System and method for monitoring health of steel grid structure

Abstract

The invention discloses a system and method for health monitoring of a steel grid structure, and belongs to the technical field of structural mechanics testing, and the method comprises the steps: obtaining the position information of steel grid nodes, generating a node position distribution diagram through a self-positioning algorithm between wireless vibration sensors, recognizing a stress concentration region in the steel grid, and generating node key parameters. According to the node key parameters, the sampling frequency of the wireless vibration sensor is adjusted, and a dynamic sparse sampling instruction is generated; executing a dynamic sparse sampling instruction, and collecting steel net rack vibration data and current environment parameters; performing multi-physical field coupling analysis on the vibration data and the current environmental parameters to generate a comprehensive damage index; and when the comprehensive damage index exceeds a preset alarm threshold value, outputting an alarm signal. According to the method, the dynamic sparse sampling technology driven by the node key parameters and the multi-physics coupling analysis model are adopted, and low-energy-consumption and high-precision structural damage real-time monitoring and self-adaptive early warning can be achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of structural mechanics testing, in particular to a system and method for steel net structure health monitoring. BACKGROUND

[0002] Steel net structure is widely used in large-span buildings such as stadiums and airport terminals, and its long-term service safety depends on structural health monitoring technology. Existing monitoring methods are mostly based on vibration signal analysis, collecting mechanical responses through sensor networks to evaluate the damage state of the structure, which belongs to the core research direction of structural mechanics performance testing field.

[0003] At present, the existing technology usually adopts a fixed density of sensor layout scheme, and obtains vibration data at a uniform sampling frequency, and determines the structural abnormalities through frequency spectrum analysis or modal parameter identification. Some improved schemes introduce strain gauges or optical fiber sensors for local strain monitoring, combined with periodic manual inspection to realize data supplementation. In addition, the alarm mechanism is mostly designed based on a static threshold, and a warning signal is triggered when the vibration amplitude exceeds the empirical threshold.

[0004] The limitations of the above-mentioned technology mainly lie in the following aspects: fixed sampling frequency leads to redundant data in non-critical areas, increasing the transmission and storage burden; the influence of environmental parameters (such as temperature and wind speed) on structural vibration characteristics is not dynamically corrected, reducing the accuracy of damage identification; static alarm threshold cannot adapt to the dynamic changes of structural degradation and external environment, and is prone to misjudgment; the filling method of missing areas of vibration signals in sparse sensor networks is low in efficiency, affecting the reliability of global state evaluation. SUMMARY

[0005] To solve the above problems, the present application provides a system and method for steel net structure health monitoring, which adopts a dynamic sparse sampling technology driven by node criticality parameters and a multi-physical field coupling analysis model, and can realize low-energy consumption, high-precision real-time monitoring and adaptive warning of structural damage.

[0006] The above-mentioned object can be achieved by the following scheme: A system and method for steel net structure health monitoring, comprising: acquiring position information of nodes of a steel net structure; generating a node position distribution map through a self-positioning algorithm between wireless vibration sensors according to the position information; identifying stress concentration areas in the steel net structure based on the node position distribution map, and generating node criticality parameters; adjusting the sampling frequency of the wireless vibration sensors according to the node criticality parameters, and generating a dynamic sparse sampling instruction; executing the dynamic sparse sampling instruction to collect vibration data of the steel net structure and current environmental parameters; performing multi-physical field coupling analysis on the vibration data and the current environmental parameters to generate a comprehensive damage index; and outputting an alarm signal when the comprehensive damage index exceeds a preset alarm threshold.

[0007] Optionally, the node criticality parameter is generated by: extracting the connection stiffness parameter of adjacent nodes according to the node position distribution map; calculating the displacement sensitivity weight coefficient of each node; and multiplying the connection stiffness parameter of the corresponding node by the displacement sensitivity weight coefficient to generate the node criticality parameter.

