A steel structure firmness monitoring method and system based on a mechanical sensor

Abstract

This invention relates to the field of mechanical sensor technology, and in particular provides a method and system for monitoring the robustness of steel structures based on mechanical sensors. The method includes performing time-frequency domain decomposition on the acquired dynamic response signal; separating steady-state vibration characteristic quantities caused solely by changes in the structure's own stiffness from the dynamic response signal based on the correspondence between each frequency component and the structure's inherent vibration modes; jointly analyzing the separated steady-state vibration characteristic quantities with the pre-established geometric topological relationships between nodes in the steel structure design drawings to calculate a stiffness distribution coefficient matrix characterizing the continuity of the overall force transmission path of the steel structure; performing point-by-point comparisons; and marking stiffness anomaly regions and their degrees of anomaly based on the offset of corresponding element values, ultimately forming a map that visually displays the robustness status of the steel structure. This invention achieves comprehensive monitoring and evaluation of the robustness of steel structures from local to overall, from signal to state, and from qualitative to quantitative analysis.

Description

Technical Field

[0001] This invention relates to the field of mechanical sensor technology, and in particular to a method and system for monitoring the robustness of steel structures based on mechanical sensors. Background Technology

[0002] As the core load-bearing system of modern buildings, bridges, industrial facilities, and large-scale public works, the safety and reliability of steel structures during long-term service are directly related to the safety of people's lives and property and the stable operation of the social economy. With the rapid development of infrastructure construction and the aging trend of existing structures in my country, how to achieve real-time, accurate, and intelligent monitoring of the health status of steel structures has become a key issue that urgently needs to be addressed in the field of engineering safety. Traditional steel structure inspection mainly relies on manual inspection, visual inspection, or periodic fixed-point inspection, which has obvious limitations: on the one hand, the inspection cycle is long and the coverage is limited, making it difficult to capture the instantaneous response and cumulative damage of the structure under dynamic loads, such as wind vibration, traffic loads, and temperature changes; on the other hand, it is highly subjective and easily affected by personnel experience and environmental conditions, and cannot achieve continuous, online condition assessment. Especially for large and complex steel structures, such as stadium space frames, long-span bridges, and core tubes of super high-rise buildings, their mechanical behavior has characteristics such as uneven spatial distribution and time-varying nonlinearity, making it even more difficult for traditional methods to fully reflect the true stress state and damage evolution law of the structure.

[0003] Prior art 1, Chinese Patent Application No. 202511521375.7, discloses a steel reinforcement strength measurement system for building steel structure construction, belonging to the field of building material testing. It includes a tensile testing device and a software system. The software system includes a test control module, a data processing and analysis module, and a visualization module. The test control module is used to set test parameters and automatically control the operation of the tensile testing device. The data processing and analysis module is used to process and analyze the collected data. The tensile testing device includes a base, a tensioning mechanism, and a measuring mechanism. Although the measuring mechanism can move along the axial direction of the steel reinforcement, and the photoelectric encoder can monitor the deformation along the entire length of the steel reinforcement, especially accurately capturing areas of severe deformation, and the ring seat drives the photoelectric encoder to measure the same cross-section from multiple angles, avoiding the limitations of a single angle and obtaining more comprehensive data to ensure accurate measurement of the elongation at break, it cannot perform in-situ, online, non-destructive strength or stability monitoring of steel structures that have already been built and are in service. The measurement process itself damages the specimens, and the measurement process is discrete and discontinuous. Strength measurements are only performed on single steel bars or local specimens, and the results cannot be generalized or used to evaluate the mechanical properties of an overall steel structure system composed of numerous components and nodes. It is completely impossible to analyze the continuity of force transmission paths, the overall stiffness distribution, and the effectiveness of node connections within the structure.

[0004] Prior art two, Chinese patent application number 202410991440.1, discloses a method, medium, and system for monitoring the corrosion degree of large-scale high-altitude steel structures after construction, belonging to the technical field of large-scale high-altitude steel structures. It includes: using multimodal analysis of visible light and ultrasonic images to comprehensively monitor the construction quality of large-scale steel structures. First, visible light and ultrasonic images from the original and monitoring periods are acquired and preprocessed. Typical monitoring areas are determined through cluster analysis, and visible light and ultrasonic images are collected at different time points after spraying citric acid solution and cleaning. Using image differencing and feature extraction, combined with multimodal weighted analysis, the corrosion degree of typical areas is calculated. A training dataset containing original and monitored area images and their corrosion degrees is established to train a neural network model. Finally, the original and monitored images are input, and the trained model is used to output a corrosion degree matrix for the entire steel structure surface, comprehensively evaluating the construction quality. Although it can comprehensively and accurately assess the corrosion status of large steel structures at high altitudes, it cannot directly detect and quantify the substantial impact of rust or other damage, such as cracks and loosening, on the internal mechanical properties of the structure, such as stiffness and load-bearing capacity. In other words, it cannot establish a direct and quantitative correlation between surface condition and internal mechanical properties; the assessment dimension is singular and has a weak correlation with the overall structural robustness.

[0005] Current technologies 1 and 2 suffer from the inability to achieve long-term online monitoring and automated early warning; they also fail to assess the overall structural safety from a mechanical perspective and lack information on the overall mechanical performance of the structure. Therefore, this invention provides a method and system for monitoring the robustness of steel structures based on mechanical sensors. Summary of the Invention

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] One aspect of the present invention provides a method for monitoring the structural integrity of steel structures based on mechanical sensors, comprising the following steps:

[0008] Mechanical sensors are deployed on the load-bearing nodes and component surfaces of the steel structure to collect dynamic response signals of the structure under external loads and environmental action in real time. Time-frequency domain decomposition is performed on the collected dynamic response signals, and steady-state vibration characteristic quantities caused only by changes in the stiffness of the structure are separated from the dynamic response signals based on the correspondence between each frequency component and the inherent vibration modes of the structure.

[0009] The separated steady-state vibration characteristic quantities are jointly analyzed with the geometric topological relationships between nodes pre-established in the steel structure design drawings. By comparing the phase difference and amplitude attenuation rate between the characteristic quantities of adjacent nodes, the stiffness distribution coefficient matrix characterizing the continuity of the overall force transmission path of the steel structure is calculated.

[0010] The calculated stiffness distribution coefficient matrix is ​​compared point by point with the pre-stored benchmark stiffness distribution matrix under the condition of structural integrity. Based on the offset of the corresponding element values, the stiffness anomaly areas and their degree of anomaly are marked, and finally a map that can intuitively display the solidity status of the steel structure is formed.

[0011] Another aspect of the present invention provides a steel structure stability monitoring system based on mechanical sensors, comprising:

[0012] The signal acquisition module is used to deploy mechanical sensors on the load-bearing nodes and component surfaces of the steel structure to acquire dynamic response signals of the structure under external loads and environmental action in real time; it performs time-frequency domain decomposition on the acquired dynamic response signals and separates the steady-state vibration characteristic quantities caused only by the change in the stiffness of the structure itself from the dynamic response signals based on the correspondence between each frequency component and the inherent vibration modes of the structure.

