A system and method for steel grid structure health monitoring

Abstract

The application discloses a system and method for steel net rack structure health monitoring, and belongs to the technical field of structural mechanics testing, which comprises the following steps: acquiring position information of a steel net rack node, generating a node position distribution map through a self-positioning algorithm between wireless vibration sensors, identifying a stress concentration area in the steel net rack, and generating a node criticality parameter; adjusting a sampling frequency of the wireless vibration sensor according to the node criticality parameter, and generating a dynamic sparse sampling instruction; executing the dynamic sparse sampling instruction, collecting steel net rack vibration data and current environmental parameters; performing multi-physical field coupling analysis on the vibration data and the current environmental parameters, and generating a comprehensive damage index; and outputting an alarm signal when the comprehensive damage index exceeds a preset alarm threshold. The application adopts a dynamic sparse sampling technology driven by a node criticality parameter and a multi-physical field coupling analysis model, and can realize low-energy-consumption, high-precision, real-time structure damage monitoring and self-adaptive early warning.

Description

TECHNICAL FIELD

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

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

[0003] Currently, existing technologies usually use a fixed density of sensor layout scheme and obtain vibration data at a uniform sampling frequency, and determine 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 supplement data. In addition, the alarm mechanism is mostly based on a static threshold design, triggering a warning signal 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 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 thresholds cannot adapt to the dynamic changes of structural degradation and external environment, and are prone to false judgments; the filling method for missing areas of vibration signals in sparse sensor networks is inefficient, 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 rack structure health monitoring, which uses a node criticality parameter driven dynamic sparse sampling technology and a multi-physical field coupling analysis model to achieve low-energy consumption, high-precision real-time monitoring and adaptive warning of structural damage.

[0006] The above-mentioned objectives can be achieved through the following solutions:

[0007] A system and method for steel grid structure health monitoring, comprising: acquiring position information of nodes of a steel grid structure; generating a node position distribution map through a self-positioning algorithm between wireless vibration sensors according to the position information; identifying a stress concentration area in the steel grid structure based on the node position distribution map, and generating a node criticality parameter; adjusting a sampling frequency of the wireless vibration sensors according to the node criticality parameter, and generating a dynamic sparse sampling instruction; executing the dynamic sparse sampling instruction to collect vibration data of the steel grid 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.

[0008] Optionally, the step of identifying a stress concentration area in the steel grid structure based on the node position distribution map, and generating a node criticality parameter comprises: extracting a connection stiffness parameter of adjacent nodes according to the node position distribution map; calculating a displacement sensitivity weight coefficient of each node; and multiplying the connection stiffness parameter of the corresponding node and the displacement sensitivity weight coefficient to generate the node criticality parameter.

[0009] Optionally, the step of adjusting a sampling frequency of the wireless vibration sensors according to the node criticality parameter, and generating a dynamic sparse sampling instruction comprises: performing real-time analysis on a 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 a low-frequency sampling instruction in the sampling instruction set based on the node criticality parameter to generate a dynamic sparse sampling instruction.

[0010] Optionally, the step of executing the dynamic sparse sampling instruction to collect vibration data of the steel grid structure and current environmental parameters comprises: acquiring 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.

[0011] Optionally, the step of performing multi-physical field coupling analysis on the vibration data and the current environmental parameters to generate a comprehensive damage index comprises: performing normalization processing on the current environmental parameters and the vibration data to generate a normalized environmental-vibration data set; modifying the normalized vibration data based on the normalized current environmental parameters based on the normalized environmental-vibration data set to generate a multi-parameter feature vector; and calculating the comprehensive damage index using the multi-parameter feature vector.

[0012] Optionally, the calculating the comprehensive damage index by using the multi-parameter feature vector comprises: establishing a vibration-environment joint distribution map of the steel net rack under a normal working condition; 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.

[0013] Optionally, the outputting the alarm signal when the comprehensive damage index exceeds a preset alarm threshold comprises: obtaining a sequence of the comprehensive damage index during a historical monitoring period to obtain a damage index historical data set; dynamically grouping the damage index historical data set to generate a current alarm threshold; and correcting the current alarm threshold when a current environmental parameter exceeds a preset environmental parameter threshold.

[0014] Optionally, the generating the current alarm threshold comprises: constructing a time-weighted data cluster according to the comprehensive damage index in the damage index historical data set and a corresponding time stamp; merging overlapping time-weighted data clusters and eliminating abnormal time-weighted data clusters by density differences of adjacent time-weighted data clusters to obtain a new data cluster set; and generating the current alarm threshold based on a center of the new data cluster set.

