A prefabricated rectangular station intelligent monitoring system and method based on an internet of things

Abstract

The application discloses a prefabricated rectangular station intelligent monitoring system and method based on Internet of Things, and belongs to the technical field of prefabricated station construction, and comprises the following steps: arranging a plurality of monitoring points at the component connecting interface; acquiring monitoring data of the monitoring points at different time points, wherein the monitoring data comprises strain data and displacement data; mapping each monitoring point with a grid point in a preset monitoring point grid to generate a monitoring point data set of a target time point corresponding to the monitoring point grid; calculating an initial stress state value of the grid point based on the monitoring data corresponding to the grid point in the monitoring point data set; calculating a structure potential value of the grid point at the target time point based on the initial stress state value of the grid point and the structure potential values of adjacent grid points of the grid point at the last time point in the monitoring point grid; and generating a structure potential distribution diagram of the prefabricated rectangular station based on the monitoring point grid and the structure potential values of each grid point at the target time point.

Description

Technical Field

[0001] This invention belongs to the field of prefabricated station construction technology, specifically relating to an intelligent monitoring system and method for prefabricated rectangular stations based on the Internet of Things. Background Technology

[0002] With the continuous advancement of urbanization and the ongoing expansion of urban rail transit networks, prefabrication and assembly technology plays an increasingly important role in the construction of modern rectangular subway stations due to its significant technological advantages. This advanced technology boasts numerous outstanding advantages, including short construction cycles, stable and reliable component quality, minimal on-site work area requirements, and minimal impact on the surrounding environment. Prefabricated rectangular stations are typically composed of standardized components such as prefabricated roof slabs, floor slabs, side walls, and central columns. These components are manufactured in a standardized manner in factories and then transported to the construction site, where they are precisely assembled using processes such as high-strength bolt connections and grouting sleeve connections.

[0003] It is worth noting that the overall stability and long-term durability of a station structure largely depend on the performance of a large number of component connection interfaces. Over decades of operation, these critical connection interfaces will continuously withstand the combined effects of multiple factors, including lateral pressure from the surrounding soil, seepage pressure from groundwater, vibration loads from train operation, and the natural aging of concrete materials. Under these complex conditions, these connection interfaces are highly susceptible to typical defects such as joint cracking, joint leakage, and uneven settlement. These defects often become the weakest links affecting the safety and service life of the entire underground structure.

[0004] Currently, the monitoring data analysis methods commonly used in the industry suffer from numerous technical bottlenecks and limitations. The most basic method employs a fixed threshold alarm mechanism, which pre-sets static safety threshold ranges for various sensors, triggering an alarm when monitoring data exceeds these ranges. The drawback of this method lies in its passivity and lag; it can only be detected when structural damage has progressed to a considerable extent, completely lacking early warning capabilities. Another relatively advanced method is to establish a refined finite element numerical model for mechanical analysis. However, this method consumes significant computational resources, making it difficult to meet the needs of real-time online monitoring. Furthermore, the complex modeling process and numerous uncertain model parameters severely impact the reliability of the analysis results.

[0005] More importantly, existing analytical methods generally suffer from a major flaw: they often process data from each monitoring point in isolation, neglecting the inherent spatial correlations and temporal evolution patterns in the development of structural damage. In reality, structural damage often exhibits a clear chain reaction characteristic; performance degradation in one area can affect the stress state of adjacent areas through stress redistribution mechanisms, thereby creating a chain transmission of damage and a risk diffusion effect. This dynamic development process is completely ignored in traditional analytical methods. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent monitoring system and method for prefabricated rectangular stations based on the Internet of Things, in order to improve the technical problems of monitoring methods in related technologies that ignore the spatiotemporal correlation of structural risks and lack predictive capabilities.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The first objective of this invention is to provide an intelligent monitoring system for prefabricated rectangular stations based on the Internet of Things, comprising:

[0009] Several monitoring points are set up at the component connection interfaces of the prefabricated rectangular station;

[0010] Acquire monitoring data at different times at the monitoring points, including strain data and displacement data;

[0011] Each monitoring point is mapped to a grid point in a preset monitoring point grid, and a monitoring point dataset corresponding to the target time is generated based on the monitoring data.

[0012] For each grid point in the monitoring point grid, the initial stress state value of that grid point is calculated based on the monitoring data corresponding to that grid point in the monitoring point dataset;

[0013] For each grid point in the monitoring grid, the structural potential value of the grid point at the target time is calculated based on the initial stress state value of the grid point and the structural potential value of the adjacent grid points in the monitoring grid at the previous time.

[0014] Based on the monitoring point grid and the structural potential value of each grid point at the target time, a structural potential distribution map of the prefabricated rectangular station is generated.

