Mass concrete construction temperature monitoring method and system

Abstract

The invention relates to a mass concrete construction temperature monitoring method and system, and specifically, the method comprises the steps: obtaining the real-time temperature sequence data of a plurality of temperature sensors distributed in a concrete structure; according to the real-time temperature sequence data of the temperature sensors, the distribution scale of volume points is determined based on the current temperature change rate, and a filtered temperature sequence of each temperature sensor is obtained through volume Kalman filtering adopting a simplified heat conduction model in state prediction; according to the temperature filtered by the temperature sensor and the space coordinates of the temperature sensor, a space tension parameter field is calculated by combining the distance between the temperature sensor and the surface of the concrete structure, and a three-dimensional temperature field of the whole concrete structure is constructed by adopting a tension spline interpolation method based on the space tension parameter field; based on the three-dimensional temperature field, the maximum temperature difference value between the core area and the surface area of the structure and the average cooling rate of the specific position of the surface of the structure in the preset time period are calculated.

Description

Technical Field

[0001] The present invention relates to the field of temperature monitoring, and in particular to a method and system for monitoring temperature during mass concrete construction. Background Art

[0002] Large-volume concrete structures have large cross-sectional dimensions, and the cement hydration process releases a significant amount of heat. Due to the poor thermal conductivity of concrete, the internal heat of hydration is difficult to dissipate, resulting in a significant increase in the structure's internal temperature. However, the surface of the structure, affected by the ambient temperature, dissipates heat more quickly, resulting in a relatively low temperature. This high internal temperature and low surface temperature condition creates a significant temperature gradient within the concrete structure, which in turn generates temperature stress. When temperature stress, especially tensile stress, exceeds the tensile strength of the concrete at the corresponding age, temperature cracks can occur. Temperature cracks not only affect the structure's appearance but, more seriously, can reduce its load-bearing capacity, durability, and impermeability, potentially jeopardizing its safety. Therefore, during the construction of large-volume concrete, real-time, accurate temperature monitoring and control is a crucial engineering step and a key measure for ensuring project quality and structural safety. Temperature monitoring of large-volume concrete typically involves embedding temperature sensors within the structure to capture time-varying temperature data at key measurement points. However, this presents two challenges. Temperature data often contains various noise interferences, such as random errors in the sensor itself, electromagnetic interference during data transmission, and transient fluctuations caused by changes in the construction environment. Furthermore, it is not feasible to have too many embedded temperature sensors. Too many would compromise the safety of large-volume concrete, while too few would make it difficult to fully understand the temperature distribution of the entire structure. The key to temperature monitoring in large-volume concrete construction is to obtain accurate temperature information at as many locations as possible using a limited number of temperature sensors. Summary of the Invention

[0003] In order to solve the above problems, the present invention provides a method for monitoring temperature during mass concrete construction, the method comprising the following steps:

[0004] Acquire real-time temperature series data from multiple temperature sensors distributed inside the concrete structure;

[0005] For the real-time temperature series 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 series of each temperature sensor is obtained by using a volumetric Kalman filter using a simplified heat conduction model in state prediction;

[0006] A spatial tension parameter field is calculated 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. A tension spline interpolation method is used based on the spatial tension parameter field to construct a three-dimensional temperature field of the entire concrete structure.

[0007] Based on the three-dimensional temperature field, the maximum temperature difference between the core area and the surface area of ​​the structure and the average temperature drop rate of a specific position on the surface of the structure within a preset time period are calculated.

[0008] Preferably, the scale of volume point distribution is determined based on the current temperature change rate, specifically:

[0009] Calculate the difference between the current real-time temperature value of each temperature sensor and the filtered temperature value at the previous moment to obtain the current temperature change;

[0010] According to the absolute value of the current temperature change, a scale for generating the volume point at the current moment is calculated by a preset function, wherein a scale parameter in the functional relationship is positively correlated with the absolute value;

[0011] The scale is used to generate a volume point set required for performing volumetric Kalman filtering at the current moment.

