A multi-sensor based concrete curing monitoring system

Abstract

The application provides a concrete curing monitoring system based on multiple sensors, and a monitoring center is used for determining a surface temperature matrix of a raft foundation according to a temperature image and original construction information, determining an abnormal temperature coordinate in the surface temperature matrix according to the surface temperature matrix, determining a temperature propagation matrix according to a temperature difference of each adjacent matrix unit in the surface temperature matrix, and determining a target abnormal sensor according to the abnormal temperature coordinate, the temperature propagation matrix and the coordinates of each sensor; determining a conduction cost based on the abnormal temperature coordinate, the target abnormal sensor and the temperature propagation matrix; and monitoring sensor data of the target abnormal sensor according to the conduction cost. The application can accurately determine a target abnormal sensor damaged by temperature influence, thereby improving fault discovery efficiency, ensuring the accuracy of raft foundation data and improving raft foundation safety.

Description

Technical Field

[0001] This invention relates to the field of anomaly monitoring, and more particularly to a concrete curing monitoring system based on multiple sensors. Background Technology

[0002] In the construction and curing stages of large-volume concrete structures, such as raft foundations, effectively controlling the temperature difference between the inner and outer surfaces of the concrete is a key measure to prevent temperature cracks. Because concrete releases a large amount of heat of hydration during hardening and its surface dissipates heat quickly, it easily forms a large temperature gradient, which can induce tensile stress and lead to cracking. Therefore, the engineering industry typically uses insulation blankets to cover the concrete surface for thermal insulation and moisture retention. However, construction sites often face harsh environmental factors such as strong winds, which can easily cause the insulation blankets to be lifted or damaged, resulting in a rapid and abnormal drop in temperature in the exposed areas. To promptly detect and warn of such risks, the industry generally adopts a combined monitoring scheme that integrates external infrared thermal imagers with internally embedded temperature sensors. This aims to use complementary multi-source data to monitor the temperature field distribution and strain state inside the structure in real time, thereby ensuring the safety and durability of the concrete structure throughout its entire life cycle.

[0003] The above-mentioned solutions have significant limitations in engineering practice. When local cold holes appear on the surface of the raft foundation due to damage to the insulation layer, the commonly used shortest spatial distance matching method in existing technology often directly regards the sensor with the closest geometric location as the affected object. This method fails to take into account the non-uniform temperature gradient distribution inside the concrete, causing the matching results to deviate from the actual heat diffusion path, omitting affected faulty sensors, and thus leading to distorted monitoring data or permanent damage to components. Summary of the Invention

[0004] This invention provides a multi-sensor-based concrete curing monitoring system. Through the technical solution of this invention, when local temperature anomalies occur on the surface of a raft foundation, the sensors affected by the temperature anomalies can be accurately identified, and then the camera can be controlled to take fixed-point shots, thereby improving the accuracy of the monitoring data of the raft foundation and ensuring the safety of the raft foundation.

[0005] In a first aspect, embodiments of the present invention provide a concrete curing monitoring system based on multiple sensors. The system includes a monitoring center, a camera, and multiple sensors, which are configured on a raft foundation. The monitoring center is used to acquire temperature images captured by the camera and the original construction information of the raft foundation, and to determine the surface temperature matrix of the raft foundation based on the temperature images and the original construction information, wherein the surface temperature matrix represents the temperature value at each grid position on the surface of the raft foundation. The monitoring center is used to determine the abnormal temperature coordinates in the surface temperature matrix based on the surface temperature matrix. The monitoring center is used to determine the temperature propagation matrix based on the temperature difference between each adjacent matrix unit in the surface temperature matrix, and to determine the target abnormal sensor based on the abnormal temperature coordinates, the temperature propagation matrix and the coordinates of each sensor, wherein the matrix unit of the temperature propagation matrix is ​​used to characterize the probability distribution of the temperature of the matrix unit being conducted to each adjacent grid. The monitoring center is used to determine the conduction cost based on the abnormal temperature coordinates, the depth of the target abnormal sensor, and the temperature propagation matrix. The conduction cost is the heat cost required to conduct heat from the abnormal temperature coordinates to the location of the target abnormal sensor. The monitoring center is used to monitor the sensor data of the target anomaly sensor based on the transmission cost.

[0006] Secondly, embodiments of the present invention provide a multi-sensor-based concrete curing monitoring method, wherein the method is configured in a monitoring center of a multi-sensor-based concrete curing monitoring system, and the method specifically includes: The temperature image captured by the camera and the original construction information of the raft foundation are acquired, and the surface temperature matrix of the raft foundation is determined based on the temperature image and the original construction information, wherein the surface temperature matrix represents the temperature value at each grid position on the surface of the raft foundation. Based on the surface temperature matrix, determine the coordinates of abnormal temperatures in the surface temperature matrix; The temperature propagation matrix is ​​determined based on the temperature difference between each adjacent matrix unit in the surface temperature matrix, and the target abnormal sensor is determined based on the abnormal temperature coordinates, the temperature propagation matrix, and the coordinates of each sensor. The matrix unit of the temperature propagation matrix is ​​used to characterize the probability distribution of the temperature of the matrix unit being conducted to each adjacent grid. Based on the abnormal temperature coordinates, the depth of the target abnormal sensor, and the temperature propagation matrix, the conduction cost is determined. The conduction cost is the heat cost required to conduct heat from the abnormal temperature coordinates to the location of the target abnormal sensor. The sensor data of the target anomaly sensor are monitored based on the transmission cost.

