A Beidou-based structural deformation monitoring method, system and storage medium

Abstract

A Beidou-based structural deformation monitoring method, system, and storage medium relate to the field of deformation monitoring. The method comprises: acquiring the engineering facility structure of a target monitoring object and performing structural analysis on its key components to accurately obtain dynamic characteristic parameters of the engineering facility structure. These dynamic characteristic parameters reflect the response characteristics of the engineering facility structure under different environmental parameters, thereby enabling the establishment of an accurate physical model of the engineering facility. By acquiring and inputting real-time monitoring data, state prediction is performed in combination with preset environmental parameters to obtain predicted monitoring data, which is then compared with the real-time monitoring data. Abnormal changes in the engineering facility structure can be detected in a timely manner. When the monitoring difference exceeds a preset change threshold, an early warning prompt message is sent to a client. This can improve the accuracy and timeliness of deformation monitoring, prevent potential structural risks, and thus ensure the safety and stability of the engineering facility.

Description

Technical Field

[0001] The present application relates to the field of deformation monitoring, and in particular to a Beidou-based structural deformation monitoring method, system, and storage medium. Background Art

[0002] With the acceleration of global urbanization and the increasing complexity of engineering projects, the safety and stability of engineering structures have become a focus of public attention. Deformation monitoring of engineering facilities plays a crucial role in preventing and controlling engineering disasters. In particular, accurate and real-time deformation monitoring technology is an important means of ensuring the safe operation of large-scale structures such as high-rise buildings, bridges, and dams.

[0003] Among the related technologies, commonly used engineering facility deformation monitoring technologies mainly include laser measurement, fiber optic sensing technology and traditional GPS measurement methods. The future deformation state of engineering facilities is predicted by combining the collected data with historical data and simple mathematical models.

[0004] However, the accuracy of relevant technologies in predicting the deformation of engineering facilities is limited, making it difficult to provide timely and accurate warnings of potential structural risks. Summary of the Invention

[0005] The present application provides a BeiDou-based structural deformation monitoring method, system, and storage medium for improving the accuracy of deformation prediction of engineering facilities.

[0006] In the first aspect, the present application provides a Beidou-based structural deformation monitoring method, which is applied to a Beidou-based structural deformation monitoring system. The method includes: obtaining the engineering facility structure of the target monitoring object, and performing structural analysis on the key components of the engineering facility structure to obtain the dynamic characteristic parameters of the engineering facility structure, which are the engineering facility parameters of the engineering facility structure under different environmental parameters; establishing an engineering facility physical model of the engineering facility structure based on the dynamic characteristic parameters; obtaining real-time monitoring data of the target monitoring object; inputting the real-time monitoring data into the engineering facility physical model, and predicting the state of the target monitoring object based on preset environmental parameters to obtain predicted monitoring data; calculating the monitoring difference between the predicted monitoring data and the real-time monitoring data, and if the monitoring difference exceeds the preset change threshold, sending an early warning prompt information to the client.

[0007] By adopting the above technical solution, by acquiring the engineering facility structure of the target monitoring object and performing structural analysis on its key components, the dynamic characteristic parameters of the engineering facility structure can be accurately obtained. These dynamic characteristic parameters reflect the response characteristics of the engineering facility structure under different environmental parameters, so that an accurate physical model of the engineering facility can be established. By acquiring and inputting real-time monitoring data, combined with preset environmental parameters for state prediction, predicted monitoring data is obtained, and compared with the real-time monitoring data, abnormal changes in the engineering facility structure can be discovered in time. When the monitoring difference exceeds the preset change threshold, an early warning prompt information will be sent to the client, which can improve the accuracy and timeliness of deformation monitoring, prevent potential structural risks, and thus ensure the safety and stability of engineering facilities.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of performing structural analysis on the key components of the engineering facility structure to obtain the dynamic characteristic parameters of the engineering facility structure specifically includes: performing simulation analysis on the key components of the engineering facility structure based on finite element analysis technology to obtain simulation analysis results; identifying the response characteristics of the engineering facility structure under various environmental parameters based on the simulation analysis results, and fitting the response characteristics to obtain the dynamic characteristic parameters.

[0009] By adopting the above technical solution and simulating and analyzing the key components of the engineering facility structure through finite element analysis technology, more accurate simulation analysis results can be obtained, which can be used to identify the response characteristics of the engineering facility structure under various environmental parameters, and obtain dynamic characteristic parameters through fitting, thereby improving the understanding of the structural characteristics of the engineering facility and enhancing the ability to predict its dynamic behavior under different conditions.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the real-time monitoring data is input into the physical model of the engineering facility, and the state of the target monitoring object is predicted according to the preset environmental parameters to obtain the predicted monitoring data, which specifically includes: preprocessing the real-time monitoring data to obtain purified data; inputting the purified data and the preset environmental parameters into the physical model of the engineering facility, and numerically simulating the strain state of the engineering facility structure under the preset environmental parameters in the physical model of the engineering facility to obtain the strain coefficient; and using the product of the purified data and the strain coefficient as the predicted monitoring data.

[0011] By adopting the above technical solution, the real-time monitoring data is preprocessed to remove noise and outliers to obtain purified data. The purified data and preset environmental parameters are then input into the physical model of the engineering facility. The strain coefficient is obtained through numerical simulation, and the product of the purified data and the strain coefficient is used as the predicted monitoring data. This effectively combines the real-time data and the calculation results of the physical model to ensure the accuracy of the predicted data, which can more accurately reflect the strain state of the engineering facility under actual environmental conditions, thereby improving the reliability and timeliness of the prediction.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the step of obtaining real-time monitoring data of the target monitoring object specifically includes: obtaining the initial monitoring data of the monitoring sensor and the arrival time and data intensity of the initial monitoring data; eliminating the data whose arrival time is greater than a preset time threshold and whose data intensity is lower than a preset intensity threshold to obtain first monitoring data; generating a preset pseudo-random code, and calculating the correlation value between the preset pseudo-random code and the first monitoring data, eliminating the first monitoring data that is lower than the preset correlation threshold to obtain real-time monitoring data.

