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

Abstract

The invention discloses a Beidou-based structural deformation monitoring method and system and a storage medium, and relates to the field of deformation monitoring, and the method comprises the steps: obtaining an engineering facility structure of a target monitoring object, carrying out the structural analysis of key parts, and accurately obtaining the dynamic characteristic parameters of the engineering facility structure, the dynamic characteristic parameters reflect response characteristics of an engineering facility structure under different environmental parameters, so that an accurate engineering facility physical model can be established, state prediction is performed by acquiring and inputting real-time monitoring data and combining preset environmental parameters to obtain predicted monitoring data, the predicted monitoring data are compared with the real-time monitoring data, and the real-time monitoring data is compared with the real-time monitoring data. According to the method, the abnormal change of the engineering facility structure can be found in time, the early warning prompt information can be sent to the client when the monitoring difference value exceeds the preset change threshold value, the precision and timeliness of deformation monitoring can be improved, potential structural risks are prevented, and therefore the safety and stability of the engineering facility are guaranteed.

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 facilities, the safety and stability of engineering facility structures have become the focus of attention. Deformation monitoring of engineering facilities plays a vital role in preventing and controlling engineering facility disasters. Especially in large-scale structural projects such as high-rise engineering facilities, bridges, and dams, accurate and real-time deformation monitoring technology is an important means to ensure their safe operation.

[0003] Among the related technologies, the commonly used deformation monitoring technologies for engineering facilities 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 relevant technology has limited accuracy in predicting the deformation of engineering facilities, 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 comprising: obtaining the engineering facility structure of the target monitoring object, and performing structural analysis on key components of the engineering facility structure to obtain dynamic characteristic parameters of the engineering facility structure, the dynamic characteristic parameters being the 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; obtaining real-time monitoring data of the target monitoring object; inputting the real-time monitoring data into the engineering facility physical model, predicting the state of the target monitoring object according to preset environmental parameters, and obtaining predicted monitoring data; calculating the monitoring difference between the predicted monitoring data and the real-time monitoring data, and if the monitoring difference exceeds a preset change threshold, sending an early warning prompt message 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, predictive monitoring data is obtained, and compared with the real-time monitoring data, abnormal changes in the structure of the engineering facility can be discovered in time. 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.

[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 step of predictive monitoring data, specifically including: 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 predictive monitoring data.

[0011] By adopting the above technical scheme, 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. 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. 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.

[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 strength of the monitoring sensor, and eliminating the data with an arrival time greater than a preset time threshold and a data strength lower than a preset strength threshold, low-quality data can be effectively filtered out, and then a preset pseudo-random code is generated and its correlation value with the initial monitoring data is calculated, and the first monitoring data lower than 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 a preset correlation threshold, and obtaining 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 collection of weight values, and performing weighted averaging on the real-time monitoring data according to the collection of weight values ​​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.

[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, after the step of 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 potential risks can be reflected more carefully, and these warning level information can be sent to the client to ensure that relevant personnel can understand the situation in time and take necessary measures, thereby improving the accuracy and timeliness of warning information, and providing users with more detailed risk assessment and response strategies, which can more effectively prevent and control the potential risks of engineering facility structures and ensure the safe and stable operation of engineering facilities.

[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, firstly, detailed data analysis reports are generated based on real-time monitoring data and predicted monitoring data, so that users can clearly understand the current status and potential risks of the engineering facility structure. Secondly, these data are marked 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, which helps technicians to accurately locate problem areas.

[0020] In the 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, and 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 the first aspect and any possible implementation method of the first aspect.

[0021] In the 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 executed 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 in the first aspect.

[0023] It can be understood 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 method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application can accurately obtain the dynamic characteristic parameters of the engineering facility structure by acquiring the engineering facility structure of the target monitoring object and performing structural analysis on its key components. 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, predictive 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 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.

