A real-time monitoring method and system for rigid skeleton

Abstract

The present invention relates to the field of data processing technology, and in particular to a real-time monitoring method and system for a rigid skeleton. When the present invention performs real-time monitoring of a rigid frame, a sensor network is arranged on the rigid skeleton. The signal sampling frequency of the sensor network is determined according to the construction stage. Data is collected from the rigid skeleton according to the signal sampling frequency of the sensor network. When the signal sampling frequency is determined, the construction process of the rigid skeleton is divided into multiple stages, and the signal sampling frequency is determined in each stage considering the influence of environmental factors. The sampling frequency of the sensor network can be adjusted in real time, and different initial sampling frequencies and adjustment coefficients are determined in different construction stages to ensure that the sensor network can dynamically adjust the sampling frequency according to changes in environmental factors, thereby improving the accuracy and reliability of monitoring data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for real-time monitoring of a rigid skeleton. Background Art

[0002] A rigid frame is a structural system composed of steel components (such as beams and columns). It is commonly used in complex projects such as high-rise buildings, large bridges, and industrial plants. It combines the high strength of steel structures with the durability of concrete, forming a highly efficient composite structure through the synergistic effect of steel and concrete. The construction process of a rigid frame involves multiple stages, including prefabrication, installation, and concrete pouring. Each stage can be affected by a variety of factors, such as load changes, ambient temperature fluctuations, and construction errors. Therefore, real-time monitoring of the rigid frame is crucial to ensuring construction quality and structural safety.

[0003] Traditional rigid skeleton monitoring methods generally set the monitoring frequency manually and then use environmental factors to correct it. This method does not consider the characteristics of different construction stages, resulting in low monitoring accuracy and reliability. At the same time, when using support vector machine models for rigid skeleton monitoring in existing technologies, the support vector machine function is not adaptable, which affects the accuracy of the monitoring results. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for real-time monitoring of a rigid skeleton, which are used to solve the problems existing in the prior art.

[0005] The present invention provides a real-time monitoring method for a rigid skeleton, comprising the following steps:

[0006] S1: Arranging a sensor network on the rigid skeleton;

[0007] S2: Determine the signal sampling frequency of the sensor network according to the construction stage;

[0008] The signal sampling frequency of the sensor network is determined according to the construction stage as follows:

[0009] S2.1: The construction process of the rigid frame is divided into multiple stages, including foundation construction, substructure construction, superstructure construction, concrete pouring, and load application.

[0010] S2.2: Determine the initial sampling frequency of the sensor network signal according to the characteristics of each construction stage;

[0011] S2.3: Determine the comprehensive adjustment coefficient based on environmental factors;

[0012] S2.4: Determine a signal sampling frequency based on the initial signal sampling frequency and the comprehensive adjustment coefficient;

[0013] S3: collecting data from the rigid skeleton according to the signal sampling frequency of the sensor network;

[0014] S4: performing data preprocessing operations on the collected monitoring data;

[0015] S5: Input the pre-processed monitoring data into the state assessment model to obtain the real-time monitoring results of the rigid skeleton.

[0016] Preferably, in S2.2, the initial sampling frequency of the signal in the foundation construction stage is once every 15 minutes; the initial sampling frequency of the signal in the lower structure construction stage is once every 10 minutes; the initial sampling frequency of the signal in the superstructure construction stage is once every 5 minutes; the initial sampling frequency of the signal in the concrete pouring stage is once every 1 minute; and the initial sampling frequency of the signal in the load loading stage is once every 3 minutes.

[0017] Preferably, in S2.2, the environmental factors are: temperature, humidity and wind speed.

[0018] Preferably,

[0019] Temperature influence coefficient α T The calculation formula is:

[0020] ;

[0021] Where: Δ T The current temperature and the reference temperature T ref The difference between β T is the temperature sensitivity coefficient;

[0022] Humidity affects the curing of concrete and the durability of the structure. The humidity adjustment coefficient α H The calculation formula is:

[0023] ;

[0024] Where: Δ H The current humidity and the reference humidity H ref The difference, β H is the humidity sensitivity coefficient;

[0025] Wind speed has a significant impact on the vibration and stability of the structure. The wind speed adjustment coefficient α WThe calculation formula is:

[0026] ;

[0027] in, The current wind speed and the reference wind speed W ref The difference, W ref is the reference wind speed, β W is the wind speed sensitivity coefficient.