[0013] By adopting the above technical solution, by obtaining the initial monitoring data, arrival time and data intensity of the monitoring sensor, and eliminating the data with an arrival time greater than a preset time threshold and a data intensity lower than a preset intensity threshold, low-quality data can be effectively filtered out. Then, a preset pseudo-random code is generated and its correlation value with the initial monitoring data is calculated, and the first monitoring data below the preset correlation threshold is eliminated, ensuring that the final real-time monitoring data has high quality and reliability, and avoiding prediction errors caused by low-quality data.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of generating a preset pseudo-random code, calculating the correlation value between the preset pseudo-random code and the first monitoring data, and eliminating the first monitoring data that is below the preset correlation threshold to obtain real-time monitoring data, the method also includes: performing quality scoring on the real-time monitoring data, assigning weight values ​​to the real-time monitoring data according to the quality score to obtain a set of weight values, and performing weighted averaging on the real-time monitoring data according to the set of weight values ​​to obtain fused data, and the quality score is calculated by the signal-to-noise ratio and signal strength of the real-time monitoring data.

[0015] By adopting the above technical solution, the real-time monitoring data is scored for quality, and weight values ​​are assigned according to the scores. The collection of these weight values ​​is used for weighted averaging to obtain fused data, which reduces the impact of single data point anomalies on the overall prediction results, thereby further improving the accuracy and representativeness of the data.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after calculating the monitoring difference between the predicted monitoring data and the real-time monitoring data, if the monitoring difference exceeds a preset change threshold, sending an early warning prompt message to the client, the method also includes: determining the risk level corresponding to the monitoring difference, and generating early warning level information based on the risk level; and sending the early warning level information to the client.

[0017] By adopting the above technical solution, by determining the risk level corresponding to the monitoring difference and generating corresponding warning level information, the severity of the potential risk can be reflected in more detail, and this warning level information can be sent to the client to ensure that relevant personnel can understand the situation in a timely manner and take necessary measures. This improves the accuracy and timeliness of the warning information, and also provides users with more detailed risk assessment and response strategies, which can more effectively prevent and control the potential risks of the engineering facility structure and ensure the safe and stable operation of the engineering facility.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the warning level information to the client, the method also includes: generating a data analysis report based on the real-time monitoring data and the predicted monitoring data; marking the real-time monitoring data and the predicted monitoring data in the physical model of the engineering facility to obtain a three-dimensional modeling image; and sending the data analysis report and the three-dimensional modeling image to the client.

[0019] By adopting the above technical solution, first, detailed data analysis reports are generated based on real-time monitoring data and predicted monitoring data, allowing users to clearly understand the current status and potential risks of the engineering facility structure. Secondly, this data is annotated in the physical model of the engineering facility to generate a three-dimensional modeling image, making the monitoring results more intuitive and easy to understand, helping technicians to accurately locate problem areas.

[0020] In second aspect, an embodiment of the present application provides a Beidou-based structural deformation monitoring system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the Beidou-based structural deformation monitoring system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product is run on a Beidou-based structural deformation monitoring system, the above-mentioned Beidou-based structural deformation monitoring system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are run on a Beidou-based structural deformation monitoring system, the Beidou-based structural deformation monitoring system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It is understandable that the BeiDou-based structural deformation monitoring system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects that can be achieved can be referenced to the beneficial effects of the corresponding methods and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. This application obtains the engineering facility structure of the target monitoring object and performs structural analysis on its key components to accurately obtain the dynamic characteristic parameters of the engineering facility structure. These dynamic characteristic parameters reflect the response characteristics of the engineering facility structure under different environmental parameters, thereby enabling the establishment of an accurate physical model of the engineering facility. By acquiring and inputting real-time monitoring data, combined with preset environmental parameters for state prediction, and obtaining predicted monitoring data, and comparing it with real-time monitoring data, abnormal changes in the engineering facility structure can be discovered in a timely manner. When the monitoring difference exceeds the preset change threshold, an early warning prompt message will be sent to the client, which can improve the accuracy and timeliness of deformation monitoring, prevent potential structural risks, and thus ensure the safety and stability of engineering facilities.

[0026] 2. This application uses finite element analysis technology to simulate and analyze the key components of the engineering facility structure, which can obtain more accurate simulation analysis results. It can be used to identify the response characteristics of the engineering facility structure under various environmental parameters, and obtain dynamic characteristic parameters through fitting, thereby improving the understanding of the structural characteristics of the engineering facility and enhancing the ability to predict its dynamic behavior under different conditions.

[0027] 3. This application adopts the above-mentioned technical solution to pre-process the real-time monitoring data, remove noise and outliers, and obtain purified data. The purified data and preset environmental parameters are then input into the physical model of the engineering facility, and the strain coefficient is obtained through numerical simulation. The product of the purified data and the strain coefficient is used as the predicted monitoring data, which effectively combines the real-time data and the calculation results of the physical model to ensure the accuracy of the predicted data. It can more accurately reflect the strain state of the engineering facility under actual environmental conditions, thereby improving the reliability and timeliness of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1This is a flowchart of a BeiDou-based structural deformation monitoring method according to an embodiment of the present application;

[0029] Figure 2 This is another flowchart of the Beidou-based structural deformation monitoring method in an embodiment of the present application;

[0030] Figure 3 This is a schematic diagram of the structure of a physical device of the Beidou-based structural deformation monitoring system in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations of one or more of the listed items.

[0032] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0033] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0034] This application is based on the BeiDou / GNSS deformation monitoring solution, which automatically collects, transmits, stores, and processes data on deformations such as bridges, dams, power towers, highway slopes, tailings ponds, settlement of goafs, and roadbed settlement in real time. It provides all-weather millimeter-level intelligent monitoring for comprehensive early warning and protection work, creates a GNSS monitoring ecological data chain, scientifically analyzes deformation evolution trends, and conducts intelligent early warning to achieve the purpose of disaster prevention and mitigation.

