CN119334236A

CN119334236A - A Beidou-based structural deformation monitoring method, system and storage medium

Info

Publication number: CN119334236A
Application number: CN202411382045.XA
Authority: CN
Inventors: 朱松; 刘泽浩; 吴丹; 华远盛; 朱家松; 李清泉
Original assignee: Shenzhen Zhiyuan Space Intelligent Technology Co ltd; Shenzhen Zhiyuan Space Innovation Technology Co ltd
Current assignee: Shenzhen Zhiyuan Space Intelligent Technology Co ltd; Shenzhen Zhiyuan Space Innovation Technology Co ltd
Priority date: 2024-09-30
Filing date: 2024-09-30
Publication date: 2025-01-21
Anticipated expiration: 2044-09-30
Also published as: CN119334236B

Abstract

A Beidou-based structural deformation monitoring method, system and storage medium relate to the field of deformation monitoring. The method comprises: by acquiring the engineering facility structure of the target monitoring object and performing structural analysis on its key components, dynamic characteristic parameters of the engineering facility structure can be accurately obtained, and 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, state prediction is performed in combination with preset environmental parameters to obtain predicted monitoring data, and the predicted monitoring data is compared with the real-time monitoring data, so that 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 a client, so that the accuracy and timeliness of deformation monitoring can be improved, potential structural risks can be prevented, and the safety and stability of the engineering facility can be guaranteed.

Description

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

Technical Field

The application relates to the field of deformation monitoring, in particular to a structural deformation monitoring method, system and storage medium based on Beidou.

Background

With the acceleration of the global urbanization process and the increasing complexity of engineering facilities, the safety and stability of engineering facility structures are the focus of attention. Deformation monitoring of engineering facilities plays a vital role in preventing and controlling engineering facility disasters. In particular, in large-scale structural engineering such as high-rise engineering facilities, bridges, dams and the like, an accurate and real-time deformation monitoring technology is an important means for ensuring safe operation of the high-rise engineering facilities, bridges, dams and the like.

In the related art, the common engineering facility deformation monitoring technology mainly comprises a laser measurement technology, an optical fiber sensing technology and a traditional GPS measurement method, and future deformation states of the engineering facility are predicted by combining collected data with historical data and a simple mathematical model.

However, the accuracy of deformation prediction of engineering facilities is limited in the related art, and potential structural risks are difficult to early warn timely and accurately.

Disclosure of Invention

The application provides a Beidou-based structural deformation monitoring method, a Beidou-based structural deformation monitoring system and a Beidou-based structural deformation monitoring storage medium, which are used for improving the precision of deformation prediction of engineering facilities.

In a first aspect, the application provides a structural deformation monitoring method based on Beidou, which is applied to a structural deformation monitoring system based on Beidou, and the method comprises the steps of obtaining an engineering facility structure of a target monitoring object, and carrying out structural analysis on key parts 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 environment parameters; the method comprises the steps of 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 environment parameters to obtain predicted monitoring data, calculating a monitoring difference value between the predicted monitoring data and the real-time monitoring data, and sending early warning prompt information to a client when the monitoring difference value exceeds a preset change threshold value.

By adopting the technical scheme, the dynamic characteristic parameters of the engineering facility structure can be accurately obtained by acquiring the engineering facility structure of the target monitoring object and carrying out structural analysis on key parts of the engineering facility structure, the dynamic characteristic parameters reflect the response characteristics of the engineering facility structure under different environment parameters, so that an accurate engineering facility physical model can be established, the state prediction is carried out by combining the preset environment parameters through the acquisition and the input of real-time monitoring data, the predicted monitoring data are obtained and are compared with the real-time monitoring data, the abnormal change of the engineering facility structure can be timely found, the early warning prompt information is sent to the client when the monitoring difference exceeds the preset change threshold, the accuracy and the timeliness of deformation monitoring can be improved, the potential structural risk is prevented, and the safety and the stability of the engineering facility are ensured.

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 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 response characteristics of the engineering facility structure under various environmental parameters according to the simulation analysis results, and fitting the response characteristics to obtain the dynamic characteristic parameters.

By adopting the technical scheme, the key components of the engineering facility structure are subjected to simulation analysis by the finite element analysis technology, so that a more accurate simulation analysis result can be obtained, the response characteristics of the engineering facility structure under various environmental parameters can be identified, dynamic characteristic parameters can be obtained by fitting, understanding of the structural characteristics of the engineering facility is improved, and the prediction capability of the dynamic behavior of the engineering facility structure under different conditions is enhanced.

In combination with some embodiments of the first aspect, in some embodiments, the step of inputting the real-time monitoring data into the engineering facility physical model and predicting the state of the target monitoring object according to a preset environmental parameter to obtain predicted monitoring data specifically includes preprocessing the real-time monitoring data to obtain purified data, inputting the purified data and the preset environmental parameter into the engineering facility physical model, performing numerical simulation on the strain state of the engineering facility structure under the preset environmental parameter in the engineering facility physical model to obtain a strain coefficient, and taking the product of the purified data and the strain coefficient as the predicted monitoring data.

By adopting the technical scheme, the real-time monitoring data is preprocessed, noise and abnormal values are removed, purified data is obtained, then the purified data and preset environmental parameters are input into an engineering facility physical model, a strain coefficient is obtained through numerical simulation, the product of the purified data and the strain coefficient is used as prediction monitoring data, the calculation results of the real-time data and the physical model are effectively combined, the accuracy of the prediction data is ensured, the strain state of the engineering facility under the actual environmental conditions can be reflected more accurately, and further the reliability and timeliness of prediction are improved.

In combination with some embodiments of the first aspect, in some embodiments, the step of acquiring real-time monitoring data of the target monitoring object specifically includes acquiring initial monitoring data of a monitoring sensor and arrival time and data intensity of the initial monitoring data, removing data with the arrival time being greater than a preset time threshold and the data intensity being lower than a preset intensity threshold to obtain first monitoring data, 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 with the arrival time being lower than a preset correlation threshold to obtain real-time monitoring data.

By adopting the technical scheme, the low-quality data can be effectively filtered by acquiring the initial monitoring data, the arrival time and the data intensity of the monitoring sensor and rejecting the data with the arrival time being greater than the preset time threshold and the data intensity being lower than the preset intensity threshold, then the preset pseudo-random code is generated and the correlation value between the low-quality data and the initial monitoring data is calculated, and the first monitoring data lower than the preset correlation threshold is rejected, so that the finally obtained real-time monitoring data has higher quality and reliability, and the prediction error caused by the low-quality data is avoided.