[0007] This invention provides a multi-sensor-based concrete curing monitoring system. Specifically, it achieves high-precision gridded digital modeling of the raft foundation surface temperature by establishing a surface temperature matrix. This allows for the rapid identification of abnormal temperature regions (abnormal temperature points) based on the surface temperature matrix. Furthermore, by using the temperature propagation matrix and the coordinates of each sensor, it identifies target abnormal sensors affected by temperature changes. By calculating the conduction cost corresponding to the target abnormal sensor, the intensity of the temperature change's impact on the sensor can be determined. In summary, the method of this invention can accurately identify target abnormal sensors affected by temperature and, by monitoring the sensor data of these sensors, ensure the accuracy of the raft foundation monitoring data, thereby improving the safety of the raft foundation. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0009] Figure 1 A schematic diagram of a multi-sensor-based concrete curing monitoring system provided in an embodiment of the present invention; Figure 2 A flowchart of a multi-sensor-based concrete curing monitoring method provided in an embodiment of the present invention. Detailed Implementation

[0010] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Example

[0011] Figure 1 This is a schematic diagram of a multi-sensor-based concrete curing monitoring system provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the system includes a monitoring center 110, a camera 120, and multiple sensors 140. The sensors are configured in a concrete raft foundation 130. The arrows indicate the communication connection relationships. The system aims to identify target abnormal sensors that may be affected by abnormal temperatures by observing the temperature change patterns on the surface of the raft foundation, and to control the camera to take directional pictures of the raft foundation. The system can be specifically applied to fields such as temperature monitoring of raft foundations and fault diagnosis of sensors inside the raft foundation.

[0012] It should be noted beforehand that the temperature conduction inside the raft foundation in this solution has been reduced in dimension. For example, the conduction cost in this invention includes a decomposition method of "two-dimensional planar diffusion and one-dimensional depth attenuation" (see below for details). This is an adaptive design made for the application scenario, input constraints and computational requirements of this solution, and does not violate the physical laws of heat conduction. The specific rationale is explained as follows: 1. The application scope of the solution is clearly defined as the physical addressing of sensors, rather than high-precision physical heat transfer simulation.

[0013] The core objective of this invention is to solve the engineering problem of "how to quickly locate embedded sensors that may be affected by surface temperature anomalies when only the surface temperature field of a raft foundation can be obtained," rather than constructing a high-precision three-dimensional numerical model of heat conduction. The calculation of conduction cost is used for a binary determination of whether the sensor is affected by temperature anomalies, rather than precisely solving for the spatiotemporal distribution of the temperature field. The two methods have fundamentally different requirements for computational accuracy: the distance between adjacent sensors is typically 0.5m to 2m. This solution only needs to determine whether the influence range of the surface anomaly covers the area where the sensor is located; it does not require precisely characterizing the microscopic propagation trajectory of heat in the medium. Coarse-grained approximate calculations fully meet the determination requirements. Therefore, this solution only needs to determine whether the influence range of the surface anomaly covers the area where the sensor is located to identify potentially damaged abnormal sensors. It does not require precisely characterizing the microscopic propagation trajectory of heat in the medium. The coarse-grained approximate calculations of this invention fully meet the determination requirements of this scenario.

[0014] 2. Two-dimensional decomposition is an inevitable choice under input constraints.

[0015] The input to this solution only includes temperature images (infrared thermal images) of the raft foundation surface captured by an infrared camera, and cannot directly obtain the continuous temperature field distribution inside the raft foundation. If the three-dimensional temperature field inside the raft foundation could be directly measured, it would only be necessary to compare the deviation between the measured temperature and the theoretical temperature of each sensor to locate abnormal sensors, thus rendering this solution ineffective. Therefore, calculations based on the two-dimensional surface temperature field are the logical premise of this solution. Since the initial distribution of the internal temperature field and the three-dimensional non-uniformity of the medium parameters are unknown, forcibly constructing a three-dimensional heat conduction model would not only be computationally redundant but also lead to unreliable calculation results due to the lack of basic data. Furthermore, in actual working conditions (such as when "cold holes" appear on the surface of the raft foundation), the internal sensor data is often damaged or invalid, further limiting the feasibility of full three-dimensional modeling. Therefore, adopting a "two-dimensional decomposition" model is a necessary technical choice to achieve the invention's objective under the strong constraint of an unknown internal temperature field, rather than a technical defect.

[0016] Specifically, such as Figure 1 As shown, the concrete curing monitoring system based on multiple sensors provided in this embodiment includes: The monitoring center is used to acquire temperature images captured by the camera and the original construction information of the raft foundation, and to determine the surface temperature matrix of the raft foundation based on the temperature images and the original construction information, wherein the surface temperature matrix represents the temperature value at each grid position on the surface of the raft foundation.

[0017] The raft foundation, a component of the building's foundation system, is made of concrete. When the building load is large and the foundation bearing capacity is weak, a concrete slab is often used to bear the building load, forming a raft foundation. It has strong integrity and stability, effectively resisting uneven ground settlement. The monitoring center is the core computing and control node of the raft foundation maintenance monitoring system. It receives multi-source sensing data, such as sensor data, camera images, and original construction information, and executes matrix calculations and issues control commands. The monitoring center can establish data connections with cameras and sensors via wired or wireless communication, and may include edge computing servers, industrial control hosts, cloud computing nodes, and supporting data processing modules. It should be noted that this invention specifically relates to the maintenance stage of the raft foundation. After the raft foundation is constructed with concrete, it needs to solidify. However, during the solidification process, differences in internal and external temperature and humidity can cause internal cracking of the concrete. Therefore, sensors need to be pre-embedded inside the raft foundation, and an external insulation layer needs to be added to ensure the stability of the raft foundation's solidification. Furthermore, the sensor involved in this solution is a sensing device pre-embedded inside the raft foundation, used to monitor the stress state, temperature and humidity inside the raft foundation. Since there are abnormal cold holes on the surface of the raft foundation (such as vegetation damage, damage to the surface antifreeze layer, etc.), the temperature difference caused by the abnormal cold holes will be conducted along the raft foundation to the sensor embedding location, thereby interfering with the data acquisition accuracy of the sensor. Therefore, one of the main objectives of this embodiment of the invention is to accurately determine the target abnormal sensor affected by abnormal temperature.