[0015] In one possible implementation, mapping each monitoring point to a grid point in a preset monitoring point grid includes:

[0016] Based on the longitudinal segmentation and circumferential structural partitioning of the prefabricated rectangular station, a two-dimensional logical grid is established as the monitoring point grid;

[0017] The physical location of each monitoring point is associated with the coordinates of a unique grid point in a two-dimensional logical grid.

[0018] In one possible implementation, the calculation of the initial stress state value of the grid point includes:

[0019] The strain and displacement data corresponding to the grid point in the monitoring point dataset are normalized to obtain normalized strain and normalized displacement values, respectively.

[0020] The initial stress state value is obtained by combining the normalized strain value and the normalized displacement value under a preset weight.

[0021] In one possible implementation, the calculation of the structural potential value of the grid point at the target time includes:

[0022] The initial stress state value of the grid point is combined with the combined value of the structural potential value of the adjacent grid point at the previous time to obtain the structural potential value of the grid point at the target time.

[0023] In one possible implementation, the calculation of the structural potential value of the grid point at the target time based on the initial stress state value of the grid point and the structural potential values ​​of the adjacent grid points in the monitoring grid at the previous time step includes...

[0024] The structural potential value of the grid point at the target time is calculated according to a first preset formula, which is:

[0025]

[0026] PotVal i,j (t) represents the structural potential energy value of the grid point at the target time, S init,i,j (t) represents the initial stress state value, PotVal k,l (t-Δt) represents the structural potential value of adjacent grid points at the previous time step, p is the inherent risk occurrence coefficient, q is the correlation influence coefficient, and N(i,j) is the set of adjacent grid points. adj This represents the number of adjacent grid points.

[0027] In one possible implementation, the structural potential values ​​of all grid points are initialized at the initial moment before acquiring monitoring data at different times of the monitoring points.

[0028] One possible implementation also includes:

[0029] Acquire environmental monitoring data and operational monitoring data for prefabricated rectangular stations;

[0030] The comprehensive correction coefficient was calculated based on environmental monitoring data and operational monitoring data.

[0031] The structural potential value of each grid point is corrected using a comprehensive correction coefficient to obtain the corrected structural potential value.

[0032] The steps for generating a structural potential distribution map of a prefabricated rectangular station include:

[0033] Based on the monitoring point grid and the structural potential value of each grid point at the target time, a structural potential distribution map of the prefabricated rectangular station is generated.

[0034] In one possible implementation, the environmental monitoring data includes temperature data and groundwater level data; the operational monitoring data includes train vibration data.

[0035] In one possible implementation, the calculation of the comprehensive correction coefficient includes:

[0036] Each data point in the environmental monitoring data is compared with its corresponding preset benchmark value to obtain its respective environmental deviation.

[0037] Each data point in the operational monitoring data is compared with its corresponding preset benchmark value to obtain the respective operational deviation.

[0038] The comprehensive correction coefficient is obtained by combining the environmental deviation and the operational deviation under preset weights.

[0039] The second objective of this invention is to provide an IoT-based intelligent monitoring system for prefabricated rectangular stations, used to implement the aforementioned IoT-based intelligent monitoring method for prefabricated rectangular stations. This system includes:

[0040] The data acquisition module acquires monitoring data at different times from the monitoring point, including strain data and displacement data.

[0041] The data processing module maps each monitoring point to a grid point in a preset monitoring point grid, and generates a monitoring point dataset for the target time corresponding to the monitoring point grid based on the monitoring data.

[0042] The state calculation module calculates the initial stress state value of each grid point in the monitoring point grid based on the monitoring data corresponding to that grid point in the monitoring point dataset.

[0043] The potential calculation module calculates the structural potential value of each grid point in the monitoring point grid at the target time based on the initial stress state value of the grid point and the structural potential value of the adjacent grid points in the monitoring point grid at the previous time.

[0044] The distribution map generation module generates a structural potential distribution map of the prefabricated rectangular station based on the monitoring point grid and the structural potential value of each grid point at the target time.

[0045] A third objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned IoT-based intelligent monitoring method for prefabricated rectangular stations.