In combination with some embodiments of the first aspect, in some embodiments, the generating a preset pseudo-random code, calculating a correlation value between the preset pseudo-random code and the first monitoring data, and rejecting the first monitoring data below a preset correlation threshold value to obtain real-time monitoring data, where after the step of obtaining real-time monitoring data, the method further includes 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 set, and performing weighted average on the real-time monitoring data according to the weight value set to obtain fusion data, where the quality score is obtained by calculating a signal-to-noise ratio and a signal strength of the real-time monitoring data.

By adopting the technical scheme, the quality scoring is carried out on the real-time monitoring data, the weight values are given according to the scoring, the weight value aggregate is used for weighted average, the fusion data is obtained, the influence of single data point abnormality on the whole prediction result is reduced, and therefore the accuracy and the representativeness of the data are further improved.

In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the early warning prompt information to the client after calculating the monitoring difference value between the predicted monitoring data and the real-time monitoring data and if the monitoring difference value exceeds the preset change threshold, the method further includes determining a risk level corresponding to the monitoring difference value, generating early warning level information according to the risk level, and sending the early warning level information to the client.

By adopting the technical scheme, the risk level corresponding to the monitoring difference value is determined, corresponding early warning level information is generated, the severity of the potential risk can be reflected more carefully, the early warning level information is sent to the client, the situation can be known timely by related personnel and necessary measures can be taken, the accuracy and timeliness of the early warning information are improved, a more detailed risk assessment and response strategy is provided for a user, the potential risk of the engineering facility structure can be prevented and controlled more effectively, and the safe and stable operation of the engineering facility is ensured.

With reference to some embodiments of the first aspect, in some embodiments, after the step of sending the pre-warning level information to the client, the method further includes generating a data analysis report according to the real-time monitoring data and the predictive monitoring data, labeling the real-time monitoring data and the predictive monitoring data in the engineering facility physical model to obtain a three-dimensional modeling image, and sending the data analysis report and the three-dimensional modeling image to the client.

By adopting the technical scheme, firstly, a detailed data analysis report is generated according to the real-time monitoring data and the predictive monitoring data, so that a user can clearly know the current state and the potential risk of the engineering facility structure, and secondly, the data are marked in the engineering facility physical model to generate a three-dimensional modeling image, so that the monitoring result is more visual and easy to understand, and the technical staff can be helped to accurately locate the problem area.

In a 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 being coupled to the one or more processors, the memory being configured to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the Beidou-based structural deformation monitoring system to perform a method as described in the first aspect and any one of the possible implementations of the first aspect.

In a third aspect, embodiments of the present application provide a computer program product comprising instructions that, when run on a beidou-based structural deformation monitoring system, cause the beidou-based structural deformation monitoring system to perform a method as described in the first aspect and any one of the possible implementations of the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, including instructions that, when executed on a beidou-based structural deformation monitoring system, cause the beidou-based structural deformation monitoring system to perform a method as described in the first aspect and any one of the possible implementations of the first aspect.

It will be appreciated that the structural deformation monitoring system based on beidou provided by the second aspect, the computer program product provided by the third aspect and the computer storage medium provided by the fourth aspect are all used for executing the method provided by the embodiment of the present application. Therefore, the advantages achieved by the method can be referred to as the advantages of the corresponding method, and will not be described herein.

One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:

1. According to the application, the dynamic characteristic parameters of the engineering facility structure can be accurately obtained by acquiring the engineering facility structure of the target monitoring object and carrying out structural analysis on key parts of the engineering facility structure, and the dynamic characteristic parameters reflect the response characteristics of the engineering facility structure under different environment parameters, so that an accurate engineering facility physical model can be established, the predicted monitoring data can be obtained by acquiring and inputting real-time monitoring data and carrying out state prediction in combination with preset environment parameters, and can be compared with the real-time monitoring data, the abnormal change of the engineering facility structure can be found in time, and when the monitoring difference exceeds a preset change threshold, early warning prompt information can be sent to a client, so that the accuracy and timeliness of deformation monitoring can be improved, and the potential structural risk can be prevented, thereby ensuring the safety and stability of the engineering facility.

2. According to the application, the key components of the engineering facility structure are subjected to simulation analysis by the finite element analysis technology, so that a more accurate simulation analysis result can be obtained, the response characteristics of the engineering facility structure under various environmental parameters can be identified, and the dynamic characteristic parameters can be obtained by fitting, so that the understanding of the structural characteristics of the engineering facility is improved, and the prediction capability of the dynamic behavior of the engineering facility structure under different conditions is enhanced.

3. According to the technical scheme, the real-time monitoring data is preprocessed, noise and abnormal values are removed, purified data is obtained, then the purified data and preset environmental parameters are input into an engineering facility physical model, a strain coefficient is obtained through numerical simulation, the product of the purified data and the strain coefficient is used as prediction monitoring data, the calculation results of the real-time data and the physical model are effectively combined, the accuracy of the prediction data is ensured, the strain state of the engineering facility under the actual environmental conditions can be reflected more accurately, and further the reliability and timeliness of prediction are improved.

Drawings

FIG. 1 is a schematic flow chart of a Beidou-based structural deformation monitoring method in an embodiment of the application;

FIG. 2 is another flow chart of a Beidou-based structural deformation monitoring method in an embodiment of the present application;

Fig. 3 is a schematic structural diagram of an entity device of the structural deformation monitoring system based on Beidou in the embodiment of the application.

Detailed Description

The terminology used in the following embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates to the contrary. It should also be understood that the term "and/or" as used in this disclosure is intended to encompass any or all possible combinations of one or more of the listed items.

The terms "first," "second," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature, and in the description of embodiments of the application, unless otherwise indicated, the meaning of "a plurality" is two or more.

In order to facilitate understanding, an application scenario of the embodiment of the present application is described below.

Based on the Beidou/GNSS deformation monitoring solution, the application automatically collects, transmits, stores and processes data in real time for deformation such as bridge, dam, electric power tower, highway slope, tailing pond, goaf settlement, roadbed settlement and the like, 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 trend, and carries out intelligent early warning, thereby achieving the purposes of disaster prevention and disaster reduction.