[0018] The temperature image is image data containing thermal radiation information captured by a camera, which can be an infrared camera. The temperature image can be an infrared thermal image, and there is a one-to-one correspondence between the pixel values ​​of the infrared thermal image and the target radiation temperature. It is used to characterize the heat distribution state of the raft foundation surface at the time of shooting. This application does not impose a unique limitation on the specific conversion relationship between pixel values ​​and surface temperature. The original construction information is static archive data formed during the design and construction phase of the raft foundation. Specifically, it can cover the three-dimensional spatial area definition data of the raft foundation, the three-dimensional position coordinate data of each embedded sensor, and the material type data of the concrete used for pouring. Different concrete materials have different thermal conductivity, which can be obtained through experimental calibration. The surface temperature matrix is ​​a two-dimensional matrix obtained by discretizing the surface of the raft foundation according to a preset spatial grid. The grid area corresponding to the matrix unit in the surface temperature matrix has a one-to-one mapping relationship with the actual physical area of ​​the raft foundation surface. The value of the matrix unit corresponds to the average surface temperature of each location point in the actual physical area. The abnormal temperature coordinates are the position indices of matrix cells in the surface temperature matrix where the temperature value deviates from the preset normal threshold range or where there is a gradient abrupt change. They are used to identify the spatial location of the suspected temperature anomaly on the surface of the raft foundation.

[0019] Optionally, the monitoring center is specifically used for: Obtain the intrinsic and extrinsic parameter matrices of the camera; based on the pixel coordinates of the temperature image, determine the actual coordinates corresponding to the pixel coordinates according to the intrinsic and extrinsic parameter matrices; determine the spatial coordinate region of the raft foundation according to the original construction information, and extract a preset two-dimensional reference plane from the spatial coordinate region; divide the preset two-dimensional reference plane according to a preset division scale to obtain multiple division grids; map the temperature values ​​corresponding to each actual coordinate to the corresponding division grids, define matrix unit indices with the row and column coordinates corresponding to each division grid, and generate the surface temperature matrix of the raft foundation with the temperature values ​​as matrix values.

[0020] The intrinsic parameter matrix of the camera is used to establish the correspondence between the image coordinate system of the temperature image and the camera coordinate system, specifically including physical quantities such as camera focal length, camera principal point coordinates, pixel tilt factor, and distortion parameters. The extrinsic parameter matrix of the camera is used to establish the correspondence between the camera coordinate system and the world coordinate system, describing the translation vector and rotation matrix of the camera in space. The actual coordinates are the real positions of the raft foundation area corresponding to the pixel in the temperature image in physical space. The preset two-dimensional reference plane is a reference plane for mesh discretization. It can be selected for the geometry of the raft foundation, such as the outer surface of the raft foundation with a specific orientation, a horizontal section or oblique section at a specific elevation, etc. For example, the plane of the raft foundation with an elevation value of 0 can be used as the preset two-dimensional reference plane. The preset partitioning scale is the spatial resolution parameter used when discretizing the preset two-dimensional reference surface into a grid, and is used to determine the granularity of the matrix units in the surface temperature matrix. The smaller the preset partitioning scale, the higher the degree of discretization of the raft foundation surface, the more matrix units the surface temperature matrix contains, and the greater the computational load of subsequent data processing. For example, a preset partitioning scale of 1m*1m can be used for grid partitioning.

[0021] Specifically, the actual coordinates corresponding to the pixel coordinates are determined using the intrinsic and extrinsic parameter matrices of the camera. Then, combining the one-to-one mapping relationship between each pixel coordinate and temperature value, the temperature value corresponding to each actual coordinate is determined. Subsequently, each actual coordinate (i.e., three-dimensional coordinate) of the raft foundation surface is oriented and projected onto a preset two-dimensional reference plane to obtain the two-dimensional projected coordinates of each actual coordinate on the preset two-dimensional reference plane. Finally, the temperature values ​​corresponding to each two-dimensional projected coordinate are filled into the grid corresponding to the preset two-dimensional reference plane to obtain the surface temperature matrix characterizing the surface temperature distribution of the raft foundation. It should be noted that the matrix value of each matrix element is the average surface temperature of each point within the actual physical region corresponding to the grid.

[0022] The monitoring center is used to determine the abnormal temperature coordinates in the surface temperature matrix based on the surface temperature matrix.

[0023] Specifically, multiple derivatives are calculated for each element of the surface temperature matrix, and anomaly matrix elements are determined based on the results. The derivative of each matrix element characterizes the temperature collapse intensity of the two-dimensional coordinates in the matrix element. The coordinates of the anomaly matrix elements are then determined as anomaly temperature coordinates.

[0024] Temperature collapse intensity is used to characterize the degree of temperature depression in a local area of ​​the raft foundation surface relative to the surrounding area. If the temperature depression is too large, it indicates that there may be a sudden temperature change in the local area.

[0025] Specifically, the derivative values ​​of each matrix element in the surface temperature matrix can characterize the specific temperature change pattern of the raft foundation surface corresponding to the matrix element. Specifically, it can cover the temperature collapse or temperature conduction of the raft foundation surface relative to adjacent positions. Therefore, by solving the multi-order derivatives of each matrix element in the surface temperature matrix, the matrix elements with abnormal temperature changes, i.e., abnormal matrix elements, can be accurately identified based on the solution results.

[0026] For example, for any matrix unit in the surface temperature matrix, its second-order spatial partial derivatives in the horizontal and vertical directions can be calculated using the finite difference method, and the second-order spatial partial derivatives in the horizontal and vertical directions can be added together to obtain the temperature Laplace value. The temperature Laplace value quantifies the degree of heat absorption from the surrounding area by the local region corresponding to the matrix unit. The larger the positive value of the temperature Laplace value, the more severe the temperature collapse at the corresponding position of the matrix unit. The temperature Laplace value can effectively highlight the characteristics of local abrupt changes. Then, based on the temperature Laplace values ​​of all matrix units, the mean and standard deviation of all temperature Laplace values ​​in the surface temperature matrix are calculated and determined for subsequent anomaly threshold setting.