[0046] A fourth objective of this invention is to provide a computer program product, including a computer program that, when executed by a processor, implements the aforementioned IoT-based intelligent monitoring method for prefabricated rectangular stations.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] The present invention first sets up several monitoring points at the component connection interface of the prefabricated rectangular station, then obtains the monitoring data of multiple monitoring points in the prefabricated rectangular station at the target time, and finally obtains the structural potential distribution map of the station by performing correlation analysis on these monitoring data. Attached Figure Description

[0049] Figure 1 This is a first flowchart of a preferred embodiment of the present invention;

[0050] Figure 2 This is a second flowchart of a preferred embodiment of the present invention;

[0051] Figure 3 This is a system block diagram of a preferred embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 A smart monitoring method for prefabricated rectangular stations based on the Internet of Things includes:

[0054] Several monitoring points are set up at the component connection interfaces of the prefabricated rectangular station;

[0055] The monitoring data of multiple monitoring points within the prefabricated rectangular station at a target time are obtained. The monitoring points are located at the component connection interfaces of the prefabricated rectangular station. The monitoring data includes strain data and displacement data.

[0056] Each monitoring point is mapped to a grid point in a preset monitoring point grid, and based on the monitoring data, a monitoring point dataset corresponding to the target time is generated for the monitoring point grid.

[0057] For each grid point in the monitoring point grid, the initial stress state value of the grid point is calculated based on the monitoring data corresponding to that grid point in the monitoring point dataset.

[0058] For each grid point in the monitoring grid, the structural potential value of the grid point at the target time is calculated based on the initial stress state value of the grid point and the structural potential value of the adjacent grid points in the monitoring grid at the previous time.

[0059] Based on the monitoring point grid and the structural potential value of each grid point at the target time, a structural potential distribution map of the prefabricated rectangular station is generated.

[0060] To better understand the technical concept of the present invention, based on the above embodiments, the following non-limiting description is provided:

[0061] The step of mapping each monitoring point to a grid point in a preset monitoring point grid includes: establishing a two-dimensional logical grid as the monitoring point grid based on the longitudinal segmentation and circumferential structural partitioning of the prefabricated rectangular station; and associating the physical location of each monitoring point with the unique grid point coordinates in the two-dimensional logical grid.

[0062] The steps for calculating the initial stress state value of the grid point include: normalizing the strain data and displacement data corresponding to the grid point in the monitoring point dataset to obtain normalized strain value and normalized displacement value respectively; and combining the normalized strain value and the normalized displacement value under a preset weight to obtain the initial stress state value.

[0063] The step of calculating the structural potential value of the grid point at the target time includes: combining the initial stress state value of the grid point with the combined value of the structural potential value of the adjacent grid point at the previous time to obtain the structural potential value of the grid point at the target time.

[0064] The step of combining the initial stress state value of the grid point with the combined value of the structural potential value of the adjacent grid point at the previous time includes: calculating the structural potential value of the grid point at the target time according to a first preset formula, wherein the first preset formula is:

[0065]

[0066] PotVal i,j (t) represents the structural potential value of the grid point at the target time, S init,i,j (t) represents the initial stress state value, PotVal k,l (t-Δt) represents the structural potential value of adjacent grid points at the previous time step, p is the inherent risk occurrence coefficient, q is the correlation influence coefficient, and N(i,j) is the set of adjacent grid points. adj This represents the number of adjacent grid points.

[0067] It also includes: before the step of acquiring monitoring data, initializing the structural potential value of all grid points at the initial moment.

[0068] It also includes: acquiring environmental monitoring data and operational monitoring data of the prefabricated rectangular station; calculating a comprehensive correction coefficient based on the environmental monitoring data and operational monitoring data; correcting the structural potential value of each grid point using the comprehensive correction coefficient to obtain a corrected structural potential value; the step of generating a structural potential distribution map of the prefabricated rectangular station includes: generating a structural potential distribution map of the prefabricated rectangular station based on the monitoring point grid and the corrected structural potential value of each grid point at the target time.

[0069] Environmental monitoring data includes temperature data and groundwater level data; the operational monitoring data includes train vibration data.

[0070] The steps for calculating the comprehensive correction coefficient include: comparing each data point in the environmental monitoring data with its corresponding preset benchmark value to obtain its respective environmental deviation; comparing each data point in the operational monitoring data with its corresponding preset benchmark value to obtain its respective operational deviation; and combining the environmental deviation and the operational deviation under preset weights to obtain the comprehensive correction coefficient.

[0071] Please see Figure 2 A smart monitoring method for prefabricated rectangular stations based on the Internet of Things includes:

[0072] S101. Obtain monitoring data of monitoring points within the prefabricated rectangular station at the target time.

[0073] In this embodiment, the monitoring system performs data acquisition through an integrated Internet of Things (IoT) sensor network. The structural integrity of the prefabricated rectangular station is highly dependent on the quality of the joints between its segments, and these component interfaces are critical areas for stress concentration and deformation. Therefore, the sensor deployment strategy focuses on these weak points.