In bridge deformation monitoring, traditional laser measurement and optical fiber sensing technologies are widely used. However, laser measurement is greatly affected by weather and environmental factors, such as heavy fog and strong light interference, which can lead to inaccurate measurement data, and optical fiber sensing, although having higher accuracy, has higher installation and maintenance costs, and is easy to damage in long-term use, which leads to data interruption. In addition, the traditional GPS measurement method has the defects in accuracy and real-time performance, and particularly in bridge construction sites with complex environments, GPS signals are easy to interfere, so that measurement data are unstable. The defects of the technologies make bridge deformation monitoring difficult to realize high-precision and high-reliability real-time monitoring and early warning, and potential risks of bridge structures cannot be effectively prevented.

The Beidou/GNSS-based deformation monitoring solution comprises a data acquisition system, a data communication system, a data processing system, an analysis early warning system and a comprehensive management system, wherein the data acquisition system consists of a reference station GNSS receiver, a GNSS antenna and various automatic sensors which are arranged at deformation monitoring points of geological and structural engineering facilities, the data communication system consists of a wired network, an optical fiber, a serial port, a wireless network bridge, a wireless radio station and 3G/4G/WiFi, beidou short message communication is supported, the most suitable data transmission mode is selected according to actual conditions, transmission of all data is completed, the data processing system consists of a server system arranged at a monitoring center and a deformation monitoring software system, the data processing system is a data processing, monitoring and analysis center of the whole monitoring system, high-precision GNSS data processing is realized, coordinate calculation and deformation analysis are carried out by the analysis early warning system, a data analysis curve is generated, overrun data is judged and alarmed by a set threshold value, a data analysis report is automatically recorded and generated, and the comprehensive management system can manage and monitor different-level users, manage original data and carry out authority analysis on the original data.

The application discloses a Beidou-based structural deformation monitoring system in a data processing system, which is characterized in that structural analysis is carried out on key components of engineering facilities such as a monitored bridge, a dam, an electric power tower, a highway slope, a tailing pond, a goaf, a roadbed and the like by receiving data in a data acquisition system, dynamic characteristic parameters of the engineering facilities under different environmental parameters are obtained by utilizing a finite element analysis technology, an engineering facility physical model of the engineering facilities is established, the engineering facility physical model is input into the engineering facility physical model for state prediction, predicted monitoring data are obtained, the predicted monitoring data are compared with real-time monitoring data, and if a monitoring difference exceeds a preset change threshold, the system can send early warning prompt information to a client.

Therefore, the method provided by the embodiment of the application can be used for acquiring dynamic characteristic parameters of engineering facilities under different environments by utilizing a finite element analysis technology on the basis of providing high-precision positioning and real-time data acquisition based on the deformation monitoring technology of the Beidou positioning system, accurately simulating the strain state of the engineering facilities, establishing a physical model of the engineering facilities, carrying out state prediction by combining with real-time data, providing more accurate deformation prediction, improving the early warning accuracy, comparing the predicted data with the actual data in real time, sending early warning prompts by the system if the difference exceeds a threshold value, helping to timely prevent structural risks, and being widely applied to safety monitoring and management of key infrastructures such as bridges, dams, electric power towers, highway slopes, tailing libraries, goafs and roadbeds, and providing an efficient and reliable deformation monitoring solution.

For ease of understanding, the method provided in this embodiment is described in the following in conjunction with the above scenario. Fig. 1 is a schematic flow chart of a structural deformation monitoring method based on beidou in an embodiment of the application.

S101, acquiring an engineering facility structure of a target monitoring object, and carrying out 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 environment parameters.

The target monitoring object refers to an engineering facility structure needing deformation monitoring, such as a large-scale infrastructure of a bridge, a dam and the like, key components of the engineering facility structure refer to parts, such as a main beam, nodes and the like, which have important influences on the overall performance and safety in the engineering facility structure, dynamic characteristic parameters refer to dynamic response characteristics of the engineering facility structure under different environmental conditions, reflect physical characteristics of the engineering facility structure, perform structural analysis refer to a process of performing mechanical performance calculation and scene simulation on the engineering facility structure by using a computer simulation method and the like, and environmental parameters refer to external conditions such as parameters of temperature, humidity, wind pressure and the like.

Specifically, the step is executed before starting deformation monitoring, and the purpose of the step is to acquire dynamic characteristic parameters of an engineering facility structure to establish an accurate physical model, firstly, determining a monitored object, acquiring complete structural design information of the monitored object, then, identifying key components in the engineering facility structure, wherein the key components directly affect the safety of the whole structure, then, modeling and numerical simulation are carried out on structural responses of the key components under different environmental parameters by utilizing computer simulation technologies such as finite element analysis and the like to obtain stress-strain characteristics of the components, and the dynamic response rules of the engineering facility structure under different environments are extracted through analysis of simulation results to fit to obtain the whole dynamic characteristic parameters of the engineering facility structure.

In some embodiments, this step may be accomplished in two ways:

Alternatively, the dynamic characteristic parameters may be obtained by actual loading tests, which are performed after the construction of the engineering facility structure is completed, such as applying a force at a critical location or measuring the structural response after a mass is placed on top, by which the dynamic characteristics of the engineering facility structure under controlled load conditions are directly measured. And then, determining parameters such as natural frequency, damping ratio, vibration mode and the like of the engineering facility structure by analyzing the measurement result, and further comprising displacement parameters monitored by Beidou positioning.

Optionally, parameter identification can be performed by combining finite element modeling with actual data, namely firstly, a fine finite element model is built based on a design drawing, then different material parameters are set for static analysis, rigidity distribution of an engineering facility structure is determined, normal modal analysis is performed to obtain frequency and vibration mode of the structure, and model parameters are adjusted to enable a frequency result to be matched with an actual measurement result, so that parameter identification is completed. It will be appreciated that other ways of obtaining the dynamic characteristic parameter may be used, and are not limited in this regard.

S102, building an engineering facility physical model of the engineering facility structure according to the dynamic characteristic parameters.

The engineering facility physical model is a physical calculation model for carrying out digital simulation and numerical calculation on an engineering facility structure by using a computer technology, integrates structural design parameters, material attribute parameters and dynamic characteristic parameters, and can carry out high-precision numerical simulation on static performance, dynamic response and the like of the structure.

Specifically, after the dynamic characteristic parameters of the engineering facility structure are obtained, a physical model needs to be built in a computer environment. First, design information such as geometric dimensions, material parameters, connection modes and the like of the structure is input to build an initial model. Then, dynamic characteristic parameters are assembled, and a quality matrix and a rigidity matrix of the model are determined. Next, boundary conditions and constraints are defined, and environmental load parameters are set. And finally, carrying out static analysis to verify the calculation result of the model, and carrying out normal modal analysis to determine dynamic characteristics so as to complete the establishment of the physical model.