[0025] 2. This application uses finite element analysis technology to simulate and analyze key components of engineering facility structures, and can obtain more accurate simulation analysis results, which can be used to identify the response characteristics of engineering facility structures under various environmental parameters, and obtain dynamic characteristic parameters through fitting, thereby improving the understanding of the characteristics of engineering facility structures and enhancing the ability to predict their dynamic behavior under different conditions.

[0026] 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

[0027] Figure 1It is a flowchart of a Beidou-based structural deformation monitoring method in an embodiment of the present application; Figure 2 is another flowchart of a Beidou-based structural deformation monitoring method in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of a Beidou-based structural deformation monitoring system in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] This application is based on the Beidou / GNSS deformation monitoring solution, and 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, providing all-weather millimeter-level intelligent monitoring for comprehensive early warning and protection work, creating a GNSS monitoring ecological data chain, scientifically analyzing deformation evolution trends, and conducting intelligent early warning to achieve the purpose of disaster prevention and mitigation.

[0032] In the deformation monitoring of bridges, traditional laser measurement and fiber optic sensing technologies are widely used. However, laser measurement is greatly affected by weather and environmental factors. For example, fog and strong light interference will cause inaccurate measurement data. Although fiber optic sensing has high accuracy, it has high installation and maintenance costs, and is prone to damage during long-term use, resulting in data interruption. In addition, traditional GPS measurement methods are also insufficient in accuracy and real-time performance. Especially in bridge construction sites with complex environments, GPS signals are easily interfered with, resulting in unstable measurement data. These technical defects make it difficult to achieve high-precision and high-reliability real-time monitoring and early warning for bridge deformation monitoring, and cannot effectively prevent potential risks to bridge structures.

[0033] The deformation monitoring solution based on Beidou / GNSS includes data acquisition system, data communication system, data processing system, analysis and early warning system and integrated management system; the data acquisition system is composed of reference station GNSS receivers, GNSS antennas and various automated sensors arranged at deformation monitoring points of geological and structural engineering facilities; the data communication system is composed of wired network, optical fiber, serial port, wireless bridge, radio station and 3G / 4G / WiFi, supports Beidou short message communication, selects the most appropriate data transmission method according to actual conditions, and completes the transmission of all data; the data processing system is composed of a server system and a deformation monitoring software system arranged in the monitoring center, which is the data processing, monitoring and analysis center of the entire monitoring system, realizing 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 alarms the over-limit data through the set threshold, and automatically records and generates data analysis reports; the integrated management system can manage and visualize the entire system, allocate different levels of user permissions, manage monitoring data, and analyze the original data.

[0034] The present application is a Beidou-based structural deformation monitoring system in a data processing system. By receiving data from a data acquisition system, a structural analysis is performed on key components of monitored engineering facilities such as bridges, dams, power poles, highway slopes, tailings ponds, goafs, and roadbeds. The finite element analysis technology is used to obtain the dynamic characteristic parameters of the engineering facilities under different environmental parameters, and an engineering facility physical model of the engineering facilities is established. The model is input into the engineering facility physical model for state prediction to obtain predicted monitoring data, and 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.

[0035] 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 the engineering facilities in different environments, accurately simulate the strain state of the engineering facilities, establish a physical model of the engineering facilities, and combine real-time data for state prediction, provide more accurate deformation prediction, improve the accuracy of early warning, compare the predicted data and the actual data in real time, if the difference exceeds the threshold, the system sends an early warning prompt to help prevent structural risks in time, and 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.

[0036] 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.

[0037] 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, where the dynamic characteristic parameters are engineering facility parameters of the engineering facility structure under different environmental parameters.

[0038] 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. The 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.

[0039] 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, which 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.

[0040] In some embodiments, this step can be implemented in the following two ways: Optionally, dynamic characteristic parameters can be obtained through actual loading tests: after the completion of the engineering facility structure, a loading test is performed on it, such as applying force at key locations or measuring the structural response after setting a mass block on the top, and the dynamic characteristics of the engineering facility structure under controllable load conditions are directly measured through loading tests. Then, the natural frequency, damping ratio, vibration mode and other parameters of the engineering facility structure are determined by analyzing the measurement results, including the displacement parameters monitored by Beidou positioning.