[0074] Optionally, the sensors may include strain sensors and displacement sensors. The types and placement of the sensors include:

[0075] The strain sensor can be a vibrating wire strain gauge or a fiber Bragg grating (FBG) strain gauge, which is attached or embedded in the inner and outer edges of the joints of precast components, especially in the corner connection areas between side walls and top and bottom slabs, as well as key points of segmental circumferential joints. The strain sensor is used to measure the strain on the concrete surface, reflecting the stress state of the structure.

[0076] The displacement sensor can be a linear variable differential transmitter (LVDT) or a wire displacement gauge, which can be connected across both sides of the joint to directly measure the relative displacement δ, such as the joint opening and shear slip.

[0077] The sensor is connected to the field data acquisition unit (DAU) via wired or wireless means (such as LoRa, NB-IoT). The DAU performs preliminary processing and packaging of the signal, and then transmits the data to the central server via 5G or industrial Ethernet.

[0078] Data acquisition is performed synchronously. The system sets a fixed time step Δt (exemplarily, this could be 10 minutes, 1 hour, or 1 day, depending on monitoring requirements and the rate of data change). At the end of each time step, this moment is defined as the target time t. The system collects the instantaneous readings of all monitoring points at this target time, or the average value within a very short time window (e.g., 1 second) before and after the target time, to filter out high-frequency noise. Understandably, the collected data will be timestamped and labeled with the sensor ID.

[0079] S201. Map multiple monitoring points to grid points in a preset monitoring point grid, and generate a monitoring point dataset for the target time corresponding to the monitoring point grid based on the monitoring data. To facilitate spatial correlation analysis, the physically discrete sensor data needs to be organized into a unified monitoring point grid.

[0080] Precast rectangular stations have clear longitudinal and circumferential structures. The longitudinal direction, along the tunnel axis, consists of precast segment rings. The circumferential direction represents the internal structure of each segment ring, such as the roof slab, floor slab, and left and right side walls. Therefore, when establishing a two-dimensional logical grid, the grid row (index i) corresponds to the longitudinal segment number of the station, from 1 to M (M being the total number of segments in the station); the grid column (index j) corresponds to the key monitoring area number within a segment ring, from 1 to N. For example, j=1 represents the connection between the left side wall and the floor slab, j=2 represents the connection between the left side wall and the roof slab, j=3 represents the connection between the right side wall and the roof slab, and j=4 represents the connection between the right side wall and the floor slab.

[0081] For example, for a station consisting of 50 segment rings, with 8 key monitoring areas in each ring, a 50×8 monitoring point grid can be established.

[0082] The mapping process involves assigning a grid coordinate (i,j) to each physical sensor (identified by its unique ID). If a grid point corresponds to multiple sensors within its area (e.g., one strain gauge on the inside and one on the outside), the average or maximum value can be taken as the data for that grid point.

[0083] After mapping, all data collected at the target time t are organized into a matrix of the same dimension as the monitoring point grid. This generates the monitoring point dataset at the target time, for example, a strain matrix E(t) = ∈ i,j (t) and a displacement matrix D(t) = δ i,j (t).

[0084] S301. For each grid point in the monitoring point grid, based on the monitoring data corresponding to that grid point in the monitoring point dataset, calculate the initial stress state value of that grid point. S301 is used to transform the original monitoring data (such as micro-strain μ∈ and millimeters) into an index ranging from [0,1], namely the initial stress state value S. init This value is used to intuitively reflect the alarm level of a single monitoring point at the current moment.

[0085] For example, for the monitoring data of grid point (i,j) at time t, ∈ i,j (t) and δ i,j (t), is normalized. The normalization formula is:

[0086]

[0087] Where, ∈ max and δ max These are preset thresholds for strain and displacement. These thresholds can be derived from alarm or control values ​​in design specifications such as the "Technical Specification for Safety Protection of Urban Rail Transit Structures," or they can be obtained through refined finite element analysis, or determined based on the statistical distribution of long-term monitoring data (such as the 95th percentile). Taking absolute values ​​is to handle tensile and compressive strains, as well as displacements in different directions.

[0088] The normalized values ​​are combined to obtain the initial stress state value S. init,i,j (t). The combination formula is:

[0089] S init,i,j (t)=α·∈ norm,i,j (t)+β·δ norm，i，j (t)

[0090] Here, α and β are preset weighting coefficients, satisfying α + β = 1. The above weighting settings are used to reflect the sensitivity and importance of different monitoring indicators for specific types of structural defects in this field.

[0091] For example, in strata rich in groundwater, joint opening (displacement) is the primary cause of water leakage, and its risk indicator may be higher than that of strain. In this scenario, the weights can be set as α = 0.4 and β = 0.6. Conversely, in sections primarily subjected to uneven earth pressure, changes in structural internal forces (strain) may be more critical, and the weights can be set as α = 0.7 and β = 0.3.