[0028] Preferably, during the foundation construction stage, the temperature sensitivity coefficient is 0.1; during the lower structure construction stage, the temperature sensitivity coefficient is 0.2; during the superstructure construction stage, the temperature sensitivity coefficient is 0.3; during the concrete pouring stage, the temperature sensitivity coefficient is 0.5; during the load loading stage, the temperature sensitivity coefficient is 0.4;

[0029] During the foundation construction phase, the humidity sensitivity coefficient is 0.05; during the lower structure construction phase, the humidity sensitivity coefficient is 0.1; during the upper structure construction phase, the humidity sensitivity coefficient is 0.15; during the concrete pouring phase, the humidity sensitivity coefficient is 0.2; during the load loading phase, the humidity sensitivity coefficient is 0.1;

[0030] During the foundation construction stage, the wind speed sensitivity coefficient is 0.1; during the lower structure construction stage, the wind speed sensitivity coefficient is 0.2; during the upper structure construction stage, the wind speed sensitivity coefficient is 0.3; during the concrete pouring stage, the wind speed sensitivity coefficient is 0.4; during the load loading stage, the wind speed sensitivity coefficient is 0.3.

[0031] Preferably, the comprehensive adjustment coefficient α The calculation formula is:

[0032] .

[0033] Preferably, in S2.4, the signal sampling frequency of the sensor network is the product of the initial signal sampling frequency of each construction stage and the comprehensive adjustment coefficient.

[0034] Preferably, in S5, the state assessment model is a support vector machine model.

[0035] Preferably, the S5 is specifically:

[0036] S5.1: Annotate the dataset.

[0037] S5.2: Establishing a support vector machine model and training the support vector machine model using the data set; the kernel function of the support vector machine includes a radial basis function part and a polynomial part;

[0038] S5.3: Input the pre-processed monitoring data into the support vector machine model to obtain the real-time monitoring results of the rigid skeleton.

[0039] According to another aspect of the present invention, a rigid skeleton real-time monitoring system is provided, wherein the system adopts the above-mentioned rigid skeleton real-time monitoring method, and the system comprises:

[0040] A placement module, configured to place a sensor network on the rigid skeleton;

[0041] a sampling frequency determination module, configured to determine the signal sampling frequency of the sensor network according to the construction stage;

[0042] A data acquisition module, configured to acquire data from the rigid skeleton according to a signal sampling frequency of the sensor network;

[0043] A preprocessing module is used to perform data preprocessing operations on the collected monitoring data;

[0044] The state assessment module is used to input the monitoring data that has undergone data preprocessing into the state assessment model to obtain real-time monitoring results of the rigid skeleton.

[0045] The embodiments of the present invention have the following technical effects:

[0046] When the present invention performs real-time monitoring of the rigid frame, a sensor network is arranged on the rigid skeleton; the signal sampling frequency of the sensor network is determined according to the construction stage; data is collected on the rigid skeleton according to the signal sampling frequency of the sensor network; data preprocessing is performed on the collected monitoring data; the monitoring data that has undergone data preprocessing is input into a state assessment model to obtain real-time monitoring results of the rigid skeleton; wherein, when the signal sampling frequency is determined, the construction process of the rigid skeleton is divided into multiple stages, including the foundation construction stage, the lower structure construction stage, the upper structure construction stage, the concrete pouring stage and the load loading stage, and the signal sampling frequency is determined in each stage considering the influence of environmental factors on it, the sampling frequency of the sensor network can be adjusted in real time, and different initial sampling frequencies and adjustment coefficients are determined in different construction stages to ensure that the sensor network can dynamically adjust the sampling frequency according to changes in environmental factors, thereby improving the accuracy and reliability of the monitoring data;