[0041] Optionally, parameter identification can be performed by combining finite element modeling with actual data: first, a detailed finite element model is built based on the design drawings, and then different material parameters are set for static analysis to determine the stiffness distribution of the engineering facility structure, and normal modal analysis is performed to obtain the frequency and vibration mode of the structure, and the model parameters are adjusted to make the frequency results consistent with the actual measurement results to complete parameter identification. It is understandable that other methods can also be used to obtain dynamic characteristic parameters, which are not limited here.

[0042] S102: Establish an engineering facility physical model of the engineering facility structure according to the dynamic characteristic parameters.

[0043] 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 on the static performance and dynamic response of the structure.

[0044] Specifically, after obtaining the dynamic characteristic parameters of the engineering facility structure, it is necessary to build a physical model in a computer environment. First, input the geometric dimensions, material parameters, connection methods and other design information of the structure to establish an initial model. Then, assemble the dynamic characteristic parameters and determine the mass matrix and stiffness matrix of the model. Next, define the boundary conditions and constraints and set the environmental load parameters. Finally, perform static analysis to verify the calculation results of the model, and perform normal modal analysis to determine the dynamic characteristics to complete the establishment of the physical model.

[0045] In some embodiments, the physical model can be established in the following two ways: Optionally, general finite element analysis software can be used for modeling: Use general commercial 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.

[0046] Optionally, use a custom structural analysis program to build a model: According to the characteristics of the monitored structure, use Python, Java and other languages ​​to independently develop a modeling and analysis program to achieve automated modeling of specific structural styles, flexibly customize the analysis process, fully consider the structural dynamic characteristic parameters, and achieve the desired simulation effect. It is understandable that other methods can also be used to build a physical model, which is not limited here.

[0047] S103: Acquire real-time monitoring data of the target monitoring object.

[0048] 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, earth 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.

[0049] Specifically, this step is performed when the physical model is established and the structure needs to be monitored for a long time. The purpose is to obtain the response data of the structure under actual conditions in real time as model input. First, the number of sensors and their layout positions need to be determined according to the monitoring plan, and then various sensors are installed and fixed at predetermined positions, and the data acquisition system is integrated. After installation, the sensors will continuously monitor the displacement, strain and other information of the structure, and transmit the data to the central server in real time at a high frequency. The server will summarize and record the data to provide model input, so that all-weather and automated monitoring of structural deformation can be achieved.

[0050] In some embodiments, real-time monitoring data can be obtained in the following two ways: Optionally, data can be obtained through a wired sensor network: using active sensors, connecting them to a central data collection box via cables, and then transmitting them to a server in real time via a local area network. This method is low-cost but complex in wiring. Optionally, data can be obtained through a wireless sensor network: using wireless sensor nodes with their own batteries, forming a wireless network through ad hoc networking technology, and sending data to a server in real time.

[0051] S104, 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.

[0052] 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.

[0053] Specifically, this step is performed after the real-time monitoring data is obtained. Its purpose is to predict the state with the help of the physical model. First, the environmental parameters that may affect the deformation of the structure need to be set in advance. These parameters will be used as one of the inputs of the model. Then the collected real-time monitoring data will be processed and integrated into a format that can be input into the model. Then the processed data and environmental parameters will be input into the physical model together, and the calculation simulation of the structure under these parameters will be run to predict its response characteristics. Finally, the predicted monitoring data such as strain and displacement can be obtained. By comparing with the real-time data, it can be judged whether the actual condition of the structure is normal.

[0054] In some embodiments, state prediction can be performed in the following two ways: 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.

[0055] 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 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 is monitored to change from 80 to 120 mm, and the maximum strain at the corner of the floor reaches 1200 microstrain. Pre-processing such as denoising and zero bias correction is performed. In the process of data fusion, the Kalman filter algorithm is used to integrate 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 and 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.