[0027] To avoid misjudgments under drastic environmental changes using a fixed threshold, the system constructs a dynamic lower limit for collapse. The calculation formula is: ;in, This represents the mean of the temperature Laplace values ​​of the surface temperature matrix; This represents the standard deviation of the temperature Laplace values ​​in the surface temperature matrix; This represents the pre-configured lower bound constant of the Laplace; This indicates taking the maximum value; specifically, this parameter means taking... + and The maximum value in.

[0028] Furthermore, if the temperature Laplace value of a matrix element in the surface temperature matrix is ​​greater than the lower limit of the dynamic collapse degree, the matrix element is determined to be an anomalous matrix element. The lower limit of the dynamic collapse degree is determined by an adaptive calculation formula, which can automatically define the boundary of significant anomalies based on the distribution characteristics of the current ambient temperature field: when the global temperature field tends to be uniform (i.e., the standard deviation of the temperature Laplace value is small), the lower limit of the dynamic collapse degree is tightened accordingly to improve the sensitivity of identifying weak anomalies; when the ambient temperature field is complex (i.e., the standard deviation of the temperature Laplace value is large), the lower limit of the dynamic collapse degree is widened accordingly to avoid misjudging environmental noise as anomalies, thereby achieving robustness of the anomaly judgment threshold as the environment changes. Simultaneously, the lower limit of the dynamic collapse degree includes a manually set physical lower limit of the Laplace value; when global uniform cooling leads to an excessively low statistical threshold, the system forcibly activates the physical lower limit of the Laplace value to prevent minor data noise from being misjudged as engineering anomalies.

[0029] The monitoring center is used to determine the temperature propagation matrix based on the temperature difference between each adjacent matrix unit in the surface temperature matrix, and to determine the target abnormal sensor based on the abnormal temperature coordinates, the temperature propagation matrix and the coordinates of each sensor, wherein the matrix unit of the temperature propagation matrix is ​​used to characterize the probability distribution of the temperature of the matrix unit being conducted to each adjacent grid.

[0030] Optionally, the monitoring center is specifically used for: For any matrix element in the surface temperature matrix, the total temperature difference is determined based on the absolute value of the temperature difference between the matrix element and its adjacent matrix elements. The temperature conduction probability of the matrix element in the matrix direction is determined based on the total temperature difference and the absolute value of the temperature difference corresponding to each matrix direction. The temperature propagation matrix corresponding to the surface temperature matrix is ​​generated based on the temperature conduction probability of each matrix element in each matrix direction.

[0031] Since the overall temperature propagation path in the discretized grid space can be considered as the superposition of local temperature transfer processes between adjacent matrix units in the surface temperature matrix, the propagation characteristics of the overall temperature field can be derived by discretizing the macroscopic temperature propagation process and analyzing the temperature conduction law between adjacent matrix units. Based on the two-dimensional thermal conductivity characteristics of the raft foundation surface, the temperature conduction process in the surface temperature matrix is ​​limited to four matrix directions: up, down, left, and right. Accordingly, for any matrix unit in the surface temperature matrix, the temperature conduction probability of that matrix unit to the four orthogonal matrix directions needs to be calculated. Furthermore, for matrix units located at the corners of the matrix, their adjacent matrix units may be less than four. It should be noted that the influence of the sensor pre-embedded thickness on temperature conduction will be discussed later.

[0032] Furthermore, according to the laws of thermodynamics, the two-point propagation of temperature is related to the temperature difference. Therefore, the probability of temperature conduction in any matrix direction can be determined by the following formula: Wherein, matrix element j is the adjacent matrix element of matrix element i. Let i be the temperature transfer probability from i to j. Let i be the absolute value of the temperature difference between i and j. It is the sum of the absolute values ​​of the temperature differences between all adjacent matrix elements k and i. This represents the set of matrix elements adjacent to element i. It should be noted that if the temperature difference between i and all its adjacent matrix elements is 0, then... It is 0.

[0033] Furthermore, the temperature propagation matrix corresponding to the surface temperature matrix can be generated by determining the temperature conduction probability of each matrix element in each matrix direction. The matrix value P(i,j) of the generated temperature propagation matrix represents the temperature conduction probability from the i-th matrix element to the j-th matrix element. It should be emphasized that if i and j are not adjacent, the probability is 0.

[0034] Specifically, the monitoring center is used for: Project the coordinates of each sensor onto the preset two-dimensional reference plane to obtain the two-dimensional coordinates of each sensor; determine the matrix unit corresponding to the two-dimensional coordinates as the target matrix unit; update the matrix values ​​of the associated units of the target matrix unit in the temperature propagation matrix to obtain the target temperature propagation matrix, wherein the target matrix unit in the target temperature propagation matrix is ​​updated to only receive temperature input from other locations and not output temperature to other locations; determine the target abnormal sensor based on the abnormal temperature coordinates and the target temperature propagation matrix.

[0035] Specifically, the implementation idea of ​​this solution is to project the coordinates of each sensor onto the preset two-dimensional reference plane to obtain the two-dimensional coordinates corresponding to each sensor; determine the matrix unit in the surface temperature matrix corresponding to the two-dimensional coordinates as the target matrix unit, which is the target position for temperature detection during the heat conduction process; if the temperature conduction data corresponding to the target matrix unit is abnormal, it indicates that the sensor corresponding to the target matrix unit may be at risk of damage due to the influence of abnormal temperature field.

[0036] Furthermore, in the initially constructed temperature propagation matrix, heat can theoretically flow bidirectionally between any adjacent matrix units. However, when considering that the target matrix unit actually corresponds to a physical sensor, the physical laws change: the sensor itself is a passive temperature measuring point. Although it is affected by the temperature of the surrounding concrete (i.e., receives heat), its small volume and mass do not significantly alter the macroscopic temperature field distribution on the surface of the large-volume concrete (i.e., it does not output heat or affect the conduction probability). Therefore, to reflect this physical characteristic, the temperature propagation matrix must be modified: the matrix values ​​of the associated units (i.e., the matrix unit itself and its adjacent matrix units) of the target matrix unit are updated. In the updated target temperature propagation matrix, the target matrix unit is set as an absorption point that only receives temperature input from other locations and does not output temperature to other locations, that is (the matrix value of a matrix unit that conducts heat relative to itself is set to 1, and the matrix value of a matrix unit that conducts heat to other adjacent matrix units is set to 0). Furthermore, based on the abnormal temperature coordinates and the target temperature propagation matrix, the target abnormal sensor is determined.