[0047] The newly designed kernel function combines RBF and polynomial components to comprehensively consider the influence of sensor data, construction phase, and environmental factors. The RBF component captures local variations in the data, making it suitable for processing small fluctuations in sensor data. The polynomial component captures global trends in the data, making it suitable for addressing the global impact of construction phase and environmental factors. By adjusting the weight parameter γ, a trade-off between local and global features can be achieved, thereby improving the accuracy of the SVM model in rigid skeleton assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 This is a flow chart of a method for real-time monitoring of a rigid skeleton provided by an embodiment of the present invention;

[0050] Figure 2 This is a flowchart of determining the signal sampling frequency of the sensor network according to the construction stage provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0052] Example 1, attached Figure 1 A flow chart of a method for real-time monitoring of a rigid skeleton is shown in the attached figure. Figure 1 As shown, a real-time monitoring method for a rigid skeleton includes the following steps:

[0053] S1: Arranging a sensor network on the rigid skeleton;

[0054] Various types of sensors, including strain sensors, displacement sensors, and acceleration sensors, are deployed at key locations of the rigid frame, such as nodes, beam-column connections, and the interface between concrete and steel structures. These sensors collect real-time data on physical quantities such as strain, displacement, and vibration of the rigid frame.

[0055] The layout of the sensor network is optimized according to the structural form and construction stage of the rigid skeleton to ensure that it can fully cover the key stress-bearing areas and deformation-sensitive areas of the structure; specifically: during the concrete pouring stage, the sensor network is mainly arranged near the contact surface between concrete and steel structure to monitor the restraining effect of concrete on the steel structure; during the construction loading stage, the sensor network is mainly arranged at the beam-column connection and node positions to monitor changes in the stress state of the structure.

[0056] S2: Determine the signal sampling frequency of the sensor network according to the construction stage;

[0057] In the real-time monitoring of the rigid skeleton, it is crucial to determine the signal sampling frequency of the sensor network. A sampling frequency that is too high will lead to an excessive amount of data, increase the burden of data processing and transmission, and may introduce unnecessary noise; a sampling frequency that is too low may miss key changes in the state of the rigid skeleton structure and fail to reflect the dynamic characteristics of the structure in a timely manner. Therefore, this embodiment provides a solution for determining the signal sampling frequency of the sensor network according to the construction stage to ensure the efficiency and reliability of the monitoring system.

[0058] Specifically, as attached Figure 2 As shown, the signal sampling frequency of the sensor network is determined according to the construction stage as follows:

[0059] S2.1: The construction process of the rigid frame is divided into multiple stages, including foundation construction, substructure construction, superstructure construction, concrete pouring, and load application.

[0060] Among them, the foundation construction phase includes the construction of foundation structures such as piers and abutments; the lower structure construction phase includes the construction of lower structures such as piers and abutments; the upper structure construction phase includes the construction of upper structures such as main beams and continuous beams; the concrete pouring phase includes the concrete pouring process of continuous beams; the load loading phase includes the loading process of construction loads and operating loads.

[0061] S2.2: Determine the initial sampling frequency of the sensor network signal according to the characteristics of each construction stage;

[0062] During the foundation construction phase, construction activities are mainly concentrated on the foundation, and the overall stress on the structure is relatively small, but the stability of the foundation is crucial to subsequent construction. The sampling frequency can be low at this stage, but it is necessary to focus on the settlement and displacement of the foundation. During the substructure construction phase, when the substructures such as piers are constructed, the structure begins to bear part of the load, and the sampling frequency should be appropriately increased to monitor the deformation and stress changes of the structure. During the superstructure construction phase, the construction of the main beam and continuous beam is a key stage, and the stress on the structure is complex. The sampling frequency should be further increased to capture the dynamic changes of the structure. During the concrete pouring phase, concrete pouring will cause the structural load to increase rapidly, and the sampling frequency should be the highest to monitor the deformation and stress changes of the structure in real time. During the load loading phase, the loading of construction loads and operational loads has a greater impact on the long-term stability of the structure, and the sampling frequency should be relatively high.

[0063] In this embodiment, the initial sampling frequency of the signal in the foundation construction stage is once every 15 minutes; the initial sampling frequency of the signal in the lower structure construction stage is once every 10 minutes; the initial sampling frequency of the signal in the superstructure construction stage is once every 5 minutes; the initial sampling frequency of the signal in the concrete pouring stage is once every 1 minute; and the initial sampling frequency of the signal in the load loading stage is once every 3 minutes.