[0056] 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.

[0057] 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 terminal of the monitoring personnel who receives the early warning information.

[0058] Specifically, this step is performed after obtaining the predicted monitoring data, in order to determine whether the structural state is abnormal. First, the system will compare the predicted monitoring data and the real-time monitoring data point by point, calculate the specific difference between the two, and then compare the calculated monitoring difference with the pre-set change threshold. If the difference exceeds the threshold, it means 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 SMS, email, etc., to remind them to take corresponding measures. If the difference does not exceed the threshold, it means that the structural state is normal.

[0059] Optionally, the threshold can be set in advance: the absolute threshold or relative threshold of deformation change is determined in advance according to the structural design indicators and historical monitoring data, and the system directly judges the structural state according to the preset value. Optionally, the threshold 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. It is understandable that the monitoring difference and judgment threshold can also be calculated by other means, which are not limited here.

[0060] 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.

[0061] 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.

[0062] First of all, the target monitoring object refers to the specific engineering facility structure that needs to be monitored for deformation, such as bridges, dams, etc. Finite element analysis technology is a digital structural analysis method. By discretizing the structure into a finite number of units, physical equations are established for numerical calculation. Key components refer to the parts of the engineering facility structure that have an important impact on the overall performance, such as main beams, nodes, etc. Simulation analysis refers to the process of computer simulation of key components using finite element analysis technology. The simulation analysis results include output information such as displacement, internal force, stress and strain of the components.

[0063] Specifically, we first establish a finite element calculation model of the structure, discretize it, identify the key components in the structure, define their material properties and connection relationships, and perform analysis settings. Then, we run the finite element software for static and modal analysis for different environmental parameters, such as temperature and load, to obtain the analysis results of key components. Finally, by processing the results, we can output information such as the stress distribution and vibration mode of the components.

[0064] For example, for a prestressed concrete simply supported beam bridge with a span of 50 meters, the 3D solid finite element model of the beam bridge is first established using ANSYS software, and the beam bridge is discretized into 100,000 units. In the model, the parameters of the prestressed concrete material are defined: the elastic modulus is 3.5×104MPa, the Poisson's ratio is 0.2, and the boundary conditions of the beam bridge are set to be simply supported at both ends. Then, a uniformly distributed load is applied to the model for static analysis. The output simulation results show that when the load is 10kN / m, the maximum displacement of the mid-span part of the beam bridge is 16.2mm and the maximum stress is 2.5MPa. In order to obtain the dynamic characteristics of the beam bridge, modal analysis is also required. The results show that the first three natural frequencies of the beam bridge are 3.2Hz, 6.5Hz and 7.9Hz respectively.

[0065] 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.

[0066] 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 and environmental changes. Fitting refers to the use of mathematical functions to approximate and describe these response characteristics. Dynamic characteristic parameters include frequency, damping, vibration mode and other dynamic parameters of engineering facility structures. Engineering facility parameters refer to the physical characteristics of the structure under environmental changes.

[0067] This step starts after the simulation analysis is completed. First, based on the analysis results, the response data of the structure in different environments are extracted to find the corresponding relationship between the environmental parameters. Then, the mathematical expression of these response characteristics is determined using methods such as curve fitting. Finally, through fitting analysis, the overall dynamic characteristic parameters of the structure, i.e., the frequency, damping coefficient and other parameters under different environments, can be obtained.

[0068] For example, a temperature gradient was set on the finite element model so that the temperature at one end of the beam bridge was 15°C and the temperature at the other end was 25°C. Static analysis was performed under multiple temperature gradient conditions, and the displacement at the mid-span was recorded. The analysis results showed that the mid-span displacement and the average temperature approximately conformed to the normal distribution relationship. In order to establish an accurate response characteristic model, the 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 load.

[0069] S203: Establish an engineering facility physical model of the engineering facility structure according to the dynamic characteristic parameters.