[0092] Suppose at grid point (10,2), the predefined ∈ max =300μ∈,,δ max =2.0mm. Weights are set as α = 0.5, β = 0.5. At the target time t, ∈ 10,2 (t)=180μ∈,δ 10,2 (t) = 1.2 mm. Therefore:

[0093] Normalized strain value: ∈ norm,10,2 (t)=min(1,|180| / 300)=0.6

[0094] Normalized displacement value: δ norm,10,2 (t) = min(1, |1.2| / 2.0) = 0.6

[0095] Initial stress state value: S init,10,2 (t)=0.5×0.6+0.5×0.6=0.6.

[0096] S401. For each grid point in the monitoring grid, calculate the structural potential value of that grid point at the target time.

[0097] This application establishes a dynamic model (first presupposed formula) describing the accumulation and diffusion of structural risks. In this model, the current comprehensive risk (structural potential value PotVal) of a point consists of two parts: one part is the "inherent risk" caused by its own current stress state, and the other part is the "related risk" "infected" by the historical risk state of its neighboring points.

[0098] The first contribution to the model represents the evolution of inherent risk directly caused by the stress state of the monitoring point itself. This risk originates from the local stress at that point, such as that caused by material fatigue or instantaneous overload, and its quantitative expression is related to the initial stress state value S. init And a risk occurrence coefficient p is related.

[0099] The model's second contribution represents spatially correlated degradation. This describes the phenomenon that the risk state of a point in a structure is influenced by the state of its neighbors; that is, risk diffuses from high-risk areas to the periphery. For example, excessive deformation of a joint can alter the stress distribution in the surrounding area, thus accelerating its degradation. The extent of this correlated effect is quantified by the average risk level of neighboring points and a correlated effect coefficient q.

[0100] The model example is as follows:

[0101]

[0102] PotVal i,j (t) represents the structural potential value of grid point (i,j) at the target time t, and its value range is also between [0,1]. The larger the value, the higher the comprehensive structural risk of that point. S init,i,j (t) represents the initial stress state value of grid point (i,j) at time t, serving as a fundamental term for the intrinsic driving factors. (1-S init,i,j (t) represents the "residual deterioration potential" of the point in its current state. This ensures that when the stress at a point is already high (S) init If it approaches 1), its space affected by its neighbors will become smaller.

[0103] N(i,j) is the set of neighboring grid points of grid point (i,j). For a two-dimensional grid, it is usually defined as its top, bottom, left, and right points. For boundary points, only its existing neighbors are considered. adj It is the total number of adjacent grid points (usually 4, 2 or 3 at the boundary).

[0104] {PotVal k,l (t-Δt) is the structural potential value of the adjacent grid point (k,l) at the previous monitoring time (t-Δt), which is used to reflect the time lag effect and spatial correlation.

[0105] p is the inherent risk factor, used to reflect the inherent tendency of a structure to deteriorate solely due to its own stress without external influences. Its value is related to factors such as material durability and construction quality. Typical values ​​range from 0.001 to 0.05. Understandably, a well-constructed structure will have a lower p value.

[0106] q is the correlation influence coefficient, used to reflect the intensity of structural risk transmission between adjacent areas. Its value is related to factors such as the overall integrity of the structure and the connection stiffness of joints. Understandably, structures with rigid connections have higher q values. Typical values ​​range from 0.1 to 0.5.

[0107] Before monitoring begins (t=0), the structural potential values ​​of all grid points are initialized to 0, i.e. Starting from the first monitoring cycle, the system performs iterative calculations based on the above formula.

[0108] For example, as in the grid point (10,2) in the example above, its S init,10,2 (t) = 0.6. Assume the model parameters are p = 0.01, q = 0.2, and at the previous time step (t-Δt), the structural potential values ​​PotVal(t-Δt) of its four neighbors (10,1), (10,3), (9,2), and (11,2) are 0.25, 0.30, 0.22, and 0.28, respectively. Then the average potential of the neighbors = (0.25 + 0.30 + 0.22 + 0.28) / 4 = 0.2625.

[0109] Calculate the structural potential energy of point (10,2) at time t:

[0110] PotVal 10,2 (t)=0.6+(1-0.6)·[0.01+0.2·0.2625]

[0111] =0.6 + 0.4 * [0.01 + 0.0525]

[0112] =0.6 + 0.4 * 0.0625

[0113] =0.6 + 0.025 = 0.625

[0114] It can be seen that, due to its own high stress state (S init =0.6), and influenced by the historical risks of surrounding points, the structural potential value of this point is calculated to be 0.625. At the next time step (t+Δt), this value of 0.625 will be used as the input when calculating the potential value of its neighboring points, thereby realizing the transmission and evolution of risk in the spatiotemporal dimension.

