Mass concrete construction temperature monitoring method and system

Abstract

This invention relates to a method and system for monitoring the temperature during the construction of large-volume concrete. Specifically, it involves acquiring real-time temperature sequence data from multiple temperature sensors distributed within the concrete structure; determining the scale of the volumetric point distribution based on the current temperature change rate using the real-time temperature sequence data from the temperature sensors; obtaining the filtered temperature sequence from each temperature sensor using a volumetric Kalman filter that employs a simplified heat conduction model in state prediction; calculating a spatial tension parameter field based on the filtered temperature and spatial coordinates of the temperature sensors, combined with the distance between the temperature sensors and the surface of the concrete structure; constructing a three-dimensional temperature field for the entire concrete structure using a tension spline interpolation method based on the spatial tension parameter field; and calculating the maximum temperature difference between the core region and the surface region of the structure, as well as the average cooling rate at a specific location on the structure surface within a preset time period, based on the three-dimensional temperature field.

Description

Technical Field

[0001] This invention relates to the field of temperature monitoring, specifically to a method and system for monitoring the temperature during the construction of large-volume concrete. Background Technology

[0002] Large-volume concrete structures have large cross-sectional dimensions, releasing a significant amount of heat during cement hydration. Due to the poor thermal conductivity of concrete, the heat of hydration generated internally is not easily dissipated, leading to a significant increase in the internal temperature of the structure. However, the surface of the structure is affected by the ambient temperature, dissipating heat more quickly and remaining relatively cool. This state of high internal temperature and low surface temperature creates a significant temperature gradient within the concrete structure, resulting in thermal stress. When thermal stress, especially tensile stress, exceeds the tensile strength of the concrete at the corresponding age, temperature cracks will occur. Temperature cracks not only affect the appearance of the structure but, more seriously, reduce its load-bearing capacity, durability, and impermeability, and may even endanger structural safety. Therefore, real-time and accurate monitoring and control of temperature during the construction of large-volume concrete is a crucial engineering aspect and a key measure to ensure project quality and structural safety.

[0003] Temperature monitoring of large-volume concrete typically involves embedding temperature sensors within the structure to acquire time-varying temperature data at key measuring points. However, this approach presents two challenges: firstly, temperature data often contains various noise interferences, such as random errors in the sensors themselves, electromagnetic interference during data transmission, and instantaneous fluctuations caused by changes in the construction environment; secondly, the number of embedded temperature sensors is limited. Too many sensors would compromise the safety of the large-volume concrete, while too few would fail to provide a comprehensive understanding of the temperature distribution across the entire structure. The key to successful temperature monitoring of large-volume concrete construction lies in obtaining accurate temperature information from as many locations as possible using a limited number of temperature sensors. Summary of the Invention

[0004] To address the above problems, this invention provides a method for monitoring the construction temperature of large-volume concrete, the method comprising the following steps:

[0005] Acquire real-time temperature sequence data from multiple temperature sensors distributed inside the concrete structure;

[0006] For the real-time temperature sequence data of the temperature sensors, the scale of the volume point distribution is determined based on the current temperature change rate, and the filtered temperature sequence of each temperature sensor is obtained by using a volume Kalman filter that employs a simplified heat conduction model in the state prediction.

[0007] Based on the filtered temperature from the temperature sensor and the spatial coordinates of the temperature sensor, the spatial tension parameter field is calculated by combining the distance between the temperature sensor and the surface of the concrete structure. Based on the spatial tension parameter field, the three-dimensional temperature field of the entire concrete structure is constructed using the tension spline interpolation method.

[0008] Based on the three-dimensional temperature field, the maximum temperature difference between the core region and the surface region of the structure is calculated, as well as the average cooling rate of a specific location on the surface of the structure within a preset time period.

[0009] Preferably, the determination of the scale of the volume point distribution based on the current rate of temperature change specifically includes:

[0010] Calculate the difference between the real-time temperature value of each temperature sensor at the current moment and the filtered temperature value at the previous moment to obtain the current temperature change.

[0011] Based on the absolute value of the current temperature change, the scale used to generate the volume point at the current moment is calculated through a preset function, and the scale parameter in the function relationship is positively correlated with the absolute value.

[0012] The scale is used to generate the set of volume points required to perform the current time-initiated capacitive Kalman filter.