[0064] S2.3: Determine the comprehensive adjustment coefficient based on environmental factors;

[0065] During construction monitoring of rigid skeletons, environmental factors (such as temperature, humidity, and wind speed) significantly impact the structural condition and the accuracy of monitoring data. The sensitivity of the structure to these factors varies at different construction stages. Therefore, a solution that dynamically adjusts the sampling frequency based on these factors and the construction stage is required to ensure the reliability of the monitoring data and the efficiency of the monitoring system.

[0066] In this step, the environmental factors mainly include: temperature, humidity and wind speed.

[0067] It is worth emphasizing that the environmental factors mentioned in this embodiment are all average values ​​of environmental factors of a fixed period; the fixed period may be 10 minutes, 15 minutes, 60 minutes, etc., and this embodiment does not specifically limit this; among them, temperature changes may cause thermal expansion and contraction of the rigid skeleton structure, affecting the deformation and stress distribution of the rigid skeleton structure; humidity changes may affect the curing of the concrete of the rigid skeleton structure and the durability of the structure; wind loads may cause vibration of the rigid skeleton structure, affecting the stability and safety of the rigid skeleton structure.

[0068] Among them, temperature changes have a greater impact on the rigid skeleton structure, especially in the concrete pouring stage and load loading stage; the temperature influence coefficient αT The calculation formula is:

[0069] ;

[0070] Where: Δ T The current temperature and the reference temperature T ref The difference (unit: ℃); β T is the temperature sensitivity coefficient, which is set according to the construction stage;

[0071] Specifically, during the foundation construction stage, the temperature sensitivity coefficient is 0.1; during the lower structure construction stage, the temperature sensitivity coefficient is 0.2; during the superstructure construction stage, the temperature sensitivity coefficient is 0.3; during the concrete pouring stage, the temperature sensitivity coefficient is 0.5; and during the load loading stage, the temperature sensitivity coefficient is 0.4.

[0072] Among them, humidity has an impact on the curing of concrete and the durability of the structure. The humidity adjustment coefficient α H The calculation formula is:

[0073] ;

[0074] Where: Δ H The current humidity and the reference humidity H ref The difference (unit: %), β H is the humidity sensitivity coefficient, which is set according to the construction stage;

[0075] Specifically, during the foundation construction stage, the humidity sensitivity coefficient is 0.05; during the lower structure construction stage, the humidity sensitivity coefficient is 0.1; during the upper structure construction stage, the humidity sensitivity coefficient is 0.15; during the concrete pouring stage, the humidity sensitivity coefficient is 0.2; and during the load loading stage, the humidity sensitivity coefficient is 0.1.

[0076] Among them, wind speed has a significant impact on the vibration and stability of the structure, and the wind speed adjustment coefficient α W The calculation formula is:

[0077] ;

[0078] in, The current wind speed and the reference wind speed W ref The difference (unit: m / s), W refis the reference wind speed (unit: m / s), usually 50% of the design wind speed. β W is the wind speed sensitivity coefficient, which is set according to the construction stage;

[0079] Specifically, during the foundation construction stage, the wind speed sensitivity coefficient is 0.1; during the lower structure construction stage, the wind speed sensitivity coefficient is 0.2; during the superstructure construction stage, the wind speed sensitivity coefficient is 0.3; during the concrete pouring stage, the wind speed sensitivity coefficient is 0.4; and during the load loading stage, the wind speed sensitivity coefficient is 0.3.

[0080] Among them, the comprehensive adjustment coefficient α The calculation formula is:

[0081] .

[0082] S2.4: Determine a signal sampling frequency based on the initial signal sampling frequency and the comprehensive adjustment coefficient;

[0083] The signal sampling frequency of the sensor network is the product of the initial signal sampling frequency of each construction phase and the comprehensive adjustment coefficient.

[0084] According to the calculated dynamic sampling frequency, the sampling frequency of the sensor network can be adjusted in real time, and different initial sampling frequencies and adjustment coefficients can be determined at different construction stages to ensure that the sensor network can dynamically adjust the sampling frequency according to changes in environmental factors, thereby improving the accuracy and reliability of monitoring data.