[0070] 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 is created according to the design drawings, and meshing is performed. The element type is selected as PIPE288, and then the material properties are defined. The elastic modulus of concrete is selected as 3.5×104MPa and the elastic modulus of steel bars is selected as 2.0×105MPa. The temperature-displacement parameters obtained in S202 are substituted into the beam bridge model, the temperature loading function is defined, the coupling relationship between the temperature field and the displacement is established, and a static analysis is performed. The results show that under a uniform temperature field of 20°C, the mid-span displacement is 10mm, which is consistent with the expected Gaussian process model results. Modal analysis is performed, and the first frequency is 3.21Hz, which is consistent with the actual measurement results. By importing dynamic characteristic parameters, a computer model reflecting the actual physical characteristics of the simply supported beam bridge is established.

[0071] S204: Acquire initial monitoring data of the monitoring sensor and the arrival time and data intensity of the initial monitoring data.

[0072] 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 acquired by the sensors. Arrival time refers to the time interval from the collection of monitoring data by the sensors to the reception and processing of the system. Data intensity indicates the quality or reliability of the monitoring data.

[0073] Specifically, this step is performed when the monitoring system starts running. It is necessary to install various sensors according to the predetermined plan and establish a connection with the data acquisition equipment. The sensors will continuously output initial monitoring data. These raw data contain a lot of noise. At the same time, the system will record the arrival time and signal strength of each data point. The arrival time reflects whether the data transmission is smooth, and the signal strength represents the data quality.

[0074] For example, in a bridge monitoring project, 32 displacement sensors were installed, which output a 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 be used as input for subsequent processing.

[0075] 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.

[0076] 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 the reliable monitoring data obtained after processing.

[0077] This step is performed after obtaining the original monitoring data. The system will check the arrival time and strength information of each data point by point, and filter the data according to the preset time threshold and strength threshold. The monitoring data that exceeds the delay threshold or the signal strength is lower than the requirement will be eliminated, and the unreliable abnormal points will be filtered out. The remaining data constitutes the first monitoring data with high reliability, which can be used as the basis for status evaluation.

[0078] For example, the delay threshold can be set to 5 seconds and the intensity threshold can be set to 60% of the design value. Then all displacement data with a delay time of more than 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.

[0079] S206: Generate a preset pseudo-random code, calculate a correlation value between the preset pseudo-random code and the first monitoring data, and remove the first monitoring data that is lower than a preset correlation threshold to obtain real-time monitoring data.

[0080] In outdoor environments, monitoring signals are affected by multipath effects during propagation, reaching sensors from different paths, causing signals to be repeatedly sampled, generating redundant noise data with low correlation. In order to reduce the impact of this multipath effect, it is necessary to use a pseudo-random code correlation test method to process it. First, the system generates a set of random number sequences as pseudo-random codes, and then calculates the correlation coefficient between each point of the monitoring data and this pseudo-random code. Since the pseudo-random code has no correlation, its correlation coefficient can reflect the redundant components caused by the multipath effect in the monitoring data. After removing the data points with poor correlation, the multipath noise components in the obtained monitoring data are effectively removed, and the quality is improved.

[0081] S207, 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, performing weighted averaging 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.

[0082] Specifically, in order to make up for the limitations of a single monitoring path, it is necessary to use the fusion of multi-source monitoring data. The quality of each path monitoring data will be different due to different influences, and it is necessary to conduct quality scoring and assign corresponding weights. 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 error of the system can be effectively controlled.

[0083] For example, the positioning data of four Beidou satellites are collected, and the signal quality scores of each are: B1 satellite 90 points, B2 satellite 80 points, B3 satellite 85 points, B4 satellite 95 points. First, the weight of each Beidou satellite can be determined according to the signal quality score: B1 weight 0.9, B2 weight 0.8, B3 weight 0.85, B4 weight 0.95, and then the weighted average fusion of 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), B4 positioning result: (longitude 0.95 degrees, latitude 1.98 degrees), and weighted calculation is performed according to the weights, and the fused Beidou positioning result can be obtained as: (longitude 1.01 degrees, latitude 2.02 degrees).