[0013] Preferably, the step of obtaining the filtered temperature sequence from each temperature sensor using a volumetric Kalman filter employing a simplified heat conduction model in state prediction specifically involves:

[0014] In the state prediction of the volumetric Kalman filter, the heat release per unit time of hydration and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transition function is constructed using the heat release per unit time of hydration and the equivalent heat dissipation coefficient.

[0015] The set of volume points from the previous time step is propagated through the state transition function to obtain the mean and covariance of the predicted state at the current time step.

[0016] In the state update, the predicted state mean and predicted state covariance are corrected by combining the real-time temperature data at the current moment to obtain the filtered temperature value at the current moment, and then the filtered temperature sequence is obtained.

[0017] Preferably, the step of calculating the spatial tension parameter field based on the filtered temperature from the temperature sensor and the spatial coordinates of the temperature sensor, combined with the distance between the temperature sensor and the surface of the concrete structure, specifically involves:

[0018] The shortest distance from any point within the concrete structure to the surface of the structure is calculated using a geometric model of the concrete structure.

[0019] The tension is calculated using the shortest distance. For the spatial points required for interpolation calculation, the specific tension parameter values ​​of each spatial point are obtained using the shortest distance and the tension, forming a spatially varying tension parameter field.

[0020] Preferably, the three-dimensional temperature field of the entire concrete structure is constructed using the tension spline interpolation method based on the spatial tension parameter field, specifically as follows:

[0021] A three-dimensional interpolation mesh is set within the volume of the concrete structure, and the governing equations for three-dimensional tension spline interpolation are established.

[0022] The filtered temperature values ​​from each sensor are used as known boundary conditions and substituted into the control equation.

[0023] The coefficients of the three-dimensional tension spline function are obtained by solving the governing equations using numerical methods.

[0024] Using the obtained coefficients and spline basis functions, the temperature values ​​of each node on the three-dimensional interpolation grid are calculated to obtain the three-dimensional temperature field of the entire concrete structure.

[0025] In a second aspect of the invention, a temperature monitoring system for large-volume concrete construction is provided, the system comprising the following modules:

[0026] The acquisition module is used to acquire real-time temperature sequence data from multiple temperature sensors distributed inside the concrete structure.

[0027] The filtering module is used to determine the scale of the volume point distribution based on the current temperature change rate for the real-time temperature sequence data of the temperature sensors, and to obtain the filtered temperature sequence of each temperature sensor by using a volume Kalman filter that employs a simplified heat conduction model in the state prediction.

[0028] The temperature field construction module is used to calculate the spatial tension parameter field based on the filtered temperature of the temperature sensor and the spatial coordinates of the temperature sensor, combined with the distance between the temperature sensor and the surface of the concrete structure. Based on the spatial tension parameter field, the tension spline interpolation method is used to construct the three-dimensional temperature field of the entire concrete structure.

[0029] The temperature monitoring module is used to calculate the maximum temperature difference between the core region and the surface region of the structure, as well as the average cooling rate of a specific location on the surface of the structure within a preset time period, based on the three-dimensional temperature field.

[0030] Preferably, the determination of the scale of the volume point distribution based on the current rate of temperature change specifically includes:

[0031] Calculate the difference between the real-time temperature value of each temperature sensor at the current moment and the filtered temperature value at the previous moment to obtain the current temperature change.

[0032] Based on the absolute value of the current temperature change, the scale used to generate the volume point at the current moment is calculated through a preset function, and the scale parameter in the function relationship is positively correlated with the absolute value.

[0033] The scale is used to generate the set of volume points required to perform the current time-initiated capacitive Kalman filter.

[0034] Preferably, the step of obtaining the filtered temperature sequence from each temperature sensor using a volumetric Kalman filter employing a simplified heat conduction model in state prediction specifically involves:

[0035] In the state prediction of the volumetric Kalman filter, the heat release per unit time of hydration and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transition function is constructed using the heat release per unit time of hydration and the equivalent heat dissipation coefficient.

[0036] The set of volume points from the previous time step is propagated through the state transition function to obtain the mean and covariance of the predicted state at the current time step.

[0037] Preferably, the step of calculating the spatial tension parameter field based on the filtered temperature from the temperature sensor and the spatial coordinates of the temperature sensor, combined with the distance between the temperature sensor and the surface of the concrete structure, specifically involves:

[0038] The shortest distance from any point within the concrete structure to the surface of the structure is calculated using a geometric model of the concrete structure.