[0037] It should be noted that the mathematical essence of the above update operation is that the system defines the target matrix unit as a fixed absorbing state node in a Markov chain. Mathematically, the probability of this node transitioning to itself is set to 1, while the probability of transitioning to all other adjacent nodes is forcibly set to zero. This operation mathematically completely severs the energy transfer path from the absorbing state node outwards. This not only conforms to the physical properties of passive temperature measurement by the sensor, but more importantly, this setting strictly satisfies the mathematical convergence premise of the absorbing Markov chain: once an abnormal heat conduction path reaches the pre-embedded sensor location, the energy will be completely absorbed and trapped, unable to diffuse outwards. Based on this convergence characteristic, the system can determine the probability of sensor damage through steady-state distribution analysis or probability calculation, eliminating the risk of misjudgment caused by false thermal feedback.

[0038] Optionally, the target anomaly sensor is determined based on the anomalous temperature coordinates and the target temperature propagation matrix, including: Based on the target temperature propagation matrix, calculate the probability distribution of temperature propagation from the abnormal temperature coordinates to the target matrix unit; in the probability distribution, determine the target abnormal matrix unit whose probability satisfies the preset probability condition, and determine the sensor corresponding to the target abnormal matrix unit as the target abnormal sensor.

[0039] Specifically, since the surface of the damaged raft foundation will generate heat to the interior, it is necessary to calculate the specific probability of the temperature influence reaching the target anomaly matrix unit starting from the abnormal temperature coordinate, in order to analyze whether the sensor corresponding to the target anomaly matrix unit is damaged.

[0040] Furthermore, the method for determining the probability distribution is as follows: Based on the target temperature propagation matrix, a first sub-matrix and a second sub-matrix are determined, wherein the first sub-matrix represents the temperature conduction probability between non-target matrix units, and the second sub-matrix represents the temperature conduction probability between the target matrix unit and each non-target matrix unit; based on the first sub-matrix and the second sub-matrix, a probability distribution matrix is ​​determined, wherein the matrix value of the probability distribution matrix represents the probability distribution of abnormal temperatures originating from each non-target matrix unit reaching the target matrix unit.

[0041] The first sub-matrix characterizes the temperature conduction probability between matrix units other than the target matrix unit. It is obtained by removing the rows and columns of the target matrix unit during matrix operations, allowing for the independent study of the internal thermal conduction characteristics of the non-sensor-covered area (i.e., the normal area) of the raft foundation surface. The second sub-matrix characterizes the temperature conduction probability from each non-target matrix unit to the target matrix unit. It is obtained by removing the rows corresponding to the target matrix unit from the target temperature propagation matrix, retaining only the columns corresponding to the target matrix unit. This characterizes the probability of heat converging from the normal area of ​​the raft foundation to the sensor location (target matrix unit). Since heat must diffuse and transfer layer by layer through multiple intermediate non-target matrix units during the conduction from the abnormal temperature coordinates to the target matrix unit, the structured decomposition of the target temperature propagation matrix decouples the complex global heat conduction problem into two independent processes: "conduction between normal areas" (first sub-matrix) and "conduction from the normal area to the sensor" (second sub-matrix). This enables refined analysis and quantitative calculation of the probability of heat conduction between adjacent matrix units.

[0042] For example, the surface temperature matrix is ​​in 2*2 form, and a sensor is embedded in the area represented by the first row and first column. The temperature conduction probability between adjacent matrix units is preset to 0.25. Then: The original temperature propagation matrix P is:

[0043] In P, (i, j) represents the probability of temperature conduction from the matrix element of the surface temperature matrix corresponding to the i-th row to the matrix element of the surface temperature matrix corresponding to the j-th column. Since each matrix element has only two adjacent matrices, 0.5 represents the self-retention or self-conduction of heat. It should be noted that the values ​​of heat conduction from the matrix element to the air are ignored in this matrix to simplify the calculation.

[0044] The updated temperature propagation matrix Q is:

[0045] The first row corresponding to the target matrix unit in Q is updated to have 100% self-propagation and 0% propagation to other adjacent matrix units.

[0046] First submatrix M:

[0047] The first submatrix M removes the rows and columns corresponding to the target matrix elements, and only represents the temperature conduction probability between non-target matrix elements; Second submatrix N:

[0048] The second submatrix N retains only the columns corresponding to the target matrix elements. At the same time, it deletes the (1,1) matrix elements in Q that represent their own heat conduction, which represent the probability of temperature conduction from non-target matrix elements to target matrix elements.

[0049] probability distribution matrix Where X is the intermediate matrix and N is the second submatrix; Where I is the identity matrix, To pre-determine a minimum value and prevent the denominator from being 0 during matrix inversion, M is the first submatrix. If N is a 3*1 matrix and M is a 3*3 matrix, then the final S is a 3*1 matrix, representing the probability that the abnormal temperatures originating from the other three non-target matrix units will reach the target matrix unit.

[0050] Furthermore, based on the target temperature propagation matrix, the probability distribution of temperature propagation from the abnormal temperature coordinates to the target matrix unit is calculated; in the probability distribution, the target abnormal matrix unit whose probability satisfies the preset probability condition is identified, and the sensor corresponding to the target abnormal matrix unit is identified as the target abnormal sensor.