[0085] S3: collecting data from the rigid skeleton according to the signal sampling frequency of the sensor network;

[0086] For example, within the rigid framework of a large bridge, a sensor network is deployed every 50 meters along the length of the bridge. Each sensor network includes 10 strain sensors, 6 displacement sensors, and 4 accelerometers. These sensors are connected to a data collector via an NB-IoT communication module, which amplifies and filters the received sensor signals. The amplification factor is adjusted based on the sensor's output signal strength to ensure signal strength and signal-to-noise ratio. The filtering algorithm uses a wavelet transform to remove noise interference from the signal, and the data collector uses a 16-bit analog-to-digital converter (ADC) to convert the analog signal into a digital signal. The ADC sampling rate is 50Hz, which meets the requirements for monitoring the rigid framework of large bridges.

[0087] S4: performing data preprocessing operations on the collected monitoring data;

[0088] Among them, the preprocessing operations include noise removal: eliminating noise introduced by sensor errors, environmental interference or transmission problems; filling missing data: processing missing values ​​that may appear during data acquisition; data correction: correcting the data format to ensure data consistency and integrity; data standardization: converting data into a unified format or dimension to facilitate subsequent analysis.

[0089] Furthermore, the noise removal is achieved by using a Butterworth filter; the missing data filling is achieved by using linear interpolation; the data correction specifically ensures consistency of data formats such as data types (such as integers, floating-point numbers) and timestamp formats; and the data standardization specifically converts the data to the interval [0,1] to facilitate subsequent analysis.

[0090] S5: Input the pre-processed monitoring data into the state assessment model to obtain the real-time monitoring results of the rigid skeleton;

[0091] In this step, the state assessment model is a support vector machine (SVM) model. The SVM is a classification algorithm based on statistical learning theory that classifies monitoring data into different categories by finding the optimal segmentation hyperplane. In real-time monitoring of rigid skeletons, SVM can be used to classify monitoring data into normal, slightly abnormal, and severely abnormal states.

[0092] Wherein, the S5 is specifically:

[0093] S5.1: Annotate the dataset.

[0094] The dataset is annotated based on the actual state of the rigid skeleton structure. The annotated categories include: normal state (the structure is safe and stable); slightly abnormal state (the structure has some minor deviations that do not affect overall safety); and severely abnormal state (the structure has obvious abnormalities that may affect safety).

[0095] S5.2: Establishing a support vector machine model, and training the support vector machine model using the data set;

[0096] Among them, the performance of the support vector machine model depends to a large extent on the selection of the kernel function. In this embodiment, in order to make the support vector machine model better adapt to the real-time monitoring of the rigid skeleton and adapt to the solution of the rigid skeleton monitoring of this embodiment that is closely related to the construction stage of the sensor data sea area, a new kernel function is proposed to improve the classification performance of the support vector machine model.

[0097] Specifically, the kernel function of the support vector machine includes a radial basis function (RBF) part and a polynomial part; the specific formula is:

[0098] ;

[0099] Where, is the kernel function of the support vector machine, x i and x j is the data point in the input data set of the support vector machine, γ is a weight parameter used to balance the contributions of the radial basis function part and the polynomial part, and its value range is [0,1]; σ is the width parameter of the radial basis function part, which controls the decay rate of the similarity between data points; c is the constant term of the polynomial part, which is used to adjust the offset of the polynomial kernel; d is the degree of the polynomial, which controls the complexity of the polynomial kernel.

[0100] The radial basis function (RBF) is a standard RBF kernel function that can handle nonlinear relationships in data. It is sensitive to local variations and suitable for capturing local features in data. It measures similarity by calculating the square of the Euclidean distance between two data points. In real-time monitoring of rigid skeletons, this radial basis function is particularly well-suited to processing local variations in sensor data, such as subtle changes in strain, displacement, and acceleration.