[0008] Optionally, the sampling frequency of the wireless vibration sensor is adjusted according to the node criticality parameter to generate a dynamic sparse sampling instruction, which includes: performing real-time analysis on the vibration signal collected by the wireless vibration sensor to obtain an instantaneous amplitude of the vibration; if the instantaneous amplitude is less than a preset stable threshold, reducing the sampling frequency to generate a low-frequency sampling instruction; if the instantaneous amplitude is greater than or equal to the stable threshold, enabling a full sampling frequency to generate a high-frequency sampling instruction; integrating the low-frequency sampling instruction and the high-frequency sampling instruction to obtain a sampling instruction set; and modifying the low-frequency sampling instruction in the sampling instruction set based on the node criticality parameter to generate a dynamic sparse sampling instruction.

[0009] Optionally, the dynamic sparse sampling instruction is executed to collect the steel grid vibration data and the current environmental parameter, which includes: obtaining historical vibration data of adjacent wireless vibration sensors; constructing a vibration waveform correlation matrix based on time synchronization; and filling in a missing section of the current steel grid vibration data according to the correlation matrix to generate a continuous vibration waveform.

[0010] Optionally, the vibration data and the current environmental parameter are subjected to multi-physical field coupling analysis to generate a comprehensive damage index, which includes: performing normalization processing on the current environmental parameter and the vibration data to generate a normalized environmental-vibration data set; modifying the normalized vibration data based on the normalized current environmental parameter based on the normalized environmental-vibration data set to generate a multi-parameter feature vector; and calculating the comprehensive damage index by using the multi-parameter feature vector.

[0011] Optionally, the comprehensive damage index is calculated by using the multi-parameter feature vector, which includes: establishing a vibration-environment joint distribution map under normal working conditions of the steel grid; extracting a deviation degree of the multi-parameter feature vector from the joint distribution map; and calculating the comprehensive damage index according to the deviation degree and a preset weight factor.

[0012] Optionally, when the comprehensive damage index exceeds a preset alarm threshold, an alarm signal is output, which includes: obtaining a sequence of comprehensive damage indexes during historical monitoring to obtain a damage index historical data set; dynamically grouping the damage index historical data set to generate a current alarm threshold; and if the current environmental parameter exceeds a preset environmental parameter threshold, modifying the current alarm threshold.

[0013] Optionally, the generating the current alarm threshold comprises: constructing time-weighted data clusters according to the comprehensive damage indices and corresponding time stamps in the damage index historical data set; merging overlapping time-weighted data clusters and eliminating abnormal time-weighted data clusters through density differences of adjacent time-weighted data clusters to obtain a new data cluster set; and generating the current alarm threshold based on the center of the new data cluster set.

[0014] Optionally, the outputting an alarm signal when the comprehensive damage index exceeds the preset alarm threshold further comprises: generating an alarm level according to a difference between the comprehensive damage index and the current alarm threshold; matching the alarm level with a preset emergency response rule library to generate a repair priority instruction; and triggering a visual warning signal of a corresponding node based on the repair priority instruction.

[0015] Based on the same inventive concept, the application further provides a system for health monitoring of a steel grid structure, which comprises: a data acquisition module for acquiring position information of nodes of the steel grid structure; a node positioning module for generating a node position distribution map through a self-positioning algorithm between wireless vibration sensors according to the position information; a parameter calculation module for identifying a stress concentration area in the steel grid structure based on the node position distribution map and generating a node criticality parameter; a sampling adjustment module for adjusting a sampling frequency of the wireless vibration sensors according to the node criticality parameter and generating a dynamic sparse sampling instruction; a parameter acquisition module for executing the dynamic sparse sampling instruction and acquiring vibration data and current environmental parameters of the steel grid structure; a damage calculation module for performing multi-physical field coupling analysis on the vibration data and the current environmental parameters and generating a comprehensive damage index; and an alarm module for outputting an alarm signal when the comprehensive damage index exceeds a preset alarm threshold.

