Basement prestress bottom plate health monitoring system based on Internet of Things

Abstract

The invention relates to the technical field of structure health monitoring, in particular to a basement prestress bottom plate health monitoring system based on the internet of things, which comprises a stress distribution analysis module, a defect diffusion analysis module, a damage characteristic evaluation module and a safety monitoring evaluation module. According to the method, accurate stress data collection and real-time monitoring are realized by capturing spatial position information and node stress values, so that stress change is carefully tracked and evaluated, stress conduction and edge weight between monitoring points are analyzed, the internal stress distribution state of the structure is accurately revealed, the defect diffusion range and path are simulated, and the damage trend is predicted in time; according to damage characteristic evaluation, through analysis of stress mutation and material strain, key data support is provided, maintenance decision making and maintenance plan optimization are effectively guided, threshold value classification and early warning implementation of safety monitoring evaluation are effectively guided, the recognition capacity of an overproof stress concentration area is improved, and the reliability of the system is improved. And the overall safety and stability of the structure are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of structural health monitoring, and in particular to a basement prestressed floor health monitoring system based on the Internet of Things. Background Art

[0002] The field of structural health monitoring technology is a technology used to evaluate the integrity and performance of engineering structures. This technology is mainly used in bridges, buildings, aircraft and critical structures. The purpose is to identify structural degradation or damage early by continuously or regularly monitoring various physical and environmental parameters of the structure. Common monitoring technologies include the use of strain gauges, accelerometers, fiber optic sensors and acoustic emission sensors. Through real-time data analysis, structural health monitoring can provide instant feedback on the status of the structure, assist maintenance personnel in optimizing repair and maintenance plans, thereby extending the service life of the structure and reducing maintenance costs.

[0003] Among them, the basement prestressed floor health monitoring system is an application of structural health monitoring technology in underground structures, specifically monitoring the health of prestressed floors. The system monitors key indicators such as prestress loss, crack development, and moisture penetration through sensors installed under the basement floor. The sensors can transmit data to the monitoring center in real time, and by analyzing data changes, potential structural safety problems can be discovered in a timely manner. Its main uses include improving the safety of the basement, preventing accidents caused by structural problems, and providing decision-making support when implementing maintenance and repair work to ensure the overall stability and safety of the building.

[0004] Although existing technologies have made some progress in real-time data analysis and damage identification, they are still limited in terms of data comprehensiveness and prediction accuracy. For example, traditional monitoring technology focuses on single data collection, such as strain or crack monitoring, and fails to fully cover the overall stress state of the structure, which leads to incomplete predictions of potential hazards and difficulty in forming effective preventive measures. Existing technologies are deficient in processing large-scale data and complex data correlation analysis, and cannot effectively achieve deep fusion and analysis of data, which to a certain extent limits the early warning and decision-making support capabilities of the monitoring system. The lack of effective defect diffusion simulation and comprehensive evaluation of damage characteristics is also a major weakness of existing technologies, which leads to the inability to timely and accurately identify and locate structural problems in actual maintenance, thereby affecting overall safety and maintenance efficiency. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a basement prestressed floor health monitoring system based on the Internet of Things.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A basement prestressed floor health monitoring system based on the Internet of Things comprises: The stress distribution analysis module captures the spatial position information and node stress values ​​of the prestressed base plate monitoring points through stress sensors, analyzes the stress conduction and correlation between monitoring points, evaluates the edge weights and node stress changes, analyzes the stress distribution status as a whole, and generates an IoT stress distribution model; The defect diffusion analysis module analyzes the stress change path of the associated edges and the stress concentration state between the nodes in the IoT stress distribution model, simulates the defect diffusion area, analyzes the stress change trend of the nodes and edges in the defect diffusion area, performs extended analysis on the time series changes, and generates a diffusion prediction model; The damage feature assessment module extracts key nodes and stress concentration areas in the diffusion prediction model, analyzes the stress mutation range and material strain characteristics between nodes, analyzes the center position and influence range of the damage area, obtains damage feature data, and evaluates the stress distribution and node status to generate a damage range assessment result; Based on the damage range assessment results, the safety monitoring and assessment module performs threshold classification on the stress concentration areas of key nodes, identifies the structural state areas beyond the safety range, triggers early warning signals, analyzes the impact of crack diffusion paths on the structure, optimizes the layout of IoT monitoring points, and generates health monitoring layout adjustment results.

[0007] As a further solution of the present invention, the steps of obtaining the IoT stress distribution model are specifically as follows: The spatial position information and node stress values ​​of the monitoring points of the prestressed bottom plate are captured by stress sensors, and the spatial coordinates and stress data are extracted from the monitoring point data to obtain a matching table of the spatial position of the monitoring point and the node stress value; The stress conduction characteristics between the monitoring points are analyzed by using the matching table of the spatial positions of the monitoring points and the stress values ​​of the nodes. The stress conduction intensity is calculated according to the spatial distance and stress difference between the monitoring points using the formula: ; The stress conduction correlation matrix is ​​obtained, where Representation Node and nodes The stress conduction strength between and Respectively represent nodes and The stress value, Representation Node and nodes The spatial distance between The weight coefficient for adjusting the node position; The edge weights and node stress change information are extracted from the stress conduction association matrix, the global stress distribution is integrated and analyzed, and the IoT stress distribution model is constructed by combining the node stress data.