[0015] Optionally, the outputting the alarm signal when the comprehensive damage index exceeds a 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.

[0016] Based on the same inventive concept, the application further provides a system for health monitoring of a steel net rack structure, which comprises: a data acquisition module for acquiring position information of nodes of the steel net rack; a node positioning module for generating a node position distribution map by 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 net rack 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 to acquire vibration data of the steel net rack and a current environmental parameter; a damage calculation module for performing multi-physical field coupling analysis on the vibration data and the current environmental parameter to generate a comprehensive damage index; and an alarm module for outputting an alarm signal when the comprehensive damage index exceeds a preset alarm threshold.

[0017] Compared with the prior art, the application has the following advantages:

[0018] 1. The application effectively balances the high-precision requirement and low-energy consumption target of the monitoring system through an adaptive dynamic sparse sampling strategy; the sampling frequency of the wireless vibration sensor is adjusted in real time according to the key parameter of the node, high-frequency collection is performed on the stress concentration area, and low-frequency sparse sampling is implemented on the non-key area, thereby significantly reducing data redundancy and computational load, while ensuring the integrity of the key node data;

[0019] 2. The application proposes a multi-physical field coupling analysis method, comprehensively considers the joint influence of vibration data and environmental parameters, and improves the accuracy and robustness of damage identification; through normalization processing and joint distribution atlas comparison, the comprehensive damage index of the structure state is quantitatively evaluated, the limitations of single signal analysis are avoided, and small abnormalities of the structure under complex working conditions can be accurately captured;

[0020] 3. The application adopts a dynamic alarm threshold generation mechanism, effectively suppresses the false alarm or missed alarm problem caused by the traditional fixed threshold through time-weighted analysis of historical data and dynamic correction of environmental parameters; the alarm threshold is adjusted according to the time-varying law of the damage index and the degree of environmental interference, thereby optimizing the sensitivity and reliability of the early warning system;

[0021] 4. The application solves the data missing problem in the sparse sampling scene through the vibration waveform filling technology based on the correlation matrix and the node self-positioning algorithm, and guarantees the spatio-temporal consistency of the monitoring data; combined with the self-organizing characteristics of the wireless sensor network, the global coverage monitoring capability of the complex steel net structure is significantly improved.

[0022] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and achieved by the structures indicated in the specification, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0024] Figure 1 is a flowchart of a method for steel net structure health monitoring according to an embodiment of the present application.

[0025] Figure 2 is a schematic diagram of three-dimensional steel net structure node distribution according to an embodiment of the present application.

[0026] Figure 3is a structural schematic diagram of a system for steel grid structure health monitoring according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0028] Referring to Figure 1 An 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 structure damage real-time monitoring and self-adaptive early warning.

[0029] The method according to the embodiment specifically includes:

[0030] obtaining position information of a steel grid node and a preset environmental parameter threshold value;

[0031] 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.

[0032] According to the position information, a node position distribution map is generated by a self-positioning algorithm between the wireless vibration sensors;

[0033] 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.

[0034] For example, each wireless vibration sensor is activated and begins to receive signals sent by surrounding sensors; the sensor calculates the relative distance between itself and other sensors based on the characteristics of the received signals (such as RSSI or ToA); the sensor transmits the calculated relative distance data to the central processing unit; the central processing unit runs a self-localization algorithm, integrates the relative position information of all sensors, calculates the global coordinates of each sensor, generates a node position distribution map, and displays the three-dimensional position of all sensors in the steel space frame structure.

[0035] Based on the node location distribution map, stress concentration areas in the steel space frame are identified, and key node parameters are generated;

[0036] Specifically, such as Figure 2 The diagram shows the node distribution of a 3D steel space frame. The color depth of the nodes indicates the strength of key parameters. Based on the node location distribution map, stress concentration areas in the steel space frame structure are identified through structural mechanics analysis and vibration characteristic analysis, and key node parameters are generated. First, based on the node location distribution map, the geometric connection relationships and structural stiffness characteristics between adjacent nodes are analyzed. The connection stiffness parameter can be represented by the values ​​of 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 weighting coefficient. The vibration response energy distribution of a node can be used to determine its key role in the overall structural vibration. Finally, the connection stiffness parameters of the node can be... and displacement sensitivity weighting coefficient Perform multiplication operations to generate key node parameters; for the ... Key parameters of node number ,have:

[0037] By generating key node parameters, the most critical stress-bearing areas in the steel space frame structure can be identified, thereby optimizing subsequent vibration sampling frequency adjustments. This optimization not only improves monitoring efficiency but also ensures that vibration data from key nodes are given focused attention and analysis, providing more accurate support for structural health monitoring.