In some embodiments, the physical model may be built by two means:

Optionally, modeling is performed by using general finite element analysis software, namely, creating a three-dimensional entity model according to a structural design drawing by using commercial general software such as ANSYS, ABAQUS and the like, setting material properties, defining connection relations, introducing dynamic characteristic parameters, and applying constraints and loads to form a fine physical calculation model.

Optionally, the self-defined structural analysis program modeling is adopted, namely, according to the characteristics of the monitored structure, the modeling analysis program is independently developed by using languages such as Python, java and the like, so that the automatic modeling of the specific structural style is realized, the analysis process can be flexibly customized, and the dynamic characteristic parameters of the structure are fully considered, so that the ideal simulation effect is achieved. It will be appreciated that other means of establishing the physical model may be employed, and are not limited in this regard.

S103, acquiring real-time monitoring data of the target monitoring object.

The real-time monitoring data are real-time deformation data acquired through various sensors arranged at key positions of the target monitoring object, the types of the sensors can comprise pore water pressure gauges, soil pressure gauges, anemometers, temperature and humidity gauges, vibration sensors, digital cameras, strain sensors, inclination sensors and the like, and the real-time data can be continuously acquired in a real-time mode, so that monitoring continuity is guaranteed.

Specifically, the time of the step is executed when long-term monitoring is needed to be carried out on the structure after the physical model is built, the purpose of the step is to acquire response data of the structure under actual conditions in real time as model input, firstly, the number and arrangement positions of the sensors are needed to be determined according to a monitoring plan, then, various sensors are installed and fixed at preset positions, the integration of a data acquisition system is completed, after the installation is completed, the sensors can continuously monitor information such as displacement, strain and the like of the structure, and data are transmitted to a central server in real time at high frequency. The server gathers and records the data to provide model input, so that all-weather and automatic monitoring of the structural deformation can be realized.

In some embodiments, the real-time monitoring data may be obtained by alternatively obtaining the data through a wired sensor network by connecting the active sensor to a central data collection box through a cable and transmitting the data to a server in real time through a local area network. The method has low cost but complex wiring, and can acquire data through a wireless sensor network, wherein the wireless sensor node with a self-charging battery is used, and the wireless network is formed through an ad hoc network technology to send the data to a server in real time.

S104, inputting the real-time monitoring data into the engineering facility physical model, and predicting the state of the target monitoring object according to the preset environmental parameters to obtain predicted monitoring data.

The preset environmental parameters refer to various environmental condition parameters such as temperature, humidity, wind power and the like which can influence the deformation of the engineering facility structure, the state prediction refers to calculation and simulation of possible reaction of the structure based on a physical model in combination with real-time data, and the prediction monitoring data refers to structural response data obtained through model prediction.

Specifically, the time of this step is executed after the acquisition of the real-time monitoring data, the purpose of which is to predict the state by means of the physical model, firstly, environmental parameters which may affect the deformation of the structure need to be preset, these parameters will be one of the inputs of the model, then the collected real-time monitoring data is processed, integrated into the format of the inputtable model, then the processed data and the environmental parameters are input into the physical model together, and the calculation simulation of the structure under these parameters is run to predict the response characteristics thereof. Finally, the predicted monitoring data such as strain, displacement and the like can be obtained, and whether the actual condition of the structure is normal can be judged by comparing the predicted monitoring data with the real-time data.

In some embodiments, state prediction may be performed in two ways:

Optionally, only the influence of environmental parameters can be considered, namely, environmental parameter data such as a temperature and humidity change curve, typical wind pressure data and the like which are counted in a history are directly input into a model to predict structural response caused by the environmental parameter data, and optionally, environmental parameters and real-time monitoring data can be considered simultaneously, namely, various environmental parameter data are collected and integrated with the real-time monitoring data in a data fusion module, and the environmental parameter data and the real-time monitoring data are input into a physical model to comprehensively calculate structural response.

For example, data of environmental parameters such as temperature, humidity, wind pressure and the like are collected, for example, on the day of 6 months and 1 day, the temperature range is 15-35 ℃, the average humidity is 65%, the maximum wind pressure reaches 8 levels, filtering smoothing treatment is carried out on the data, noise is removed, real-time monitoring data of a structure are obtained from a displacement sensor, a strain sensor and the like, for example, the displacement of span in a girder is monitored to be 80-120mm, the maximum strain of a floor corner point reaches 1200 micro-strain, pretreatment such as denoising, zero offset correction and the like is carried out, in the data fusion process, a Kalman filtering algorithm is adopted, temperature and humidity, wind pressure parameters, displacement and strain monitoring data are integrated according to time sequences, a three-dimensional solid model is built in ABAQUS software according to structural characteristics, the data fused after the pretreatment is input, a finite element type is selected, a connection relation is defined, structural dynamic analysis of static force and modal dynamic step analysis is selected for the current environmental conditions, iteration solving parameters, the running temperature load, wind pressure load and displacement boundary conditions are set, displacement response, internal force distribution and strain time of different parts of the structure are extracted from analysis calculation results as prediction data, whether the prediction results are abnormal structure state data is compared with actual monitoring results or not.

S105, calculating a monitoring difference value of the predicted monitoring data and the real-time monitoring data, and sending early warning prompt information to the client if the monitoring difference value exceeds a preset change threshold value.

The monitoring difference value refers to the numerical difference between the predicted monitoring data and the real-time monitoring data, the preset change threshold value refers to a key value for judging whether the structural state is abnormal, the early warning prompt information refers to warning information automatically sent by the system when the monitoring difference value exceeds the threshold value, and the client side refers to a monitoring personnel terminal for receiving the early warning information.

Specifically, the timing of this step is performed after the predictive monitor data is obtained, in order to determine whether the structural state is abnormal. Firstly, the system can compare predicted monitoring data and real-time monitoring data point by point, calculate specific difference values between the predicted monitoring data and the real-time monitoring data, then compare the calculated monitoring difference values with a preset change threshold value, if the difference values exceed the threshold value, the abnormal structural state is indicated, accidents are likely to occur, at the moment, the system can automatically generate early warning prompt information, and the early warning prompt information is immediately sent to monitoring staff through short messages, mails and the like, so that corresponding measures are reminded to be taken. If the difference value does not exceed the threshold value, the structural state is normal.