[0012] Preferably, the temperature sequence after filtering of each temperature sensor is obtained by using a volumetric Kalman filter with a simplified heat conduction model in state prediction, specifically:

[0013] In the state prediction of the volumetric Kalman filter, the hydration heat release per unit time and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transfer function is constructed using the hydration heat release per unit time and the equivalent heat dissipation coefficient;

[0014] Propagating the volume point set at the previous moment through the state transfer function to obtain the predicted state mean and predicted state covariance at the current moment;

[0015] In the state update, the predicted state mean and predicted state covariance are corrected in combination with the real-time temperature data at the current moment to obtain the filtered temperature value at the current moment, and then obtain the filtered temperature sequence.

[0016] Preferably, the spatial tension parameter field is calculated 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, specifically:

[0017] The geometric model of the concrete structure is used to calculate the shortest distance from any point in the structure to the surface of the structure;

[0018] The tension is calculated using the shortest distance, and for the spatial points required for interpolation calculation, the specific tension parameter value of each spatial point is obtained using the shortest distance and the tension, thereby forming a spatially varying tension parameter field.

[0019] Preferably, the three-dimensional temperature field of the entire concrete structure is constructed by a tension spline interpolation method based on the spatially varying tension parameter field, specifically as follows:

[0020] A three-dimensional interpolation grid is set within the concrete structure volume, and the governing equations of the three-dimensional tension spline interpolation are established;

[0021] Substituting the filtered temperature values ​​of each sensor into the control equation as known boundary conditions;

[0022] Solving the control equations using a numerical method to obtain coefficients of a three-dimensional tension spline function;

[0023] The obtained coefficients and spline basis functions are used to calculate the temperature value of each node on the three-dimensional interpolation grid to obtain the three-dimensional temperature field of the entire concrete structure.

[0024] In a second aspect of the present invention, a mass concrete construction temperature monitoring system is provided, the system comprising the following modules:

[0025] An acquisition module is used to obtain real-time temperature series data from multiple temperature sensors distributed inside the concrete structure;

[0026] a filtering module for determining the scale of volume point distribution based on the current temperature change rate for the real-time temperature series data of the temperature sensor, and obtaining a filtered temperature series of each temperature sensor using a volumetric Kalman filter using a simplified heat conduction model in state prediction;

[0027] A temperature field construction module is used to calculate a 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, and to construct a three-dimensional temperature field of the entire concrete structure using a tension spline interpolation method based on the spatial tension parameter field;

[0028] The temperature monitoring module is used to calculate the maximum temperature difference between the core area and the surface area of ​​the structure and the average cooling rate of a specific position on the surface of the structure within a preset time period based on the three-dimensional temperature field.

[0029] Preferably, the scale of volume point distribution is determined based on the current temperature change rate, specifically:

[0030] Calculate the difference between the current real-time temperature value of each temperature sensor and the filtered temperature value at the previous moment to obtain the current temperature change;

[0031] According to the absolute value of the current temperature change, a scale for generating the volume point at the current moment is calculated by a preset function, wherein a scale parameter in the functional relationship is positively correlated with the absolute value;

[0032] The scale is used to generate a volume point set required for performing volumetric Kalman filtering at the current moment.

[0033] Preferably, the temperature sequence after filtering of each temperature sensor is obtained by using a volumetric Kalman filter with a simplified heat conduction model in state prediction, specifically:

[0034] In the state prediction of the volumetric Kalman filter, the hydration heat release per unit time and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transfer function is constructed using the hydration heat release per unit time and the equivalent heat dissipation coefficient;

[0035] The volume point set at the previous moment is propagated through the state transfer function to obtain the predicted state mean and predicted state covariance at the current moment.