[0035] Traditional laser measurement and fiber optic sensing technologies are widely used in bridge deformation monitoring. However, laser measurement is significantly affected by weather and environmental factors. For example, heavy fog and strong light interference can lead to inaccurate measurement data. While fiber optic sensing offers high accuracy, it is expensive to install and maintain, and is prone to damage over time, resulting in data interruptions. Furthermore, traditional GPS measurement methods lack accuracy and real-time performance. Especially in complex bridge construction sites, GPS signals are susceptible to interference, resulting in unstable measurement data. These technical shortcomings make it difficult to achieve high-precision and reliable real-time monitoring and early warning for bridge deformation monitoring, and are unable to effectively prevent potential risks to bridge structures.

[0036] The Beidou / GNSS-based deformation monitoring solution includes a data acquisition system, a data communication system, a data processing system, an analysis and early warning system, and an integrated management system. The data acquisition system consists of reference station GNSS receivers, GNSS antennas, and various automated sensors deployed at deformation monitoring points of geological and structural engineering facilities. The data communication system consists of wired networks, optical fibers, serial ports, wireless bridges, radio stations, and 3G / 4G / WiFi. It supports Beidou short message communication and selects the most appropriate data transmission method based on actual conditions to complete the transmission of all data. The data processing system, consisting of a server system and deformation monitoring software system deployed in the monitoring center, serves as the data processing, monitoring, and analysis center for the entire monitoring system, enabling high-precision GNSS data processing, coordinate solution, and deformation analysis. The analysis and early warning system analyzes the solved data, generates data analysis curves, judges and issues alarms for out-of-limit data based on set thresholds, and automatically records and generates data analysis reports. The integrated management system can manage and visualize the entire system, assign different levels of user permissions, manage monitoring data, and analyze the raw data.

[0037] This application is a Beidou-based structural deformation monitoring system in a data processing system. By receiving data from a data acquisition system, it performs structural analysis on key components of monitored engineering facilities such as bridges, dams, power towers, highway slopes, tailings ponds, goafs, and roadbeds. It uses finite element analysis technology to obtain the dynamic characteristic parameters of the engineering facilities under different environmental parameters, establishes an engineering facility physical model of the engineering facility, and inputs it into the engineering facility physical model for state prediction to obtain predicted monitoring data. The predicted monitoring data is compared with the real-time monitoring data. If the monitoring difference exceeds the preset change threshold, the system will send an early warning prompt message to the client.

[0038] It can be seen that by adopting the method in the embodiment of the present application, on the basis of the deformation monitoring technology based on the Beidou positioning system, which can provide high-precision positioning and real-time data acquisition, the finite element analysis technology is used to obtain the dynamic characteristic parameters of engineering facilities in different environments, accurately simulate the strain state of engineering facilities, establish a physical model of engineering facilities, and combine real-time data for state prediction to provide more accurate deformation prediction, improve the accuracy of early warning, and compare the predicted data and actual data in real time. If the difference exceeds the threshold, the system will send an early warning prompt to help prevent structural risks in a timely manner. It is widely used in the safety monitoring and management of key infrastructure such as bridges, dams, power towers, highway slopes, tailings ponds, goafs and roadbeds, providing efficient and reliable deformation monitoring solutions.

[0039] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of the Beidou-based structural deformation monitoring method in an embodiment of the present application.

[0040] S101. Acquire the engineering facility structure of the target monitoring object, and perform structural analysis on key components of the engineering facility structure to obtain dynamic characteristic parameters of the engineering facility structure. The dynamic characteristic parameters are engineering facility parameters of the engineering facility structure under different environmental parameters.

[0041] Among them, the target monitoring object refers to the engineering facility structure that needs to be monitored for deformation, such as large-scale infrastructure such as bridges and dams. The key components of the engineering facility structure refer to the parts of the engineering facility structure that have an important impact on the overall performance and safety, such as main beams, nodes, etc. Dynamic characteristic parameters refer to the dynamic response characteristics of the engineering facility structure under different environmental conditions, reflecting the physical characteristics of the engineering facility structure. Structural analysis refers to the process of calculating the mechanical properties and scenario simulation of the engineering facility structure using computer simulation and other methods. Environmental parameters refer to external conditions, such as temperature, humidity, wind pressure and other parameters.

[0042] Specifically, this step is performed before starting deformation monitoring. Its purpose is to obtain the dynamic characteristic parameters of the engineering facility structure in order to establish an accurate physical model. First, it is necessary to determine the monitoring object and obtain its complete structural design information. Then, identify the key components in the engineering facility structure. These key components directly affect the safety of the overall structure. Then, use computer simulation techniques such as finite element analysis to model and numerically simulate the structural responses of each key component under different environmental parameters to obtain the stress-strain characteristics of the components. By analyzing the simulation results, the dynamic response laws of the engineering facility structure under different environments are extracted, and the overall dynamic characteristic parameters of the engineering facility structure are obtained by fitting.

[0043] In some embodiments, this step can be implemented in the following two ways:

[0044] Alternatively, dynamic characteristic parameters can be obtained through actual loading tests: After the construction of the facility structure is completed, loading tests are performed on it. For example, forces are applied to key locations or a mass is placed on top to measure the structural response. This loading test directly measures the dynamic characteristics of the facility structure under controlled load conditions. The measurement results are then analyzed to determine parameters such as the natural frequency, damping ratio, and mode shape of the facility structure, as well as displacement parameters detected by Beidou positioning monitoring.

[0045] Alternatively, parameter identification can be performed by combining finite element modeling with actual data: first, a detailed finite element model is constructed based on the design drawings. Then, static analysis is performed using different material parameters to determine the stiffness distribution of the facility structure. Normal modal analysis is then performed to determine the structure's frequency and mode shape. The model parameters are then adjusted to ensure that the resulting frequency matches the actual measurement results, completing parameter identification. It is understood that other methods can also be used to obtain dynamic characteristic parameters, which are not limited here.

[0046] S102: Establish an engineering facility physical model of the engineering facility structure according to the dynamic characteristic parameters.

[0047] Among them, the physical model of engineering facilities refers to a physical calculation model that uses computer technology to perform digital simulation and numerical calculations on the structure of engineering facilities. This model integrates structural design parameters, material property parameters and dynamic characteristic parameters, and can perform high-precision numerical simulations of the static performance and dynamic response of the structure.