[0039] The tension is calculated using the shortest distance. For the spatial points required for interpolation calculation, the specific tension parameter values ​​of each spatial point are obtained using the shortest distance and the tension, forming a spatially varying tension parameter field.

[0040] Preferably, the three-dimensional temperature field of the entire concrete structure is constructed using the tension spline interpolation method based on the spatial tension parameter field, specifically as follows:

[0041] A three-dimensional interpolation mesh is set within the volume of the concrete structure, and the governing equations for three-dimensional tension spline interpolation are established.

[0042] The filtered temperature values ​​from each sensor are used as known boundary conditions and substituted into the control equation.

[0043] The coefficients of the three-dimensional tension spline function are obtained by solving the governing equations using numerical methods.

[0044] Using the obtained coefficients and spline basis functions, the temperature values ​​of each node on the three-dimensional interpolation grid are calculated to obtain the three-dimensional temperature field of the entire concrete structure.

[0045] Finally, the present invention also provides a computer storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0046] This invention introduces a volumetric point distribution scale that is adaptively adjusted based on the current rate of temperature change into the volumetric Kalman filter, enabling the filtering algorithm to dynamically adjust its sensitivity according to the severity of temperature changes. Furthermore, a simplified heat conduction model considering hydration heat release and heat dissipation is incorporated into the state prediction step of the volumetric Kalman filter. By using physical law information to constrain the filtering process, filtering results that better reflect the actual physical process can be obtained even when the substrate has sparse measurement data or high noise. Moreover, tension spline interpolation is performed using a spatially varying tension parameter field, which correlates the tension parameter with the distance of the measuring point from the structural surface. This allows the interpolation to better distinguish between the steep temperature gradient in the surface area and the gentle temperature gradient in the core area, resulting in a three-dimensional temperature field that more closely approximates the actual temperature distribution of large-volume concrete. Attached Figure Description

[0047] Figure 1 This is a flowchart of Example 1;

[0048] Figure 2 A schematic diagram showing the arrangement of temperature sensors in a large volume of concrete is shown.

[0049] Figure 3 This is a schematic diagram of the temperature of a large-volume concrete structure.

[0050] Figure 4 This is a structural diagram of Example 2. Detailed Implementation

[0051] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[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] Figure 1 A flowchart of the first embodiment of the present invention is shown, as follows: Figure 1 As shown, the method includes the following steps:

[0054] S1, acquire real-time temperature sequence data from multiple temperature sensors distributed inside the concrete structure;

[0055] During the concrete pouring process, temperature sensors are pre-embedded at key locations in the structure according to the design, such as... Figure 2 As shown, the key locations include, but are not limited to, the geometric center, the cross-sectional center, points at different depths, surface points near the template, the windward side, and the leeward side. Temperature sensors continuously measure the temperature at their location, and the temperature data is transmitted to the processing system via wired or wireless means.

[0056] S2, for the real-time temperature sequence data of the temperature sensor, the scale of the volume point distribution is determined based on the current temperature change rate, and the filtered temperature sequence of each temperature sensor is obtained by using volume Kalman filtering with a simplified heat conduction model in state prediction.

[0057] Concrete temperature changes are influenced by various factors, and the process is not a simple linear relationship. This invention employs a volumetric Kalman filter (CKF) to filter the temperature sequence. The CKF approximates the probability distribution of the temperature state by selecting a specific set of representative points, i.e., volume points. In this invention, volume points are further generated based on the scale of the volume point distribution, and this scale is dynamically adjusted according to the current rate of temperature change. The scale of the volume point distribution determines the dispersion range of the volume points around the current estimated temperature. For example, if the temperature suddenly rises rapidly, the scale parameter will be adjusted accordingly, for example, by increasing it, so that the volume points are more widely dispersed, allowing the filter to better capture and adapt to such rapid changes; conversely, if the temperature change is gradual, the scale parameter will also be adjusted, for example, by decreasing it, so that the volume points are more concentrated, improving the estimation accuracy.

[0058] In one embodiment, determining the scale of the volume point distribution based on the current rate of temperature change specifically involves:

[0059] Calculate the difference between the real-time temperature value of each temperature sensor at the current moment and the filtered temperature value at the previous moment to obtain the current temperature change.