[0038] Based on the key parameters of the node, the sampling frequency of the wireless vibration sensor is adjusted to generate a dynamic sparse sampling command.

[0039] Specifically, vibration signals from wireless vibration sensors are collected and analyzed in real time. The instantaneous amplitude of the vibration signal is extracted using methods such as Fourier transform or wavelet transform. ,have:

[0040] wherein, represents the vibration signal of the i-th sensor at time t, is the number of sensors.

[0041] 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 the optimization of the frequency and energy consumption of data sampling 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 , there is:

[0042] wherein, is the frequency of the low-frequency sampling instruction before modification.

[0043] 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.

[0044] executing the dynamic sparse sampling instruction, collecting steel grid vibration data and current environmental parameters;

[0045] ​​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.

[0046] Exemplarily, the construction process of the vibration waveform correlation matrix is as follows: let the vibration signals of sensors and be and respectively, and the correlation coefficient of the two can be expressed as:

[0047] 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.

[0048] performing multi-physical field coupling analysis on the vibration data and the current environmental parameters to generate a comprehensive damage index;

[0049] Specifically, 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.

[0050] When the comprehensive damage index exceeds a preset alarm threshold, an alarm signal is output.

[0051] Specifically, 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:

[0052] 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 (with a value range of ), is the time stamp corresponding to the th comprehensive damage index, is the current time.

[0053] Then, by calculating the density difference of adjacent data clusters, overlapping clusters are merged and abnormal clusters are removed. For the data cluster and density difference ,have: in, and For data clusters and The standard deviation. If If the density difference is less than a preset threshold, the two clusters are merged. If the average density difference of a cluster exceeds three times the global median, it is identified as an abnormal cluster and removed. The weighted sum and center point of the merged new data cluster set are used as the current alarm threshold. Current environmental parameters (such as temperature and humidity) are monitored in real time and compared with preset environmental parameter thresholds. If the current environmental parameter exceeds the limit, a correction coefficient is calculated based on the degree of deviation. For example, for every 1°C exceeding the temperature 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 with an initial correction coefficient of 1, the corrected correction coefficient would be 1 x 0.8 x 0.8 = 0.64. The corrected alarm threshold is generated using this correction coefficient. :

[0054] in The weighted sum of the new data cluster set and the mean of its centroids represent the alarm threshold before correction. This is a correction factor. When the real-time comprehensive damage index exceeds the current alarm threshold, the difference is calculated. Based on the difference, a preset emergency response rule base is used. For example: a difference less than or equal to 0.3 times the current alarm threshold corresponds to a Level 1 alarm (yellow warning); a difference 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 but 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 warning signals are sent to the corresponding node's visualization terminal, displaying the fault location and emergency handling priority.

[0055] By dynamically grouping data based on time weighting and density differences, interference from outdated data is effectively suppressed, allowing the system to focus more on recent structural trends. An environmental parameter correction mechanism prevents misjudgments of thresholds due to extreme weather, ensuring precise matching of alarm levels with actual damage levels. Visualized early warning systems enable maintenance personnel to quickly locate vulnerable nodes and prioritize repairs, significantly improving the efficiency of safety maintenance for steel space frame structures.

[0056] Based on the same inventive concept, such as Figure 3 As shown, the present invention also provides a system for health monitoring of steel space frame structures, the system comprising:

[0057] A data acquisition module is configured to acquire position information of the nodes of the steel grid structure.

[0058] A node positioning module is configured to generate a node position distribution map by a self-positioning algorithm among the wireless vibration sensors according to the position information.

[0059] 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.

[0060] A sampling adjustment module is configured to adjust a sampling frequency of the wireless vibration sensors according to the node criticality parameter and generate a dynamic sparse sampling instruction.

[0061] A parameter acquisition module is configured to execute the dynamic sparse sampling instruction and acquire vibration data of the steel grid structure and current environmental parameters.

[0062] A damage calculation module is configured to perform a multi-physical field coupling analysis on the vibration data and the current environmental parameters and generate a comprehensive damage index.

[0063] An alarm module is configured to output an alarm signal when the comprehensive damage index exceeds a preset alarm threshold.

[0064] It should be noted that the above formulas can be converted into unitless standard values or same-dimension superimposable parameters by the principle of dimensional consistency 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.