[0115] S501. Based on the monitoring point grid and the structural potential value of each grid point, generate a structural potential distribution map of the prefabricated rectangular station.

[0116] For example, the system calculates the structural potential value matrix PotVal(t) = PotVal, which has the same dimension as the monitoring point grid. i,j (t) is used for visualization rendering. The station's two-dimensional logical grid is used as the canvas, and the color of each grid cell is determined by its corresponding structural potential value PotVal. i,j (t) determines. The system pre-sets a color map, for example:

[0117] 0≤PotVal<0.3: Blue (Safe)

[0118] 0.3≤PotVal<0.6: Green (Normal)

[0119] 0.6≤PotVal<0.8: Yellow (Attention / Warning)

[0120] 0.8≤PotVal≤1.0: Red (Alarm / Danger)

[0121] In this way, the structural health status of the entire station is presented as a two-dimensional map using multiple colors. Engineers no longer need to examine the data curves of hundreds of sensors individually; a glance at the map is enough to quickly locate the "hot spots" marked in yellow or red. These areas represent the highest structural risk and are most likely to develop defects, and should be the focus of subsequent manual inspections, repairs, and reinforcements.

[0122] Following S401 and preceding S501, it also includes:

[0123] S451. Obtain environmental monitoring data and operational monitoring data for prefabricated rectangular stations.

[0124] In addition to integrating structural sensors, the monitoring system can also integrate other types of sensors to capture external factors that affect the structure. For example, temperature sensors deployed at different locations within the station can obtain temperature data to reflect the impact of thermal expansion and contraction on structural joints. Piezometers or water level gauges deployed outside the station can obtain groundwater level data to reflect the impact of water pressure on external loads on the structure. Humidity sensors inside the station can obtain humidity data to reflect the impact of high humidity environments on steel reinforcement corrosion.

[0125] Accelerometers installed near the track or on the structure can also be used to collect peak vibration acceleration or spectral characteristics (train vibration data) caused by trains passing by. Data such as train frequency and axle load can be obtained from the Metro Operations Control Center (OCC) to quantify the cumulative effect of dynamic loads.

[0126] S452. Based on environmental monitoring data and operational monitoring data, a comprehensive correction coefficient is calculated.

[0127] For each external factor, calculate its deviation from the baseline state.

[0128] Temperature deviation Where T current It is the current temperature, T base It is the design reference temperature (e.g., 20℃), ΔT design It refers to the design temperature difference range.

[0129] water level deviation Where H current This is the current water level, H. safe It's the safe water level, H. design That is the highest water level designed for.

[0130] Vibration deviation Where A current It is the currently measured peak vibration acceleration, A. base It is the vibration reference value under normal operating conditions.

[0131] By weighting and combining all deviations, we obtain the comprehensive correction coefficient C. corr The coefficient is a multiplier greater than or equal to 1.

[0132] C corr =1+w temp ·D temp +w water ·D water +w vib ·D vib +...

[0133] Among them, w temp ,w water ,w vib These are the weighting coefficients for various external factors. These weights can be set based on expert experience and regional geological, hydrological, and operational characteristics.

[0134] For example, for a station located on soft soil foundation with abundant groundwater, the impact of water level changes may be the greatest, with a weight w. water It can be set to 0.4; the impact of train vibration is secondary, w vib Set to 0.3; temperature has a relatively small effect, w temp Set it to 0.1. In this example, the sum of these weights does not need to be 1.

[0135] Assume T base =20,ΔT design =15. The current temperature is 30℃, then D temp =|30-20| / 15≈0.67.

[0136] Assume H safe =5m,H design =10m. Current water level is 8m, then D water = (8-5) / (10-5) = 0.6.

[0137] Assuming the peak vibration value is 1.2 times the reference value, then D vib =1.2.

[0138] Using the above weights, the overall correction coefficient is:

[0139] C corr =1+0.1×0.67+0.3×1.2+0.4×0.6=1+0.067+0.36+0.24=1.667.

[0140] S453. The structural potential value of each grid point is corrected using a comprehensive correction coefficient.

[0141] The original structural potential value PotVal calculated in S401 is used. i,j (t), and the comprehensive correction coefficient C corr By combining the results, the corrected structural potential value PotVal is obtained. corr,i,j (t).