[0084] S208: pre-process the real-time monitoring data to obtain purified data.

[0085] Here, preprocessing refers to the process of preliminary adjustments such as format conversion and zero drift processing on real-time monitoring data, and purified data refers to clean monitoring data that can be directly used for modeling and analysis after preprocessing.

[0086] Specifically, this step is performed after acquiring real-time monitoring data. The collected raw monitoring data needs to be preprocessed, such as coordinate conversion, unit conversion, timing interpolation, etc. This can eliminate the system error introduced by the sensor installation error and improve the reliability of the data. Then, zero drift compensation is performed to eliminate the drift error caused by environmental changes. Finally, noise is reduced through filtering and smoothing and other methods. After the above preprocessing, the monitoring data becomes continuous and stable and can be used as purified data input for subsequent modeling and analysis.

[0087] For example, the collected raw strain data can be transformed into coordinate systems, converted into units into microstrain, and subjected to linear zero drift compensation and filtering and denoising to obtain purified strain data that can be directly input into modeling.

[0088] S209, inputting the purification 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.

[0089] Here, the preset environmental parameters refer to the simulated environmental loading conditions, such as temperature, wind pressure, etc. The strain state is the internal force deformation state of the structure under the action of environmental parameters, and the strain coefficient is the numerical strain result of each part of the structure obtained through simulation.

[0090] This step is performed after obtaining the purification monitoring data, and the cleaned monitoring data and the environmental parameters to be analyzed are loaded into the previously established structural calculation model. The finite element simulation is run to analyze the internal force deformation state of the structure under the set environment, and the small deformation, stress and strain distribution of each unit of the structure can be obtained. Finally, the strain time series or extreme value of a specific part is output as the strain coefficient obtained by simulation.

[0091] For example, the temperature field and wind pressure parameters can be set, loaded into the bridge calculation model, and the 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.

[0092] Then, the finite element model of the simply supported beam bridge established in S202, in which unit No. 10 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 unit No. 10 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 over time, define wind pressure loading, establish a pressure distribution of 0.5KN / m 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-strain result data of unit No. 10, and output the strain time history as the simulation coefficient.

[0093] S210: taking the product of the purification data and the strain coefficient as the predicted monitoring data.

[0094] Here, purified data refers to monitoring data that can be directly used for analysis after preprocessing. The strain coefficient is the strain response result of the structure under environmental load obtained through modeling simulation. The predicted monitoring data is the monitoring value corrected according to the model calculation results.

[0095] This step is performed after obtaining the purification monitoring data and the simulated strain coefficient. First, the time coordinate alignment relationship between the purification monitoring data and the simulated strain coefficient is checked and synchronized. Then, the purification monitoring data is multiplied by the strain coefficient at the corresponding moment point by point to generate a corrected predicted monitoring data set. It is necessary to check whether the prediction result is within a reasonable range.

[0096] 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 result is: purification 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 purification monitoring displacement data by the strain coefficient at the corresponding moment point by point, and forming the calculation 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.

[0097] S211, calculating the monitoring difference between the predicted monitoring data and the real-time monitoring data, and if the monitoring difference exceeds a preset change threshold, sending an early warning prompt message to the client.

[0098] 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.

[0099] This step is based on the predicted monitoring data and the real-time monitoring data. First, the difference between the two is calculated point by point. Then, it is determined whether the difference exceeds the preset change threshold. If it exceeds, it means that the state of the structure has changed beyond the normal range and an early warning is required. At this time, the system will automatically generate an early warning message and immediately push it to the client user through the monitoring terminal. The early warning message will include the location of the limit, the monitoring value and the time.

[0100] For example, if the normal displacement change threshold is set to 2mm, when the predicted monitoring result is 10mm and the real-time monitoring result is 15mm, the difference between the two is 5mm, which exceeds the threshold limit of 2mm. At this time, the system will push an early warning message "The displacement detection of bridge point A exceeds the threshold at xx time" to remind users to pay attention to abnormal changes in the structural status.