[0048] Specifically, after obtaining the dynamic characteristic parameters of the engineering facility structure, a physical model needs to be constructed in a computer environment. First, the initial model is established by inputting design information such as the structure's geometric dimensions, material parameters, and connection methods. Next, the dynamic characteristic parameters are assembled to determine the model's mass matrix and stiffness matrix. Next, boundary conditions and constraints are defined, and environmental load parameters are set. Finally, a static analysis is performed to verify the model's calculation results, and a normal modal analysis is performed to determine the dynamic characteristics, completing the physical model.

[0049] In some embodiments, a physical model can be established in the following two ways:

[0050] Optionally, general finite element analysis software can be used for modeling: Use commercial general-purpose software such as ANSYS, ABAQUS, etc. to create a three-dimensional solid model according to the structural design drawings, set material properties, define connection relationships, import dynamic characteristic parameters, and apply constraints and loads to form a sophisticated physical calculation model.

[0051] Optionally, a customized structural analysis program can be used for modeling: Based on the characteristics of the monitored structure, a modeling and analysis program can be independently developed using languages ​​such as Python and Java to achieve automated modeling of specific structural styles. This allows for flexible customization of the analysis process, fully considering the dynamic characteristics of the structure to achieve the desired simulation results. It is understood that other methods can also be used to establish the physical model, and this is not limited here.

[0052] S103: Acquire real-time monitoring data of the target monitoring object.

[0053] Among them, real-time monitoring data refers to the real-time deformation data collected by various sensors arranged at key positions of the target monitoring object. The types of sensors may include pore water pressure gauges, soil pressure gauges, anemometers, temperature and humidity meters, vibration sensors, digital cameras, strain sensors and tilt sensors, etc. Real-time means that data can be obtained continuously and uninterruptedly to ensure the continuity of monitoring.

[0054] Specifically, this step is performed after the physical model is established, when the structure needs long-term monitoring. Its purpose is to obtain real-time response data of the structure under actual conditions as model input. First, the number and placement of sensors must be determined according to the monitoring plan. Then, the various sensors must be installed and fixed in the predetermined locations, and the data acquisition system must be integrated. Once installed, the sensors continuously monitor the structure's displacement, strain, and other information, transmitting the data to a central server at a high frequency and in real time. The server aggregates and records the data to provide model input, enabling all-weather, automated monitoring of structural deformation.

[0055] In some embodiments, real-time monitoring data can be acquired through the following two methods: Alternatively, data can be acquired through a wired sensor network: active sensors are connected to a central data collection box via cables, and then transmitted to a server in real time via a local area network. This method is low-cost but complex in terms of wiring. Alternatively, data can be acquired through a wireless sensor network: wireless sensor nodes with built-in batteries are used to form a wireless network using ad hoc networking technology, and data is transmitted to a server in real time.

[0056] S104: Input the real-time monitoring data into the physical model of the engineering facility, predict the state of the target monitoring object according to preset environmental parameters, and obtain predicted monitoring data.

[0057] Among them, preset environmental parameters refer to various environmental condition parameters that may affect the structural deformation of the engineering facility, such as temperature, humidity, wind force, etc. State prediction refers to the calculation and simulation of possible structural responses based on physical models combined with real-time data. Predictive monitoring data refers to the structural response data obtained through model prediction.

[0058] Specifically, this step is performed after real-time monitoring data is acquired. Its purpose is to predict the state using the physical model. First, environmental parameters that may affect structural deformation must be pre-set. These parameters will serve as one of the model's inputs. The collected real-time monitoring data is then processed and integrated into a format that can be input into the model. This processed data, along with the environmental parameters, is then fed into the physical model. A computational simulation of the structure under these parameters is run to predict its response characteristics. Finally, predicted monitoring data such as strain and displacement are obtained. By comparing this with the real-time data, it is possible to determine whether the structure is actually functioning normally.

[0059] In some embodiments, state prediction can be performed in the following two ways:

[0060] Optionally, only the influence of environmental parameters can be considered: historical statistical environmental parameter data, such as temperature and humidity change curves, typical wind pressure data, etc. are directly input into the model to predict the structural response caused by them; optionally, environmental parameters and real-time monitoring data can be considered at the same time: various environmental parameter data are collected and integrated with real-time monitoring data in the data fusion module, and both are input into the physical model to comprehensively calculate the structural response. It can be understood that other methods can also be used to predict the state, which is not limited here.

[0061] For example, data on environmental parameters such as temperature, humidity, and wind pressure are collected. For example, on June 1, the temperature range was 15-35°C, the average humidity was 65%, and the maximum wind pressure reached level 8. The data is then filtered and smoothed to remove noise. Real-time monitoring data of the structure is obtained from displacement sensors and strain sensors. For example, the displacement of the span in the main beam was monitored to change from 80 to 120 mm, and the maximum strain at the corner of the floor reached 1200 microstrain. Preprocessing such as denoising and zero bias correction is also performed. During the data fusion process, the Kalman filter algorithm is used to integrate the temperature and humidity in time series. , wind pressure parameters and displacement and strain monitoring data. According to the structural characteristics, a three-dimensional solid model is established in ABAQUS software, the pre-processed fused data is input, the finite element type is selected, the connection relationship is defined, and according to the current environmental conditions, static and modal dynamic step analysis are selected, the iterative solution parameters are set, and the structural dynamic analysis of temperature load, wind pressure load and displacement boundary conditions is performed. From the analysis and calculation results, the displacement response, internal force distribution and strain time history of different parts of the structure are extracted as prediction monitoring data. The prediction results are compared with the actual collected data to determine whether the structural state is abnormal.

[0062] S105: Calculate the monitoring difference between the predicted monitoring data and the real-time monitoring data. If the monitoring difference exceeds a preset change threshold, send an early warning message to the client.

[0063] Among them, the monitoring difference refers to the numerical difference between the predicted monitoring data and the real-time monitoring data, the preset change threshold refers to the key value used to determine whether the structural state is abnormal, the early warning prompt information refers to the warning information automatically sent by the system when the monitoring difference exceeds the threshold, and the client refers to the monitoring personnel terminal that receives the early warning information.