[0060] Based on the absolute value of the current temperature change, the scale used to generate the volume point at the current moment is calculated through a preset function, and the scale parameter in the function relationship is positively correlated with the absolute value.

[0061] The scale is used to generate the set of volume points required to perform the current time-initiated capacitive Kalman filter.

[0062] At the current moment, the raw temperature value directly measured by the sensor is obtained. Then, the filtered temperature estimate calculated by the Kalman filter at the previous moment is found, and the difference between the two is calculated to obtain the current temperature change. The temperature change reflects how much the temperature has actually jumped since the last filtered estimate, serving as an indicator of the drasticness of temperature change.

[0063] The absolute value of the temperature change is calculated and input into a preset function. The preset function outputs the scale parameter used to generate the volume points at the current moment. In one embodiment, the scale parameter in the preset function is positively correlated with the absolute value of the temperature change; that is, if the temperature change is large, the calculated scale parameter will be relatively large, and vice versa. The scale parameter quantifies the extent to which the dispersion range of the volume points is adjusted based on recent temperature fluctuations. A larger scale parameter indicates that the temperature is changing rapidly, requiring a wider dispersion range to capture the change; a smaller scale parameter indicates that the temperature is more stable, and the volume points should be closer together to improve estimation accuracy. In one embodiment, a linear function is used as the preset function, but it is not limited to linear functions.

[0064] The capacitive Kalman filter generates a specific number of volume points based on the best state estimate from the previous time step, typically 2^n, where n is the state dimension; however, since this invention only estimates temperature, n=1, or 2 points. The dispersion of these points is determined by the state covariance matrix from the previous time step, which represents the uncertainty of the previous estimate. This invention integrates the calculated dynamic scale into the volume point generation formula as an additional scaling factor. In a more specific embodiment, the product of the scale and the accumulated uncertainty from the past is first calculated, and this product is used as the weights of the basis vectors before the volume points are calculated.

[0065] Furthermore, the CKF prediction incorporates a simplified heat conduction model. This model incorporates fundamental physical laws, including, in one embodiment, the heat released during cement hydration and the heat dissipated from concrete to the surrounding environment. For example, using the simplified heat conduction model, the predicted temperature for the next moment is approximately equal to the current temperature plus a small increase due to hydration heat minus a small decrease due to heat dissipation into the air. By combining this physical model, the CKF filtering results more closely approximate the actual physical process. Finally, step S2 processes the raw temperature sequences from each sensor, outputting a smoother and more accurate filtered temperature sequence.

[0066] In one embodiment, the step of obtaining the filtered temperature sequence from each temperature sensor using a volumetric Kalman filter employing a simplified heat conduction model in state prediction specifically involves:

[0067] In the state prediction of the volumetric Kalman filter, the heat release per unit time of hydration and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transition function is constructed using the heat release per unit time of hydration and the equivalent heat dissipation coefficient.

[0068] The set of volume points from the previous time step is propagated through the state transition function to obtain the mean and covariance of the predicted state at the current time step.

[0069] In the state update, the predicted state mean and predicted state covariance are corrected by combining the real-time temperature data at the current moment to obtain the filtered temperature value at the current moment, and then the filtered temperature sequence is obtained.

[0070] To ensure the predictions better reflect actual temperature propagation and dissipation, this invention employs a simplified heat conduction model to apply volumetric Kalman filtering to the temperature sequence. This simplified heat conduction model incorporates the continuous heat generation within the concrete due to cement hydration and the cooling effect caused by heat loss from the measuring point to surrounding cooler areas. The simplified heat conduction model simplifies complex boundary conditions and material properties, primarily focusing on heat generation and transfer. The heat release per unit time due to hydration is obtained through this model, representing the expected heat release during cement hydration within a unit of time under the current conditions. The simplified heat conduction model also includes an equivalent heat dissipation coefficient, which indicates the ease with which heat dissipates at the temperature sensor location. This simplified heat conduction model is constructed using experimental data, empirical formulas, or more complex hydration models.