[0008] As a further solution of the present invention, the step of obtaining the defect diffusion region is specifically as follows: According to the basic parameters of the IoT stress distribution model, including node position, connection edge type, elastic coefficient and density, the initial stress state of each node and edge is numerically set, and the calibration result of the initial stress state is obtained by referring to the standard values ​​of external conditions including temperature and pressure; Based on the calibration result of the initial stress state, the dynamic change parameters of the simulation are set, including the amplitude of temperature increase and decrease, the periodic change range of pressure, the stress change path under the differentiated conditions and the stress concentration state between nodes are monitored, and data are recorded to obtain the stress dynamic recording results; The stress dynamic recording results are used to identify risk nodes and edges, perform local simulation on risk areas, monitor the defect diffusion process caused by stress concentration, predict the damaged area in the future time period, and generate the defect diffusion area.

[0009] As a further solution of the present invention, the step of acquiring the diffusion prediction model is specifically as follows: The node stress and edge stress data in the defect diffusion region are sorted, the node and edge stress values ​​at each time point in the time series change are extracted, the time point information is paired with the stress value, and the connection relationship between the nodes and the length of the edge are analyzed in combination with the geometric characteristics of the diffusion region to generate a basic time series stress matrix; The stress variation trends of nodes and edges are extracted from the basic time series stress matrix, and the relationship between nodes and edges is extended and analyzed using the formula: ; Calculation time period The stress diffusion characteristic quantity is used to generate the time series stress diffusion trend matrix, where: Indicates time period The stress diffusion characteristic quantity, and The time periods and At the node The stress value at Representation Node The side length of is the adjusted weight of the edge, M represents the total number of nodes; According to the data of the time series stress diffusion trend matrix, combined with the boundary changes and node distribution of the region, the diffusion range in the future time period is evaluated through the node stress diffusion speed and the edge stress change rate, and a diffusion prediction model is established.

[0010] As a further solution of the present invention, the step of acquiring the damage characteristic data is specifically as follows: Analyze the diffusion prediction model, identify key nodes and stress concentration areas, calculate the stress value and concentration factor of each area through the correlation between stress and strain and the response sensitivity of the node, and obtain key stress information of the area; Based on the key stress information of the area and combined with the strain characteristics of the material, the formula is: ; The strain non-uniformity value of the region is calculated, where: Indicates the strain non-uniformity value of the region, represents the stress value of the kth region, represents the average value of regional stress, represents the strain characteristic parameter of the kth region, is the maximum strain characteristic parameter, is the total number of regions; The strain non-uniformity value of the region is used to evaluate the central position and impact range of the damaged region, and screening is performed based on the threshold to obtain damage characteristic data.

[0011] As a further solution of the present invention, the steps of obtaining the damage range assessment result are specifically as follows: Based on the damage feature data, damage parameters of the region are extracted, the data is divided into multiple sections according to the damage parameter distribution, key points in the sections are screened, damage data points and positions are marked, and damage feature positioning results are established; Based on the damage feature positioning result, damage data of the marked area is selected, stress estimation is performed point by point on the data in combination with the mechanical model, and stress distribution data is classified and grouped, stress direction and magnitude of each area are marked, and a stress distribution map is obtained; Based on the stress distribution diagram, the stress data corresponding to each node and the mechanical characteristics of the surrounding area are extracted, the node status is classified according to the node stress conditions, and the damaged nodes in the overall structure are calibrated through the connection relationship to generate a damage range assessment result.

[0012] As a further solution of the present invention, the step of obtaining the warning signal is specifically as follows: According to the damage range assessment result, the stress value and the corresponding material characteristic parameter of the stress concentration area of ​​the key node are extracted, the stress value is compared with the safety threshold, and the stress value state is classified by segmented logic to generate a preliminary classification result beyond the safety range; Using the preliminary classification results beyond the safety range, combined with the node distribution density and stress distribution mean in the region, a weighted analysis is performed on each node beyond the safety range using the formula: ; Generate node overlimit value, where is the node overlimit value, is the average stress value beyond the safety range, is the node distribution density, are the weight coefficients of stress and density respectively; According to the node exceeding limit value, a trigger threshold is set, and the area where the node exceeding limit value is higher than the set threshold is marked as a warning area. An alarm mechanism is established, and a warning signal is triggered through the marked warning area.