[0064] Specifically, this step is performed after obtaining the predicted monitoring data, with the purpose of determining whether the structural state is abnormal. First, the system compares the predicted monitoring data with the real-time monitoring data point by point, calculating the specific difference between the two. The calculated monitoring difference is then compared with a pre-set change threshold. If the difference exceeds the threshold, it indicates that the structural state is abnormal and an accident may occur. At this time, the system will automatically generate an early warning message and immediately send it to the monitoring personnel via text message, email, etc., reminding them to take appropriate measures. If the difference does not exceed the threshold, the structural state is normal.

[0065] Optionally, a threshold value can be set in advance: based on the structural design indicators and historical monitoring data, the absolute threshold value or relative threshold value of the deformation change is predetermined, and the system directly judges the structural state according to the preset value. Optionally, the threshold value can be dynamically updated through machine learning: a large amount of historical monitoring data is collected, and machine learning methods such as neural networks are used to train and optimize the threshold judgment model to achieve dynamic updating of the threshold value. It is understandable that the monitoring difference and judgment threshold value can also be calculated by other means, which are not limited here.

[0066] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the Beidou-based structural deformation monitoring method in an embodiment of the present application.

[0067] S201. Acquire the engineering facility structure of the target monitoring object, and perform simulation analysis on key components of the engineering facility structure based on finite element analysis technology to obtain simulation analysis results.

[0068] First, the target monitoring object refers to the specific engineering facility structures that require deformation monitoring, such as bridges and dams. Finite element analysis is a digital structural analysis method that discretizes the structure into a finite number of elements and establishes physical equations for numerical calculations. Key components refer to those parts of the engineering facility structure that have a significant impact on the overall performance, such as main beams and joints. Simulation analysis is the process of computer simulation of key components using finite element analysis technology. The simulation analysis results include output information such as component displacement, internal forces, stress and strain.

[0069] Specifically, a finite element model of the structure is first established and discretized. Key components are identified, their material properties and connections defined, and analysis settings implemented. Next, static and modal analysis is performed using finite element software against various environmental parameters, such as temperature and load, to obtain analysis results for key components. Finally, the results are processed to output information such as component stress distribution and mode shapes.

[0070] For example, for a 50-meter span prestressed concrete simply supported beam bridge, a 3D solid finite element model of the bridge was first created using ANSYS software, discretizing the bridge into 100,000 elements. Within the model, the prestressed concrete material parameters were defined: an elastic modulus of 3.5 × 10⁴ MPa and a Poisson's ratio of 0.2. The boundary conditions for the bridge were set to simply supported at both ends. A uniformly distributed load was then applied to the model, and a static analysis was performed. The simulation results showed that, at a load of 10 kN / m, the maximum displacement in the mid-span of the bridge was 16.2 mm, and the maximum stress was 2.5 MPa. To determine the dynamic characteristics of the bridge, a modal analysis was performed, revealing the first three natural frequencies of the bridge to be 3.2 Hz, 6.5 Hz, and 7.9 Hz, respectively.

[0071] S202. Identify the response characteristics of the engineering facility structure under various environmental parameters based on the simulation analysis results, and fit the response characteristics to obtain the dynamic characteristic parameters, which are the engineering facility parameters of the engineering facility structure under different environmental parameters.

[0072] Response characteristics refer to the dynamic response laws of engineering facility structures under different environmental conditions, such as the characteristic relationship between displacement and strain as the environment changes. Fitting refers to the use of mathematical functions to approximate and describe these response characteristics. Dynamic characteristic parameters include dynamic parameters such as frequency, damping, and vibration mode of the engineering facility structure. Engineering facility parameters refer to the physical characteristics of the structure under environmental changes.

[0073] This step begins after the simulation analysis is complete. First, based on the analysis results, the response data of the structure under different environments are extracted, and the corresponding relationships between the environmental parameters are found. Then, using methods such as curve fitting, the mathematical expression of these response characteristics is determined. Finally, through fitting analysis, the overall dynamic characteristic parameters of the structure, namely the frequency, damping coefficient, and other parameters under different environments, can be obtained.

[0074] For example, a temperature gradient was set on the finite element model, resulting in a temperature of 15°C at one end of the beam bridge and 25°C at the other. Static analyses were run under multiple temperature gradient conditions, and the displacement at the midspan was recorded. The analysis results showed that the midspan displacement and the average temperature approximately followed a normal distribution. To establish an accurate response characteristic model, a Gaussian process regression method was used, with the average temperature as the independent variable and the displacement as the dependent variable. A Gaussian process regression model was trained, and the final temperature-displacement response function was determined as: y = 20*exp(-((x-20)^2) / 50)+2, where x is the average temperature and y is the corresponding displacement. Through parameter identification, the temperature-displacement dynamic characteristic parameters of the beam bridge were obtained, which can reflect its response law under temperature gradient loads.

[0075] S203: Establish an engineering facility physical model of the engineering facility structure according to the dynamic characteristic parameters.

[0076] In S202, taking a simply supported beam bridge as an example, the dynamic characteristic parameters of its temperature field-displacement were obtained through parameter identification, and a Gaussian process regression model was established to represent the influence of temperature on displacement. Now, this dynamic characteristic parameter will be used to establish the engineering facility physical model of the simply supported beam bridge: In the ANSYS finite element analysis software, a three-dimensional solid model of the beam bridge was created according to the design drawings, and meshing was performed. The element type was selected as PIPE288. Then, the material properties were defined, and the elastic modulus of concrete was selected as 3.5×104MPa and the elastic modulus of steel was selected as 2.0×105MPa. The temperature-displacement parameters obtained in S202 were substituted into the beam bridge model, and the temperature loading function was defined. The coupling relationship between the temperature field and displacement was established, and a static analysis was performed. The results showed that under a uniform temperature field of 20°C, the mid-span displacement was 10mm, which was consistent with the expected Gaussian process model results. Modal analysis was performed, and the first frequency was 3.21Hz, which was consistent with the actual measurement results. By importing the dynamic characteristic parameters, a computer model reflecting the actual physical characteristics of the simply supported beam bridge was established.

[0077] S204: Acquire initial monitoring data of the monitoring sensor and the arrival time and data intensity of the initial monitoring data.