[0051] The probability distribution matrix represents the state transition probability distribution of heat migration from each non-target matrix unit to each target matrix unit. Specifically, the matrix unit (i, j) of the probability distribution matrix characterizes the cumulative probability that heat originates from the i-th non-target matrix unit in the surface temperature matrix, travels through the conduction path defined by the target temperature propagation matrix, and ultimately reaches the j-th target matrix unit (i.e., the sensor). Based on the above definition, if the matrix value P of the matrix unit (x, y) of the probability distribution matrix is ​​greater than a preset probability threshold (artificially set according to the safety level of the raft foundation; for example, this preset probability threshold can be 0.05), then it indicates that a proportion P of the heat from the x-th non-target matrix unit will be transferred to the y-th target matrix unit. Since the abnormal temperature coordinates are located at specific matrix units, if the conduction probability P between the matrix unit and the target matrix unit satisfies the preset probability condition, it indicates that the heat generated by the abnormal temperature coordinates has the potential to significantly affect the temperature field of the y-th target matrix unit. Accordingly, the y-th target matrix unit is determined to be a target abnormal matrix unit affected by the abnormal temperature coordinates, and the sensor corresponding to the target abnormal matrix unit is identified as the target abnormal sensor to be warned.

[0052] The previous section introduced the specific method for determining the target anomaly sensor; the following section describes the specific method for determining the transmission cost.

[0053] The monitoring center is used to determine the conduction cost based on the abnormal temperature coordinates, the depth of the target abnormal sensor, and the temperature propagation matrix. The conduction cost is the heat cost required to conduct heat from the abnormal temperature coordinates to the location of the target abnormal sensor.

[0054] Specifically, since heat dissipates along its path due to thermal resistance during propagation in the medium, the conduction cost quantifies the energy loss in this dissipation process. It is important to emphasize that the temperature calculation process described in this application is based on a two-dimensional matrix model, not by directly inferring the three-dimensional spatial coordinates of the target anomaly sensor from the two-dimensional matrix. Instead, a two-stage decoupled analysis method of "two-dimensional planar diffusion + one-dimensional depth attenuation" is employed: In the first stage, the projected coordinates P of the target anomaly sensor on the preset two-dimensional reference plane are determined, and the surface propagation cost (i.e., two-dimensional path cost) of heat spreading from the anomaly temperature coordinates to the projected coordinates P is calculated based on the target temperature propagation matrix. In the second stage, the depth attenuation process of heat vertically conducting from the projected coordinates P to the physical location of the target anomaly sensor along the depth direction is considered. Since the thermal conductivity of concrete is relatively constant, heat transfer in this depth direction mainly exhibits an exponential attenuation with increasing burial depth.

[0055] Based on this, when calculating the conduction cost, the surface conduction cost is first obtained by solving the target temperature propagation matrix. Then, a depth attenuation coefficient matching the embedment depth of the target anomaly sensor is introduced to quantify the proportion of heat energy lost during the process of penetrating the concrete surface and entering the internal sensor embedment point. Finally, the surface conduction cost and the depth attenuation coefficient are coupled (e.g., weighted summation or multiplication) to obtain the total conduction cost. This application, through the above engineering approximation principle, decomposes the complex three-dimensional heat conduction problem into a combination of two-dimensional planar conduction and one-dimensional depth attenuation. While ensuring the accuracy of engineering monitoring, it significantly reduces the complexity of data processing, achieving the technical effect of high-precision assessment of the potential impact of surface temperature anomalies on deep internal sensors using only easily obtainable two-dimensional infrared image data.

[0056] Specifically, for any matrix unit in the temperature propagation matrix, the single-step cost of heat conduction from the matrix unit to the adjacent matrix unit is determined based on the temperature conduction probability from the matrix unit to the adjacent matrix unit; the shortest propagation path is determined with the abnormal temperature coordinates as the endpoint and the two-dimensional coordinates of the target abnormal sensor as the starting point; and the conduction cost is determined based on the single-step cost of each adjacent matrix unit in the shortest propagation path and the depth of the target abnormal sensor relative to the preset two-dimensional reference plane.

[0057] The shortest propagation path represented by the start and end points is the shortest heat loss path. When cold holes appear on the surface of the raft foundation, the heat around the sensor will be lost outward, which will lead to sensor damage.

[0058] Specifically, the greater the probability of temperature conduction between two matrix units, the lower the cost.

[0059]

[0060] in, Characterizes the single-step cost of heat conduction from matrix element i to adjacent matrix element j. The probability of heat conduction from matrix element i to its adjacent matrix element j can be determined using the temperature propagation matrix. If... A value of 0 indicates that there is no temperature difference between i and j, meaning that heat transfer cannot occur between i and j. Therefore, the single-step cost can be set to a preset maximum value.

[0061] Furthermore, the shortest propagation path can be determined by taking the matrix cell containing the abnormal temperature coordinates as the endpoint and the matrix cell containing the two-dimensional coordinates of the target anomaly sensor as the starting point. The single-step costs of each adjacent matrix cell along the shortest propagation path are then summed to obtain the surface propagation cost. Subsequently, the conduction cost is determined based on the depth of the target anomaly sensor relative to the preset two-dimensional reference plane (the distance between the target anomaly sensor's actual three-dimensional coordinates and the preset two-dimensional reference plane) and the surface propagation cost. It is understood that the greater the depth, the greater the cost; the specific formula is not limited here.

[0062] Furthermore, the formula for determining the transmission cost can be: ; Where S is the shortest propagation path, Here, cost is the transit index between matrix elements along the shortest propagation path, and cost is the single-step cost from matrix element i to j. The depth attenuation coefficient is a function value related to the depth of the target anomaly sensor relative to a preset two-dimensional reference surface, specifically an exponential attenuation function related to the thermal conductivity of concrete. The following condition must be met: the deeper the sensor is buried (T), the greater its attenuation coefficient (i.e., the greater the heat loss), which in turn leads to a lower total conduction cost. This increases accordingly. This exponential decay function is strictly related to the thermophysical properties of concrete; the specific mathematical form is not limited here to accommodate the monitoring needs of different materials. For example, Where T represents depth, in meters (m). The thermal conductivity of concrete is expressed in W / (m*K), where k is the preset conductivity coefficient. The value of k can be 1, and the specific value can be determined through laboratory calibration.