[0013] As a further solution of the present invention, the steps of obtaining the health monitoring layout adjustment result are specifically as follows: Based on the early warning signal, the direction, expansion rate and diffusion impact range of the crack diffusion path are analyzed, the trend of stress concentration in the differentiated area of ​​the structure is analyzed through the crack path, and the path characteristic parameters are combined with the material property parameters of the area to generate the crack diffusion path impact assessment result; Using the impact assessment results of the crack diffusion path and combining the current IoT monitoring point layout information, the coverage and redundancy between the crack impact area and the monitoring point distribution are analyzed using the formula: ; Calculate the layout coverage index and generate the optimization analysis result of the layout coverage index, where: is the layout coverage index, is the density of monitoring points within the crack influence range, is the average distance of the crack affected area, is the crack propagation rate, is the redundancy rate of the current monitoring point; According to the optimization analysis result of the layout coverage index, the positions of the monitoring points within the crack diffusion influence range are adjusted, the density, coverage and redundancy of the monitoring points of the new layout are progressively optimized, and the health monitoring layout adjustment result is generated.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, stress sensors are installed at the monitoring points of the prestressed bottom plate to capture spatial position information and node stress values, so as to achieve more accurate stress distribution data collection and real-time monitoring, thereby making the tracking and evaluation of stress changes more detailed. By analyzing the stress conduction relationship between the monitoring points and evaluating the edge weights, the stress distribution state inside the structure can be accurately revealed. In the defect diffusion analysis, by simulating the defect diffusion range and path, the damage development trend can be predicted in time, and the ability to prevent and respond to structural emergencies can be enhanced. The damage feature assessment provides key data support for repair work by focusing on the analysis of stress mutations and material strain characteristics, which helps to more effectively guide maintenance decisions and optimize maintenance plans, threshold classification of safety monitoring assessment and implementation of early warning, improve the ability to identify excessive stress concentration areas, and ensure the overall safety and stability of the structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the Internet of Things stress distribution model in the present invention; Figure 3 It is a flow chart of the defect diffusion area in the present invention; Figure 4 is a flow chart of the diffusion prediction model in the present invention; Figure 5 It is a flow chart of damage characteristic data in the present invention; Figure 6 A flow chart showing the damage range assessment results in the present invention; Figure 7 It is a flow chart of the early warning signal in the present invention; Figure 8 It is a flow chart of the health monitoring layout adjustment results in the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 , a basement prestressed floor health monitoring system based on the Internet of Things includes: The stress distribution analysis module captures the spatial position information and node stress values ​​of the prestressed base plate monitoring points through stress sensors, analyzes the stress conduction and correlation between monitoring points, evaluates the edge weights and node stress changes, analyzes the stress distribution status as a whole, and generates an IoT stress distribution model; The defect diffusion analysis module analyzes the stress change path of the associated edges and the stress concentration state between nodes in the IoT stress distribution model, simulates the defect diffusion area, analyzes the stress change trend of the nodes and edges in the defect diffusion area, performs extended analysis on the time series changes, and generates a diffusion prediction model; The damage feature assessment module extracts key nodes and stress concentration areas in the diffusion prediction model, analyzes the stress mutation range and material strain characteristics between nodes, analyzes the center position and impact range of the damage area, obtains damage feature data, and evaluates stress distribution and node status to generate damage range assessment results; Based on the damage range assessment results, the safety monitoring and assessment module performs threshold classification on the stress concentration areas of key nodes, identifies the structural state areas beyond the safety range, triggers early warning signals, analyzes the impact of crack diffusion paths on the structure, optimizes the layout of IoT monitoring points, and generates health monitoring layout adjustment results.

[0019] The IoT stress distribution model includes node spatial position information, node stress value, and stress conduction relationship between monitoring points. The defect diffusion area includes the defect starting point, influence range, and diffusion speed. The diffusion prediction model includes the defect diffusion path, diffusion area range, and node and edge stress change trend. The damage characteristic data includes the key node position, stress concentration area, and material strain characteristics. The damage range assessment results include the center position of the damage area, the influence range, and the node stress mutation range. The early warning signals include key node stress anomalies, over-threshold area alarms, and structural damage diffusion trend prompts. The health monitoring layout adjustment results include optimized monitoring point layout, key node stress concentration area adjustment, and monitoring coverage optimization.

[0020] See also Figure 2 ,The specific steps for obtaining the IoT stress distribution model are: The spatial position information and node stress values ​​of the monitoring points of the prestressed bottom plate are captured by stress sensors, and the spatial coordinates and stress data are extracted from the monitoring point data to obtain a matching table of the spatial position of the monitoring point and the node stress value; The monitoring point data captured by the stress sensor can accurately obtain the spatial position of each monitoring point and the corresponding node stress value. First, the monitoring data is preprocessed, including signal amplification, filtering and denoising, to ensure the accuracy of the data. The spatial position information is located using high-precision three-dimensional scanning technology and the global positioning system (GPS) to ensure that the location information of the monitoring point is accurate. The node stress value is measured by a high-sensitivity pressure sensor. The sensor can adapt to different environmental conditions, such as temperature, humidity and pressure changes, and automatically adjust the measurement parameters to obtain the best measurement effect. The monitoring data also includes a timestamp to record the specific collection time of each data point, so that data analysis can refer to time factors, such as analyzing data trends and periodic changes. The comprehensive data is transmitted to the central processing unit through the data interface. After algorithm processing, a matching table of the spatial position of the monitoring point and the node stress value is generated. This table not only records the spatial position and stress value of each monitoring point, but also includes the monitoring time and environmental conditions, providing basic data support for subsequent data analysis and stress assessment.

[0021] Using the matching table of the spatial position of the monitoring points and the stress values ​​of the nodes, the stress conduction characteristics between the monitoring points are analyzed. According to the spatial distance and stress difference between the monitoring points, the stress conduction intensity is calculated using the formula: ; The stress conduction correlation matrix is ​​obtained, where Representation Node and nodes The stress conduction strength between and Respectively represent nodes and The stress value, Representation Node and nodes The spatial distance between The weight coefficient for adjusting the node position; The benefit of the formula is that by referring to the square of the stress difference between nodes and the square of the distance, combined with the weight factor, the stress conduction efficiency in the material can be more accurately evaluated, which is particularly important for heterogeneous materials or structures. It can be adjusted according to material properties and structural design to reflect the stress transfer characteristics of different areas; Refer to two nodes and , if the node The stress value is MPa, node The stress value is MPa, distance between two points m, weight coefficient , considering the influence of material inhomogeneity and the actual distance between nodes, it can be calculated: ; The results show that at the node and The stress conduction intensity between them is 312.5, which indicates that the area is subjected to large stress differences in the structure and requires further inspection and reinforcement.