[0065] 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 steel grid structure health monitoring, characterized in that, The method comprises: obtaining position information of a steel grid node; generating a node position distribution map through a self-positioning algorithm between wireless vibration sensors according to the position information; identifying a stress concentration area in the steel grid based on the node position distribution map, and generating a node criticality parameter; adjusting a sampling frequency of the wireless vibration sensor according to the node criticality parameter, and generating a dynamic sparse sampling instruction; executing the dynamic sparse sampling instruction to collect steel grid vibration data 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; wherein the current environmental parameters and the vibration data are normalized to generate a normalized environmental-vibration data set; based on the normalized environmental-vibration data set, the normalized vibration data is corrected through the normalized current environmental parameters to generate a multi-parameter feature vector; a vibration-environment joint distribution map under normal working conditions of the steel grid is established; the deviation of the multi-parameter feature vector from the joint distribution map is extracted; and the comprehensive damage index is calculated according to the deviation and a preset weight factor; when the comprehensive damage index exceeds a preset alarm threshold, an alarm signal is output.

2. The method for steel grid structure health monitoring according to claim 1, characterized in that, The identification of the stress concentration area in the steel grid based on the node position distribution map to generate the node criticality parameter comprises: extracting the connection stiffness parameter of adjacent nodes according to the node position distribution map; calculating the displacement sensitivity weight coefficient of each node; multiplying the connection stiffness parameter of the corresponding node by the displacement sensitivity weight coefficient to generate the node criticality parameter.

3. The method for steel truss structure health monitoring according to claim 1, wherein, The adjustment of the sampling frequency of the wireless vibration sensor according to the node criticality parameter to generate the dynamic sparse sampling instruction comprises: performing 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 stable threshold, the sampling frequency is reduced to generate a low-frequency sampling instruction; if the instantaneous amplitude is greater than or equal to the stable threshold, the full sampling frequency is enabled 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; correcting the low-frequency sampling instruction in the sampling instruction set based on the node criticality parameter to generate a dynamic sparse sampling instruction.

4. The method for steel truss structure health monitoring according to claim 1, wherein, The execution of the dynamic sparse sampling instruction to collect the steel grid vibration data and the current environmental parameters comprises: obtaining historical vibration data of adjacent wireless vibration sensors; constructing a vibration waveform correlation matrix based on time synchronization; filling in the missing sections of the current steel grid vibration data according to the correlation matrix to generate a continuous vibration waveform.

5. The method for steel truss structure health monitoring according to claim 1, wherein, When the comprehensive damage index exceeds the preset alarm threshold, the alarm signal is output, which comprises: 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; if the current environmental parameter exceeds a preset environmental parameter threshold, the current alarm threshold is corrected.

6. The method for steel grid structure health monitoring according to claim 5, characterized in that, The generation of the current alarm threshold comprises: constructing a time-weighted data cluster according to the comprehensive damage indexes in the damage index historical data set and the corresponding time stamps; The overlapping time-weighted data clusters are merged and the abnormal time-weighted data clusters are removed by weighting the density difference of adjacent time-weighted data clusters, to obtain a new data cluster set; A current alarm threshold is generated based on the center of the new data cluster set.

7. The method for steel grid structure health monitoring according to claim 6, characterized in that, The output of the alarm signal when the comprehensive damage index exceeds the preset alarm threshold further includes: An alarm level is generated according to the difference between the comprehensive damage index and the current alarm threshold; The alarm level is matched with a preset emergency response rule library to generate a repair priority instruction; A visual warning signal of the corresponding node is triggered based on the repair priority instruction.

8. A system for steel grid structure health monitoring, applied to the method for steel grid structure health monitoring according to any one of claims 1-7, characterized in that, The system includes: A data acquisition module for acquiring position information of a steel grid node; 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 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 sensor according to the node criticality parameter and generating a dynamic sparse sampling instruction; A parameter acquisition module for executing the dynamic sparse sampling instruction to collect steel grid vibration data and current environmental parameters; A damage calculation module for performing multi-physical field coupling analysis on the vibration data and the current environmental parameters to generate a comprehensive damage index, including: normalizing the current environmental parameters and the vibration data to generate a normalized environment-vibration data set; correcting the normalized vibration data based on the normalized current environmental parameters based on the normalized environment-vibration data set to generate a multi-parameter feature vector; 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 and the joint distribution map; and calculating the comprehensive damage index according to the deviation degree and a preset weight factor; An alarm module for outputting an alarm signal when the comprehensive damage index exceeds a preset alarm threshold.

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