[0016] Compared with the prior art, the application has the following advantages: 1. The application effectively balances the high-precision requirement and the low-energy-consumption target of the monitoring system through an adaptive dynamic sparse sampling strategy; the sampling frequency of the wireless vibration sensors is adjusted in real time according to the node criticality parameter, high-frequency acquisition is performed on the stress concentration area, and low-frequency sparse sampling is implemented on non-critical areas, thereby significantly reducing data redundancy and computational load while ensuring the integrity of the data of the critical nodes; 2. The application proposes a multi-physical field coupling analysis method, which comprehensively considers the joint influence of vibration data and environmental parameters, improves the accuracy and robustness of damage identification, quantitatively evaluates the comprehensive damage index of the structure state through normalization processing and joint distribution atlas comparison, avoids the limitations of single signal analysis, and can accurately capture small abnormalities of the structure under complex working conditions; 3. This invention adopts a dynamic alarm threshold generation mechanism. Through time-weighted analysis of historical data and dynamic correction of environmental parameters, it effectively suppresses the false alarm or missed alarm problems caused by traditional fixed thresholds. The alarm threshold is adjusted according to the time-varying law of the damage index and the degree of environmental interference, optimizing the sensitivity and reliability of the early warning system. 4. The present invention solves the data missing problem in sparse sampling scenarios through the vibration waveform filling technology based on the correlation matrix and the node self-positioning algorithm, ensuring the temporal and spatial consistency of the monitoring data; combined with the self-organizing characteristics of the wireless sensor network, it significantly improves the full-area coverage monitoring capability of complex steel grid structures.

[0017] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the 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.

[0019] Figure 1 The present invention is a flow chart of a method for health monitoring of a steel grid structure according to an embodiment of the present invention.

[0020] Figure 2 Schematic diagram of the node distribution of a three-dimensional steel grid according to an embodiment of the present invention.

[0021] Figure 3 1 is a structural diagram of a system for monitoring the health of a steel grid structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0023] Reference Figure 1An embodiment of the present application provides a method for steel grid structure health monitoring, which adopts a node criticality parameter driven dynamic sparse sampling technology and a multi-physical field coupling analysis model, and can realize low energy consumption, high precision, real-time monitoring and adaptive early warning of structural damage.

[0024] The method of the embodiment specifically comprises: obtaining position information of a steel grid node and a preset environmental parameter threshold value; Specifically, the position information of each node is collected by a plurality of wireless vibration sensors arranged on the steel grid structure. The position information can be obtained in real time by a positioning module (such as an RFID tag or a GPS positioning module) built in the sensor, and transmitted to a central processing unit through a wireless communication module. At the same time, the preset environmental parameter threshold value (such as temperature, humidity, wind speed, etc.) needs to be set in advance according to the design specification of the steel grid structure and the actual use environment, and stored in the system database for correcting the alarm threshold value. The position information needs to be calibrated by a spatial distribution algorithm of the sensor array to ensure the accuracy of the data. The whole process needs to be completed under the cooperative work of the sensor network, to ensure that the position information of each node can be updated in real time.

[0025] According to the position information, a node position distribution map is generated by a self-positioning algorithm between the wireless vibration sensors; Specifically, the node position distribution map is generated by a self-positioning algorithm between the sensors, using the relative position information between adjacent sensors, combined with the obtained node position data. The self-positioning algorithm is usually based on the communication and signal characteristics between the sensors, such as signal strength indication (RSSI) or time of arrival (ToA). These sensors estimate the relative distance by sharing signal characteristics, and finally determine the absolute position of each sensor through multiple rounds of iterative calculation. The whole process involves data fusion and calibration to ensure the accuracy of the position distribution map.

[0026] Illustratively, each wireless vibration sensor is started and begins to receive signals sent by surrounding sensors; the sensor calculates the relative distance between other sensors according to the received signal characteristics (such as RSSI or ToA); the sensor transmits the calculated relative distance data to the central processing unit; the central processing unit runs the self-positioning algorithm, integrates the relative position information of all sensors, calculates the global coordinates of each sensor, generates the node position distribution map, and displays the three-dimensional position of all sensors in the steel grid structure.