[0078] First of all, monitoring sensors refer to various types of sensors installed at key locations of engineering facility structures, which are used to collect deformation monitoring data of the structures. Initial monitoring data refers to the analog or digital measurement data originally obtained by the sensor. Arrival time refers to the time interval from the collection of monitoring data by the sensor to the reception and processing of the system. Data intensity indicates the quality or reliability of the monitoring data.

[0079] Specifically, this step is performed when the monitoring system begins operation. Various sensors must be installed according to a pre-defined plan and connected to data acquisition equipment. The sensors continuously output initial monitoring data, which contains a significant amount of noise. The system also records the arrival time and signal strength of each data point. The arrival time reflects the smoothness of data transmission, while the signal strength represents data quality.

[0080] For example, in a bridge monitoring project, 32 displacement sensors were installed, which output displacement data every 2 minutes. The system would extract the acquisition time and signal amplitude of each data point. These raw displacement data and their time and intensity information would serve as input for subsequent processing.

[0081] S205: Eliminate the data whose arrival time is greater than a preset time threshold and whose data intensity is lower than a preset intensity threshold to obtain first monitoring data.

[0082] The preset time threshold refers to the maximum allowed data arrival delay. The preset strength threshold refers to the expected minimum data quality standard. The first monitoring data represents reliable monitoring data obtained after processing.

[0083] This step is performed after acquiring the raw monitoring data. The system examines the arrival time and intensity of each data point, filtering the data based on preset time and intensity thresholds. Data exceeding the delay threshold or with signal strength below the required level is discarded, filtering out unreliable outliers. The remaining data constitutes the highly reliable primary monitoring data set, which serves as the basis for status assessment.

[0084] For example, you can set the delay threshold to 5 seconds and the intensity threshold to 60% of the design value. Then, all displacement data with a delay exceeding 5 seconds or an intensity lower than 60% of the design value will be removed, and the remaining reliable data will be retained for subsequent analysis.

[0085] S206: Generate a preset pseudo-random code, calculate a correlation value between the preset pseudo-random code and the first monitoring data, and eliminate the first monitoring data below a preset correlation threshold to obtain real-time monitoring data.

[0086] In outdoor environments, monitoring signals are affected by multipath during propagation, reaching the sensor along different paths. This causes repeated sampling of the signal, generating redundant noise data with low correlation. To mitigate the impact of this multipath effect, a pseudo-random code correlation check is employed. First, the system generates a random number sequence as a pseudo-random code. The correlation coefficient between each monitoring data point and this pseudo-random code is then calculated. Because pseudo-random codes are uncorrelated, their correlation coefficients reflect the redundant components caused by multipath in the monitoring data. By removing data points with poor correlation, the multipath noise component in the resulting monitoring data is effectively removed, improving its quality.

[0087] S207. Perform a quality score on the real-time monitoring data, assign a weight value to the real-time monitoring data according to the quality score to obtain a weight value collection, and perform weighted averaging on the real-time monitoring data according to the weight value collection to obtain fused data. The quality score is calculated based on the signal-to-noise ratio and signal strength of the real-time monitoring data.

[0088] Specifically, to overcome the limitations of a single monitoring path, it is necessary to fuse multi-source monitoring data. Each path's monitoring data will have varying quality due to different influences, requiring a quality score and corresponding weighting. By weighted averaging multi-source monitoring data, the reliability of the results can be improved, the impact of individual path errors can be reduced, and the overall system error can be effectively controlled.

[0089] For example, if positioning data from four Beidou satellites is collected, and their signal quality scores are: B1 satellite 90 points, B2 satellite 80 points, B3 satellite 85 points, and B4 satellite 95 points, then a weight can be assigned to each Beidou satellite based on the signal quality score: B1 weight 0.9, B2 weight 0.8, B3 weight 0.85, and B4 weight 0.95. Then, a weighted average fusion of the Beidou positioning results is performed. Assume that the positioning results of the four satellites are: B1 positioning result: (longitude 1 degree, latitude 2 degrees), B2 positioning result: (longitude 1.1 degrees, latitude 2.1 degrees), B3 positioning result: (longitude 1.2 degrees, latitude 2.05 degrees), and B4 positioning result: (longitude 0.95 degrees, latitude 1.98 degrees). After weighted calculation based on the weights, the fused Beidou positioning result is: (longitude 1.01 degrees, latitude 2.02 degrees).

[0090] S208: Preprocess the real-time monitoring data to obtain purified data.

[0091] Here, preprocessing refers to the process of performing preliminary adjustments such as format conversion and zero drift processing on real-time monitoring data. Purified data refers to clean monitoring data that can be directly used for modeling and analysis after preprocessing.

[0092] Specifically, this step is performed after obtaining real-time monitoring data. The collected raw monitoring data needs to be preprocessed, such as coordinate conversion, unit conversion, time series interpolation, etc. This can eliminate the system error introduced by sensor installation error and improve data reliability. Then zero drift compensation is performed to eliminate the drift error caused by environmental changes. Finally, noise is reduced through filtering and smoothing methods. After the above preprocessing, the monitoring data becomes continuous and stable, and can be used as purified data input for subsequent modeling analysis.

[0093] For example, the collected raw strain data can be transformed into coordinate systems, converted into units of microstrain, and subjected to linear zero drift compensation and filtering and denoising to obtain purified strain data that can be directly input into modeling.

[0094] S209: input the purification data and the preset environmental parameters into the physical model of the engineering facility, and perform numerical simulation on the strain state of the engineering facility structure under the preset environmental parameters in the physical model of the engineering facility to obtain a strain coefficient.

[0095] Here, the preset environmental parameters refer to the simulated environmental loading conditions, such as temperature and wind pressure. The strain state is the internal force deformation state of the structure under the influence of the environmental parameters, and the strain coefficient is the numerical strain result of each part of the structure obtained through simulation.

[0096] This step is performed after obtaining the cleansing monitoring data. The cleansed monitoring data and the environmental parameters to be analyzed are loaded into the previously established structural calculation model. Finite element simulation is then run to analyze the internal forces and deformations of the structure under the specified environmental conditions. This analysis reveals the micro-deformations, stress, and strain distributions of each structural element. Finally, the strain time series or extreme values ​​at specific locations are output as the simulated strain coefficients.