[0101] The polynomial component is a polynomial kernel function that captures global features in the data. It is highly capable of handling linear relationships and high-order interactions in the data. In real-time monitoring of rigid skeletons, this polynomial component is particularly well suited to addressing the global impact of construction phases and environmental factors. For example, the global impact of changes in construction phases (such as foundation construction and concrete pouring) and environmental factors (such as temperature, humidity, and wind speed) on the structural state can be modeled using the polynomial component.

[0102] By adjusting γ, a trade-off can be made between the radial basis function part and the polynomial function part. When γ=1, the kernel function completely depends on the RBF part; when γ=0, the kernel function completely depends on the polynomial part.

[0103] The newly designed kernel function in this embodiment combines RBF and polynomial components to comprehensively consider the influence of sensor data, construction phase, and environmental factors. The RBF component captures local variations in the data, making it suitable for processing small fluctuations in sensor data. The polynomial component captures global trends in the data, making it suitable for addressing the global impact of construction phase and environmental factors. By adjusting the weight parameter γ, a trade-off between local and global features can be achieved, thereby improving the accuracy of the SVM model in rigid skeleton assessment.

[0104] In this embodiment, cross-validation and optimization algorithms (such as grid search or Bayesian optimization) are used to select the optimal parameters γ, σ, c and d to find the best parameter combination; the above process is a prior art and will not be discussed in detail in this embodiment.

[0105] S5.3: Inputting the pre-processed monitoring data into the support vector machine model to obtain a real-time monitoring result of the rigid skeleton;

[0106] The processed feature data is input into the SVM model, and the model outputs the corresponding structural state category.

[0107] Through the above steps, the support vector machine model can be used to effectively evaluate the structural status of the rigid skeleton of large bridges, providing strong technical support for safety management and quality control during the construction process.

[0108] In Example 2, the present invention further provides a rigid skeleton real-time monitoring system, wherein the system adopts a rigid skeleton real-time monitoring method of Example 1, and the system comprises:

[0109] A placement module, configured to place a sensor network on the rigid skeleton;

[0110] a sampling frequency determination module, configured to determine the signal sampling frequency of the sensor network according to the construction stage;

[0111] A data acquisition module, configured to acquire data from the rigid skeleton according to a signal sampling frequency of the sensor network;

[0112] A preprocessing module is used to perform data preprocessing operations on the collected monitoring data;

[0113] The state assessment module is used to input the monitoring data that has undergone data preprocessing into the state assessment model to obtain real-time monitoring results of the rigid skeleton.

[0114] Example 3: The present invention also provides an electronic device, including one or more processors and a memory.

[0115] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0116] The memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may run the program instructions to implement a rigid skeleton real-time monitoring method and / or other desired functions of any embodiment of the present application described above. Various contents such as initial external parameters, thresholds, etc. may also be stored in the computer-readable storage medium.

[0117] In one example, the electronic device may further include an input device and an output device, these components interconnected via a bus system and / or other connection mechanisms (not shown). The input device may include, for example, a keyboard, a mouse, etc. The output device may output various information to the outside, including warning information, braking force, etc. The output device may include, for example, a display, a speaker, a printer, a communication network, and remote output devices connected thereto.

[0118] Of course, for the sake of simplicity, components such as buses, input / output interfaces, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0119] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to implement the functions of a real-time monitoring method for a rigid skeleton provided by any embodiment of the present application.

[0120] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0121] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the processor implements a method for real-time monitoring of a rigid skeleton provided by any embodiment of the present application.