[0027] Based on the node position distribution map, a stress concentration area in the steel grid is identified, and a node criticality parameter is generated; Specifically, as Figure 2The three-dimensional steel grid node distribution is shown. The node color depth indicates the strength of key parameters. Based on the node position distribution diagram, the stress concentration area in the steel grid structure is identified through structural mechanics analysis and vibration characteristics analysis, and the key parameters of the nodes are generated. First, according to the node position distribution diagram, the geometric connection relationship and structural stiffness characteristics between adjacent nodes are analyzed. The connection stiffness parameters can be expressed as the adjacent nodes and The stiffness coefficient matrix between Then, based on the characteristics of the vibration signal, the displacement sensitivity of each node is calculated. Displacement sensitivity weight coefficient The energy distribution of the node's vibration response can be used to determine the key role the node plays in the overall structural vibration. and displacement sensitivity weight coefficient Perform multiplication operation to generate node key parameters. Key parameters of the node ,have:

[0028] By generating key node parameters, we can identify the most critical stress-bearing areas within the steel grid structure, thereby optimizing subsequent vibration sampling frequency adjustments. This optimization not only improves monitoring efficiency but also ensures that vibration data from key nodes is focused on and analyzed, providing more accurate support for structural health monitoring.

[0029] According to the key parameters of the node, the sampling frequency of the wireless vibration sensor is adjusted to generate a dynamic sparse sampling instruction; Specifically, the vibration signal of the wireless vibration sensor is collected and analyzed in real time. The instantaneous amplitude of the vibration signal is extracted by Fourier transform or wavelet transform. ,have: in, Indicates the Sensors at time The vibration signal, is the number of sensors.

[0030] Next, the instantaneous amplitude is compared with a preset stability threshold. The stability threshold is an empirical parameter that is preset according to the vibration characteristics of the steel grid structure under normal working conditions. If the instantaneous amplitude is less than the stability threshold, it indicates that the structure is in a relatively stable working condition, and the sampling frequency can be reduced to reduce the burden and energy consumption of data acquisition. The sampling instruction after reducing the sampling frequency is called a low-frequency sampling instruction. If the instantaneous amplitude is greater than or equal to the stability threshold, it indicates that the structure may be in a state of greater stress, and the full sampling frequency needs to be enabled to obtain more detailed vibration data. The enabled full sampling frequency is called a high-frequency sampling instruction. Then, the low-frequency sampling instruction and the high-frequency sampling instruction are integrated to obtain a sampling instruction set. The sampling instruction set is a frequency set dynamically adjusted according to the instantaneous amplitude of the sensor, which ensures that the frequency and energy consumption of data sampling are optimized on the premise of ensuring data integrity. Next, the low-frequency sampling instruction is modified based on the node criticality parameter to generate a dynamic sparse sampling instruction. The node criticality parameter reflects the importance of each node in the steel grid structure, and the dynamic sparse sampling instruction is a proper sparse sampling of non-critical nodes on the basis of ensuring the sampling frequency of critical nodes. For the frequency of the modified low-frequency sampling instruction , wherein, is the frequency of the low-frequency sampling instruction before modification.

[0031] Through the dynamic sparse sampling strategy, the sampling frequency and data acquisition amount of the sensor are significantly optimized. The dynamic sparse sampling can dynamically adjust the sampling frequency according to the importance of the node and the actual stress condition, thereby reducing the overall data amount and energy consumption on the premise of ensuring the data integrity of the critical nodes, and improving the efficiency and reliability of the system. This dynamic adjustment strategy can better adapt to the actual running state of the steel grid structure, avoid unnecessary energy waste, and at the same time ensure the accuracy and effectiveness of the monitoring data. In addition, this method can be flexibly adjusted according to different working condition requirements, enhancing the adaptability and flexibility of the system, and providing high-quality data support for subsequent health monitoring.

[0032] execute the dynamic sparse sampling instruction to collect steel grid vibration data and current environmental parameters; Specifically, first, according to the dynamic sparse sampling instruction, a sampling frequency adjustment instruction is sent to each wireless vibration sensor. Each sensor adjusts its own sampling frequency according to the received instruction to ensure that the sampling frequency of the key node is not lower than the preset minimum sampling frequency. Subsequently, the sensor starts collecting the vibration data of the steel net rack, including acceleration, displacement and other characteristic parameters, and simultaneously collects environmental parameters such as temperature, humidity and wind speed. During the vibration data collection process, if it is detected that the vibration signal of a certain area is missing or incomplete, the missing section is filled by constructing a vibration waveform correlation matrix based on time synchronization. The construction of the vibration waveform correlation matrix is based on the historical vibration data of adjacent sensors, and by analyzing the correlation of vibration signals between sensors, a complementary analysis function is calculated for each missing section. Finally, a continuous vibration waveform is generated to ensure the integrity and time synchronization of the vibration data.