[0097] For example, the temperature field and wind pressure parameters can be set, loaded into the bridge calculation model, and stress-strain analysis can be run. Based on the displacement of the monitoring points, the simulated strain time history can be extracted as the strain coefficient result.

[0098] Then, the finite element model of the simply supported beam bridge established in S202 is used, in which the No. 10 unit in the middle of the beam bridge is the sensor monitoring point: prepare to purify the monitoring strain data, import the strain sequence {120, 130, 125, 135, ...} microstrain of the No. 10 unit at the measuring point into ANSYS, then define the environmental parameters, and establish temperature loading in ANSYS: define the temperature field distribution function T(x, y, z, t) = 15 + 10sin(πt / 24) to represent the sinusoidal change of temperature with time, define wind pressure loading, establish a 0.5KN / m pressure distribution on the end face of the beam bridge, then load and apply, select the beam unit, use the "BF" command to load the temperature field distribution function, couple it with the unit temperature field, use the "SFA" command to load the end face pressure, apply wind pressure distribution, run the finite element analysis solver, perform static and thermal stress coupling analysis, take the stress and strain result data of unit No. 10, and output the strain time history as the simulation coefficient.

[0099] S210: The product of the purification data and the gauge coefficient is used as the predicted monitoring data.

[0100] Here, cleaned data refers to monitoring data that has undergone preprocessing and can be directly used for analysis. The strain factor is the strain response of a structure under environmental loads, as determined through modeling simulations. Predicted monitoring data is the monitoring value corrected based on the model calculation results.

[0101] This step is performed after obtaining the purification monitoring data and simulated strain coefficients. First, the time coordinate alignment of the purification monitoring data and the simulated strain coefficient is verified and synchronized. Then, the purification monitoring data is multiplied by the strain coefficient at the corresponding time point to generate a revised predicted monitoring data set. It is necessary to check whether the predicted results are within a reasonable range.

[0102] For example, in the simply supported beam bridge model established in S202, the middle unit No. 10 is selected as the sensor measurement point, and its measurement results are: purified monitoring displacement data: {15.1, 15.3, 15.2, ...} mm. Through the modeling calculation in S209, the strain coefficient at the corresponding simulation moment is obtained: {1.02, 0.98, 1.01, ...}. The specific steps for generating the predicted monitoring displacement data are: processing the time coordinates of the two sets of data to ensure the consistency of the time series, multiplying the purified monitoring displacement data with the strain coefficient at the corresponding moment point by point, and forming the calculated results into a predicted monitoring displacement sequence: {15.402, 14.985, 15.352, ...} mm. Check whether the result is within a reasonable physical range, and output the predicted displacement time series as the corrected monitoring data.

[0103] S211: Calculate the monitoring difference between the predicted monitoring data and the real-time monitoring data. If the monitoring difference exceeds a preset change threshold, send an early warning message to the client.

[0104] Here, the monitoring difference refers to the numerical difference between the predicted monitoring data and the real-time monitoring data. The preset change threshold is the threshold criterion for judging whether the structural state has changed significantly. The early warning prompt information will be sent when the monitoring difference exceeds the threshold to inform the abnormal change of the structural state.

[0105] This step is based on both predicted and real-time monitoring data. First, the difference between the two is calculated point by point. Then, a check is performed to determine whether the difference exceeds a pre-set threshold. If so, this indicates a state change beyond normal for the structure, necessitating an alert. At this point, the system automatically generates an alert message and immediately pushes it to the client via the monitoring terminal. This alert message includes the location of the violation, the monitored value, and the time.

[0106] For example, if the threshold for normal displacement change is set at 2mm, and the predicted monitoring result is 10mm, while the real-time monitoring result is 15mm, the difference is 5mm, exceeding the 2mm threshold. In this case, the system will push an alert message, "Bridge point A displacement detection exceeded the threshold at time xx," to alert users to abnormal changes in the structural state.

[0107] S212: Determine the risk level corresponding to the monitoring difference, and generate warning level information according to the risk level.

[0108] Here, the risk level is a qualitative assessment of the dangerousness of the structural state based on the monitoring difference, and the warning level is the result of grading the severity of the warning.

[0109] This step is based on the size of the monitoring difference. The system will determine the corresponding risk level for different ranges of monitoring differences, such as general, large, serious, etc., and then convert the risk level into a matching warning level, such as reminder, warning, serious warning, etc. Finally, a prompt message containing the warning level is generated and sent to the user, such as "Point A displacement is abnormal, warning". The distinction between warning levels can help users more accurately assess the structural status risk and take appropriate emergency measures.

[0110] S213: Send the warning level information to the client.

[0111] This step is performed after obtaining the warning level information. The system will package the warning content containing the risk level into a standard format telegram or message text, and then select the client access method, which can be the Internet, mobile network or independent wireless network, etc. Through the selected network interface, the warning telegram will be pushed to the registered client with the appropriate communication protocol. After the client receives the warning information, it needs to prompt the user, usually using obvious methods such as sound and light.

[0112] For example, the warning message "Serious displacement abnormality has occurred in area A, please check!" can be pushed to the mobile client APP of the monitoring system through the 5G network using the MQTT protocol.

[0113] S214: Generate a data analysis report based on the real-time monitoring data and the predicted monitoring data.

[0114] The data analysis report is a statistical analysis result of the structural operation status generated based on the monitoring data. This step is performed after obtaining the real-time monitoring data and the predicted data. The system can perform comparative statistical analysis on the two sets of data and generate statistical reports containing elements such as data overview, distribution characteristics, and correlation analysis. At the same time, it can use vivid charts to present the time pattern of the monitoring data and its relationship with the predicted results. The report can be automatically generated by the professional analysis module, and a tool for generating visual reports can also be provided for analysts to use. The generated report can more intuitively reflect the operating status and data quality of the structure, and provide management and maintenance personnel with a reference for decision-making.

[0115] S215: Mark the real-time monitoring data and the predicted monitoring data in the physical model of the engineering facility to obtain a three-dimensional modeling image.

[0116] Here, real-time monitoring data and predictive monitoring data are two important data sources for the structural operating status. The physical model of the engineering facility is a three-dimensional computer model created based on the actual structure. The three-dimensional modeling image is the effect display after data visualization annotation on the computer model.

