% change rate significantly deviates from the normal change range (mean μ=25%) set based on similar engineering experience, the function outputs a high "regional anomaly index";

[0141] Quality Inspection Layer: The model's decision layer first assigns a "safety contribution weight" to the R1 region based on the fact that it belongs to the "high temperature sensitive area"; then, it multiplies this weight by the anomaly index; finally, it sums up the weighted anomalies of all 9 regions to obtain the final "arch dam quality anomaly coefficient".

[0142] The system automatically generates and issues a quality warning report, indicating that there is a risk to the overall quality of the arch dam, and highlights the significant anomalies in the response parameters of the R1 area.

[0143] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An Internet of Things-based water conservancy quality acceptance data acquisition system, characterized in that, The method comprises the following steps: The temperature field division module collects the structural data of the initial state of the arch dam to obtain initial structural data; According to the sunlight irradiation law, the temperature of the surface of the arch dam is monitored to obtain temperature distribution data; a temperature field model of the arch dam structure is constructed, and the temperature field of the arch dam is divided according to the initial structural data and the temperature distribution data to obtain the temperature field region of the arch dam; The process of dividing the temperature field of the arch dam structure by the temperature field model of the arch dam structure comprises the following steps: By using a clustering analysis algorithm, the time-space characteristics of the temperature distribution data and the initial structural data are extracted, and the point sets with similar thermal behaviors are divided into the same temperature field region of the arch dam by combining the geometric orientation of the arch dam structure, the daily temperature variation amplitude and the temperature response phase delay; According to the temperature gradient change rate and the structural stress sensitivity, the temperature field region of the arch dam is divided into a high-temperature sensitive area, a medium-temperature sensitive area and a low-temperature sensitive area; the structural stress sensitivity is identified according to the structural parameters; The water-free acceptance module periodically collects the structural data of the arch dam in the water-free state to identify water-free deformation data; according to the water-free deformation data and the temperature distribution data of the temperature field region of the arch dam, the deformation degree of each temperature field region of the arch dam under the influence of different temperature distributions is identified to obtain water-free deformation parameters; The water-acceptance module collects the coupled temperature data and the water pressure load data under the influence of the water body and periodically collects the structural data of the arch dam in the water state to obtain water deformation data; a quality acceptance model of the arch dam is constructed to identify the coupled temperature data, the water pressure load data and the water deformation data of the temperature field region of the arch dam, and the quality is judged in combination with the water-free deformation parameters; The process of judging the quality of the arch dam by the quality acceptance model of the arch dam comprises the following steps: The coupled temperature data, the water pressure load data and the water deformation data of the temperature field region of the arch dam are identified to obtain water deformation parameters; the water deformation parameters comprise water linear expansion coefficient, water temperature stress coefficient, water deformation coordination coefficient and water structure stability index; By comparing and analyzing the change degree of the water deformation parameters of each temperature field region of the arch dam with the corresponding water-free deformation parameters and evaluating the numerical rationality of the water deformation parameters themselves, a regional abnormal index is identified; The safety contribution weight is obtained by considering the contribution degree of the deformation control of the high-temperature sensitive area, the force transmission coordination of the medium-temperature sensitive area and the stable support of the low-temperature sensitive area to the overall safety of the arch dam; the regional abnormal index is weighted and fused according to the safety contribution weight to obtain an arch dam quality abnormal coefficient.

2. The water conservancy project quality acceptance data acquisition system based on the Internet of Things according to claim 1, characterized in that, The structural data comprises arch dam geometric characteristics, material physical properties, mechanical response benchmarks and structural integrity parameters; The arch dam geometric characteristics comprise arch crown beam elevation, arch end thrust pier coordinates, arch axis curvature radius and arch ring thickness distribution; the material physical properties comprise arch dam concrete density distribution, elastic modulus and compressive strength; the mechanical response benchmarks comprise initial stress state and deformation benchmark value; and the structural integrity parameters comprise arch crown, arch seat and arch end structural integrity data.

3. The water conservancy project quality acceptance data acquisition system based on the Internet of Things according to claim 1, characterized in that, The process of monitoring the temperature of the surface of the arch dam according to the sunlight irradiation law to obtain temperature distribution data comprises the following steps: The arch dam surface is divided into a sunlight direct irradiation area and a sunlight non-direct irradiation area based on the geometric relationship between the solar incident angle and the arch dam structure; the sunlight direct irradiation area is an arch dam surface area directly irradiated by sunlight, and the sunlight non-direct irradiation area is a surface area unable to receive direct sunlight due to the arch dam structure and surrounding terrain shielding; The temperature change frequency and temperature distribution uniformity of different regions are identified by calculating the spatial geometric relationship between the solar trajectory and each structural component of the arch dam, combining the temperature response differences of the different geometric characteristic regions of the arch crown thin-wall area, arch shoulder transition area, arch seat thick area and arch end constraint area; the temperature sensor density of different regions is adjusted according to the temperature change frequency and temperature distribution uniformity, and the temperature distribution data is identified.

4. The water conservancy project quality acceptance data acquisition system based on the Internet of Things according to claim 1, characterized in that, The anhydrous deformation data includes geometric changes, stress changes and displacement changes of the arch dam; The geometric changes are obtained by periodically and repeatedly measuring the geometric parameter changes of the arch crown beam elevation, arch end thrust pier coordinates, arch axis curvature radius and arch ring thickness distribution through high-precision measuring equipment; the stress changes are obtained by continuously monitoring the stress state of each arch ring of the arch dam through a strain monitoring system, and identifying the stress redistribution and stress concentration areas by comparing with the initial stress reference value; the displacement changes are obtained by measuring the spatial displacement of key parts of the arch dam through a displacement monitoring network, including the radial displacement of the arch crown area, the tangential displacement of the arch seat area and the constraint displacement of the arch end area.

5. The water conservancy project quality acceptance data acquisition system based on the Internet of Things according to claim 1, characterized in that, The anhydrous deformation parameter acquisition process includes an anhydrous linear expansion coefficient, an anhydrous temperature stress coefficient, an anhydrous deformation coordination coefficient and an anhydrous structure stability index; The anhydrous linear expansion coefficient value reflects the difference in thermal expansion and contraction of each region under the condition of temperature change; The anhydrous temperature stress coefficient value represents the efficiency difference of the conversion of temperature load to structure stress of each region; The anhydrous deformation coordination coefficient value describes the difference in deformation coordination ability between each region and adjacent region; The anhydrous structure stability index value evaluates the difference in the ability of each region to maintain structural integrity under the action of temperature.

6. The water conservancy project quality acceptance data acquisition system based on Internet of Things according to claim 1, characterized in that, The acquisition process of the coupled temperature data and water pressure load data includes: The water pressure load data is obtained by using an underwater pressure sensor network to collect the static water pressure distribution data acting on the upstream surface of the arch dam, and obtaining the spatial distribution characteristics of the water load on each arch ring of the arch dam; The coupled temperature data is obtained by synchronously collecting the reservoir water temperature, arch dam surface temperature and internal temperature through a reservoir water temperature monitoring system and an arch dam internal temperature monitoring network, and forming water body-arch dam coupled temperature field data.

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