[0013] The joint analysis module is used to perform joint analysis between the separated steady-state vibration characteristic quantities and the geometric topological relationships between each node pre-established in the steel structure design drawings. By comparing the phase difference and amplitude attenuation rate between the characteristic quantities of adjacent nodes, the stiffness distribution coefficient matrix characterizing the continuity of the overall force transmission path of the steel structure is calculated.

[0014] The point-by-point comparison module is used to compare the calculated stiffness distribution coefficient matrix with the pre-stored benchmark stiffness distribution matrix under the condition of structural integrity. Based on the offset of the corresponding element values, it marks the stiffness anomaly areas and their degree of anomaly, and finally forms a map that can intuitively display the solidity status of the steel structure.

[0015] This invention achieves real-time, multi-location synchronous acquisition of structural dynamic response signals by deploying mechanical sensors on the load-bearing nodes and component surfaces of steel structures. The deployment method directly captures the mechanical behavior of the structure under the coupling effect of real loads and the environment, providing a high spatiotemporal resolution raw data foundation for analysis. The acquired signals are decomposed in the time-frequency domain, and steady-state vibration characteristic quantities are separated based on the correspondence between frequency components and the structure's inherent vibration modes. This effectively filters out signal interference caused by non-structural stiffness factors such as environmental noise and instantaneous impacts, thereby extracting core characteristic parameters that directly and stably reflect the structure's own stiffness state. Secondly, the separated steady-state vibration characteristic quantities are jointly analyzed with the geometric topological relationships in the steel structure design drawings. By calculating the phase difference and amplitude attenuation rate between adjacent node characteristic quantities, a quantitative analysis of the structural force transmission path is achieved. The measurement information of discrete points is transformed into a spatial description of the overall structural force transmission continuity. The calculated stiffness distribution coefficient matrix can characterize the spatial distribution of structural stiffness at the system level, overcoming the limitation of traditional point-based monitoring that cannot reflect the evolution of the overall mechanical performance of the structure. Finally, the stiffness distribution coefficient matrix calculated in real time is compared point by point with the pre-stored benchmark matrix under the condition of structural integrity. Based on the numerical offset, the stiffness anomaly areas and their degree of anomaly are identified and marked. This realizes a direct mapping from data to state. Through the generated visualization map, the overall and local solidity status of the steel structure can be clearly and intuitively presented. It can not only locate damaged or weak areas, but also classify and evaluate the degree of anomaly, providing a clear quantitative basis for structural safety early warning and maintenance decisions. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of the steel structure stability monitoring method based on mechanical sensors provided in Embodiment 1 of the present invention;

[0018] Figure 2 This is a schematic diagram of the steel structure stability monitoring method based on mechanical sensors provided in Embodiment 1 of the present invention;

[0019] Figure 3 This is a process diagram of separating steady-state vibration characteristic quantities caused solely by changes in the structure's own stiffness from the dynamic response signal, as provided in Embodiment 2 of the present invention.

[0020] Figure 4 This is a diagram illustrating the process of calculating the stiffness distribution coefficient matrix characterizing the continuity of the overall force transmission path of the steel structure, as provided in Embodiment 5 of the present invention.

[0021] Figure 5 This is a process diagram showing the marking of stiffness anomaly regions and their degree of anomaly in Embodiment 7 of the present invention;

[0022] Figure 6 This is a block diagram of the steel structure stability monitoring system based on mechanical sensors provided in Embodiment 13 of the present invention;

[0023] Figure 7 A block diagram of the electronic device provided by the present invention;

[0024] Figure 8 A block diagram of a computer-readable storage medium provided for this invention.

[0025] Reference numerals: 1. Signal acquisition module; 2. Joint analysis module; 3. Point-by-point comparison module; 4. Central processing unit / microprocessor / main control chip; 5. Storage medium; 6. Data bus; 7. Input / output bus / external bus / device bus; 8. Display; 9. Input / output device; 10. Computer-readable instructions; 11. Non-transitory computer-readable storage medium. Detailed Implementation

[0026] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0027] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0028] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0029] In this embodiment of the invention, directional terms such as "up," "down," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0030] Example 1: As Figure 1 As shown, this embodiment of the invention provides a method for monitoring the structural integrity of steel structures based on mechanical sensors, comprising the following steps:

[0031] Step S100: Install mechanical sensors on the load-bearing nodes and component surfaces of the steel structure to collect dynamic response signals of the structure under external loads and environmental action in real time; perform time-frequency domain decomposition on the collected dynamic response signals, and separate the steady-state vibration characteristic quantities caused only by the change in the stiffness of the structure itself from the dynamic response signals based on the correspondence between each frequency component and the inherent vibration modes of the structure.

[0032] Step S200: The separated steady-state vibration characteristic quantities are jointly analyzed with the geometric topological relationships between nodes pre-established in the steel structure design drawings. By comparing the phase difference and amplitude attenuation rate between the characteristic quantities of adjacent nodes, the stiffness distribution coefficient matrix characterizing the continuity of the overall force transmission path of the steel structure is calculated.

[0033] Step S300: Compare the calculated stiffness distribution coefficient matrix with the pre-stored benchmark stiffness distribution matrix under the condition of structural integrity point by point. Based on the offset of the corresponding element values, mark the stiffness anomaly areas and their degree of anomaly, and finally form a map that can intuitively display the solidity status of the steel structure.

[0034] Among them, load-bearing nodes and component surfaces are key connection points and stress-bearing parts of the force transmission path in steel structures. Changes in their mechanical state can directly reflect changes in the overall or local load-bearing capacity of the structure. Using these as sensor placement locations can obtain the most direct and sensitive raw information for judging structural robustness. Dynamic response signals are the real-time mechanical responses of steel structures under the continuous influence of external loads and environmental effects. They contain information directly related to robustness, such as changes in structural stiffness and damage accumulation, and are the basic data source for structural state analysis. The correspondence between each frequency component and the structure's inherent vibration modes indicates that the geometric dimensions, material properties, and boundary conditions of the steel structure determine its unique vibration patterns. When the structural stiffness changes due to damage or degradation, its inherent vibration modes will also change accordingly. Based on the correspondence, the parts that truly reflect changes in the structure's own properties can be filtered out from the dynamic response. Steady-state vibration characteristic quantities are vibration characteristics extracted from complex dynamic responses and determined solely by the structure's own stiffness. They eliminate the interference of environmental noise and instantaneous impacts and can stably and continuously reflect the trend and degree of changes in steel structure stiffness. They are the core indicators for quantitatively assessing structural robustness. The geometric topological relationships between nodes originate from the clearly defined component connection methods and spatial layout in the steel structure design drawings. They define the load transmission path and sequence within the structure, serving as a spatial framework that links the vibration characteristic quantities of discrete measuring points and analyzes the structural force transmission continuity from a holistic perspective. The stiffness distribution coefficient matrix, calculated by integrating the steady-state vibration characteristic quantities of each node and their geometric topological relationships, quantitatively describes the distribution of stiffness in various parts of the steel structure and its continuity along the force transmission path, systematically presenting the overall and local structural robustness levels.