[0117] This step is performed after obtaining both types of monitoring data. First, the 3D physical model of the engineering facility structure is imported into the visualization software. Then, the real-time monitoring data and predicted data are annotated to the relevant locations on the model, distinguished by different colors. By setting the deformation magnification factor, the deformation effect of the structure under different monitoring conditions can be more intuitively visualized, and an animation of the data time series can be displayed. Finally, a 3D model visualization image with the monitoring data annotations is generated.

[0118] For example, the floor lateral displacement results obtained by real-time monitoring are marked in red, the predicted floor lateral displacement results are marked in blue, and the images are magnified 100 times for animation display to obtain a three-dimensional visual comparison image of the displacement monitoring results of various parts of the engineering facility structure.

[0119] S216: Send the data analysis report and the 3D modeling image to the client.

[0120] This step is executed after obtaining the report and image. The system will package the report and image files into a standard format and add necessary descriptive text information. Then, it will select a wired network or a wireless network to send the data packet to the registered client terminal. After the client receives the data, it needs to extract the report and image and present them to the user in an appropriate manner, such as organizing them into a monitoring data browsing page in the APP. In this way, the user can intuitively view the structural status analysis report and make judgments conveniently.

[0121] The following describes the Beidou-based structural deformation monitoring system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of the Beidou-based structural deformation monitoring system in an embodiment of the present application.

[0122] It should be noted that Figure 3 The structure of the Beidou-based structural deformation monitoring system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0123] like Figure 3 As shown, the Beidou-based structural deformation monitoring system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes, such as the methods described in the above embodiments, based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage unit 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for system operation. CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0124] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, push button switches, and the like; an output section 307 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the removable media can be installed in the storage section 308 as needed.

[0125] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the present invention.

[0126] It should be noted that specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0128] Specifically, the Beidou-based structural deformation monitoring system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the Beidou-based structural deformation monitoring method provided by the above embodiment is implemented.

[0129] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the Beidou-based structural deformation monitoring system described in the above embodiments, or may exist independently and not be incorporated into the Beidou-based structural deformation monitoring system. The storage medium carries one or more computer programs, which, when executed by a processor of the Beidou-based structural deformation monitoring system, enable the Beidou-based structural deformation monitoring system to implement the Beidou-based structural deformation monitoring method provided in the above embodiments.

[0130] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0131] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0132] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A BeiDou-based structural deformation monitoring method, characterized in that: Applied to a BeiDou-based structural deformation monitoring system, the method includes: Acquire the engineering facility structure of the target monitoring object, and perform structural analysis on key components of the engineering facility structure to obtain dynamic characteristic parameters of the engineering facility structure, wherein the dynamic characteristic parameters are engineering facility parameters of the engineering facility structure under different environmental parameters; establishing an engineering facility physical model of the engineering facility structure according to the dynamic characteristic parameters; Acquire initial monitoring data from a monitoring sensor and the arrival time and data intensity of the initial monitoring data; Eliminate the data whose arrival time is greater than a preset time threshold and whose data intensity is lower than a preset intensity threshold to obtain first monitoring data; Generate a preset pseudo-random code, calculate a correlation value between the preset pseudo-random code and the first monitoring data, and eliminate the first monitoring data below a preset correlation threshold to obtain real-time monitoring data; Performing a quality score on the real-time monitoring data, assigning a weight value to the real-time monitoring data according to the quality score to obtain a weight value collection, and performing a weighted average on the real-time monitoring data according to the weight value collection to obtain fused data, wherein the quality score is calculated based on the signal-to-noise ratio and signal strength of the real-time monitoring data; Inputting the real-time monitoring data into the physical model of the engineering facility, predicting the state of the target monitoring object according to preset environmental parameters, and obtaining predicted monitoring data; The difference between the predicted monitoring data and the real-time monitoring data is calculated, and if the difference exceeds a preset change threshold, an early warning message is sent to the client.

2. The method according to claim 1, characterized in that The step of performing structural analysis on key components of the engineering facility structure to obtain dynamic characteristic parameters of the engineering facility structure specifically includes: Performing simulation analysis on key components of the engineering facility structure based on finite element analysis technology to obtain simulation analysis results; The response characteristics of the engineering facility structure under various environmental parameters are identified based on the simulation analysis results, and the response characteristics are fitted to obtain the dynamic characteristic parameters.

3. The method according to claim 1, characterized in that The step of inputting the real-time monitoring data into the physical model of the engineering facility, predicting the state of the target monitoring object according to preset environmental parameters, and obtaining predicted monitoring data specifically includes: Preprocessing the real-time monitoring data to obtain purified data; Inputting the purification data and the preset environmental parameters into the engineering facility physical model, and numerically simulating the strain state of the engineering facility structure under the preset environmental parameters in the engineering facility physical model to obtain a strain coefficient; The product of the purification data and the gauge factor is used as the predicted monitoring data.

4. The method according to claim 1, wherein After calculating the difference between the predicted monitoring data and the real-time monitoring data, and sending an early warning message to the client if the difference exceeds a preset change threshold, the method further includes: Determining the risk level corresponding to the monitoring difference, and generating warning level information according to the risk level; The warning level information is sent to the client.

5. The method according to claim 4, characterized in that After the step of sending the warning level information to the client, the method further includes: Generate a data analysis report based on the real-time monitoring data and the predicted monitoring data; Annotating the real-time monitoring data and the predicted monitoring data in the physical model of the engineering facility to obtain a three-dimensional modeling image; The data analysis report and the three-dimensional modeling image are sent to the client.

6. A Beidou-based structural deformation monitoring system, characterized in that: The Beidou-based structural deformation monitoring system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the Beidou-based structural deformation monitoring system to execute the method described in any one of claims 1 to 5.

7. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a BeiDou-based structural deformation monitoring system, the BeiDou-based structural deformation monitoring system is enabled to execute the method according to any one of claims 1 to 5.

8. A computer program product, characterized in that When the computer program product runs on a Beidou-based structural deformation monitoring system, the Beidou-based structural deformation monitoring system is enabled to execute the method according to any one of claims 1 to 5.

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