[0033] Exemplarily, the construction process of the vibration waveform correlation matrix is as follows: let the vibration signals of sensor and sensor be and respectively, and the correlation coefficient of the two can be expressed as: wherein, and are the start time and end time of the vibration signal respectively. By calculating the correlation coefficient of all sensor pairs, the vibration waveform correlation matrix is constructed, and the matrix element is the correlation coefficient of sensor and sensor . Based on the correlation matrix, the complementary analysis function of the missing vibration signal can be accurately calculated to ensure that the filled vibration waveform is highly consistent with the actual structure vibration characteristics.

[0034] performing multi-physical field coupling analysis on the vibration data and the current environmental parameters to generate a comprehensive damage index; In particular, the current environmental parameters include temperature, humidity, etc., and the vibration data is the vibration signal of the node. The vibration data and the current environmental parameters are normalized, and all the normalized data are aligned by time to generate a two-dimensional matrix form of the normalized environmental-vibration data set. The normalized current environmental parameters correct the normalized vibration data to generate a feature vector containing multiple parameters (such as temperature, humidity, vibration amplitude, etc.), i.e., a multi-parameter feature vector. By collecting the vibration data and environmental parameters of the steel grid structure under normal working conditions, a vibration-environment joint distribution map is established to reflect the vibration and environmental characteristics of the steel grid structure under normal conditions. By comparing the multi-parameter feature vector with the joint distribution map, the deviation between them is calculated, which reflects the difference between the current state and the normal state of the steel grid structure. According to the deviation and the preset weight factor (which reflects the influence degree of different parameters on the damage of the steel grid structure), a comprehensive damage index is calculated by weighted average method. For the comprehensive damage index , there are: is a preset weight factor, reflecting the contribution of different parameters to damage, is the deviation of each parameter, is the parameter index. This index can comprehensively reflect the damage of the steel grid structure under the action of multiple physical fields, and provide an important basis for subsequent health monitoring and early warning.

[0035] When the comprehensive damage index exceeds a preset alarm threshold, an alarm signal is output.

[0036] In particular, the comprehensive damage index during the previous monitoring period is extracted from the historical database to form a damage index historical data set. Each data point is attached with a time stamp for associating the historical data with the current monitoring result. According to the time stamp and the comprehensive damage index value of the historical data, a time-weighted data cluster is constructed. The weight of each data cluster is determined by a time decay factor, and the damage index with a closer time occupies a higher weight. The calculation formula of the time-weighted data cluster is: wherein, is the weighted sum of the data cluster, is the th comprehensive damage index in the damage index historical data set, is a decay factor (value range ), is the time stamp corresponding to the th comprehensive damage index, is the current time.

[0037] Then, by calculating the density difference of adjacent data clusters, overlapping clusters are merged and abnormal clusters are removed. For data clusters and Density difference ,have: in, and For data clusters and If If the value is less than the preset threshold, the two clusters are merged; if the mean density difference of a cluster exceeds 3 times the global median, it is determined to be an abnormal cluster and removed. The weighted sum center point of the merged new data cluster set is used as the current alarm threshold. Real-time monitoring of current environmental parameters (such as temperature and humidity) is performed, and the current environmental parameters are compared with the preset environmental parameter thresholds. If the current environmental parameters exceed the limit, the correction coefficient is calculated according to the degree of deviation of the environmental parameters. For example, for every 1°C the temperature exceeds the limit, the current correction coefficient is multiplied by 0.8. The initial correction coefficient can be 1. For example, if the temperature exceeds the limit by 2°C, then if the initial correction coefficient is 1, the corrected correction coefficient is 1X0.8X0.8=0.64; the correction coefficient is used to generate the corrected alarm threshold : in is the mean of the weighted and central points of the new data cluster set, that is, the alarm threshold before correction, is the correction coefficient. When the real-time comprehensive damage index exceeds the current alarm threshold, the difference is calculated and matched against the preset emergency response rule base based on the difference. For example, a difference of less than or equal to 0.3 times the current alarm threshold corresponds to a level 1 alarm (yellow warning); a difference of less than or equal to 0.3 times the current alarm threshold corresponds to a level 1 alarm (yellow warning); a difference greater than 0.3 times the current alarm threshold and less than or equal to 0.6 times the current alarm threshold corresponds to a level 2 alarm (orange warning); and a difference greater than 0.6 times the current alarm threshold corresponds to a level 3 alarm (red warning). Repair priority instructions are generated based on the alarm level, and a warning signal is sent to the corresponding node's visualization terminal, displaying the fault location and emergency response priority.