[0022] Extract edge weights and node stress change information from the stress conduction correlation matrix, integrate and analyze the global stress distribution, and build an IoT stress distribution model based on node stress data. Using the generated stress conduction correlation matrix, a dedicated analysis software is used to comprehensively refer to the edge weights and node stress changes to conduct an in-depth analysis of the stress distribution in the entire monitoring area, including multi-dimensional visualization of the data, mapping the stress values ​​to different colors to distinguish different stress levels, so that engineers can intuitively identify potential risk areas. The analysis software can also simulate the stress response under various external loading conditions and predict future damage and crack development trends. The advanced data processing function integrated in the software can correlate and analyze stress data with different types of monitoring data, such as displacement and vibration data, to conduct comprehensive structural health monitoring, which not only enhances the predictive ability of the model, but also provides a scientific basis for the maintenance and repair of the structure. Through comprehensive analysis, the IoT stress distribution model is constructed, which provides a powerful tool for the long-term operation and maintenance and safety assessment of the structure.

[0023] See also Figure 3 , the specific steps for obtaining the defect diffusion area are: According to the basic parameters of the IoT stress distribution model, including node location, edge type, elastic coefficient and density, the initial stress state of each node and edge is numerically set, and the calibration result of the initial stress state is obtained by referring to the standard values ​​of external conditions including temperature and pressure; The three-dimensional coordinate data of each node is determined according to the node position, and the connection type of the edge is classified through actual measurement or existing models, and the material properties and associated parameters of the edge are set. For example, the elastic coefficient of each connecting edge can be obtained through tensile testing in a laboratory environment, and the density is obtained by consulting the material standard table or density measurement equipment in combination with the physical properties of the edge material. Based on the standard values ​​of external conditions including temperature and pressure, the temperature response coefficient and pressure response coefficient of each node are corrected. The standard stress calculation of the nodes is performed one by one using the regional setting method, and the initial stress distribution matrix is ​​generated. During the calibration process, the initial stress data of each node is compared with the standard value, and the difference is corrected by adjusting the parameters of the connecting edge, and the final stress state of the node and edge is recorded to obtain the calibration result of the initial stress state.

[0024] Based on the calibration results of the initial stress state, set the dynamic change parameters of the simulation, including the amplitude of temperature increase and decrease, the periodic change range of pressure, monitor the stress change path under differentiated conditions and the stress concentration state between nodes, and record the data to obtain the stress dynamic recording results; By setting standard heating and cooling amplitude values, for example, the temperature increase amplitude is 3 to 10 degrees Celsius, and the temperature decrease amplitude is 2 to 8 degrees Celsius, and combined with the pressure cycle change range, such as setting the pressure cycle to 1 to 5 minutes and the change amplitude to 5 to 15 Pa, dynamic parameters are loaded on each node and connecting edge in turn. The loading of dynamic change parameters is achieved by externally applying simulation conditions. For example, in a laboratory environment, the Internet of Things system is physically loaded using temperature control equipment and pressure controllers, and the stress changes of nodes and edges are monitored in real time. After each cycle, the stress change path of the edge is recorded, the stress concentration area between nodes is marked by a stress concentration distribution diagram, and the dynamic data at different time points are stored to form a stress dynamic recording result.

[0025] Using the stress dynamic recording results, identify risk nodes and edges, perform local simulation on the risk area, monitor the defect diffusion process caused by stress concentration, predict the damage area in the future time period, and generate the defect diffusion area; Data screening is performed on the stress dynamic recording results to find out the areas where the edge stress value exceeds the safe range, and the corresponding nodes and connecting edges are marked as risk areas. When local simulation is performed on the area, the elastic limit point of the edge and the stress distribution change of the node are monitored by loading refined external conditions, such as further increasing the temperature change range or increasing the pressure fluctuation frequency. The time and impact range of the defect diffusion from the edge to the node are recorded, especially for high-risk edges, focusing on monitoring the speed and direction of the defect diffusion. Based on the data obtained, the defect diffusion area in the next 24 hours or longer is predicted in combination with the diffusion path and time to clarify the damaged area and provide repair suggestions.

[0026] See also Figure 4 , the specific steps for obtaining the diffusion prediction model are: The node stress and edge stress data in the defect diffusion area are sorted, the node and edge stress values ​​at each time point in the time series change are extracted, the time point information is paired with the stress value, and the connection relationship between nodes and the length of the edge are analyzed in combination with the geometric characteristics of the diffusion area to generate a basic time series stress matrix; The node and edge stress data in the defect diffusion area are sorted out. The node data comes from the time series records of the stress sensor, which records the node position and stress value at each time point. The edge stress data is obtained by analyzing the physical connection relationship between adjacent nodes, including the length of the edge and the stress value distributed on the edge. The time point data is determined by matching the timestamp with the stress data to form a time series distribution. Next, the data is mapped to the geometric model, and the node connection relationship and edge distribution are adjusted according to the topological characteristics of the regional boundary to ensure that all nodes and edges contain spatial coordinates, stress values ​​and time characteristics. The adjusted data is summarized into a basic time series stress matrix, which is used to describe the stress distribution characteristics of the defect diffusion area in time series.