[0101] S212: Determine the risk level corresponding to the monitoring difference, and generate warning level information according to the risk level.

[0102] 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.

[0103] 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, severe, etc., and then convert the risk level into a matching warning level, such as reminder, warning, severe 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 of warning levels can help users more accurately assess the risk of structural status and take appropriate emergency measures.

[0104] S213: Send the warning level information to the client.

[0105] 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's 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.

[0106] 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.

[0107] S214: Generate a data analysis report based on the real-time monitoring data and the predicted monitoring data.

[0108] 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, vivid charts can be used 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 tools 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 reference for decision-making.

[0109] S215: Annotate 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.

[0110] 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.

[0111] This step is performed after obtaining two types of monitoring data. First, the three-dimensional physical model of the engineering facility structure is imported into the visualization software. Then, the real-time monitoring data and the predicted data are marked to the relevant positions of the model respectively, which can be distinguished by different colors. By setting the deformation magnification coefficient, the deformation effect of the structure under different monitoring conditions can be more intuitively seen, and the animation of the data time series change can also be displayed. Finally, a three-dimensional modeling visualization image with monitoring data annotations is generated.

[0112] 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 magnified 100 times for animation display, to obtain a three-dimensional visual comparison image of the displacement monitoring results of each part of the engineering facility structure.

[0113] S216: Send the data analysis report and the 3D modeling image to the client.

[0114] 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, the wired network or wireless network is selected to send the data packet to the registered client terminal. After the client receives the data, the report and image need to be extracted and presented 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.

[0115] 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 structure of a physical device of a Beidou-based structural deformation monitoring system in an embodiment of the present application.

[0116] 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.

[0117] like Figure 3As shown, the Beidou-based structural deformation monitoring system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0118] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. 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. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0119] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0120] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a 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 may be used by or in combination with an instruction execution system, apparatus, or device.

[0121] 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 may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a 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 may also occur in an order different from that marked in the accompanying drawings.

[0122] 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 in the above embodiment is implemented.

[0123] 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 embodiment; or may exist independently without being assembled into the Beidou-based structural deformation monitoring system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the Beidou-based structural deformation monitoring system, the Beidou-based structural deformation monitoring system implements the Beidou-based structural deformation monitoring method provided in the above embodiment.

[0124] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned 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.

[0125] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0126] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A Beidou-based structural deformation monitoring method, characterized in that: Applied to a Beidou-based structural deformation monitoring system, the method comprises: 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, where 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 real-time monitoring data of the target monitoring object; 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 monitoring difference between the predicted monitoring data and the real-time monitoring data is calculated, and if the monitoring difference exceeds a preset change threshold, an early warning prompt 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: Perform 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, characterized in that: The step of obtaining the real-time monitoring data of the target monitoring object specifically includes: Acquire initial monitoring data of a monitoring sensor and 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; A preset pseudo-random code is generated, and a correlation value between the preset pseudo-random code and the first monitoring data is calculated, and the first monitoring data below a preset correlation threshold is eliminated to obtain real-time monitoring data.

5. The method according to claim 4, characterized in that After the step of generating a preset pseudo-random code, calculating a correlation value between the preset pseudo-random code and the first monitoring data, and removing the first monitoring data below a preset correlation threshold to obtain real-time monitoring data, the method further includes: 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, perform weighted averaging on the real-time monitoring data according to the weight value collection to obtain fused data, and the quality score is calculated based on the signal-to-noise ratio and signal strength of the real-time monitoring data.

6. The method according to claim 1, characterized in that After calculating the monitoring difference between the predicted monitoring data and the real-time monitoring data, and sending an early warning message to the client if the monitoring 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.

7. The method according to claim 6, 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.

8. 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 1-7.

9. 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 7.

10. 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 7.

Images