[0038] Through dynamic grouping based on time-weighted and density-differentiated data, the interference of older data is effectively suppressed, allowing the system to focus on recent trends in structural conditions. An environmental parameter correction mechanism prevents misjudgment of thresholds during extreme weather events, ensuring that alarm levels accurately match the actual extent of damage. Visualized early warnings enable maintenance personnel to quickly locate weak points and prioritize repairs, significantly improving the safety and efficiency of steel grid structures.

[0039] Based on the same inventive concept, Figure 3 As shown, the present invention also provides a system for monitoring the health of a steel grid structure, the system comprising: Data acquisition module, used to obtain the location information of steel grid nodes; A node positioning module is configured to generate a node position distribution map by a self-positioning algorithm among wireless vibration sensors according to the position information. A parameter calculation module is configured to identify a stress concentration area in the steel grid structure based on the node position distribution map and generate a node criticality parameter. A sampling adjustment module is configured to adjust a sampling frequency of the wireless vibration sensor according to the node criticality parameter and generate a dynamic sparse sampling instruction. A parameter acquisition module is configured to execute the dynamic sparse sampling instruction and acquire the steel grid vibration data and the current environmental parameter. A damage calculation module is configured to perform a multi-physical field coupling analysis on the vibration data and the current environmental parameter and generate a comprehensive damage index. An alarm module is configured to output an alarm signal when the comprehensive damage index exceeds a preset alarm threshold.

[0040] It should be noted that the above formulas can be converted into unitless standard values or same-dimension superimposable parameters by the dimensional consistency principle and mathematical standardization means (for example, normalization processing, dimensionless parameter conversion or unit system unification), so as to eliminate the interference of different dimensions on the operation logic, make the formula retain the original data distribution characteristics, and have mathematical operation rationality and objective law adaptability. It is a conventional technical means, and will not be described here. The electrical connection between the above-mentioned units does not necessarily mean direct connection, and indirect connection can also be used as long as the purpose of the application is achieved. The above-mentioned is only an exemplary embodiment of the application, and cannot limit the scope of the application.

[0041] That is, any equivalent changes and modifications made according to the teachings of the present application are still within the scope of the present application. Other embodiments of the present application will be readily apparent to those skilled in the art upon considering the specification and practice of the disclosure. The present application is intended to cover any variations, uses or adaptive changes to the present application following the general principles of the present application and including common knowledge or conventional technical means in the art not disclosed by the present application.

Claims

1. A method for health monitoring of steel grid structures, characterized in that: The method comprises: Get the location information of steel grid nodes; generating a node location distribution map based on the location information through a self-positioning algorithm among wireless vibration sensors; Based on the node position distribution diagram, identifying the stress concentration area in the steel grid and generating key node parameters; According to the key parameters of the node, the sampling frequency of the wireless vibration sensor is adjusted to generate a dynamic sparse sampling instruction; Executing the dynamic sparse sampling instruction to collect steel grid vibration data and current environmental parameters; Performing multi-physics field coupling analysis on the vibration data and current environmental parameters to generate a comprehensive damage index; When the comprehensive damage index exceeds a preset alarm threshold, an alarm signal is output.

2. The method for health monitoring of steel grid structures according to claim 1, characterized in that: The method of identifying the stress concentration area in the steel grid based on the node position distribution diagram and generating the key parameters of the node includes: Extracting connection stiffness parameters of adjacent nodes according to the node position distribution diagram; Calculate the displacement sensitivity weight coefficient of each node; The connection stiffness parameter of the corresponding node is multiplied by the displacement sensitivity weight coefficient to generate the node critical parameter.