[0027] Extract the stress change trend of nodes and edges from the basic time series stress matrix, and extend the relationship between nodes and edges to analyze the relationship between them. The formula is: ; Calculation time period The stress diffusion characteristic quantity is used to generate the time series stress diffusion trend matrix, where: Indicates time period The stress diffusion characteristic quantity, and The time periods and At the node The stress value at Representation Node The side length of is the adjusted weight of the edge, M represents the total number of nodes; The benefit of the formula is that by normalizing the stress changes of nodes in the time series and introducing edge length and weight factors, it can more accurately reflect the dynamic characteristics of stress transfer between nodes, and is particularly suitable for stress diffusion analysis in non-uniform structures; Set time is the current moment, time For the last moment, select an edge in the defect diffusion area Calculate the nodes at time The stress value is MPa, at time The stress value is MPa, the length of the side m, weight coefficient , substitute the data into the formula: ; The result shows that the change intensity of the stress diffusion trend of nodes and edges at the current time point is 8, which is used to generate the time series stress diffusion trend matrix.

[0028] According to the data of the time series stress diffusion trend matrix, combined with the boundary changes and node distribution of the region, the diffusion range of the future time period is evaluated through the node stress diffusion speed and edge stress change rate, and a diffusion prediction model is established; The stress diffusion rate of nodes and edges at each time point is analyzed, the node positions, stress values ​​and edge connection relationships recorded in the time series matrix are extracted, a time series map is established, the map is divided into time periods, the data of node stress diffusion rate and edge stress change rate are matched with time intervals respectively, the node stress change rate is calculated by difference, and the edge stress change rate is calculated by the ratio of the difference between adjacent nodes and the edge length. The diffusion range in the future time period is analyzed based on the data, and the diffusion trend at future time points is predicted by extrapolation calculation by combining regional geometric characteristics and material properties. The defect diffusion area in the future time period is reflected in the model, and a diffusion prediction model is established.

[0029] See also Figure 5 , the specific steps for obtaining damage feature data are: Analyze the diffusion prediction model, identify key nodes and stress concentration areas, calculate the stress value and concentration factor of each area through the correlation between stress and strain and the response sensitivity of the node, and obtain the key stress information of the area; Analyze the diffusion prediction model, extract the key nodes and stress concentration areas in the model, and call the stress values ​​and material characteristic parameters of the nodes such as elastic modulus based on the correlation between the nodes and the stress concentration areas in the model. By calculating the stress change rate and sensitivity around the nodes, the stress concentration degree in the area is weighted in combination with the stress mutation range. At the same time, the range of uneven stress distribution between nodes is compared, and the contribution of the nodes to the overall model is calculated. The model nodes are then sorted according to their importance, and the key stress nodes are screened. The stress concentration area is analyzed by the geometric distribution of the area, and the weight of the concentrated area is combined with the node distribution characteristics. The key stress values ​​between the nodes in the area are recursively calculated layer by layer to obtain the key stress information of the area.

[0030] Based on the key stress information of the region and combined with the strain characteristics of the material, the formula is: ; The strain non-uniformity value of the region is calculated, where: Indicates the strain non-uniformity value of the region, represents the stress value of the kth region, represents the average value of regional stress, represents the strain characteristic parameter of the kth region, is the maximum strain characteristic parameter, is the total number of regions; The benefit of the formula is that, by introducing the joint calculation method of stress and strain characteristics, the contribution of stress changes in the region to the overall model is comprehensively analyzed, and combined with the calculation of the degree of heterogeneity, the influence range of the damaged area is quantified, thereby improving the accuracy and reliability of the model results. The regional stress value is , , , , , the regional strain parameter is , , , , , the maximum strain characteristic parameter is The average stress is , substitute the value into the formula: ; The calculation results are: ; ; The result shows that the strain non-uniformity value is 1.77, which indicates the imbalance between stress distribution and strain characteristics in the region, and can be used as a basis for the next step of identifying the damaged area.

[0031] The strain non-uniformity value of the region is used to evaluate the center position and impact range of the damaged area, and the damage characteristic data is obtained by screening according to the threshold. Based on the strain non-uniformity value, the area with a value higher than the threshold is marked as the critical damage area, and the distribution center position of the area is further evaluated. The stress weight distribution of the nodes inside the area is used to perform weighted calculation of the central coordinates. The central node position is adjusted in combination with the regional geometry, and the geometric center of the stress concentration area is calculated. The stress range near the center position is combined with the material characteristic parameters to calculate the contribution value. At the same time, the important nodes and boundaries between regions are screened by decreasing the threshold, and non-critical nodes with lower influence are eliminated. The adjusted model performs layered annotation on the range of the damaged area, and further combines the stress value and influence radius of the damage center area in the model to screen out the damage characteristic data.

[0032] See also Figure 6 , the specific steps for obtaining the damage range assessment results are as follows: Based on the damage feature data, the damage parameters of the area are extracted, the data is divided into multiple sections according to the damage parameter distribution, and the key points in the sections are screened, the damage data points and positions are marked, and the damage feature positioning results are established; The data points in each area are analyzed one by one. By comparing the stress change value, deformation amount and stress gradient change rate of adjacent points, the key parameters representing the regional damage characteristics, such as the stress peak point and the maximum deformation point, are extracted. The data is divided into multiple sections according to the distribution characteristics of the damage parameters. For example, based on the stress concentration and deformation amplitude, the high stress concentration area and the large deformation area are divided into one section, and the low stress concentration area is divided into another section. Key points are screened in each section, and the specific locations of damage points and potential risk points are determined according to the change range and position of the characteristic points. The damage data points and their three-dimensional position coordinates are annotated on the visual interface through the marking function to generate damage feature positioning results, providing basic data support for subsequent stress analysis.