[0071] A state transition function is constructed using the heat released by hydration per unit time and the equivalent heat dissipation coefficient. In one embodiment, the temperature predicted by the state transition function is the filtered temperature plus the temperature rise caused by the heat of hydration, and then the temperature drop caused by heat dissipation is subtracted. Then, the set of volume points generated at the previous time step is substituted into the constructed state transition function, and each volume point is independently calculated using the state transition function, resulting in a predicted volume point position for each. Using the new set of volume points propagated through the state transition function and their respective weights (preferably, the weights are the reciprocal of the number of volume points), the predicted state statistics for the current time step are calculated. The predicted state statistics include the predicted state mean and the predicted state covariance. In one embodiment, the predicted state mean is obtained by weighted averaging of the propagated volume points, and the predicted state covariance is obtained by calculating the weighted dispersion of the propagated volume points relative to the predicted mean.

[0072] After the prediction phase of the volumetric Kalman filter, the update phase of the volumetric Kalman filter is initiated. The actual measured temperature is compared with the predicted temperature, the difference between them is calculated, and a weighting factor is calculated. In one embodiment, the weighting factor, i.e., the gain, depends on the covariance of the prediction and the covariance of the measurement noise. It determines the extent to which the current measurement value should be trusted when correcting the prediction. In another embodiment, the final corrected value is obtained by correcting the prediction value; the correction is the difference between the actual measured temperature and the predicted temperature, multiplied by the Kalman gain.

[0073] S3. Based on the filtered temperature and spatial coordinates of the temperature sensor, and combined with the distance between the temperature sensor and the concrete structure surface, the spatial tension parameter field is calculated. Based on the spatial tension parameter field, the tension spline interpolation method is used to construct the three-dimensional temperature field of the entire concrete structure.

[0074] In large-volume concrete structures, since temperature sensors cannot be removed, embedding too many temperature sensors can affect the stress of the large-volume concrete structure, while embedding too few cannot comprehensively monitor the temperature of the large-volume concrete structure. In this invention, the temperature field of the large-volume concrete structure is constructed by further utilizing the known temperature values ​​and three-dimensional spatial coordinates of the sensors. In one embodiment, a tension spline interpolation method is used to construct the three-dimensional temperature field. For each temperature sensor, a spatial tension parameter field is calculated based on its distance from the concrete structure surface. The tension used in the tension spline interpolation differs at different locations on the structure. In one embodiment, the calculation of the spatial tension parameter field based on the filtered temperature of the temperature sensor, the spatial coordinates of the temperature sensor, and the distance of the temperature sensor from the concrete structure surface specifically involves:

[0075] The shortest distance from any point within the concrete structure to the surface of the structure is calculated using a geometric model of the concrete structure.

[0076] The tension is calculated using the shortest distance. For the spatial points required for interpolation calculation, the specific tension parameter values ​​of each spatial point are obtained using the shortest distance and the tension, forming a spatially varying tension parameter field.

[0077] For any point P inside the structure, calculate the distance from point P to every point on all surfaces of the structure. Find the minimum of these distances. For complex geometries, discretize the surface into a large number of small patches, then calculate the distance from points to these patches and take the minimum value. Map the calculated shortest distance to a specific tension parameter value. In one embodiment, the smaller the distance, the greater the corresponding tension. Apply this to all points requiring temperature interpolation to form a distribution of tension values ​​that varies with spatial location, i.e., a spatial tension parameter field. In one embodiment, mapping the shortest distance to a specific tension parameter value uses an inverse proportional function or an exponential decay function.

[0078] After obtaining the spatially varying tension parameter field, the three-dimensional temperature field of the entire concrete structure is further constructed using the tension spline interpolation method based on the spatial tension parameter field, specifically as follows:

[0079] A three-dimensional interpolation mesh is set within the volume of the concrete structure, and the governing equations for three-dimensional tension spline interpolation are established.

[0080] The filtered temperature values ​​from each sensor are used as known boundary conditions and substituted into the control equation.

[0081] The coefficients of the three-dimensional tension spline function are obtained by solving the governing equations using numerical methods.

[0082] Using the obtained coefficients and spline basis functions, the temperature values ​​of each node on the three-dimensional interpolation grid are calculated to obtain the three-dimensional temperature field of the entire concrete structure.