3. The method for health monitoring of steel grid structures according to claim 1, characterized in that: The step of adjusting the sampling frequency of the wireless vibration sensor according to the key node parameters and generating a dynamic sparse sampling instruction includes: Perform real-time analysis on the vibration signal collected by the wireless vibration sensor to obtain the instantaneous amplitude of the vibration; If the instantaneous amplitude is less than a preset stability threshold, the sampling frequency is reduced to generate a low-frequency sampling instruction; If the instantaneous amplitude is greater than or equal to the stability threshold, enabling full sampling frequency and generating a high-frequency sampling instruction; Integrating the low-frequency sampling instruction and the high-frequency sampling instruction to obtain a sampling instruction set; The low-frequency sampling instructions in the sampling instruction set are modified based on the point criticality parameters to generate dynamic sparse sampling instructions.

4. The method for health monitoring of steel grid structures according to claim 1, characterized in that: The execution of the dynamic sparse sampling instruction to collect steel grid vibration data and current environmental parameters includes: Obtain historical vibration data from adjacent wireless vibration sensors; Construct a vibration waveform correlation matrix based on time synchronization; According to the correlation matrix, missing sections of the current steel grid vibration data are filled to generate a continuous vibration waveform.

5. The method for health monitoring of steel grid structures according to claim 1, characterized in that: The performing multi-physics field coupling analysis on the vibration data and current environmental parameters to generate a comprehensive damage index includes: Normalizing the current environmental parameters and the vibration data to generate a normalized environment-vibration data set; Based on the normalized environment-vibration data set, the normalized vibration data is corrected by using the normalized current environment parameters to generate a multi-parameter feature vector; The comprehensive damage index is calculated using the multi-parameter eigenvector.

6. The method for health monitoring of steel grid structures according to claim 5, characterized in that: The method of calculating the comprehensive damage index using the multi-parameter eigenvector includes: Establish the vibration-environment joint distribution map under normal working conditions of steel grid; Extracting the deviation between the multi-parameter feature vector and the joint distribution map; A comprehensive damage index is calculated based on the deviation and a preset weight factor.

7. The method for health monitoring of steel grid structures according to claim 1, characterized in that: When the comprehensive damage index exceeds a preset alarm threshold, outputting an alarm signal includes: Obtain the comprehensive damage index sequence during the historical monitoring period to obtain the damage index historical data set; Dynamically grouping the damage index historical data set to generate a current alarm threshold; If the current environmental parameters exceed the preset environmental parameter threshold, the current alarm threshold is corrected.

8. The method for health monitoring of steel grid structures according to claim 7, characterized in that: Generating the current alarm threshold comprises: Construct a time-weighted data cluster based on the comprehensive damage index and the corresponding timestamp in the damage index historical data set; By comparing the density differences of adjacent time-weighted data clusters, overlapping time-weighted data clusters are merged and abnormal time-weighted data clusters are eliminated to obtain a new data cluster set. Based on the new data cluster set center, the current alarm threshold is generated.

9. The method for health monitoring of steel grid structures according to claim 8, characterized in that: When the comprehensive damage index exceeds a preset alarm threshold, outputting an alarm signal also includes: generating an alarm level according to a difference between the comprehensive damage index and the current alarm threshold; Matching the alarm level with a preset emergency response rule library to generate a repair priority instruction; Based on the repair priority instruction, a visual early warning signal of the corresponding node is triggered.

10. A system for monitoring the health of a steel grid structure, applied to the method for monitoring the health of a steel grid structure according to any one of claims 1 to 9, characterized in that: The system comprises: Data acquisition module, used to obtain the location information of steel grid nodes; A node positioning module is used to generate a node location distribution map based on the location information by using a self-positioning algorithm between wireless vibration sensors; A parameter calculation module is used to identify the stress concentration area in the steel grid based on the node position distribution map and generate key node parameters; A sampling adjustment module, configured to adjust the sampling frequency of the wireless vibration sensor according to the key parameters of the node and generate a dynamic sparse sampling instruction; A parameter acquisition module is used to execute the dynamic sparse sampling instruction to collect steel grid vibration data and current environmental parameters; a damage calculation module, configured to perform multi-physics field coupling analysis on the vibration data and current environmental parameters to generate a comprehensive damage index; The alarm module is used to output an alarm signal when the comprehensive damage index exceeds a preset alarm threshold.

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