[0033] Based on the damage feature location results, the damage data of the marked area is selected, and the stress of the data is estimated point by point in combination with the mechanical model. The stress distribution data is classified and grouped, and the stress direction and magnitude of each area are marked to obtain the stress distribution map; By decomposing the stress conditions of each marked point, it is divided into the direction of the resultant force of axial force, tangential force and external force. When estimating point-by-point stress, the stress data of unmarked points are calculated by interpolation method according to the regional boundary and internal data distribution, and added to the overall stress distribution map of the damaged area. After completing the point-by-point calculation, the stress distribution data is classified and grouped. Combined with the force transmission path between points, points with similar stress magnitudes in the same area are grouped together, and the stress direction of each group of data is marked. Combined with the classification and grouping results, a stress distribution map is generated, which clearly shows the stress flow direction and concentration degree of each area, and can intuitively reflect the mechanical state of the damage characteristic area.

[0034] Based on the stress distribution diagram, the stress data corresponding to each node and the mechanical characteristics of the surrounding area are extracted, the node status is classified according to the node stress conditions, and the damaged nodes in the overall structure are calibrated through the connection relationship to generate the damage range assessment result; Based on the stress distribution diagram, the node status is classified into three states: normal, overloaded or damaged by analyzing the local stress value and overall stress direction of each node. For the classified nodes, the nodes with abnormal stress in the structure and their connection paths are calibrated through connection relationship analysis. During the calibration process, the stress transfer between the damaged nodes and the surrounding nodes is checked one by one, and the connection relationship in the high stress gradient area is focused on. The key nodes that cause the overall change of the structure are marked. Combined with the above analysis results, the damage range assessment results are generated. The assessment results not only show the specific location of the damaged nodes in the current structure, but also include the scope and trend of the potential expansion area, which is convenient for taking corresponding remedial measures or structural optimization design.

[0035] See also Figure 7 , the specific steps for obtaining the early warning signal are: According to the damage range assessment results, the stress values ​​and corresponding material characteristic parameters of the stress concentration areas of the key nodes are extracted, the stress values ​​are compared with the safety threshold, and the stress value states are classified using segmented logic to generate preliminary classification results that exceed the safety range; The stress data of each key node in the model are sorted according to their stress amplitude. Combined with the material properties of the area where the node is located, such as elastic modulus and plastic limit, the stress of the node is further evaluated by point-by-point comparison to see whether it exceeds the safety range. This operation not only marks the nodes that exceed the range as abnormal, but also deepens the understanding of the safety status of each area by classifying the abnormal nodes into the corresponding stress set area. The comprehensive stress value and characteristic density of each area are also calculated. The data is used to evaluate and distinguish the safety level of each area, and a preliminary classification result that exceeds the safety range is generated, providing a basis for further analysis and response measures.

[0036] Using the preliminary classification results beyond the safety range, combined with the node distribution density and stress distribution mean in the region, a weighted analysis is performed on each node beyond the safety range using the formula: ; Generate node overlimit value, where is the node overlimit value, is the average stress value beyond the safety range, is the node distribution density, are the weight coefficients of stress and density respectively; The benefit of the formula is that it effectively distinguishes the severity of the over-limit state of the region and improves the accuracy of the early warning by comprehensively considering the two important parameters, the average stress value and the node distribution density of the reference area, and introducing a weight coefficient to adjust the relative contribution rate of the two. This calculation mode enables the early warning system to dynamically adjust according to real-time data, reflecting which areas have increased risk levels and need immediate attention and processing; Assume that the average stress value of the region is MPa, the node distribution density is nodes / m2, and the weight coefficients are and , substitute into the formula to calculate: ; ; ; The result shows that the node over-limit value is 82, which is used to characterize the comprehensive over-limit status of the area. It is further compared with the preset threshold to determine whether to trigger the early warning signal. The parameters in the process reflect the actual situation of structural safety in the area, thereby providing the corresponding basis for triggering the early warning signal, ensuring that relevant departments can respond to potential safety risks in a timely manner.

[0037] According to the node over-limit value, a trigger threshold is set, and the area where the node over-limit value is higher than the set threshold is marked as a warning area. An alarm mechanism is established to trigger a warning signal through the marked warning area; According to the node overlimit degree value, the calculated value is compared with the preset safety threshold point by point, and the area where the nodes above the threshold are located is marked as the warning area. By calling the regional geometry and stress distribution characteristics, the boundary range and center position of the warning area are comprehensively analyzed. The central stress value of the marked warning area is combined with the influence radius to evaluate its warning significance. By marking the overlimit area and its characteristic information in the model, the docking alarm system is triggered at the same time, the early warning mechanism is established, and the early warning signal output is completed, ensuring that the early warning system can respond quickly when potential risks appear at key nodes, providing scientific and real-time data support for on-site management and emergency response, and improving the efficiency and effectiveness of handling emergencies.