[0035] The principles described in the above embodiments are referenced in the appendix. Figure 2This embodiment achieves real-time, multi-location synchronous acquisition of structural dynamic response signals by deploying mechanical sensors on the load-bearing nodes and component surfaces of the steel structure. This deployment method directly captures the mechanical behavior of the structure under the coupling effect of real loads and the environment, providing a high spatiotemporal resolution foundation for analysis. The acquired signals are decomposed in the time-frequency domain, and steady-state vibration characteristic quantities are separated based on the correspondence between frequency components and the structure's inherent vibration modes. This effectively filters out signal interference caused by non-structural stiffness factors such as environmental noise and instantaneous impacts, thereby extracting core characteristic parameters that directly and stably reflect the structure's own stiffness state. Secondly, the separated steady-state vibration characteristic quantities are jointly analyzed with the geometric topological relationships in the steel structure design drawings. By calculating the phase difference and amplitude attenuation rate between adjacent node characteristic quantities, a quantitative analysis of the structural force transmission path is achieved. The measurement information of discrete points is transformed into a spatial description of the overall structural force transmission continuity. The calculated stiffness distribution coefficient matrix can characterize the spatial distribution of structural stiffness at the system level, overcoming the limitation of traditional point-based monitoring that cannot reflect the evolution of the overall mechanical performance of the structure. Finally, the stiffness distribution coefficient matrix calculated in real time is compared point by point with the pre-stored benchmark matrix under the condition of structural integrity. Based on the numerical offset, the stiffness anomaly areas and their degree of anomaly are identified and marked. This realizes a direct mapping from data to state. Through the generated visualization map, the overall and local solidity status of the steel structure can be clearly and intuitively presented. It can not only locate damaged or weak areas, but also classify and evaluate the degree of anomaly, providing a clear quantitative basis for structural safety early warning and maintenance decisions.

[0036] In summary, this embodiment achieves comprehensive monitoring and evaluation of the structural integrity of steel structures from local to overall, from signal to status, and from qualitative to quantitative, significantly improving the systematicness, accuracy, and engineering practicality of the monitoring.

[0037] Example 2: Figure 3 As shown, based on Example 1, the process of separating the steady-state vibration characteristic quantity caused solely by the change in the stiffness of the structure itself from the dynamic response signal in step S100 of this embodiment of the invention specifically includes the following steps:

[0038] Step S101: The dynamic response signals synchronously collected by the mechanical sensors deployed on each load-bearing node and component surface are aligned according to the time sequence to form a set of spatially discrete raw response data; the raw response data is subjected to time-frequency transformation to convert the dynamic response signal of each measuring point into a set of vibration components divided by frequency range. The set of vibration components includes the vibration response of the steel structure at all frequencies under external loads and environmental action.

[0039] Step S102: Extract the spatial coordinates of each node and the connection relationship of the components from the steel structure design drawings to generate a geometric correlation diagram describing the force transmission path of the structure; based on the geometric correlation diagram, perform spatial correlation analysis on the components of each frequency range of the vibration component concentration, identify those frequency ranges that exhibit phase stable transmission and amplitude attenuation in accordance with the material damping law between adjacent nodes, and mark the frequency ranges as characteristic frequency bands associated with the overall stiffness state of the structure.

[0040] Step S103: Extract all vibration components belonging to the characteristic frequency band from the vibration component set, smooth the amplitude of the vibration components over time, remove sudden fluctuations caused by instantaneous impact, and obtain a continuous numerical sequence that only reflects the evolution of the stiffness of the steel structure itself. The continuous numerical sequence is the steady-state vibration characteristic quantity.

[0041] In the above embodiments, this embodiment constructs the basic structure of the original response data by synchronously aligning and spatially discretizing the dynamic response signals of multiple measurement points; it uses time-frequency transformation to convert the time-domain signal into a set of frequency-domain vibration components, realizing the separate expression of vibration responses at different frequencies; it combines the structural geometric correlation diagram to perform spatial correlation analysis, identifying the characteristic frequency bands associated with the overall stiffness state of the structure from the set of vibration components, excluding vibration components caused by non-structural stiffness factors; it extracts the corresponding vibration components based on the characteristic frequency bands and performs time series smoothing to eliminate interference fluctuations caused by instantaneous impacts, finally obtaining a continuous steady-state vibration characteristic sequence that only reflects the changes in the stiffness of the structure itself, providing a stable and reliable data foundation for structural stiffness state monitoring and damage identification.

[0042] Example 3: Based on Example 2, the process of identifying those frequency ranges in step S102 of this embodiment of the invention that exhibit stable phase transmission and amplitude attenuation conforming to the material damping law specifically includes the following steps:

[0043] Step S1021: Associate the vibration component set with the geometric correlation diagram, and add the node number and spatial coordinate attribute of the measuring point in the geometric correlation diagram to each measuring point component in each frequency range of the vibration component set, forming a set of frequency component data tables with spatial identification.

[0044] Step S1022: Based on the connection relationship between adjacent nodes defined in the geometric correlation diagram, extract the two vibration components of each pair of adjacent nodes in the same frequency range from the frequency component data table; calculate the phase difference sequence of the two vibration components at multiple consecutive time sampling points, and calculate the statistical standard deviation of the phase difference sequence; at the same time, calculate the amplitude ratio sequence of the two vibration components, and fit the amplitude ratio sequence with the distance between adjacent nodes and the material damping coefficient pre-stored in the geometric correlation diagram to obtain the amplitude attenuation compliance coefficient;

[0045] Step S1023: Set the phase stability threshold and the amplitude attenuation compliance threshold. Compare the statistical standard deviation and compliance coefficient of all adjacent node pairs in each frequency interval with the corresponding thresholds. The number of adjacent node pairs in the statistical frequency interval that simultaneously satisfy the statistical standard deviation being lower than the phase stability threshold and the compliance coefficient being higher than the amplitude attenuation compliance threshold. When the proportion of the number of adjacent node pairs exceeds the preset proportion threshold, the frequency interval is marked as a characteristic frequency band associated with the overall stiffness state of the structure.