[0036] Preferably, the spatial tension parameter field is calculated 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, specifically:

[0037] The geometric model of the concrete structure is used to calculate the shortest distance from any point in the structure to the surface of the structure;

[0038] The tension is calculated using the shortest distance, and for the spatial points required for interpolation calculation, the specific tension parameter value of each spatial point is obtained using the shortest distance and the tension, thereby forming a spatially varying tension parameter field.

[0039] Preferably, the three-dimensional temperature field of the entire concrete structure is constructed by a tension spline interpolation method based on the spatially varying tension parameter field, specifically as follows:

[0040] A three-dimensional interpolation grid is set within the concrete structure volume, and the governing equations of the three-dimensional tension spline interpolation are established;

[0041] Substituting the filtered temperature values ​​of each sensor into the control equation as known boundary conditions;

[0042] Solving the control equations using a numerical method to obtain coefficients of a three-dimensional tension spline function;

[0043] The obtained coefficients and spline basis functions are used to calculate the temperature value of each node on the three-dimensional interpolation grid to obtain the three-dimensional temperature field of the entire concrete structure.

[0044] Finally, the present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method described in the first aspect.

[0045] The present invention introduces a volume point distribution scale that is adaptively adjusted based on the current temperature change rate into the volumetric Kalman filter, so that the filtering algorithm can dynamically adjust its sensitivity according to the severity of the temperature change. A simplified heat conduction model that takes into account hydration heat release and heat dissipation is incorporated into the state prediction step of the volumetric Kalman filter, and the filtering process is constrained by physical law information. When the substrate has sparse measurement data or large noise, a filtering result that is more consistent with the actual physical process can be obtained. A spatially varying tension parameter field is used for tension spline interpolation, and the tension parameter is associated with the distance of the measuring point from the structural surface, so that the interpolation can better distinguish between the steep temperature gradient in the surface area and the gentle temperature gradient in the core area. The generated three-dimensional temperature field is closer to the actual temperature distribution of large-volume concrete. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of Example 1;

[0047] Figure 2 A schematic diagram showing the arrangement of temperature sensors in a bulk concrete;

[0048] Figure 3 It is a temperature diagram of mass concrete;

[0049] Figure 4 This is a structural diagram of Example 2. DETAILED DESCRIPTION

[0050] In this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] Figure 1 1 shows a flow chart of a first embodiment of the present invention, as shown in FIG. Figure 1 As shown, the method includes the following steps:

[0053] S1, obtains real-time temperature series data from multiple temperature sensors distributed inside the concrete structure;

[0054] During the concrete pouring process, temperature sensors are pre-buried at key locations of the structure according to the design, such as Figure 2 As shown, the key positions include but are not limited to the geometric center, the cross-sectional center, points at different depths, surface points close to the template, the windward side and the leeward side, etc. The temperature sensor continuously measures the temperature at its location, and the temperature data is transmitted to the processing system via wired or wireless means.

[0055] S2, for the real-time temperature series data of the temperature sensor, determining the scale of the volume point distribution based on the current temperature change rate, and using a volumetric Kalman filter using a simplified heat conduction model in state prediction to obtain a filtered temperature series of each temperature sensor;

[0056] The temperature change of concrete is affected by many factors, and the process is not a simple linear relationship. The present invention uses a volumetric Kalman filter (CKF) to filter the temperature sequence. CKF approximates the probability distribution of the temperature state by selecting a set of specific and representative points, namely volume points. In the present invention, volume points are further generated according to the scale of the volume point distribution, and the scale of the volume point distribution is dynamically adjusted according to the current temperature change rate. The scale of the volume point distribution determines the distribution 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, such as increasing, so that the volume points are more spread out, so that the filter can better capture and adapt to such rapid changes; conversely, if the temperature changes slowly, the scale parameter will also be adjusted, such as decreasing, so that the volume points are more concentrated, thereby improving the accuracy of the estimate.