Optionally, a threshold value may be preset, that is, an absolute threshold value or a relative threshold value of deformation change is predetermined according to structural design indexes and historical monitoring data, the system directly judges a structural state according to a preset value, optionally, a large amount of historical monitoring data may be collected and dynamically updated through machine learning, a neural network and other machine learning methods are used for training and optimizing a threshold value judging model, dynamic updating of the threshold value is achieved, it is understood that monitoring difference values and judging threshold values may be calculated in other modes, and the method is not limited herein.

The method provided in this embodiment will be described in more detail. Fig. 2 is a schematic flow chart of a structural deformation monitoring method based on beidou according to an embodiment of the present application.

S201, acquiring an engineering facility structure of a target monitoring object, and performing simulation analysis on key components of the engineering facility structure based on a finite element analysis technology to obtain a simulation analysis result.

First, the target monitoring object refers to a specific engineering facility structure such as a bridge, a dam, etc. which needs deformation monitoring. The finite element analysis technology is a digital structure analysis method, a physical equation is established to carry out numerical calculation by discretizing a structure into a finite number of units, a key component refers to a part, such as a main beam, a node and the like, which has important influence on the whole performance in an engineering facility structure, the simulation analysis refers to a process of carrying out computer simulation on the key component by using the finite element analysis technology, and a simulation analysis result comprises output information such as displacement, internal force, stress strain and the like of the component.

Specifically, a finite element calculation model of the structure is firstly established, discretized, key components in the structure are identified, material properties and connection relations of the key components are defined, analysis setting is carried out, and then finite element software is operated for static force and modal analysis aiming at different environmental parameters such as temperature and load, so that analysis results of the key components are obtained. Finally, the information such as stress distribution and vibration mode of the component can be outputted by processing the result.

For example, for a prestressed concrete simply supported girder bridge with a span of 50 meters, first, a three-dimensional solid finite element model of the girder bridge is built by ANSYS software, and the girder bridge is discretized into 100000 units. In the model, the parameters of the prestressed concrete material are defined that the elastic modulus is 3.5 multiplied by 104MPa, the Poisson ratio is 0.2, the boundary condition of the bridge is set as simple branches at two ends, then uniform load is applied on the model, static analysis is carried out, and the output simulation result shows that when the load is 10kN/m, the maximum displacement of the midspan part of the bridge is 16.2mm, the maximum stress is 2.5MPa, and in order to obtain the dynamic characteristic of the bridge, modal analysis is also needed, and the result shows that the first 3 natural frequencies of the bridge are 3.2Hz, 6.5Hz and 7.9Hz respectively.

S202, identifying response characteristics of the engineering facility structure under various environmental parameters according to the simulation analysis result, and fitting the response characteristics to obtain the dynamic characteristic parameters, wherein the dynamic characteristic parameters are engineering facility parameters of the engineering facility structure under different environmental parameters.

The response characteristics refer to dynamic response rules of the engineering facility structure under different environmental conditions, such as characteristic relation of displacement and strain along with environmental change, the fitting refers to the approximation of mathematical functions to describe the response characteristics, dynamic characteristic parameters comprise dynamic parameters such as frequency, damping and vibration mode of the engineering facility structure, and engineering facility parameters refer to physical characteristics of the structure under environmental change.

This step starts after the simulation analysis is completed. Firstly, response data of the structure under different environments are extracted according to analysis results, corresponding relations among all environment parameters are found, then mathematical expression forms of the response characteristics are determined by using curve fitting and other methods, and finally, overall dynamic characteristic parameters of the structure, namely parameters such as frequency, damping coefficient and the like under different environments, can be obtained through fitting analysis.

For example, on a finite element model, a temperature gradient is set such that the bridge has a temperature of 15 ℃ at one end and 25 ℃ at the other end. And running static analysis under a plurality of temperature gradient conditions, recording displacement at a midspan, and displaying analysis results, wherein the midspan displacement approximately accords with a normal distribution relation with the average temperature, in order to establish an accurate response characteristic model, a Gaussian process regression method is adopted, the average temperature is taken as an independent variable, the displacement is taken as a dependent variable, a Gaussian process regression model is trained, and a finally determined temperature-displacement response function is y=20 x+exp (- ((x-20)/(2)/50) +2), wherein x is the average temperature, y is the corresponding displacement, and the temperature-displacement dynamic characteristic parameter of the bridge can be obtained through parameter identification, so that the response rule of the bridge under the temperature gradient load can be reflected.

S203, building an engineering facility physical model of the engineering facility structure according to the dynamic characteristic parameters.

In S202, taking a simple girder bridge as an example, obtaining dynamic characteristic parameters of temperature field-displacement by parameter identification, establishing a Gaussian process regression model to represent the influence of temperature on displacement, establishing an engineering facility physical model of the simple girder bridge by utilizing the dynamic characteristic parameters, establishing a three-dimensional solid model of the girder bridge according to a design drawing in ANSYS finite element analysis software, carrying out grid division, selecting element types as PIPE288, defining material properties, selecting concrete elastic modulus as 3.5X104 MPa, reinforcing steel bar elastic modulus as 2.0X105 MPa, substituting the parameters of temperature-displacement obtained in S202 into the girder bridge model, defining a temperature loading load function, establishing a coupling relation of a temperature field and displacement, carrying out static analysis, and displaying that the midspan displacement is 10mm under the condition of uniform temperature field of 20 ℃, carrying out modal analysis, the first frequency is 3.21Hz, and the actual measurement result is coincident, and establishing a computer model reflecting the actual physical characteristics of the simple girder bridge by importing the dynamic characteristic parameters.

S204, acquiring initial monitoring data of the monitoring sensor and the arrival time and data intensity of the initial monitoring data.

Firstly, the monitoring sensor is used for collecting deformation monitoring data of a structure, the initial monitoring data is analog or digital measurement data originally obtained by the sensor, the arrival time is the time interval from the collection of the monitoring data from the sensor to the receiving and processing of the system, and the data intensity represents the quality or reliability of the monitoring data.

Specifically, this step is performed when the monitoring system starts to operate. The system is required to install various sensors according to a preset scheme, and establish connection with data acquisition equipment, the sensors can continuously output initial monitoring data, the initial monitoring data contain a large amount of noise, meanwhile, the system can record the arrival time and the signal intensity of each data point, the arrival time reflects whether data transmission is smooth or not, and the signal intensity represents data quality.