[0046] In the above embodiments, this embodiment forms a technical process for automatically identifying and extracting characteristic frequency bands that are strongly correlated with the overall stiffness characteristics of the structure from the original vibration signal through data spatialization, dual-parameter quantitative analysis of adjacent node pairs, and a judgment mechanism based on statistical proportions. This improves the accuracy, interpretability, and automation level of capturing the overall dynamic characteristics of the structure in condition monitoring, and provides filtered and verified key frequency domain information for steel structure health assessment and diagnosis.

[0047] Example 4: Based on Example 3, the process of adding the node number and spatial coordinate attributes of the measuring point in the geometric correlation diagram to each measuring point component in each frequency range of the vibration component set in step S1021 of this embodiment of the invention specifically includes the following steps:

[0048] Step S10211: Extract the list of node numbers and the spatial coordinate values ​​corresponding to each number from the generated geometric association diagram. At the same time, extract the list of correspondences between each measuring point and its corresponding node recorded when the mechanical sensors are deployed. Each measuring point in the correspondence list is associated with a unique node number. Merge the node number list, spatial coordinate values ​​and the measuring point correspondence list to generate a measuring point-node mapping table indexed by the measuring point identifier and containing node numbers and spatial coordinates.

[0049] Step S10212: Divide the obtained vibration component set according to frequency intervals to obtain several measurement point component sets under a single frequency interval; iterate through each measurement point component set under a single frequency interval in turn; for each measurement point component in the measurement point component set, query the measurement point-node mapping table according to the measurement point identifier of the measurement point component to obtain the node number and spatial coordinates corresponding to the measurement point; and append the obtained node number and spatial coordinates as two new data fields to the data structure of the measurement point component.

[0050] Step S10213: Reassemble all the measurement point components with node numbers and spatial coordinate attributes added under all frequency ranges according to the frequency range to form a new set of data tables; each data entry in the data table contains the vibration component amplitude, the corresponding frequency range, the measurement point identifier, the node number and the spatial coordinates, and the data table is a frequency component data table with spatial identifiers.

[0051] In the above embodiments, this embodiment generates spatially indexable frequency component data, which not only preserves the original vibration information, but also deeply integrates the position and topology of the structure, enabling subsequent analysis to be performed directly within the spatial framework of the structure. This provides the necessary data foundation and format support for structural state assessment based on vibration propagation paths, phase relationships, and attenuation laws.

[0052] Example 5: Figure 4 As shown, based on Example 1, the process of calculating the stiffness distribution coefficient matrix characterizing the continuity of the overall force transmission path of the steel structure in step S200 of this embodiment of the invention specifically includes the following steps:

[0053] Step S201: Extract the continuous numerical sequence of each node from the obtained steady-state vibration characteristic quantities, and extract the connection relationship of adjacent node pairs from the generated geometric correlation graph; match the numerical sequence of each node according to its node number in the geometric correlation graph to form a path feature table with adjacent node paths as index and containing the characteristic quantity sequences of the nodes at both ends of the path.

[0054] Step S202: Traverse each adjacent node path in the path feature table, calculate the phase difference sequence of the feature quantity sequences of the two ends of the adjacent node path at the same time sampling point; merge all the values ​​in the phase difference sequence into a single value representing the overall phase shift of the adjacent node path; at the same time, calculate the amplitude ratio sequence of the feature quantity sequences of the two ends of the node, and merge all the values ​​in the amplitude ratio sequence into a single value representing the overall amplitude attenuation of the adjacent node path; use the two single values ​​as two quantization indicators of the adjacent node path.

[0055] Step S203: Input the two quantitative indicators of each adjacent node path into the path direction coefficient calculation program, and output a path coefficient that characterizes the force transmission continuity of the adjacent node path; arrange the path coefficients calculated for all adjacent node paths according to the connection relationship of the nodes in the geometric relationship diagram to form a two-dimensional coefficient array with the node number as the row and column index. The two-dimensional coefficient array is a stiffness distribution coefficient matrix.

[0056] In the above embodiments, this embodiment abstracts and integrates node-level vibration time series data into path-level force transmission continuity coefficients through path extraction, dual-parameter quantization, and comprehensive evaluation. Finally, it constructs a stiffness distribution coefficient matrix covering all major connection relationships of the structure. The matrix represents complex dynamic vibration response information as a spatial distribution map that can intuitively reflect the efficiency and continuity of force flow transmission within the structure. It provides a quantitative, topologically based core criterion for evaluating the overall stiffness state of the structure and identifying weak links or abnormal areas in the force transmission path.

[0057] Example 6: Based on Example 5, the process of outputting a path coefficient characterizing the continuity of force transmission between adjacent nodes in step S203 of this embodiment of the invention specifically includes the following steps:

[0058] Step S2031: Retrieve the pre-stored reference phase offset value and reference amplitude attenuation value of the adjacent node path under the condition of structural integrity from the geometric correlation diagram; calculate the difference between the obtained single value representing the overall phase offset and the reference phase offset value to obtain the phase offset difference of the adjacent node path; at the same time, calculate the ratio between the obtained single value representing the overall amplitude attenuation and the reference amplitude attenuation value to obtain the amplitude attenuation change rate of the adjacent node path.

[0059] Step S2032: Retrieve two pre-set weighting coefficients from the data storage area. The first weighting coefficient corresponds to the influence ratio of the phase offset difference in the force transmission continuity evaluation, and the second weighting coefficient corresponds to the influence ratio of the amplitude attenuation change rate. Multiply the phase offset difference by the first weighting coefficient to obtain the phase offset weighted value. Multiply the amplitude attenuation change rate by the second weighting coefficient to obtain the amplitude attenuation weighted value. Add the phase offset weighted value and the amplitude attenuation weighted value to obtain the force transmission continuity score of the adjacent node path.

[0060] Step S2033: Retrieve the pre-set upper and lower limits of the scoring threshold from the data storage area; compare the force transmission continuity score with the upper and lower limits of the scoring threshold in turn; if the force transmission continuity score is greater than the upper limit of the scoring threshold, use the value of the upper limit of the scoring threshold as the path coefficient of the path; if the force transmission continuity score is less than the lower limit of the scoring threshold, use the value of the lower limit of the scoring threshold as the path coefficient of the adjacent node path; if the force transmission continuity score is between the lower and upper limits of the scoring threshold, directly use the score value as the path coefficient of the adjacent node path.

[0061] In the above embodiments, this embodiment achieves state relativization through benchmarking, integrates multi-dimensional information through weighted fusion, and normalizes and robusts the output results through threshold limiting. The final output path coefficient is a dimensionless, comparable, and robust comprehensive index that reflects the degree of change in force transmission continuity between adjacent nodes relative to the healthy benchmark state, providing standardized and interference-resistant input elements for the subsequent formation of the stiffness distribution coefficient matrix.