[0057] In one embodiment, the scale of the volume point distribution is determined based on the current temperature change rate, specifically:

[0058] Calculate the difference between the current real-time temperature value of each temperature sensor and the filtered temperature value at the previous moment to obtain the current temperature change;

[0059] According to the absolute value of the current temperature change, a scale for generating the volume point at the current moment is calculated by a preset function, wherein a scale parameter in the functional relationship is positively correlated with the absolute value;

[0060] The scale is used to generate a volume point set required for performing volumetric Kalman filtering at the current moment.

[0061] At the current moment, the raw temperature value directly measured by the sensor is obtained. The Kalman filter's filtered temperature estimate calculated at the previous moment is then used to find the difference between the two, giving the current temperature change. The temperature change reflects the actual temperature jump from the last filtered estimate to the present, serving as an indicator of the severity of the temperature change.

[0062] The absolute value of the temperature change is calculated and input into a preset function, which outputs a scale parameter used to generate the volume point 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 spread of the volume points is adjusted according to the recent temperature fluctuations. A larger scale parameter indicates that the temperature is changing rapidly and a wider spread is required to capture the changes; a smaller scale parameter indicates that the temperature is more stable and the volume points are closer together to improve the estimation accuracy. In one embodiment, a linear function is used as the preset function, but it is of course not limited to a linear function.

[0063] The volumetric Kalman filter generates a specific number of volume points based on the best state estimate at the previous moment, generally 2n, where n is the state dimension; since the present invention only estimates temperature, n = 1, that is, 2 points. The degree of dispersion of these points is determined by the state covariance matrix at the previous moment, which represents the uncertainty of the previous estimate. The present 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 past accumulated uncertainty is first calculated, and the product is used as the weight of the basis vector, and then the volume point is calculated.

[0064] In addition, a simplified heat conduction model is used in CKF's prediction. The simplified heat conduction model contains basic physical laws. In one embodiment, it includes the fact that cement hydration releases heat and that concrete dissipates heat to the surrounding environment. For example, when using the simplified heat conduction model, the predicted temperature at the next moment is approximately equal to the current temperature + a slight increase in temperature due to hydration heat - a slight decrease in temperature due to heat dissipation to the air. By combining the physical model in this way, the filtering results of CKF are closer to the actual physical process. Finally, after step S2 processes the original temperature sequence of each sensor, the output is a smoother and more accurate filtered temperature sequence.

[0065] In one embodiment, the filtered temperature sequence of each temperature sensor is obtained by using a volumetric Kalman filter with a simplified heat conduction model in state prediction, specifically:

[0066] In the state prediction of the volumetric Kalman filter, the hydration heat release per unit time and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transfer function is constructed using the hydration heat release per unit time and the equivalent heat dissipation coefficient;

[0067] Propagating the volume point set at the previous moment through the state transfer function to obtain the predicted state mean and predicted state covariance at the current moment;

[0068] In the state update, the predicted state mean and predicted state covariance are corrected in combination with the real-time temperature data at the current moment to obtain the filtered temperature value at the current moment, and then obtain the filtered temperature sequence.

[0069] In order to make the prediction more consistent with the actual temperature propagation and dissipation, the present invention uses a simplified heat conduction model to perform volumetric Kalman filtering on the temperature series. The simplified heat conduction model includes the cooling caused by the continuous generation of heat due to the cement hydration reaction inside the concrete and the heat at the measuring point being dissipated to the surrounding area with lower temperature. The simplified heat conduction model simplifies complex boundary conditions and material properties, and mainly considers heat generation and transfer. The hydration heat release per unit time is obtained through the simplified heat conduction model, and the hydration heat release per unit time indicates how much heat is expected to be released by cement hydration per unit time under the current state; the simplified heat conduction model also includes an equivalent heat dissipation coefficient, which indicates the difficulty of heat dissipation at the location of the temperature sensor. The simplified heat conduction model is constructed through experimental data, empirical formulas or more complex hydration models.