[0122] The computer-readable storage medium may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or 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.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A real-time monitoring method for a rigid skeleton, characterized in that: The following steps are involved: S1: Arranging a sensor network on the rigid skeleton; S2: Determine the signal sampling frequency of the sensor network according to the construction stage; The signal sampling frequency of the sensor network is determined according to the construction stage as follows: S2.1: The construction process of the rigid frame is divided into multiple stages, including foundation construction, substructure construction, superstructure construction, concrete pouring, and load application. S2.2: Determine the initial sampling frequency of the sensor network signal according to the characteristics of each construction stage; S2.3: Determine the comprehensive adjustment coefficient based on environmental factors; in S2.3, the environmental factors are: temperature, humidity and wind speed; among them, the temperature influence coefficient α T The calculation formula is: ； Where: Δ T The current temperature and the reference temperature T ref The difference between β T is the temperature sensitivity coefficient; Humidity affects the curing of concrete and the durability of the structure. The humidity adjustment coefficient α H The calculation formula is: ； Where: Δ H The current humidity and the reference humidity H ref The difference, β H is the humidity sensitivity coefficient; Wind speed has a significant impact on the vibration and stability of the structure. The wind speed adjustment coefficient α W The calculation formula is: ； in, The current wind speed and the reference wind speed W ref The difference, W ref is the reference wind speed, β W is the wind speed sensitivity coefficient; the comprehensive adjustment coefficient α The calculation formula is: ； S2.4: Determine a signal sampling frequency based on the initial signal sampling frequency and the comprehensive adjustment coefficient; S3: collecting data from the rigid skeleton according to the signal sampling frequency of the sensor network; S4: performing data preprocessing operations on the collected monitoring data; S5: Input the pre-processed monitoring data into the state assessment model to obtain the real-time monitoring results of the rigid skeleton.

2. A method for real-time monitoring of a rigid skeleton according to claim 1, characterized in that: In S2.2, the initial sampling frequency of the signal in the foundation construction stage is once every 15 minutes; the initial sampling frequency of the signal in the lower structure construction stage is once every 10 minutes; the initial sampling frequency of the signal in the upper structure construction stage is once every 5 minutes; the initial sampling frequency of the signal in the concrete pouring stage is once every 1 minute; and the initial sampling frequency of the signal in the load loading stage is once every 3 minutes.

3. The method for real-time monitoring of a rigid skeleton according to claim 1, characterized in that: During the foundation construction phase, the temperature sensitivity coefficient is 0.1; during the lower structure construction phase, the temperature sensitivity coefficient is 0.2; during the upper structure construction phase, the temperature sensitivity coefficient is 0.3; during the concrete pouring phase, the temperature sensitivity coefficient is 0.5; during the load loading phase, the temperature sensitivity coefficient is 0.4; During the foundation construction phase, the humidity sensitivity coefficient is 0.05; during the lower structure construction phase, the humidity sensitivity coefficient is 0.1; during the upper structure construction phase, the humidity sensitivity coefficient is 0.15; during the concrete pouring phase, the humidity sensitivity coefficient is 0.2; during the load loading phase, the humidity sensitivity coefficient is 0.1; During the foundation construction stage, the wind speed sensitivity coefficient is 0.1; during the lower structure construction stage, the wind speed sensitivity coefficient is 0.2; during the upper structure construction stage, the wind speed sensitivity coefficient is 0.3; during the concrete pouring stage, the wind speed sensitivity coefficient is 0.4; during the load loading stage, the wind speed sensitivity coefficient is 0.

3.

4. The method for real-time monitoring of a rigid skeleton according to claim 1, characterized in that: In S2.4, the signal sampling frequency of the sensor network is the product of the initial signal sampling frequency of each construction stage and the comprehensive adjustment coefficient.

5. The method for real-time monitoring of a rigid skeleton according to claim 1, characterized in that: In S5, the state assessment model is a support vector machine model.

6. The method for real-time monitoring of a rigid skeleton according to claim 5, characterized in that: The S5 is specifically: S5.1: Annotate the dataset. S5.2: Establishing a support vector machine model and training the support vector machine model using the data set; the kernel function of the support vector machine includes a radial basis function part and a polynomial part; S5.3: Input the pre-processed monitoring data into the support vector machine model to obtain the real-time monitoring results of the rigid skeleton.

7. A real-time monitoring system for a rigid skeleton, characterized in that: The system adopts a real-time monitoring method for a rigid skeleton according to any one of claims 1 to 6, and the system comprises: A placement module, configured to place a sensor network on the rigid skeleton; a sampling frequency determination module, configured to determine the signal sampling frequency of the sensor network according to the construction stage; A data acquisition module, configured to acquire data from the rigid skeleton according to a signal sampling frequency of the sensor network; A preprocessing module is used to perform data preprocessing operations on the collected monitoring data; The state assessment module is used to input the monitoring data that has undergone data preprocessing into the state assessment model to obtain real-time monitoring results of the rigid skeleton.

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