[0062] Example 7: Figure 5 As shown, based on Example 1, the process of marking the stiffness anomaly region and its degree of anomaly in step S300 of this embodiment of the invention specifically includes the following steps:

[0063] Step S301: Compare the obtained stiffness distribution coefficient matrix with the reference stiffness distribution matrix retrieved from the structural history database element by element, and calculate the absolute value of the difference between each corresponding element; arrange all the calculated absolute values ​​of the difference according to their row and column indices in the stiffness distribution coefficient matrix to form a difference matrix with the same dimension as the stiffness distribution coefficient matrix; then traverse each element in the difference matrix, compare each absolute value of the difference with the preset anomaly initiation threshold, filter out all element positions and their values ​​that are greater than the anomaly initiation threshold, and generate a list of the correspondence between anomaly element positions and values;

[0064] Step S302: Retrieve several pre-divided continuous anomaly level intervals from the data storage area. Each anomaly level interval corresponds to a fixed numerical range. Iterate through the generated corresponding list and match the absolute value of each difference in the corresponding list with the numerical range of each anomaly level interval to determine the anomaly level interval to which the absolute value of the difference belongs. Based on the matching results, attach a level identifier representing the degree of anomaly to each anomaly element to form an anomaly status detail table containing node position, absolute value of difference, and anomaly level identifier.

[0065] Step S303: Extract the geometric outline of the structure from the steel structure design drawings as the base map, and locate the coordinates of each node in the generated abnormal state details table on the base map; at each location point, according to the abnormality level identifier of the node, retrieve the pre-stored fill color or fill pattern corresponding to the level, and draw the fill color or fill pattern at the location point; at the same time, the location points with the absolute value of the difference greater than the preset warning threshold are circled with a thick outline line; after all the location points are drawn, generate a legend labeling the corresponding color or pattern for each abnormality level on the edge of the base map, and finally obtain a steel structure solidity status map.

[0066] In the above embodiments, this embodiment identifies and visualizes the process of structural stiffness anomaly regions. Through matrix comparison, hierarchical classification, and graphical representation, it realizes the transformation from discrete numerical differences to a structured and interpretable structural state map. By comparing the current stiffness distribution coefficient matrix with the historical benchmark matrix element by element, a difference matrix is ​​generated, and preliminary screening is performed based on a preset anomaly initiation threshold. This completes the process of extracting potential anomalies from the overall data, realizing data dimensionality reduction and preliminary anomaly localization. The difference calculation quantifies the local deviation of the current state relative to the historical healthy benchmark, while the threshold screening focuses attention on significant change areas that exceed the normal fluctuation range, providing a target dataset. By introducing a pre-defined continuous anomaly level range, the selected anomaly differences are classified and categorized, and a level label is attached to each anomaly point. Continuous numerical differences are discretized into a finite number of anomaly levels with clear engineering significance. This achieves the standardization and semantic expression of anomaly severity, transforming numerical differences that are difficult to understand intuitively into level labels representing different degrees of severity, such as slight, moderate, and severe, providing a hierarchical basis for decision support. By spatially mapping the anomaly state details table with the structural geometric base map and rendering it using different visual elements—colors, patterns, and outlines—based on the anomaly level, a state map is finally generated. This completes the transformation from an abstract data table to an intuitive spatial distribution map. Its technical effect lies in realizing the spatial visualization and enhanced expression of structural state information; by encoding anomaly levels with colors and patterns, highlighting high-risk points with bold outlines, and providing decoding explanations through legends, complex differences in overall stiffness distribution and anomaly severity can be quickly and intuitively perceived and understood; the map integrates discrete nodal anomaly information into the real geometric context of the structure, clearly revealing the spatial location, range clustering, and relative severity of different locations of anomaly areas.

[0067] In summary, this embodiment achieves anomaly region location through threshold screening, quantifies and grades the degree of anomaly through level classification, and enhances the readability and operability of the analysis results through spatial visualization. The final output steel structure stability status map is a comprehensive visual representation tool integrating location, numerical, and level information. It can intuitively display the spatial distribution pattern and severity gradient of structural stiffness anomalies, providing a graphical basis for rapid assessment of structural health status, precise investigation of key areas, and determination of maintenance priorities.

[0068] Example 8: Based on Example 7, the process of calculating the absolute value of the difference between each corresponding element in step S301 of this embodiment of the invention specifically includes the following steps:

[0069] Step S3011: Align the stiffness distribution coefficient moment and the reference stiffness distribution matrix according to their respective row index order and column index order, establish a one-to-one correspondence between the two matrices at each same row and column index position, and form a four-field correspondence table containing row index value, column index value, stiffness distribution coefficient value and reference stiffness distribution coefficient value.

[0070] Step S3012: Traverse each row of data in the four-field correspondence table. For each row, retrieve the stiffness distribution coefficient value and the reference stiffness distribution coefficient value stored in the row, perform the subtraction operation of the former minus the latter, and take the absolute value of the result to obtain a non-negative numerical result. Add the numerical result as a new field to the data structure of the current row, while keeping the original row index value and column index value unchanged. After all rows have been processed, the four-field correspondence table is expanded into a five-field data table with calculation results.

[0071] Step S3013: Traverse each row of the five-field data table, extract the row index value, column index value, and newly added numerical result field from each row, and combine the three data into a triple consisting of the row index, column index, and absolute difference value; gather all the triples extracted from all rows together, arrange them in ascending order of row index value and column index value, and generate a list of original absolute difference values ​​organized in row and column order, where each entry is the absolute difference value of the corresponding element and its position identifier.

[0072] In the above embodiments, this embodiment achieves precise matching of computational objects by ensuring data position alignment; it achieves scalar quantification of the degree of difference by performing subtraction and absolute value operations; finally, through data recombination and sorting, it generates a list of absolute difference values ​​with a clear structure, complete positional information, and spatial logical organization. This list, as an intermediate data product, completely and unambiguously records the stiffness deviation of each computational point relative to the baseline state, providing accurate quantitative input for threshold screening and anomaly detection. This ensures that the conversion from the original matrix data to the point difference data is traceable, computationally deterministic, and data consistent.

[0073] Example 9: Based on Example 8, the process of appending the numerical result as a new field to the data structure of the current row in step S3012 of this embodiment of the invention specifically includes the following steps:

[0074] Step S30121: Read the data of the current row from the generated four-field correspondence table. The row data contains four fields: row index value, column index value, stiffness distribution coefficient value, and reference stiffness distribution coefficient value. Take the numerical result obtained by performing subtraction and taking the absolute value as an independent temporary data item, which together with the four field values ​​of the row constitutes a temporary data set containing five data items.

[0075] Step S30122: Create a new empty data structure for the current row. The empty data structure reserves five field positions, corresponding to the row index, column index, stiffness distribution coefficient, baseline stiffness distribution coefficient, and newly added numerical result field, respectively. Write the five data items in the temporary data set into the five field positions of the new data structure in sequence according to the corresponding relationship, and complete the conversion of the current row data from four-field format to five-field format.