For example, in a bridge monitoring project, where 32 displacement sensors are installed, and displacement data is output every 2 minutes, the system will extract the acquisition time and signal amplitude for each data point, and these raw displacement data and their time and intensity information will be input for subsequent processing.

And S205, eliminating the data with the arrival time being greater than a preset time threshold and the data intensity being lower than a preset intensity threshold to obtain first monitoring data.

The preset time threshold refers to the maximum data arrival delay allowed. The preset intensity threshold is a minimum expected data quality standard, and the first monitoring data represents reliable monitoring data obtained after processing.

The method comprises the steps that after original monitoring data are obtained, the system checks the arrival time and intensity information of each data point by point, and the data are screened according to a preset time threshold value and an intensity threshold value. The monitoring data exceeding the time delay threshold or having the signal strength lower than the requirement can be removed, unreliable abnormal points are filtered, and the rest data form first monitoring data with high reliability, so that the first monitoring data can be used as the basis of state evaluation.

For example, the delay threshold may be set at 5 seconds and the intensity threshold at 60% of the design value. All displacement data reaching more than 5 seconds or 60% less than the design value will be removed, leaving the remaining reliable data for subsequent analysis.

S206, generating a preset pseudo-random code, calculating the correlation value of the preset pseudo-random code and the first monitoring data, and eliminating the first monitoring data lower than a preset correlation threshold value to obtain real-time monitoring data.

In an outdoor environment, the monitoring signal is affected by multipath effects in the propagation process, and reaches the sensor from different paths, so that the signal is repeatedly sampled, and redundant noise data with low correlation is generated. In order to reduce the influence of the multipath effect, a pseudo-random code correlation test mode is adopted to process, firstly, a system generates a group of random number sequences to serve as pseudo-random codes, then, the correlation coefficient between each point of the monitoring data and the pseudo-random codes is calculated, and the correlation coefficient can reflect redundant components caused by the multipath effect in the monitoring data because the pseudo-random codes have no correlation, and multipath noise components in the obtained monitoring data are effectively removed after the data points with poor correlation are removed, so that the quality is improved.

S207, carrying out quality grading on the real-time monitoring data, giving a weight value to the real-time monitoring data according to the quality grading to obtain a weight value set, carrying out weighted average on the real-time monitoring data according to the weight value set to obtain fusion data, and calculating the quality grading according to the signal-to-noise ratio and the signal strength of the real-time monitoring data.

Specifically, to compensate for the limitations of a single monitoring path, fusion of multi-source monitoring data is required. The quality of each path monitoring data is different due to different influences, quality grading is needed, weights are correspondingly given, the reliability of the result can be improved, the influence of individual path errors is reduced, and the overall error of the system is effectively controlled through weighted average of the multi-source monitoring data.

For example, positioning data of 4 Beidou satellites are collected, the respective signal quality scores are that the satellite B1 is 90, the satellite B2 is 80, the satellite B3 is 85 and the satellite B4 is 95, firstly, weights of 0.9 for the satellite B1, 0.8 for the satellite B2, 0.85 for the satellite B3 and 0.95 for the satellite B4 can be determined according to the signal quality scores, and then weighted average fusion of Beidou positioning results is carried out. The positioning results of the 4 satellites are respectively B1 positioning results (longitude 1 degree and latitude 2 degrees), B2 positioning results (longitude 1.1 degree and latitude 2.1 degrees), B3 positioning results (longitude 1.2 degrees and latitude 2.05 degrees), B4 positioning results (longitude 0.95 degrees and latitude 1.98 degrees), and weighting calculation is carried out according to the weights, so that the fused Beidou positioning results (longitude 1.01 degrees and latitude 2.02 degrees) can be obtained.

S208, preprocessing the real-time monitoring data to obtain purified data.

The preprocessing refers to a process of performing preliminary adjustment such as format conversion and zero drift treatment on real-time monitoring data, and the purified data is clean monitoring data which can be directly used for modeling analysis after the preprocessing.

Specifically, the step is performed after acquiring real-time monitoring data, and necessary preprocessing is required to be performed on the acquired original monitoring data, for example, coordinate conversion, unit conversion, time sequence compensation and the like, so that systematic errors caused by sensor installation errors can be eliminated, the reliability of the data is improved, zero drift compensation is performed, drift errors caused by environmental changes are eliminated, finally, noise is reduced through filtering smoothing and other methods, and the monitoring data becomes continuous and stable after the preprocessing, and can be used as purified data input for subsequent modeling analysis.

For example, the collected strain raw data can be subjected to coordinate system conversion, unit conversion to micro-strain, linear zero drift compensation and filtering denoising, so that purified strain data which can be directly input into modeling can be obtained.

S209, inputting the purification data and the preset environmental parameters into the engineering facility physical model, and carrying out numerical simulation on the strain state of the engineering facility structure under the preset environmental parameters in the engineering facility physical model to obtain a strain coefficient.

Here, the preset environmental parameters refer to environmental loading conditions such as temperature, wind pressure, etc. under which the simulation is performed. 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.

The step is carried out after the purification monitoring data are obtained, and the cleaned monitoring data and the environmental parameters to be analyzed are loaded into a structural calculation model established before. And running finite element simulation, analyzing the internal force deformation state of the structure under the set environment, obtaining the micro deformation, stress and strain distribution of each unit of the structure, and finally outputting the strain time sequence or extremum of a specific part as the strain coefficient obtained by simulation.

For example, temperature field and wind pressure parameters can be set, the parameters are loaded into a bridge calculation model, stress-strain analysis is performed, and the strain time history obtained by simulation is extracted according to the displacement of the monitoring points and is used as a strain coefficient result.

The established simple bridge finite element model in S202 is characterized in that a bridge middle No. 10 unit is a sensor monitoring point, purifying monitoring strain data is prepared, a strain sequence {120,130,125,135, & gt } micro-strain of the measuring point No. 10 unit is imported into ANSYS, then environment parameters are defined, temperature loading is established in the ANSYS, a temperature field distribution function T (x, y, z, T) =15+10sin (pi T/24) is defined to represent the sinusoidal change of temperature along with time, wind pressure loading is defined, a pressure distribution of 0.5KN/m is established at the bridge end face, then an application is loaded, a beam unit is selected, loading the temperature field distribution function is used for coupling with a unit temperature field, loading end face pressure is used for SFA command, wind pressure distribution is applied, a finite element analysis solver is operated, static force and thermal stress coupling analysis is conducted, stress strain result data of the number 10 unit is taken, and strain time history is output as a simulation coefficient.