[0070] A state transfer function is constructed using the hydration heat release per unit time and the equivalent heat dissipation coefficient. In one embodiment, the state transfer function predicts a temperature that is the filtered temperature plus the temperature increase due to hydration heat, minus the temperature decrease due to heat dissipation. The set of volume points generated at the previous moment is then substituted into the constructed state transfer function. Each volume point is independently calculated using the state transfer function, resulting in a predicted volume point position. Using the new set of volume points propagated through the state transfer function and their respective weights (preferably, the weights are the inverse of the number of volume points), the predicted state statistics for the current moment are calculated. These predicted state statistics include a predicted state mean and a predicted state covariance. In one embodiment, the predicted state mean is obtained by taking a weighted average 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.

[0071] After the VCKF prediction phase, the update phase of the VCKF begins. 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, or gain, depends on the covariance of the prediction and the covariance of the measurement noise. This factor determines how much confidence should be placed in the current measurement when revising the predicted value. In one embodiment, the predicted value is corrected to obtain a final corrected value. The correction factor is the difference between the actual measured temperature and the predicted temperature, multiplied by the Kalman gain.

[0072] S3, calculating a spatial tension parameter field based on the filtered temperature of the temperature sensor and the spatial coordinates of the temperature sensor in combination with the distance between the temperature sensor and the surface of the concrete structure, and constructing a three-dimensional temperature field of the entire concrete structure using a tension spline interpolation method based on the spatial tension parameter field;

[0073] In a large-volume concrete structure, since the temperature sensors cannot be removed, burying too many temperature sensors will affect the stress of the large-volume concrete structure, and burying too few will make it impossible to fully monitor the temperature of the large-volume concrete structure. In the present invention, the temperature value and three-dimensional spatial coordinates of the known sensors are further utilized to construct the temperature field of the large-volume concrete structure. In one embodiment, the three-dimensional temperature field is constructed by tension spline interpolation. For each temperature sensor, the spatial tension parameter field is calculated based on the distance from the surface of the concrete structure. At different positions of the structure, the tension used in the tension spline interpolation is different. In one embodiment, the spatial tension parameter field is calculated based on the filtered temperature of the temperature sensor and the spatial coordinates of the temperature sensor, combined with the distance of the temperature sensor from the surface of the concrete structure, as follows:

[0074] The geometric model of the concrete structure is used to calculate the shortest distance from any point in the structure to the surface of the structure;

[0075] The tension is calculated using the shortest distance, and for the spatial points required for interpolation calculation, the specific tension parameter value of each spatial point is obtained using the shortest distance and the tension, thereby forming a spatially varying tension parameter field.

[0076] For any point P within 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 the point to these patches and take the minimum value. Map the calculated shortest distance to a specific tension parameter value. In one embodiment, smaller distances correspond to greater tension. This distance is applied to all points requiring temperature interpolation, forming a distribution of tension values ​​that varies with spatial position, i.e., a spatial tension parameter field. In one embodiment, the shortest distance is mapped to a specific tension parameter value using an inverse proportional function or an exponential decay function.

[0077] After obtaining the spatially varying tension parameter field, further, the three-dimensional temperature field of the entire concrete structure is constructed by a tension spline interpolation method based on the spatially varying tension parameter field, specifically as follows:

[0078] A three-dimensional interpolation grid is set within the concrete structure volume, and the governing equations of the three-dimensional tension spline interpolation are established;

[0079] Substituting the filtered temperature values ​​of each sensor into the control equation as known boundary conditions;

[0080] Solving the control equations using a numerical method to obtain coefficients of a three-dimensional tension spline function;

[0081] The obtained coefficients and spline basis functions are used to calculate the temperature value of each node on the three-dimensional interpolation grid to obtain the three-dimensional temperature field of the entire concrete structure.