[0076] Step S30123: Add the five-field data structure obtained after the current row is transformed to a newly created data table container according to its original row order; repeat the processing of all rows in the four-field correspondence table, and append the five-field data of each row to the end of the data table container after the transformation is completed; after all rows are processed, a complete five-field data table is formed in the data table container.

[0077] In the above embodiments, this embodiment achieves temporary storage of calculation results by constructing a temporary data set; achieves data format conversion by expanding data structure fields; and achieves batch processing and generation of complete data tables by sequentially appending data table containers. This ensures the structural consistency, sequential integrity, and processing efficiency of data conversion, providing an extended structured data foundation for data operations.

[0078] Example 10: Based on Example 9, the process of creating a new empty data structure in the current row in step 30122 of this embodiment of the invention specifically includes the following steps:

[0079] Step S301221: Extract the field names corresponding to the five data items from the obtained temporary data set. The five field names are row index, column index, stiffness distribution coefficient, reference stiffness distribution coefficient and numerical result; arrange the five field names in their natural order in the data set to form a field definition list.

[0080] Step S301222: Create an empty record carrier in memory according to the field definition list. The empty record carrier reserves five storage units in the order of the fields in the list. Each storage unit is initialized to an empty state. At the same time, a temporary memory address identifier is allocated to the record carrier.

[0081] Step S301223: Use the original row sequence number of the current row in the four-field correspondence table as the row identifier of the newly created empty record carrier, and associate and bind the row identifier with the memory address identifier; after the binding is completed, the record carrier with the row identifier and all storage units are empty is the target empty data structure that can be written to by the current row data.

[0082] In the above embodiments, this embodiment determines the field composition of the data structure by generating a field definition list; pre-allocates storage space by creating a memory record carrier; establishes an addressable association of the data carrier by binding row identifiers with memory addresses; and provides an empty data structure with clear field definitions, pre-allocated storage space, and row identifiers for data writing, ensuring the accuracy of data writing and the effectiveness of memory management.

[0083] Example 11: Based on Example 10, the process of associating and binding the row identifier with the memory address identifier in step S301223 of this embodiment of the invention specifically includes the following steps:

[0084] Step S3012231: Starting from the determined row identifier and the allocated memory address identifier, write the two identifiers as a set of corresponding entries into a blank mapping table for recording the correspondence; the blank mapping table is pre-created in memory and contains two parallel field areas, the left area is used to store the row identifier, and the right area is used to store the memory address identifier corresponding to the row identifier.

[0085] Step S3012232: Convert the format of the row identifier written to the corresponding entry to generate a positioning code with the same value as the row identifier; append the positioning code to the end of the memory address identifier to form an extended address identifier composed of the memory address identifier and the positioning code. The extended address identifier can directly point to the specific starting position of the row data storage in memory.

[0086] Step S3012233: Write the extended address identifier back to the right-hand field area corresponding to the row identifier in the blank mapping table, replacing the original memory address identifier; the row identifier and the extended address identifier form a fixed correspondence in the mapping table. The extended address identifier is obtained by querying the row identifier in the blank mapping table, thereby realizing the location and access of the target empty data structure.

[0087] In the above embodiments, this embodiment establishes an association record between row identifiers and memory addresses through the creation and entry writing of a mapping table; it achieves the expansion and precise pointing of address information through the generation and appending of location codes; and it optimizes and fixes the mapping relationship through the write-back and replacement of extended address identifiers. This process constructs a queryable mapping table, enabling efficient acquisition of the extended precise memory address through the row identifier, providing a reliable addressing mechanism for the location and access of data structures, and ensuring the accuracy and efficiency of memory operations.

[0088] Example 12: Based on Example 11, the process of obtaining the extended address identifier by querying the row identifier in the blank lookup mapping table in step S3012233 of this embodiment of the invention specifically includes the following steps:

[0089] Step S30122331: Extract all written extended address identifiers from the right field area of ​​the updated blank mapping table, and at the same time extract all row identifiers corresponding to the extended address identifiers from the left field area; match the extracted row identifiers with the extended address identifiers according to their original arrangement order in the mapping table to form a retrieval reference table containing the correspondence between row identifiers and extended address identifiers.

[0090] Step S30122332: Compare the target row identifier to be queried with the row identifier sequence in the retrieval table in sequence. Starting from the beginning of the retrieval table, check the numerical equality of the target row identifier with each row identifier in the table in turn. When a row identifier with the same value as the target row identifier is found, record the position number of the row identifier in the retrieval table.

[0091] Step S30122333: Based on the position number of the record, locate the extended address identifier corresponding to the same number in the lookup table, and retrieve the extended address identifier from the lookup table; the retrieved extended address identifier is the final result obtained by querying the row identifier, and the result is used to point to the starting position of the target empty data structure in memory.

[0092] In the above embodiments, this embodiment converts the mapping relationship into a sequentially accessible data structure by generating a lookup table; it achieves the matching and location determination of the target row identifier through sequential comparison and numerical judgment; it obtains the corresponding extended address identifier through sequence number positioning and identifier extraction; and it establishes a query mechanism based on sequential retrieval, which enables the reliable acquisition of the corresponding extended address identifier through the row identifier, providing effective addressing support for locating data structures in memory and ensuring the accuracy and repeatability of the query operation.

[0093] Example 13: As Figure 6As shown, based on Examples 1-12, the steel structure stability monitoring system based on mechanical sensors provided in this embodiment of the invention includes:

[0094] Signal acquisition module 1 is used to deploy mechanical sensors on the load-bearing nodes and component surfaces of the steel structure to acquire dynamic response signals of the structure under external loads and environmental action in real time; perform time-frequency domain decomposition on the acquired dynamic response signals, and separate the steady-state vibration characteristic quantities caused only by the change in the stiffness of the structure itself from the dynamic response signals based on the correspondence between each frequency component and the inherent vibration modes of the structure.

[0095] The joint analysis module 2 is used to perform joint analysis with the separated steady-state vibration characteristic quantities and the geometric topological relationship between each node pre-established in the steel structure design drawings. By comparing the phase difference and amplitude attenuation rate between the characteristic quantities of adjacent nodes, the stiffness distribution coefficient matrix characterizing the continuity of the overall force transmission path of the steel structure is calculated.

[0096] The point-by-point comparison module 3 is used to compare the calculated stiffness distribution coefficient matrix with the pre-stored benchmark stiffness distribution matrix under the condition of structural integrity. Based on the offset of the corresponding element values, it marks the stiffness anomaly areas and their degree of anomaly, and finally forms a map that can intuitively display the solidity status of the steel structure.