[0083] The concrete structure is discretized, preferably into a three-dimensional mesh, where each point is a square or triangular grid. The goal of tension spline interpolation is to find a three-dimensional function that is as smooth as possible, passing through all known sensor temperature points. Simultaneously, the three-dimensional function must satisfy tension constraints; the greater the tension, the more taut the function becomes between data points. When establishing these governing equations, for each spatial location involved in the equations, the spatial variation tension parameter value corresponding to that location is extracted from the obtained spatial tension parameter field and substituted into the equation. If a grid node happens to coincide with the sensor location, the temperature of that node is directly set as the sensor's temperature value. If the sensor is located between grid nodes, its temperature value serves as a constraint condition in the equation system, and the final solution of the temperature field function at that sensor location is equal to or very close to the sensor's measured value. A linear or nonlinear system of equations is established, where the unknowns are typically the coefficients of the basis functions defining the three-dimensional tension spline function. Due to the large scale of the equation system, a computer numerical method is needed to solve it, finding the unique set of coefficients that satisfy all conditions, resulting in a set of values ​​that determine the desired three-dimensional tension spline function. For any node on the grid, its coordinates can be substituted into the tension spline function expression consisting of coefficients and basis functions to calculate the estimated temperature of that node. Figure 3 A schematic diagram showing the temperature of a cross-section of a large-volume concrete structure is presented.

[0084] S4. Based on the three-dimensional temperature field, calculate the maximum temperature difference between the core region and the surface region of the structure, as well as the average cooling rate of a specific location on the surface of the structure within a preset time period.

[0085] After calculating the three-dimensional temperature field, several important indicators can be calculated, including but not limited to the maximum temperature difference between the core and surface regions, and the average cooling rate. The maximum temperature difference is the largest temperature difference between the core and surface regions of the structure. This is achieved by finding the point with the highest internal temperature and the point with the lowest surface temperature in the three-dimensional temperature field and calculating the difference between them. For example, if the highest internal temperature is 68℃ and the lowest surface temperature is 23℃, the maximum temperature difference is 45℃. This temperature difference is a primary indicator for assessing the risk of concrete cracking. During construction, it is also necessary to monitor the temperature drop over time at one or more key points on the surface. Preferably, this involves calculating the average temperature drop per hour over a specific time period. For example, calculating the average hourly temperature drop of a surface point over the past day as 0.8℃. An excessively rapid cooling rate can lead to surface cracking. After calculating these indicators, they are compared with control standards to determine whether the current thermal state of the structure is safe. Based on the temperature changes, subsequent maintenance measures are determined, such as whether insulation adjustments are needed and when formwork can be removed.

[0086] Figure 4 A structural diagram of a second embodiment of the present invention is shown, as follows. Figure 4 As shown, the system includes the following modules:

[0087] The acquisition module is used to acquire real-time temperature sequence data from multiple temperature sensors distributed inside the concrete structure.

[0088] The filtering module is used to determine the scale of the volume point distribution based on the current temperature change rate for the real-time temperature sequence data of the temperature sensors, and to obtain the filtered temperature sequence of each temperature sensor by using a volume Kalman filter that employs a simplified heat conduction model in the state prediction.

[0089] The temperature field construction module is used to calculate the spatial tension parameter field based on the filtered temperature of the temperature sensor and the spatial coordinates of the temperature sensor, combined with the distance between the temperature sensor and the surface of the concrete structure. Based on the spatial tension parameter field, the tension spline interpolation method is used to construct the three-dimensional temperature field of the entire concrete structure.

[0090] The temperature monitoring module is used to calculate the maximum temperature difference between the core region and the surface region of the structure, as well as the average cooling rate of a specific location on the surface of the structure within a preset time period, based on the three-dimensional temperature field.

[0091] Preferably, the determination of the scale of the volume point distribution based on the current rate of temperature change specifically includes:

[0092] Calculate the difference between the real-time temperature value of each temperature sensor at the current moment and the filtered temperature value at the previous moment to obtain the current temperature change.

[0093] Based on the absolute value of the current temperature change, the scale used to generate the volume point at the current moment is calculated through a preset function, and the scale parameter in the function relationship is positively correlated with the absolute value.

[0094] The scale is used to generate the set of volume points required to perform the current time-initiated capacitive Kalman filter.

[0095] Preferably, the step of obtaining the filtered temperature sequence from each temperature sensor using a volumetric Kalman filter employing a simplified heat conduction model in state prediction specifically involves:

[0096] In the state prediction of the volumetric Kalman filter, the heat release per unit time of hydration and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transition function is constructed using the heat release per unit time of hydration and the equivalent heat dissipation coefficient.

[0097] The set of volume points from the previous time step is propagated through the state transition function to obtain the mean and covariance of the predicted state at the current time step.