[0142] PotVal corr,i,j (t)=min(1,PotVal i,j (t)·C corr )

[0143] The original potential value of grid point (10,2) is PotVal. 10,2 (t) = 0.625.

[0144] Under the current harsh external conditions, its corrected potential value is:

[0145] PotVal corr，10，2 (t)=min(1,0.625×1.667)=min(1,1.041875)=1.0.

[0146] This indicates that, after comprehensively considering external factors, the risk level at this point has been drastically increased to the highest level. The aforementioned correction makes the system more sensitive to sudden risks caused by the coupling of multiple factors.

[0147] Finally, S501 will base its analysis on these corrected potential values ​​PotVal, which better reflect real-world operating conditions. corr,i,j (t) is used to generate a structural potential distribution map, providing a more reliable basis for decision-making.

[0148] This invention also provides an IoT-based intelligent monitoring system for prefabricated rectangular stations, which can be a physical or software implementation of the methods described above. The system can be deployed on a cloud server, providing services through a web interface, or it can be deployed on an edge computing server located at the station.

[0149] like Figure 3 As shown, the system may include:

[0150] The data acquisition module is responsible for interfacing with the front-end data acquisition unit (DAU) or IoT platform, receiving real-time data streams from various sensors, and handling data parsing, cleaning, and initial storage.

[0151] The data processing module maintains a mapping table from sensor IDs to grid coordinates (i,j). It periodically retrieves raw data at the target time from the database, performs gridding mapping, and generates structured monitoring point datasets such as strain matrices and displacement matrices. This module can be implemented using Python's Pandas and NumPy libraries for efficient matrix operations.

[0152] The state calculation module receives the monitoring point dataset, normalizes and weights the data for each grid point, and outputs the initial stress state value matrix S. init (t). The module is configured with threshold and weight parameters for various monitoring data, which can be adjusted through the interface.

[0153] The potential calculation module is the core of the system's computation; it receives the S value at the current moment. init The PotVal(t) matrix and the PotVal(t-Δt) matrix from the previous time step are used to calculate the PotVal(t) matrix at the current time step by applying a diffusion model to each grid point through iterative or parallel computation. The module internally configures the model parameters p and q.

[0154] The distribution map generation module receives the final structural potential value matrix and renders it into a heatmap using graphics libraries (such as Python's Matplotlib, Plotly, or front-end tools like ECharts, D3.js). This module also provides interactive features, such as displaying specific values ​​on mouse hover, zooming, and interaction with the station's 3D BIM model.

[0155] The correction module is specifically designed to acquire and process environmental and operational data to calculate the comprehensive correction factor C. corr After obtaining PotVal(t), the potential calculation module will call this module to get the current C. corr The value is used to correct the result, and then the final result is output to the distribution map generation module.

[0156] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0157] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or 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 apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0158] The units described as separate components may or may not be physically separate. A component shown as a unit can be one physical unit or multiple physical units; that is, it can be located in one place or distributed in multiple different places. Depending on actual needs, some or all of the units can be selected to achieve the purpose of this embodiment.

[0159] Furthermore, the functional units in the various embodiments of this application 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 described above can be implemented in hardware.

[0160] Please see Figure 3 A smart monitoring system for prefabricated rectangular stations based on the Internet of Things, comprising:

[0161] The data acquisition module is used to acquire monitoring data from multiple monitoring points within the prefabricated rectangular station at a target time. The monitoring points are located at the component connection interfaces of the prefabricated rectangular station, and the monitoring data includes strain data and displacement data.

[0162] The data processing module is used to map each monitoring point to a grid point in a preset monitoring point grid, and generate a monitoring point dataset for a target time corresponding to the monitoring point grid based on the monitoring data.

[0163] The state calculation module is used to calculate the initial stress state value of each grid point in the monitoring point grid based on the monitoring data corresponding to that grid point in the monitoring point dataset.

[0164] The potential calculation module is used to calculate the structural potential value of each grid point in the monitoring point grid at the target time based on the initial stress state value of the grid point and the structural potential value of the adjacent grid points in the monitoring point grid at the previous time.

[0165] The distribution map generation module is used to generate a structural potential distribution map of the prefabricated rectangular station based on the monitoring point grid and the structural potential value of each grid point at the target time.

[0166] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described IoT-based intelligent monitoring method for prefabricated rectangular stations.

[0167] A fourth objective of this invention is to provide a computer program product, including a computer program that, when executed by a processor, implements the aforementioned IoT-based intelligent monitoring method for prefabricated rectangular stations.