S210, taking the product of the purge data and the strain coefficient as the prediction monitoring data.

The purification data herein refers to monitoring data that can be directly used for analysis after pretreatment. The strain coefficient is the strain response result of the structure under the environmental load, which is obtained through modeling simulation. The predicted monitoring data is a monitoring value corrected according to the model calculation result.

The method comprises the steps of obtaining purification monitoring data and simulation strain coefficients, firstly, checking time coordinate alignment relation of the purification monitoring data and the simulation strain coefficients, performing synchronization processing, and then multiplying the purification monitoring data by the strain coefficients at corresponding moments point by point to generate a corrected prediction monitoring data set. It is necessary to check whether the prediction result is within a reasonable range.

For example, in the simple girder bridge model established in the S202, a middle 10-number unit is selected as a sensor measuring point, the measurement result is that purifying monitoring displacement data: {15.1,15.3,15.2, & gt.. } mm, a strain coefficient corresponding to a simulation moment: {1.02,0.98,1.01, & gt} is obtained through modeling calculation in the S209, the specific steps of generating predicted monitoring displacement data are that time coordinates of two groups of data are processed to ensure that time sequences are consistent, the purifying monitoring displacement data are multiplied by the strain coefficient corresponding to the moment point by point, a calculation result forms a predicted monitoring displacement sequence: {15.402,14.985,15.352, & gt.} mm, whether a detection result is in a reasonable physical range or not, and the predicted displacement time sequence is output as corrected monitoring data.

S211, calculating a monitoring difference value of the predicted monitoring data and the real-time monitoring data, and sending early warning prompt information to the client if the monitoring difference value exceeds a preset change threshold value.

Here, the monitoring difference value refers to a numerical difference between the predicted monitoring data and the real-time monitoring data, the preset change threshold value is a threshold value criterion for judging whether the structural state is obviously changed, and the early warning prompt information is sent when the monitoring difference value exceeds the threshold value and is used for informing the abnormal change of the structural state.

This step is performed based on the predictive 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 a preset change threshold. If the state of the structure is changed beyond the normal range, the system automatically generates early warning information and immediately pushes the early warning information to a client user through the monitoring terminal, and the early warning information contains the contents such as an overrun position, a monitoring value, time and the like.

For example, when the normal displacement change threshold is set to be 2mm and the predicted monitoring result is 10mm, and the real-time monitoring is performed to obtain 15mm, the difference between the two is 5mm, and the threshold limit of 2mm is exceeded. At this time, the system may push an early warning message "bridge a-bit displacement detection exceeds a threshold value" at the moment xx. To prompt the user to pay attention to the abnormal change in the state of the structure.

S212, determining a risk level corresponding to the monitoring difference value, and generating early warning level information according to the risk level.

Here, the risk level is a qualitative assessment of the structural state risk according to the monitoring difference, and the early warning level is a result of grading the severity of the early warning.

Based on the monitoring difference, the system determines corresponding risk levels, such as general, larger, serious and the like, according to the monitoring difference in different ranges, then converts the risk levels into matched early warning levels, such as reminding, warning, serious warning and the like, finally generates prompt information containing the early warning levels and sends the prompt information to a user, such as 'A-level displacement abnormality, warning', and the early warning level distinction can help the user to evaluate structural state risks more accurately, and appropriate emergency measures are adopted.

S213, the early warning level information is sent to the client.

The method comprises the steps that after early warning level information is obtained, early warning content containing risk levels is packaged into a message or message text in a standard format by a system, then an access mode of a client, which can be the Internet, a mobile network or an independent wireless network, is selected, the early warning message is pushed to a registered client through a selected network interface by a proper communication protocol, and after the client receives the early warning information, a user needs to be prompted, and obvious modes such as sound and light are usually adopted.

For example, the early warning message of' region A has serious displacement abnormality and please check-.

S214, generating a data analysis report according to the real-time monitoring data and the predictive monitoring data.

The data analysis report is a statistical analysis result of the structure running condition generated according to the monitoring data, the step is carried out after the real-time monitoring data and the prediction data are obtained, the system can carry out comparison statistical analysis on the two groups of data to generate a statistical report containing factors such as data overview, distribution characteristics, correlation analysis and the like, meanwhile, a vivid chart can be used for presenting the time law of the monitoring data and the relation between the time law and the prediction result, the report can be automatically generated by a professional analysis module, a tool for generating a visual report can also be provided for an analyst, the generated report can more intuitively reflect the running state and the data quality of the structure and is provided for a manager and a maintainer to carry out decision reference.

S215, labeling the real-time monitoring data and the predicted monitoring data in the engineering facility physical model to obtain a three-dimensional modeling image.

The real-time monitoring data and the predictive monitoring data are two important data sources of the running state of the structure, the engineering facility physical model is a three-dimensional computer model created based on the actual structure, and the three-dimensional modeling image is the effect display after the data visual labeling is carried out on the computer model.

The method comprises the steps of obtaining two types of monitoring data, firstly, importing a three-dimensional physical model of an engineering facility structure into visual software, and then, respectively and correspondingly marking the real-time monitoring data and the prediction data to relevant positions of the model, wherein the real-time monitoring data and the prediction data can be distinguished by different colors. By setting the deformation amplification coefficient, the deformation effect of the structure in different monitoring states can be more intuitively seen, and the animation of the time sequence change of the data can be displayed. And finally, generating a three-dimensional modeling visual image with the monitoring data labels.

For example, the floor side shift result obtained by real-time monitoring is marked with red, the floor side shift result is predicted by blue mark, and the floor side shift result is amplified by 100 times for animation display, so that a three-dimensional visual comparison image of the displacement monitoring result of each part of the engineering facility structure is obtained.

S216, the data analysis report and the three-dimensional modeling image are sent to the client.

The method comprises the steps that after a report and an image are obtained, the system packages the report and the image file into a standard format, necessary description text information is added, then a wired network or a wireless network is selected to send a data packet to a registered client terminal, after the client terminal receives data, the report and the image are required to be extracted and displayed to a user in a proper mode, such as an APP (application) is organized into a monitoring data browsing page, and therefore the user can intuitively check a structural state analysis report and conveniently judge.