[0097] In the above embodiments, this embodiment realizes the full automation and intelligence of steel structure stability monitoring from data acquisition to condition assessment. Specific technical effects are as follows: The signal acquisition module, by deploying mechanical sensors at key locations of the steel structure, achieves continuous real-time monitoring of the structure's dynamic response; it performs time-frequency domain decomposition on the acquired signals and extracts steady-state vibration characteristic quantities based on the correspondence between frequency and the structure's inherent modes, effectively filtering environmental noise and transient interference, and obtaining core characteristic parameters that stably reflect the structure's own stiffness state. The joint analysis module combines the extracted steady-state vibration characteristics with the structural design topology, and by analyzing the phase difference and amplitude attenuation rate of characteristic quantities between adjacent nodes, it transforms discrete measurement point information into a continuous description of the overall force transmission path of the structure; the output stiffness distribution coefficient matrix quantifies the spatial distribution characteristics of structural stiffness at the system level, achieving a macroscopic characterization of the overall mechanical performance of the structure. The point-to-point comparison module performs a fine comparison between the real-time calculated stiffness distribution matrix and the health baseline state, accurately identifies stiffness anomaly areas and quantifies the degree of anomaly based on the numerical offset; the generated visualization map transforms the complex matrix data into an intuitive display of the structural robustness status, realizing accurate location of anomalies and classification of damage levels.

[0098] In summary, this embodiment enables long-term online monitoring of the structural integrity of steel structures, early damage warning, and quantitative safety assessment, significantly improving the automation level, analytical depth, and engineering practical value of structural health monitoring.

[0099] Figure 7 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0100] The electronic device may include a central processing unit / microprocessor / main control chip 4; and a storage medium 5 coupled to the central processing unit / microprocessor / main control chip 4 and storing computer-executable instructions therein for performing the steps of various methods of embodiments of the present invention when executed by the processor.

[0101] The central processing unit / microprocessor / main control chip 4 may include, but is not limited to, one or more processors or microprocessors.

[0102] Storage medium 5 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0103] In addition, the electronic device may include (but is not limited to) a data bus 6, an input / output bus / external bus / device bus 7, a display 8, and input / output devices 9 (e.g., keyboard, mouse, speaker, etc.).

[0104] The central processing unit / microprocessor / main control chip 4 can communicate with external devices (8, 9, etc.) via wired or wireless networks (not shown) through the input / output bus / external bus / device bus 7.

[0105] The storage medium 5 may also store at least one computer-executable instruction for performing the steps of various functions and / or methods in the embodiments described herein when the central processing unit / microprocessor / main control chip 4 is running.

[0106] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0107] Figure 8 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0108] like Figure 8As shown, the non-transitory computer-readable storage medium 11 stores instructions, such as computer-readable instructions 10. When the computer-readable instructions 10 are executed by a processor, the various methods described above can be performed. The non-transitory computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium 11 can be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions 10 stored on the non-transitory computer-readable storage medium 11, the various methods described above can be performed.

[0109] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0113] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the tightness of a steel structure based on a mechanical sensor, characterized by, Includes the following steps: The separated steady-state vibration characteristic quantities are jointly analyzed with the geometric topological relationships between nodes pre-established in the steel structure design drawings. By comparing the phase difference and amplitude attenuation rate between the characteristic quantities of adjacent nodes, the stiffness distribution coefficient matrix characterizing the continuity of the overall force transmission path of the steel structure is calculated. The calculated stiffness distribution coefficient matrix is ​​compared point by point with the pre-stored benchmark stiffness distribution matrix under the condition of structural integrity. Based on the offset of the corresponding element values, the stiffness anomaly areas and their degree of anomaly are marked, and finally a map that can intuitively display the solidity status of the steel structure is formed.

2. The method of claim 1, wherein the mechanical sensor-based steel structure tightness monitoring method is characterized by, The process of calculating the stiffness distribution coefficient matrix, which characterizes the continuity of the overall force transmission path of a steel structure, includes the following steps: Extract the continuous numerical sequence of each node from the obtained steady-state vibration characteristic quantities, and extract the connection relationship of adjacent node pairs from the generated geometric correlation graph; match the numerical sequence of each node according to its node number in the geometric correlation graph to form a path feature table with adjacent node paths as index and containing the characteristic quantity sequences of the nodes at both ends of the path. Traverse each adjacent node path in the path feature table, calculate the phase difference sequence of the feature quantity sequences of the two ends of the adjacent node path at the same time sampling point; merge all the values ​​in the phase difference sequence into a single value representing the overall phase shift of the adjacent node path; at the same time, calculate the amplitude ratio sequence of the feature quantity sequences of the two ends of the node, and merge all the values ​​in the amplitude ratio sequence into a single value representing the overall amplitude attenuation of the adjacent node path; use the two single values ​​as two quantitative indicators of the adjacent node path. Two quantitative indicators of each adjacent node path are input into the path direction coefficient calculation program, and a path coefficient representing the force transmission continuity of the adjacent node path is output. The path coefficients calculated for all adjacent node paths are arranged according to the connection relationship of the nodes in the geometric relationship diagram to form a two-dimensional coefficient array with the node number as the row and column index. The two-dimensional coefficient array is the stiffness distribution coefficient matrix.

3. The method of claim 1, wherein the mechanical sensor-based steel structure tightness monitoring method is characterized by, The process of identifying regions of stiffness aberration and their degree of aberration includes the following steps: The obtained stiffness distribution coefficient matrix is ​​compared element by element with the reference stiffness distribution matrix retrieved from the structural history database, and the absolute value of the difference between the elements at each corresponding position is calculated. All the calculated absolute values ​​of the difference are arranged according to their row and column indices in the stiffness distribution coefficient matrix to form a difference matrix with the same dimension as the stiffness distribution coefficient matrix. Then, iterate through each element in the difference matrix, compare the absolute value of each difference with the preset anomaly initiation threshold, filter out the positions and values ​​of all elements that are greater than the anomaly initiation threshold, and generate a list of the correspondence between the positions and values ​​of anomaly elements. Retrieve several pre-divided consecutive anomaly level intervals from the data storage area. Each anomaly level interval corresponds to a fixed numerical range. Iterate through the generated corresponding list and match the absolute value of each difference in the corresponding list with the numerical range of each anomaly level interval to determine the anomaly level interval to which the absolute value of the difference belongs. Based on the matching results, attach a level identifier representing the degree of anomaly to each anomaly element to form an anomaly status detail table. Extract the geometric outline of the structure from the steel structure design drawings as the base map, and locate the coordinates of each node in the generated abnormal state details table on the base map. At each location point, according to the abnormality level identifier of the node, retrieve the pre-stored fill color or fill pattern corresponding to the level, and draw the fill color or fill pattern at the location point to obtain a steel structure stability status map.