[0082] The concrete structure is discretized, preferably by dividing the concrete into a three-dimensional grid, where each point in the grid is a square or triangular grid. The goal of tension spline interpolation is to find a smooth three-dimensional function that passes through all known sensor temperature points. At the same time, the function must satisfy tension constraints; the greater the tension, the more the function tends to be stretched between data points. When establishing these governing equations, for each spatial location involved in the equations, the spatially varying tension parameter value corresponding to that location is extracted from the calculated spatial tension parameter field and substituted into the equations. If a grid node coincides with a sensor location, the temperature of that node is directly set to the sensor's temperature value. If a sensor is located between grid nodes, its temperature value serves as a constraint in the equations, and the resulting 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, and the unknowns in this system are typically the coefficients of the basis functions that define the three-dimensional tension spline function. Due to the large size of the system, numerical computer methods are required to solve it, finding a unique set of coefficients that satisfies all the conditions and obtaining a set of values ​​that define the desired three-dimensional tension spline function. For any node on the grid, the estimated temperature of the node can be calculated by substituting its coordinates into the tension spline function expression composed of coefficients and basis functions. Figure 3 Schematic diagram showing the temperature of a profiled surface of mass concrete.

[0083] S4, based on the three-dimensional temperature field, calculating the maximum temperature difference between the core area and the surface area of ​​the structure, and the average cooling rate of a specific position on the surface of the structure within a preset time period.

[0084] After calculating the three-dimensional temperature field, several important indicators can be calculated based on this data, including but not limited to the maximum temperature difference between the core and surface areas, and the average cooling rate. The maximum temperature difference is the maximum temperature difference between the core and surface areas of the structure. The highest internal temperature point and the lowest surface temperature point are found in the three-dimensional temperature field, and the difference between them is calculated. For example, if the highest internal temperature is 68°C and the lowest surface temperature is 23°C, the maximum temperature difference is 45°C. This temperature difference is the 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 surface points. Preferably, the average hourly temperature drop over a specific time period is calculated. For example, the average hourly temperature drop at a surface point over the past day is calculated to be 0.8°C. Excessively rapid cooling rates can lead to surface cracking. After calculating these indicators, they are compared with control standards to determine whether the structure's current thermal state is safe. Based on the temperature changes, subsequent maintenance measures, such as whether insulation adjustments are needed and when formwork can be removed, can be determined.

[0085] Figure 4 1 shows a structural diagram of a second embodiment of the present invention, as shown in FIG. Figure 4 As shown, the system includes the following modules:

[0086] An acquisition module is used to obtain real-time temperature series data from multiple temperature sensors distributed inside the concrete structure;

[0087] a filtering module for determining the scale of volume point distribution based on the current temperature change rate for the real-time temperature series data of the temperature sensor, and obtaining a filtered temperature series of each temperature sensor using a volumetric Kalman filter using a simplified heat conduction model in state prediction;

[0088] A temperature field construction module is used to calculate a 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, and to construct a three-dimensional temperature field of the entire concrete structure using a tension spline interpolation method based on the spatial tension parameter field;

[0089] The temperature monitoring module is used to calculate the maximum temperature difference between the core area and the surface area of ​​the structure and the average cooling rate of a specific position on the surface of the structure within a preset time period based on the three-dimensional temperature field.

[0090] Preferably, the scale of volume point distribution is determined based on the current temperature change rate, specifically:

[0091] Calculate the difference between the current real-time temperature value of each temperature sensor and the filtered temperature value at the previous moment to obtain the current temperature change;

[0092] According to the absolute value of the current temperature change, a scale for generating the volume point at the current moment is calculated by a preset function, wherein a scale parameter in the functional relationship is positively correlated with the absolute value;

[0093] The scale is used to generate a volume point set required for performing volumetric Kalman filtering at the current moment.