The following describes the structural deformation monitoring system based on the Beidou in the embodiment of the present application from the perspective of hardware processing, please refer to fig. 3, which is a schematic structural diagram of an entity device of the structural deformation monitoring system based on the Beidou in the embodiment of the present application.

It should be noted that, the structure of the Beidou-based structural deformation monitoring system shown in fig. 3 is only an example, and should not bring any limitation to the functions and the application scope of the embodiment of the present invention.

As shown in fig. 3, the beidou-based structural deformation monitoring system includes a central processing unit (Central Processing Unit, CPU) 301, which can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 302 or a program loaded from a storage portion 308 into a random access Memory (Random Access Memory, RAM) 303. In the RAM303, various programs and data required for the system operation are also stored. The CPU 301, ROM 302, and RAM303 are connected to each other through a bus 304. An Input/Output (I/O) interface 305 is also connected to bus 304.

Connected to the I/O interface 305 are an input section 306 including an audio input device, a push button switch, and the like, an output section 307 including a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD) and an audio output device, an indicator lamp, 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, a modem, and the like. The communication section 309 performs communication processing via a network such as the internet. The 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, or the like is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.

In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 309, and/or installed from the removable medium 311. When the computer program is executed by a Central Processing Unit (CPU) 301, various functions defined in the present invention are performed.

Specific examples of a computer-readable storage medium include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.

Specifically, the Beidou-based structural deformation monitoring system of the embodiment comprises a processor and a memory, wherein a computer program is stored in the memory, and the Beidou-based structural deformation monitoring method provided by the embodiment is realized when the computer program is executed by the processor.

In another aspect, the present invention further provides a computer readable storage medium, where the storage medium may be included in the beidou-based structural deformation monitoring system described in the foregoing embodiment, or may exist alone, and not be assembled 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, cause the beidou-based structural deformation monitoring system to implement the beidou-based structural deformation monitoring method provided in the foregoing embodiment.

While the application has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that the foregoing embodiments may be modified or equivalents may be substituted for some of the features thereof, and that the modifications or substitutions do not depart from the spirit of the embodiments.

As used in the above embodiments, the term "when..is interpreted as meaning" if..or "after..or" in response to determining..or "in response to detecting..is" depending on the context. Similarly, the phrase "when determining..or" if (a stated condition or event) is detected "may be interpreted to mean" if determined.+ -. "or" in response to determining.+ -. "or" when (a stated condition or event) is detected "or" in response to (a stated condition or event) "depending on the context.

Those of ordinary skill in the art will appreciate that implementing all or part of the above-described method embodiments may be accomplished by a computer program to instruct related hardware, the program may be stored in a computer readable storage medium, and the program may include the above-described method embodiments when executed. The storage medium includes a ROM or a random access memory RAM, a magnetic disk or an optical disk, and other various media capable of storing program codes.

Claims

1. The utility model provides a structural deformation monitoring method based on big dipper, which is characterized in that is applied to structural deformation monitoring system based on big dipper, and the method includes:

Acquiring an engineering facility structure of a target monitoring object, and carrying out 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 environment parameters;

establishing an engineering facility physical model of the engineering facility structure according to the dynamic characteristic parameters;

acquiring 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 according to preset environmental parameters to obtain predicted monitoring data;

And calculating a monitoring difference value of the predicted monitoring data and the real-time monitoring data, and sending early warning prompt information to the client if the monitoring difference value exceeds a preset change threshold value.

2. The method according to claim 1, wherein the step of performing structural analysis on the key components of the engineering facility structure to obtain dynamic characteristic parameters of the engineering facility structure specifically comprises:

performing simulation analysis on key components of the engineering facility structure based on a finite element analysis technology to obtain a simulation analysis result;

And identifying the response characteristics of the engineering facility structure under each environmental parameter according to the simulation analysis result, and fitting the response characteristics to obtain the dynamic characteristic parameters.

3. The method according to claim 1, wherein the step of inputting the real-time monitoring data into the engineering facility physical model, predicting the state of the target monitoring object according to a preset environmental parameter, 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 performing numerical simulation on the strain state of the engineering facility structure under the preset environmental parameters in the engineering facility physical model to obtain a strain coefficient;

Taking the product of the purge data and the strain coefficient as the predictive monitoring data.

4. The method according to claim 1, wherein the step of acquiring real-time monitoring data of the target monitoring object specifically comprises:

acquiring initial monitoring data of a monitoring sensor and arrival time and data intensity of the initial monitoring data;

removing the data with the arrival time being greater than a preset time threshold and the data intensity being lower than a preset intensity threshold to obtain first monitoring data;

generating a preset pseudo-random code, calculating the correlation value of the preset pseudo-random code and the first monitoring data, and eliminating the first monitoring data lower than a preset correlation threshold value to obtain real-time monitoring data.

5. The method according to claim 4, wherein after the step of generating the preset pseudo-random code, calculating a correlation value between the preset pseudo-random code and the first monitoring data, and rejecting the first monitoring data below a preset correlation threshold value to obtain real-time monitoring data, the method further comprises:

And carrying out quality grading on the real-time monitoring data, giving a weight value to the real-time monitoring data according to the quality grading to obtain a weight value set, carrying out weighted average on the real-time monitoring data according to the weight value set to obtain fusion data, and calculating the quality grading according to the signal-to-noise ratio and the signal strength of the real-time monitoring data.

6. The method of claim 1, wherein after the step of calculating a 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, the method further comprises:

determining a risk level corresponding to the monitoring difference value, and generating early warning level information according to the risk level;

and sending the early warning level information to the client.

7. The method of claim 6, wherein after the step of sending the alert level information to the client, the method further comprises:

generating a data analysis report according to the real-time monitoring data and the predictive monitoring data;

Labeling the real-time monitoring data and the prediction monitoring data in the engineering facility physical model to obtain a three-dimensional modeling image;

and sending the data analysis report and the three-dimensional modeling image to the client.

8. A beidou-based structural deformation monitoring system comprising one or more processors and a memory, the memory coupled with the one or more processors, the memory for storing computer program code comprising computer instructions, the one or more processors invoking the computer instructions to cause the beidou-based structural deformation monitoring system to perform the method of any of claims 1-7.

9. A computer readable storage medium comprising instructions which, when run on a beidou based structural deformation monitoring system, cause the beidou based structural deformation monitoring system to perform the method of any one of claims 1-7.

10. A computer program product, characterized in that the computer program product, when run on a beidou-based structural deformation monitoring system, causes the beidou-based structural deformation monitoring system to perform the method of any one of claims 1-7.