[0038] See also Figure 8 ,The specific steps for obtaining the health monitoring layout adjustment results are: Based on the early warning signal, the direction, expansion rate and diffusion impact range of the crack diffusion path are analyzed. The trend of stress concentration in the differentiated area of ​​the structure is analyzed through the crack path. The path characteristic parameters are combined with the material property parameters of the area to generate the crack diffusion path impact assessment results. Based on the early warning signals, we analyze how the crack diffusion path affects the integrity of the structure. By investigating the starting point and expansion trend of the crack in detail, we analyze its behavior patterns on different material types and calculate the stress concentration effect caused by crack diffusion and its potential damage to the performance of surrounding materials in combination with the principles of structural mechanics. The analysis results help predict the behavior of future cracks and provide a scientific basis for formulating countermeasures. We can understand the impact of the crack diffusion path on the entire structure and make a preliminary assessment of the safety of key load-bearing parts, thereby providing a quantitative reference for the layout of monitoring points and generating crack diffusion path impact assessment results.

[0039] Using the impact assessment results of the crack diffusion path and combining the current IoT monitoring point layout information, the coverage and redundancy between the crack impact area and the monitoring point distribution are analyzed using the formula: ; Calculate the layout coverage index and generate the optimization analysis result of the layout coverage index, where: is the layout coverage index, is the density of monitoring points within the crack influence range, is the average distance of the crack affected area, is the crack propagation rate, is the redundancy rate of the current monitoring point; The benefit of the formula is that it can effectively evaluate the spatial layout efficiency of the IoT monitoring system by comprehensively considering the density of monitoring points in the crack diffusion area and the average distance between the monitoring points and the cracks, as well as the crack diffusion rate and the redundancy rate of the monitoring points, thereby guiding the optimization of the monitoring point layout and ensuring that the monitoring system has a higher response speed and coverage rate for key areas; Set the density of monitoring points within the crack influence range 5 points / square meter, average distance is 2 m, the crack propagation rate The redundancy rate of monitoring points is 0.8 m / h. is 0.2, substitute the value into the formula for calculation: ; ; The result shows that the layout coverage index is 2.66. This index reflects the coverage efficiency of the current monitoring layout for the crack diffusion area. The higher the index value, the better the coverage effect of the monitoring system, so that the dynamic information of crack diffusion can be more effectively captured in actual monitoring.

[0040] According to the optimization analysis results of the layout coverage index, the locations of the monitoring points within the crack diffusion influence range are adjusted, and the density, coverage and redundancy of the monitoring points of the new layout are progressively optimized to generate the health monitoring layout adjustment results; According to the optimization analysis results of the layout coverage index, the layout of the IoT monitoring points is adjusted to increase the density of monitoring points, especially in the center and critical path of the crack diffusion area, reduce the average distance between monitoring points, improve the coverage and response speed of the monitoring network, and dynamically adjust the location of monitoring points to respond to changes in crack diffusion. The redundancy rate of the monitoring network is optimized to reduce maintenance costs and improve data accuracy. The health monitoring layout adjustment results are generated to ensure that the monitoring network can more effectively collect real-time data and monitor cracks. The effectiveness of its implementation is ensured through precise data analysis and field testing, thereby providing a more scientific and accurate layout plan for structural health monitoring.

[0041] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A basement prestressed floor health monitoring system based on the Internet of Things, characterized in that: The system comprises: The stress distribution analysis module captures the spatial position information and node stress values ​​of the prestressed base plate monitoring points through stress sensors, analyzes the stress conduction and correlation between monitoring points, evaluates the edge weights and node stress changes, analyzes the stress distribution status as a whole, and generates an IoT stress distribution model; The defect diffusion analysis module analyzes the stress change path of the associated edges and the stress concentration state between the nodes in the IoT stress distribution model, simulates the defect diffusion area, analyzes the stress change trend of the nodes and edges in the defect diffusion area, performs extended analysis on the time series changes, and generates a diffusion prediction model; The damage feature assessment module extracts key nodes and stress concentration areas in the diffusion prediction model, analyzes the stress mutation range and material strain characteristics between nodes, analyzes the center position and influence range of the damage area, obtains damage feature data, and evaluates the stress distribution and node status to generate a damage range assessment result; Based on the damage range assessment results, the safety monitoring and assessment module performs threshold classification on the stress concentration areas of key nodes, identifies the structural state areas beyond the safety range, triggers early warning signals, analyzes the impact of crack diffusion paths on the structure, optimizes the layout of IoT monitoring points, and generates health monitoring layout adjustment results.

2. The basement prestressed floor health monitoring system based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the IoT stress distribution model are specifically as follows: The spatial position information and node stress values ​​of the monitoring points of the prestressed bottom plate are captured by stress sensors, and the spatial coordinates and stress data are extracted from the monitoring point data to obtain a matching table of the spatial position of the monitoring point and the node stress value; The stress conduction characteristics between the monitoring points are analyzed by using the matching table of the spatial positions of the monitoring points and the stress values ​​of the nodes. The stress conduction intensity is calculated according to the spatial distance and stress difference between the monitoring points using the formula: ； The stress conduction correlation matrix is ​​obtained, where Representation Node and nodes The stress conduction strength between and Respectively represent nodes and The stress value, Representation Node and nodes The spatial distance between The weight coefficient for adjusting the node position; The edge weights and node stress change information are extracted from the stress conduction association matrix, the global stress distribution is integrated and analyzed, and the IoT stress distribution model is constructed by combining the node stress data.