[0098] In the state update, the predicted state mean and predicted state covariance are corrected by combining the real-time temperature data at the current moment to obtain the filtered temperature value at the current moment, and then the filtered temperature sequence is obtained.

[0099] Preferably, the step of calculating the spatial tension parameter field based on the filtered temperature from the temperature sensor and the spatial coordinates of the temperature sensor, combined with the distance between the temperature sensor and the surface of the concrete structure, specifically involves:

[0100] The shortest distance from any point within the concrete structure to the surface of the structure is calculated using a geometric model of the concrete structure.

[0101] The tension is calculated using the shortest distance. For the spatial points required for interpolation calculation, the specific tension parameter values ​​of each spatial point are obtained using the shortest distance and the tension, forming a spatially varying tension parameter field.

[0102] Preferably, the three-dimensional temperature field of the entire concrete structure is constructed using the tension spline interpolation method based on the spatial tension parameter field, specifically as follows:

[0103] A three-dimensional interpolation mesh is set within the volume of the concrete structure, and the governing equations for three-dimensional tension spline interpolation are established.

[0104] The filtered temperature values ​​from each sensor are used as known boundary conditions and substituted into the control equation.

[0105] The coefficients of the three-dimensional tension spline function are obtained by solving the governing equations using numerical methods.

[0106] Using the obtained coefficients and spline basis functions, the temperature values ​​of each node on the three-dimensional interpolation grid are calculated to obtain the three-dimensional temperature field of the entire concrete structure.

[0107] The present invention also provides a computer-readable storage medium on which a computer program is stored, the computer program implementing the method described in Embodiment 1 when executed by a processor.

[0108] In addition, the present invention provides a computer device, the computer device including at least a memory and a processor, wherein a computer program is stored in the memory, and the computer program, when executed by the processor, implements the method as described in Embodiment 1.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of a necessary general-purpose hardware platform, or by a combination of hardware and software. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a computer product. The present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Other embodiments may also be used. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the construction temperature of large-volume concrete, characterized in that, The method includes the following steps: Acquire real-time temperature sequence data from multiple temperature sensors distributed inside the concrete structure; For the real-time temperature sequence data of the temperature sensors, the scale of the volume point distribution is determined based on the current temperature change rate, and the filtered temperature sequence of each temperature sensor is obtained by using a volume Kalman filter that employs a simplified heat conduction model in the state prediction. Based on the filtered temperature from the temperature sensor and the spatial coordinates of the temperature sensor, the spatial tension parameter field is calculated by combining the distance between the temperature sensor and the surface of the concrete structure. Based on the spatial tension parameter field, the three-dimensional temperature field of the entire concrete structure is constructed using the tension spline interpolation method. Based on the three-dimensional temperature field, the maximum temperature difference between the core region and the surface region of the structure is calculated, as well as the average cooling rate of a specific location on the surface of the structure within a preset time period. The process of obtaining the filtered temperature sequences from each temperature sensor using a volumetric Kalman filter with a simplified heat conduction model in state prediction is as follows: In the state prediction of the volumetric Kalman filter, the heat release per unit time of hydration and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transition function is constructed using the heat release per unit time of hydration and the equivalent heat dissipation coefficient. The set of volume points from the previous time step is propagated through the state transition function to obtain the mean and covariance of the predicted state at the current time step. In the state update, the predicted state mean and predicted state covariance are corrected by combining the real-time temperature data at the current moment to obtain the filtered temperature value at the current moment, and then the filtered temperature sequence is obtained.

2. The method as described in claim 1, characterized in that, The scale for determining the volume point distribution based on the current rate of temperature change is specifically as follows: Calculate the difference between the real-time temperature value of each temperature sensor at the current moment and the filtered temperature value at the previous moment to obtain the current temperature change. Based on the absolute value of the current temperature change, the scale used to generate the volume point at the current moment is calculated through a preset function, and the scale parameter in the function relationship is positively correlated with the absolute value. The scale is used to generate the set of volume points required to perform the current time-initiated capacitive Kalman filter.

3. The method as described in claim 1, characterized in that, The spatial tension parameter field is calculated based on the filtered temperature from the temperature sensor, the spatial coordinates of the temperature sensor, and the distance between the temperature sensor and the concrete structure surface. Specifically: The shortest distance from any point within the concrete structure to the surface of the structure is calculated using a geometric model of the concrete structure. The tension is calculated using the shortest distance. For the spatial points required for interpolation calculation, the specific tension parameter values ​​of each spatial point are obtained using the shortest distance and the tension, forming a spatially varying tension parameter field.