[0063] It should be noted that heat conduction within the raft foundation is a three-dimensional continuum physical process influenced by multiple parameters. Directly performing time-series thermal partial differential equations at the three-dimensional voxel level would introduce significant computational delays, failing to meet the real-time tracking requirements of the monitoring system. Therefore, this embodiment employs a dimension-reduction approximation calculation method. Specifically, the influence of temperature anomalies is decomposed along the structure into mutually orthogonal horizontal surface components (representing the influence along the shortest propagation path along the raft foundation surface) and vertical components. This operation is essentially an approximate solution.

[0064] It should be emphasized that if the target anomaly matrix cell and the matrix cell corresponding to the anomaly temperature coordinate are the same matrix cell, then only the depth of the sensor corresponding to the target anomaly matrix cell needs to be used to calculate the transmission cost.

[0065] Optionally, if the transmission cost is less than a preset cost threshold and the sensor data of the target abnormal sensor meets the data monitoring conditions, a raft foundation surface maintenance instruction is generated; if the transmission cost is greater than the preset cost threshold and the sensor data of the target abnormal sensor meets the data monitoring conditions, a sensor maintenance instruction is generated.

[0066] Specifically, within a preset monitoring period, sensor data from the target anomaly sensor is continuously collected; if the value of the sensor data deviates from a preset value range, it is determined that the sensor data triggers a data monitoring condition. The preset value range is defined as the data interval corresponding to when the target anomaly sensor is in normal working condition.

[0067] Specifically, if the transmission cost is less than the preset cost threshold and the data monitoring conditions are met, it indicates that the sensor data anomaly is due to the temperature effect of the surface cold hole. Therefore, it is only necessary to generate a raft foundation surface maintenance command to repair the cold hole on the raft foundation surface and ensure that the temperature is normal to restore the sensor data to normal. If the transmission cost exceeds the preset cost threshold and the data monitoring conditions are met, it is determined that the thermal loss effect of the surface cold hole cannot be effectively transmitted to the target abnormal sensor, and the cause of the target abnormal sensor's anomaly is not due to temperature. Therefore, it is necessary to generate a sensor maintenance command.

[0068] Furthermore, if cold holes appear in the raft foundation, abnormal temperature coordinates may also affect other invisible raft foundation surface areas along the shortest propagation path. To address this, the two-dimensional coordinates of each point along the shortest propagation path can be reconstructed into actual three-dimensional coordinates. Combined with the camera's installation coordinates, current shooting angle, and the actual three-dimensional coordinates, a camera monitoring command is generated to capture these actual three-dimensional coordinates, thereby enabling monitoring of all risk areas of the raft foundation along the shortest propagation path. Specifically, the reconstruction method involves recording the mapping relationship between each two-dimensional coordinate and the actual three-dimensional coordinate when reducing the actual three-dimensional coordinates of the raft foundation surface to two dimensions. This mapping relationship can then be directly invoked during the reconstruction process. Specifically, the camera monitoring command may include the camera's shooting tilt angle, shooting posture, and shooting time. The camera control system can perform directional shooting based on the camera monitoring command, providing workers with specific images of the damaged surface areas, facilitating raft foundation maintenance.

[0069] This invention provides a multi-sensor-based concrete curing monitoring system. The system includes a monitoring center, cameras, and multiple sensors configured on a raft foundation. The monitoring center acquires temperature images captured by the cameras and original construction information of the raft foundation. Based on the temperature images and the original construction information, it determines a surface temperature matrix of the raft foundation, where the surface temperature matrix represents the temperature value at each grid position on the surface of the raft foundation. The monitoring center also determines abnormal temperature coordinates within the surface temperature matrix. Furthermore, the monitoring center determines a temperature propagation matrix based on the temperature difference between adjacent matrix units in the surface temperature matrix. Based on the abnormal temperature coordinates, the temperature propagation matrix, and the coordinates of each sensor, it identifies a target abnormal sensor. The matrix units of the temperature propagation matrix represent the probability distribution of temperature conduction from the matrix unit to adjacent grid units. Finally, the monitoring center determines a conduction cost based on the abnormal temperature coordinates, the depth of the target abnormal sensor, and the temperature propagation matrix. The conduction cost is the heat cost required to conduct heat from the abnormal temperature coordinates to the location of the target abnormal sensor. Specifically, by determining the temperature propagation matrix corresponding to the surface temperature matrix, the temperature propagation pattern at each location point of the raft foundation can be determined in a directional manner. This allows for the identification of target anomaly sensors that may be affected by temperature changes. Furthermore, by monitoring the raft foundation area associated with the anomaly sensors using cameras, the accuracy of raft foundation monitoring data can be improved, ensuring the safety of the raft foundation.

[0070] Example 2 Figure 2 A flowchart illustrating a multi-sensor-based concrete curing monitoring method provided in this embodiment of the invention. The method is configured in a monitoring center of a multi-sensor-based concrete curing monitoring system, and specifically includes: Step 210: Obtain the temperature image captured by the camera and the original construction information of the raft foundation, and determine the surface temperature matrix of the raft foundation based on the temperature image and the original construction information, wherein the surface temperature matrix represents the temperature value of each grid position on the surface of the raft foundation.

[0071] Step 220: Determine the abnormal temperature coordinates in the surface temperature matrix based on the surface temperature matrix.

[0072] Step 230: Determine the temperature propagation matrix based on the temperature difference between each adjacent matrix unit in the surface temperature matrix, and determine the target abnormal sensor based on the abnormal temperature coordinates, the temperature propagation matrix and the coordinates of each sensor, wherein the matrix unit of the temperature propagation matrix is ​​used to characterize the probability distribution of the temperature of the matrix unit being conducted to each adjacent grid.

[0073] Step 240: Based on the abnormal temperature coordinates, the depth of the target abnormal sensor, and the temperature propagation matrix, determine the conduction cost, which is the heat cost required to conduct heat from the abnormal temperature coordinates to the location of the target abnormal sensor.

[0074] Step 250: Monitor the sensor data of the target anomaly sensor according to the transmission cost.