[0168] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent monitoring of prefabricated rectangular stations based on the Internet of Things, characterized in that, include: Several monitoring points are set up at the component connection interfaces of the prefabricated rectangular station; Acquire monitoring data at different times at the monitoring points, including strain data and displacement data; Each monitoring point is mapped to a grid point in a preset monitoring point grid, and a monitoring point dataset corresponding to the target time is generated based on the monitoring data. For each grid point in the monitoring point grid, the initial stress state value of that grid point is calculated based on the monitoring data corresponding to that grid point in the monitoring point dataset; For each grid point in the monitoring grid, the structural potential value of the grid point at the target time is calculated based on the initial stress state value of the grid point and the structural potential value of the adjacent grid points in the monitoring grid at the previous time. Specifically, it includes: The structural potential value of the grid point at the target time is calculated according to a first preset formula, which is: ； in, Let (i,j) be the structural potential value of grid point (i,j) at the target time. This represents the initial stress state value. The structural potential value of the adjacent grid points at the previous time step. This is the inherent risk occurrence coefficient. The correlation coefficient is the coefficient of influence. It is the set of adjacent grid points. This represents the number of adjacent grid points. Based on the monitoring point grid and the structural potential value of each grid point at the target time, a structural potential distribution map of the prefabricated rectangular station is generated.

2. The intelligent monitoring method for prefabricated rectangular stations based on the Internet of Things according to claim 1, characterized in that, The process of mapping each monitoring point to a grid point in a preset monitoring point grid includes: Based on the longitudinal segmentation and circumferential structural partitioning of the prefabricated rectangular station, a two-dimensional logical grid is established as the monitoring point grid; The physical location of each monitoring point is associated with the coordinates of a unique grid point in a two-dimensional logical grid.

3. The intelligent monitoring method for prefabricated rectangular stations based on the Internet of Things according to claim 1, characterized in that, The initial stress state value of the grid point obtained by the calculation includes: The strain and displacement data corresponding to the grid point in the monitoring point dataset are normalized to obtain normalized strain and normalized displacement values, respectively. The initial stress state value is obtained by combining the normalized strain value and the normalized displacement value under a preset weight.

4. The intelligent monitoring method for prefabricated rectangular stations based on the Internet of Things according to claim 1, characterized in that, The calculated structural potential value of the grid point at the target time includes: The initial stress state value of the grid point is combined with the combined value of the structural potential value of the adjacent grid point at the previous time to obtain the structural potential value of the grid point at the target time.

5. The intelligent monitoring method for prefabricated rectangular stations based on the Internet of Things according to claim 1, characterized in that, Also includes: Acquire environmental monitoring data and operational monitoring data for prefabricated rectangular stations; The environmental monitoring data includes temperature data and groundwater level data; the operational monitoring data includes train vibration data. The comprehensive correction coefficient was calculated based on environmental monitoring data and operational monitoring data. The structural potential value of each grid point is corrected using a comprehensive correction coefficient to obtain the corrected structural potential value. The steps for generating a structural potential distribution map of a prefabricated rectangular station include: Based on the monitoring point grid and the structural potential value of each grid point at the target time, a structural potential distribution map of the prefabricated rectangular station is generated.

6. The intelligent monitoring method for prefabricated rectangular stations based on the Internet of Things according to claim 5, characterized in that, The calculated comprehensive correction coefficient includes: Each data point in the environmental monitoring data is compared with its corresponding preset benchmark value to obtain its respective environmental deviation. Each data point in the operational monitoring data is compared with its corresponding preset benchmark value to obtain the respective operational deviation. The comprehensive correction coefficient is obtained by combining the environmental deviation and the operational deviation under preset weights.

7. A prefabricated rectangular station intelligent monitoring system based on the Internet of Things, characterized in that, For implementing the IoT-based intelligent monitoring method for prefabricated rectangular stations as described in any one of claims 1-6, the system comprises: The data acquisition module acquires monitoring data at different times from the monitoring point, including strain data and displacement data. The data processing module maps each monitoring point to a grid point in a preset monitoring point grid, and generates a monitoring point dataset for the target time corresponding to the monitoring point grid based on the monitoring data. The state calculation module calculates the initial stress state value of each grid point in the monitoring point grid based on the monitoring data corresponding to that grid point in the monitoring point dataset. The potential calculation module calculates the structural potential value of each grid point in the monitoring point grid at the target time based on the initial stress state value of the grid point and the structural potential value of the adjacent grid points in the monitoring point grid at the previous time. The distribution map generation module generates a structural potential distribution map of the prefabricated rectangular station based on the monitoring point grid and the structural potential value of each grid point at the target time.

8. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the Internet of Things-based intelligent monitoring method for prefabricated rectangular stations as described in any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the IoT-based intelligent monitoring method for prefabricated rectangular stations as described in any one of claims 1-6.

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