4. The method of claim 3, wherein the mechanical sensor-based steel structure tightness monitoring method is characterized by, The process of calculating the absolute value of the difference between elements at corresponding positions includes the following steps: Align the stiffness distribution coefficient moment and the reference stiffness distribution matrix according to their respective row index order and column index order, establish a one-to-one correspondence between the two matrices at each same row and column index position, and form a four-field correspondence table containing row index value, column index value, stiffness distribution coefficient value and reference stiffness distribution coefficient value. Iterate through each row of data in the four-field correspondence table. For each row, retrieve the stiffness distribution coefficient value stored in the row and the reference stiffness distribution coefficient value. Perform the subtraction operation between the former and the latter and take the absolute value of the result to obtain a non-negative numerical result. Add the numerical result as a new field to the data structure of the current row, while keeping the original row index value and column index value unchanged. After all rows have been processed, the four-field correspondence table is expanded into a five-field data table with calculation results; Iterate through each row of the five-field data table, extract the row index value, column index value, and newly added numerical result field from each row, and combine the three data into a triple consisting of the row index, column index, and absolute difference value; gather all the triples extracted from all rows together, arrange them in ascending order of row index value and ascending order of column index value, and generate a list of original absolute difference values ​​organized in row and column order, where each entry is the absolute difference value of the corresponding element and its position identifier.

5. The method of claim 4, wherein the mechanical sensor-based steel structure tightness monitoring method is characterized by, The process of appending a numerical result as a new field to the data structure of the current row includes the following steps: Read the data of the current row from the generated four-field correspondence table. The row data contains four fields: row index value, column index value, stiffness distribution coefficient value, and reference stiffness distribution coefficient value. The numerical result obtained by performing subtraction and taking the absolute value is used as an independent temporary data item, which together with the four field values ​​of the row constitutes a temporary data set containing five data items. The current row creates a new empty data structure with five reserved field positions, corresponding to the row index, column index, stiffness distribution coefficient, baseline stiffness distribution coefficient, and newly added numerical result field. Write the five data items in the temporary data set into the five field positions of the new data structure in sequence according to their correspondence, thus completing the conversion of the current row of data from a four-field format to a five-field format; The five-field data structure obtained after the current row is transformed is added to a newly created data table container in its original row order; all rows in the four-field correspondence table are processed repeatedly, and the five-field data of each row is appended to the end of the data table container after each row is transformed; after all rows have been processed, a complete five-field data table is formed in the data table container.

6. The mechanical sensor based steel structure soundness monitoring method according to claim 5, wherein The process of creating a new empty data structure for the current row includes the following steps: Extract the field names corresponding to the five data items from the obtained temporary dataset. The five field names are row index, column index, stiffness distribution coefficient, reference stiffness distribution coefficient, and numerical result. Arrange the five field names in their natural order in the dataset to form a field definition list. An empty record carrier is created in memory based on the field definition list. The empty record carrier reserves five storage units in the order of the fields in the list, and each storage unit is initialized to an empty state. At the same time, a temporary memory address identifier is allocated to the record carrier. The original row sequence number of the current row in the four-field correspondence table is used as the row identifier of this newly created empty record carrier, and the row identifier is associated and bound with the memory address identifier; Once the binding is complete, the record carrier with row identifiers and all storage units are empty becomes the target empty data structure that can be written to for the current row data.

7. The mechanical sensor based steel structure soundness monitoring method according to claim 6, wherein The process of associating and binding row identifiers with memory address identifiers includes the following steps: Starting from the determined row identifier and the allocated memory address identifier, the two identifiers are written as a set of corresponding entries into a blank mapping table for recording the correspondence. The blank mapping table is pre-created in memory and contains two parallel field areas: the left area is used to store the row identifier, and the right area is used to store the memory address identifier corresponding to the row identifier. The row identifier written to the corresponding entry is formatted and a positioning code with the same value as the row identifier is generated. The positioning code is appended to the end of the memory address identifier to form an extended address identifier composed of the memory address identifier and the positioning code. The extended address identifier can directly point to the specific starting position of the row data in memory. The extended address identifier is written back to the right-hand field area corresponding to the row identifier in the blank mapping table, replacing the original memory address identifier. A fixed correspondence is formed between the row identifier and the extended address identifier in the mapping table. The extended address identifier is obtained by querying the row identifier in the blank mapping table, thereby realizing the location and access of the target empty data structure.

8. The mechanical sensor based steel structure soundness monitoring method according to claim 7, wherein, The process of obtaining the extended address identifier by querying the row identifier in the blank lookup mapping table includes the following steps: Extract all written extended address identifiers from the right field area of ​​the updated blank mapping table, and extract all row identifiers corresponding to the extended address identifiers from the left field area; match the extracted row identifiers with the extended address identifiers according to their original order in the mapping table to form a retrieval reference table containing the correspondence between row identifiers and extended address identifiers. The target row identifier to be queried is compared sequentially with the row identifier sequence in the retrieval table. Starting from the beginning of the retrieval table, the target row identifier is compared with each row identifier in the table for numerical equality. When a row identifier with the same value as the target row identifier is found, the position number of the row identifier in the retrieval table is recorded. Based on the position number of the record, locate the extended address identifier corresponding to the same number in the lookup table, and retrieve the extended address identifier from the lookup table; the retrieved extended address identifier is the final result obtained by querying the row identifier, and the result is used to point to the starting position of the target empty data structure in memory.

9. The method for monitoring the structural integrity of steel structures based on mechanical sensors as described in claim 1, characterized in that, Mechanical sensors are deployed on the load-bearing nodes and component surfaces of the steel structure to collect dynamic response signals of the structure under external loads and environmental influences in real time. Time-frequency domain decomposition is performed on the collected dynamic response signals, and steady-state vibration characteristic quantities caused only by changes in the stiffness of the structure are separated from the dynamic response signals based on the correspondence between each frequency component and the inherent vibration modes of the structure.

10. A steel structure stability monitoring system based on mechanical sensors, used to implement the steel structure stability monitoring method based on mechanical sensors as described in any one of claims 1 to 9, characterized in that, include: The signal acquisition module is used to deploy mechanical sensors on the load-bearing nodes and component surfaces of steel structures to acquire dynamic response signals of the structure under external loads and environmental effects in real time. The collected dynamic response signal is decomposed in the time-frequency domain. Based on the correspondence between each frequency component and the structure's natural vibration modes, the steady-state vibration characteristic quantity caused only by the change in the structure's own stiffness is separated from the dynamic response signal. The joint analysis module is used to perform joint analysis between the separated steady-state vibration characteristic quantities and the geometric topological relationships between each node pre-established in the steel structure design drawings. By comparing the phase difference and amplitude attenuation rate between the characteristic quantities of adjacent nodes, the stiffness distribution coefficient matrix characterizing the continuity of the overall force transmission path of the steel structure is calculated. The point-by-point comparison module is used to compare the calculated stiffness distribution coefficient matrix with the pre-stored benchmark stiffness distribution matrix under the condition of structural integrity. Based on the offset of the corresponding element values, it marks the stiffness anomaly areas and their degree of anomaly, and finally forms a map that can intuitively display the solidity status of the steel structure.

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