[0075] It should be noted that the method steps in this embodiment may specifically include any method steps and technical features executed by the monitoring center in the above embodiments, and have the same beneficial effects, which will not be elaborated here.

[0076] The monitoring center of this invention is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The monitoring center can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

Claims

1. A multi-sensor-based concrete curing monitoring system, characterized in that, The system includes a monitoring center, cameras, and multiple sensors configured within a concrete raft foundation. The monitoring center is used to acquire temperature images of the raft foundation surface captured by the camera and the original construction information of the raft foundation, and to determine the surface temperature matrix of the raft foundation based on the temperature images and the original construction information, wherein the surface temperature matrix represents the temperature value at each grid position on the surface of the raft foundation. The monitoring center is used to determine the abnormal temperature coordinates in the surface temperature matrix based on the surface temperature matrix. The monitoring center is used to determine the temperature propagation matrix based on the temperature difference between each adjacent matrix unit in the surface temperature matrix, and to determine the target abnormal sensor based on the abnormal temperature coordinates, the temperature propagation matrix and the coordinates of each sensor, wherein the matrix unit of the temperature propagation matrix is ​​used to characterize the probability distribution of the temperature of the matrix unit being conducted to each adjacent grid. The monitoring center is used to determine the conduction cost based on the abnormal temperature coordinates, the depth of the target abnormal sensor, and the temperature propagation matrix. The conduction cost is the heat cost required to conduct heat from the abnormal temperature coordinates to the location of the target abnormal sensor. The monitoring center is used to monitor the sensor data of the target anomaly sensor based on the transmission cost.

2. The system according to claim 1, characterized in that, The monitoring center is specifically used for: Obtain the intrinsic and extrinsic parameter matrices of the camera; Based on the pixel coordinates of the temperature image, the actual coordinates corresponding to the pixel coordinates are determined according to the intrinsic parameter matrix and the extrinsic parameter matrix; The spatial coordinate region of the raft foundation is determined based on the original construction information, and a preset two-dimensional reference plane is extracted from the spatial coordinate region. Based on a preset division scale, a preset two-dimensional reference surface is divided to obtain multiple division meshes; The temperature values ​​corresponding to each actual coordinate are mapped to the corresponding grid, and the matrix cell index is defined by the row and column coordinates corresponding to each grid. The surface temperature matrix of the raft foundation is generated by using the temperature value as the matrix value.

3. The system according to claim 1, characterized in that, The monitoring center is specifically used for: The multi-order derivatives of each matrix element of the surface temperature matrix are solved, and the abnormal matrix element is determined based on the solution results. The derivative results of the matrix element characterize the temperature collapse intensity of the two-dimensional coordinates in the matrix element. The coordinates of the abnormal matrix unit are determined as abnormal temperature coordinates.

4. The system according to claim 1, characterized in that, The monitoring center is specifically used for: For any matrix element in the surface temperature matrix, the total temperature difference is determined based on the absolute value of the temperature difference between the matrix element and its adjacent matrix elements, and the temperature conduction probability of the matrix element in the matrix direction is determined based on the total temperature difference and the absolute value of the temperature difference corresponding to each matrix direction. The temperature propagation matrix corresponding to the surface temperature matrix is ​​generated based on the temperature conduction probability of each matrix element in each matrix direction.

5. The system according to claim 2, characterized in that, The monitoring center is specifically used for: The coordinates of each sensor are projected onto the preset two-dimensional reference plane to obtain the two-dimensional coordinates of each sensor; The matrix element corresponding to the two-dimensional coordinates is determined as the target matrix element; The matrix values ​​of the associated units of the target matrix unit in the temperature propagation matrix are updated to obtain the target temperature propagation matrix. The target matrix unit in the target temperature propagation matrix is ​​updated to only receive temperature input from other locations and not output temperature to other locations. The target anomaly sensor is determined based on the abnormal temperature coordinates and the target temperature propagation matrix.

6. The system according to claim 5, characterized in that, The monitoring center is specifically used for: Based on the target temperature propagation matrix, calculate the probability distribution of temperature propagation reaching the target matrix cell starting from the abnormal temperature coordinate; In the probability distribution, target anomaly matrix units whose probabilities satisfy preset probability conditions are identified, and the sensors corresponding to the target anomaly matrix units are identified as target anomaly sensors.

7. The system according to claim 6, characterized in that, The monitoring center is specifically used for: Based on the target temperature propagation matrix, a first submatrix and a second submatrix are determined, wherein the first submatrix represents the temperature conduction probability between non-target matrix elements, and the second submatrix represents the temperature conduction probability between target matrix elements and each non-target matrix element. Based on the first submatrix and the second submatrix, a probability distribution matrix is ​​determined, wherein the matrix value of the probability distribution matrix represents the probability distribution of abnormal temperatures originating from each non-target matrix unit reaching the target matrix unit.

8. The system according to claim 1, characterized in that, The monitoring center is specifically used for: For any matrix unit in the temperature propagation matrix, the single-step cost of the matrix unit conducting heat to the adjacent matrix unit is determined based on the temperature conduction probability of the matrix unit to the adjacent matrix unit. Using the abnormal temperature coordinates as the endpoint and the two-dimensional coordinates of the target abnormal sensor as the starting point, the shortest propagation path is determined; The propagation cost is determined based on the single-step cost of each adjacent matrix unit in the shortest propagation path and the depth of the target anomaly sensor relative to the preset two-dimensional reference plane.

9. The system according to claim 8, characterized in that, The monitoring center is specifically used for: If the transmission cost is less than the preset cost threshold and the sensor data of the target abnormal sensor meets the data monitoring conditions, then a raft foundation surface repair instruction is generated. If the transmission cost is greater than a preset cost threshold, and the sensor data of the target abnormal sensor meets the data monitoring conditions, then a sensor maintenance command is generated.

10. The system according to claim 9, characterized in that, The monitoring center is specifically used for: Within a preset monitoring period, the sensor data of the target abnormal sensor is monitored. If the value or change pattern of the sensor data is not within the preset value range, the sensor data is determined to meet the data monitoring conditions.

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