4. The method as described in claim 1, characterized in that, The three-dimensional temperature field of the entire concrete structure is constructed using the tension spline interpolation method based on the spatial tension parameter field, specifically as follows: A three-dimensional interpolation mesh is set within the volume of the concrete structure, and the governing equations for three-dimensional tension spline interpolation are established. The filtered temperature values ​​from each sensor are used as known boundary conditions and substituted into the control equation. The coefficients of the three-dimensional tension spline function are obtained by solving the governing equations using numerical methods. Using the obtained coefficients and spline basis functions, the temperature values ​​of each node on the three-dimensional interpolation grid are calculated to obtain the three-dimensional temperature field of the entire concrete structure.

5. A temperature monitoring system for large-volume concrete construction, characterized in that, The system includes the following modules: The acquisition module is used to acquire real-time temperature sequence data from multiple temperature sensors distributed inside the concrete structure. The filtering module is used to determine the scale of the volume point distribution based on the current temperature change rate for the real-time temperature sequence data of the temperature sensors, and to obtain the filtered temperature sequence of each temperature sensor by using a volume Kalman filter that employs a simplified heat conduction model in the state prediction. The temperature field construction module is used to calculate the spatial tension parameter field based on the filtered temperature of the temperature sensor and the spatial coordinates of the temperature sensor, combined with the distance between the temperature sensor and the surface of the concrete structure. Based on the spatial tension parameter field, the tension spline interpolation method is used to construct the three-dimensional temperature field of the entire concrete structure. The temperature monitoring module is used to calculate the maximum temperature difference between the core area and the surface area of ​​the structure, as well as the average cooling rate of a specific location on the surface of the structure within a preset time period, based on the three-dimensional temperature field. The process of obtaining the filtered temperature sequences from each temperature sensor using a volumetric Kalman filter with a simplified heat conduction model in state prediction is as follows: In the state prediction of the volumetric Kalman filter, the heat release per unit time of hydration and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transition function is constructed using the heat release per unit time of hydration and the equivalent heat dissipation coefficient. The set of volume points from the previous time step is propagated through the state transition function to obtain the mean and covariance of the predicted state at the current time step. In the state update, the predicted state mean and predicted state covariance are corrected by combining the real-time temperature data at the current moment to obtain the filtered temperature value at the current moment, and then the filtered temperature sequence is obtained.

6. The system as described in claim 5, characterized in that, The scale for determining the volume point distribution based on the current rate of temperature change is specifically as follows: Calculate the difference between the real-time temperature value of each temperature sensor at the current moment and the filtered temperature value at the previous moment to obtain the current temperature change. Based on the absolute value of the current temperature change, the scale used to generate the volume point at the current moment is calculated through a preset function, and the scale parameter in the function relationship is positively correlated with the absolute value. The scale is used to generate the set of volume points required to perform the current time-initiated capacitive Kalman filter.

7. The system as described in claim 5, characterized in that, The spatial tension parameter field is calculated based on the filtered temperature from the temperature sensor, the spatial coordinates of the temperature sensor, and the distance between the temperature sensor and the concrete structure surface. Specifically: The shortest distance from any point within the concrete structure to the surface of the structure is calculated using a geometric model of the concrete structure. The tension is calculated using the shortest distance. For the spatial points required for interpolation calculation, the specific tension parameter values ​​of each spatial point are obtained using the shortest distance and the tension, forming a spatially varying tension parameter field.

8. The system as described in claim 5, characterized in that, The three-dimensional temperature field of the entire concrete structure is constructed using the tension spline interpolation method based on the spatial tension parameter field, specifically as follows: A three-dimensional interpolation mesh is set within the volume of the concrete structure, and the governing equations for three-dimensional tension spline interpolation are established. The filtered temperature values ​​from each sensor are used as known boundary conditions and substituted into the control equation. The coefficients of the three-dimensional tension spline function are obtained by solving the governing equations using numerical methods. Using the obtained coefficients and spline basis functions, the temperature values ​​of each node on the three-dimensional interpolation grid are calculated to obtain the three-dimensional temperature field of the entire concrete structure.

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