3. The basement prestressed floor health monitoring system based on the Internet of Things according to claim 2 is characterized in that: The steps for obtaining the defect diffusion area are specifically as follows: According to the basic parameters of the IoT stress distribution model, including node position, connection edge type, elastic coefficient and density, the initial stress state of each node and edge is numerically set, and the calibration result of the initial stress state is obtained by referring to the standard values ​​of external conditions including temperature and pressure; Based on the calibration result of the initial stress state, the dynamic change parameters of the simulation are set, including the amplitude of temperature increase and decrease, the periodic change range of pressure, the stress change path under the differentiated conditions and the stress concentration state between nodes are monitored, and data are recorded to obtain the stress dynamic recording results; The stress dynamic recording results are used to identify risk nodes and edges, perform local simulation on risk areas, monitor the defect diffusion process caused by stress concentration, predict the damaged area in the future time period, and generate the defect diffusion area.

4. The basement prestressed floor health monitoring system based on the Internet of Things according to claim 3 is characterized in that: The steps for obtaining the diffusion prediction model are specifically as follows: The node stress and edge stress data in the defect diffusion region are sorted, the node and edge stress values ​​at each time point in the time series change are extracted, the time point information is paired with the stress value, and the connection relationship between the nodes and the length of the edge are analyzed in combination with the geometric characteristics of the diffusion region to generate a basic time series stress matrix; The stress variation trends of nodes and edges are extracted from the basic time series stress matrix, and the relationship between nodes and edges is extended and analyzed using the formula: ； Calculation time period The stress diffusion characteristic quantity is used to generate the time series stress diffusion trend matrix, where: Indicates time period The stress diffusion characteristic quantity, and The time periods and At the node The stress value at Representation Node The side length of is the adjusted weight of the edge, M represents the total number of nodes; According to the data of the time series stress diffusion trend matrix, combined with the boundary changes and node distribution of the region, the diffusion range in the future time period is evaluated through the node stress diffusion speed and the edge stress change rate, and a diffusion prediction model is established.

5. The basement prestressed floor health monitoring system based on the Internet of Things according to claim 4 is characterized in that: The steps for obtaining the damage characteristic data are specifically as follows: Analyze the diffusion prediction model, identify key nodes and stress concentration areas, calculate the stress value and concentration factor of each area through the correlation between stress and strain and the response sensitivity of the node, and obtain key stress information of the area; Based on the key stress information of the area and combined with the strain characteristics of the material, the formula is: ； The strain non-uniformity value of the region is calculated, where: Indicates the strain non-uniformity value of the region, represents the stress value of the kth region, represents the average value of regional stress, represents the strain characteristic parameter of the kth region, is the maximum strain characteristic parameter, is the total number of regions; The strain non-uniformity value of the region is used to evaluate the central position and impact range of the damaged region, and screening is performed based on the threshold to obtain damage characteristic data.

6. The basement prestressed floor health monitoring system based on the Internet of Things according to claim 5 is characterized in that: The steps for obtaining the damage range assessment result are specifically as follows: Based on the damage feature data, damage parameters of the region are extracted, the data is divided into multiple sections according to the damage parameter distribution, key points in the sections are screened, damage data points and positions are marked, and damage feature positioning results are established; Based on the damage feature positioning result, damage data of the marked area is selected, stress estimation is performed point by point on the data in combination with the mechanical model, and stress distribution data is classified and grouped, stress direction and magnitude of each area are marked, and a stress distribution map is obtained; Based on the stress distribution diagram, the stress data corresponding to each node and the mechanical characteristics of the surrounding area are extracted, the node status is classified according to the node stress conditions, and the damaged nodes in the overall structure are calibrated through the connection relationship to generate a damage range assessment result.

7. The basement prestressed floor health monitoring system based on the Internet of Things according to claim 6 is characterized in that: The steps for obtaining the early warning signal are specifically as follows: According to the damage range assessment result, the stress value and the corresponding material characteristic parameter of the stress concentration area of ​​the key node are extracted, the stress value is compared with the safety threshold, and the stress value state is classified by segmented logic to generate a preliminary classification result beyond the safety range; Using the preliminary classification results beyond the safety range, combined with the node distribution density and stress distribution mean in the region, a weighted analysis is performed on each node beyond the safety range using the formula: ； Generate node overlimit value, where is the node overlimit value, is the average stress value beyond the safety range, is the node distribution density, are the weight coefficients of stress and density respectively; According to the node exceeding limit value, a trigger threshold is set, and the area where the node exceeding limit value is higher than the set threshold is marked as a warning area. An alarm mechanism is established, and a warning signal is triggered through the marked warning area.

8. The basement prestressed floor health monitoring system based on the Internet of Things according to claim 7 is characterized in that: The steps for obtaining the health monitoring layout adjustment result are specifically as follows: Based on the early warning signal, the direction, expansion rate and diffusion impact range of the crack diffusion path are analyzed, the trend of stress concentration in the differentiated area of ​​the structure is analyzed through the crack path, and the path characteristic parameters are combined with the material property parameters of the area to generate the crack diffusion path impact assessment result; Using the impact assessment results of the crack diffusion path and combining the current IoT monitoring point layout information, the coverage and redundancy between the crack impact area and the monitoring point distribution are analyzed using the formula: ； Calculate the layout coverage index and generate the optimization analysis result of the layout coverage index, where: is the layout coverage index, is the density of monitoring points within the crack influence range, is the average distance of the crack affected area, is the crack propagation rate, is the redundancy rate of the current monitoring point; According to the optimization analysis result of the layout coverage index, the positions of the monitoring points within the crack diffusion influence range are adjusted, the density, coverage and redundancy of the monitoring points of the new layout are progressively optimized, and the health monitoring layout adjustment result is generated.

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