[0094] Preferably, the temperature sequence after filtering of each temperature sensor is obtained by using a volumetric Kalman filter with a simplified heat conduction model in state prediction, specifically:

[0095] In the state prediction of the volumetric Kalman filter, the hydration heat release per unit time and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transfer function is constructed using the hydration heat release per unit time and the equivalent heat dissipation coefficient;

[0096] Propagating the volume point set at the previous moment through the state transfer function to obtain the predicted state mean and predicted state covariance at the current moment;

[0097] In the state update, the predicted state mean and predicted state covariance are corrected in combination with the real-time temperature data at the current moment to obtain the filtered temperature value at the current moment, and then obtain the filtered temperature sequence.

[0098] Preferably, the spatial tension parameter field is calculated 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, specifically:

[0099] The geometric model of the concrete structure is used to calculate the shortest distance from any point in the structure to the surface of the structure;

[0100] The tension is calculated using the shortest distance, and for the spatial points required for interpolation calculation, the specific tension parameter value of each spatial point is obtained using the shortest distance and the tension, thereby forming a spatially varying tension parameter field.

[0101] Preferably, the three-dimensional temperature field of the entire concrete structure is constructed by a tension spline interpolation method based on the spatially varying tension parameter field, specifically as follows:

[0102] A three-dimensional interpolation grid is set within the concrete structure volume, and the governing equations of the three-dimensional tension spline interpolation are established;

[0103] Substituting the filtered temperature values ​​of each sensor into the control equation as known boundary conditions;

[0104] Solving the control equations using a numerical method to obtain coefficients of a three-dimensional tension spline function;

[0105] The obtained coefficients and spline basis functions are used to calculate the temperature value of each node on the three-dimensional interpolation grid to obtain the three-dimensional temperature field of the entire concrete structure.

[0106] The present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in the first embodiment is implemented.

[0107] In addition, the present invention also provides a computer device, which includes at least a memory and a processor. A computer program is stored in the memory, and when the computer program is executed by the processor, it implements the method described in the first embodiment.

[0108] Through the description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by adding the necessary general hardware platform, or of course, by combining hardware and software. Based on this understanding, the essence of the above technical solution or the portion 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.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it, and other embodiments may also be used. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for monitoring temperature during mass concrete construction, characterized in that: The method comprises the following steps: Acquire real-time temperature series data from multiple temperature sensors distributed inside the concrete structure; For the real-time temperature series 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 series of each temperature sensor is obtained by using a volumetric Kalman filter using a simplified heat conduction model in state prediction; A spatial tension parameter field is calculated 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. A tension spline interpolation method is used based on the spatial tension parameter field to construct a three-dimensional temperature field of the entire concrete structure. Based on the three-dimensional temperature field, the maximum temperature difference between the core area and the surface area of ​​the structure and the average temperature drop rate of a specific position on the surface of the structure within a preset time period are calculated.

2. The method according to claim 1, wherein The scale of volume point distribution is determined based on the current temperature change rate, specifically: Calculate the difference between the current real-time temperature value of each temperature sensor and the filtered temperature value at the previous moment to obtain the current temperature change; According to the absolute value of the current temperature change, a scale for generating the volume point at the current moment is calculated by a preset function, wherein a scale parameter in the functional relationship is positively correlated with the absolute value; The scale is used to generate a volume point set required for performing volumetric Kalman filtering at the current moment.

3. The method according to claim 1, wherein The temperature sequence of each temperature sensor after filtering is obtained by using a volumetric Kalman filter with a simplified heat conduction model in state prediction, specifically: In the state prediction of the volumetric Kalman filter, the hydration heat release per unit time and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transfer function is constructed using the hydration heat release per unit time and the equivalent heat dissipation coefficient; Propagating the volume point set at the previous moment through the state transfer function to obtain the predicted state mean and predicted state covariance at the current moment; In the state update, the predicted state mean and predicted state covariance are corrected in combination with the real-time temperature data at the current moment to obtain the filtered temperature value at the current moment, and then obtain the filtered temperature sequence.

4. The method according to claim 1, wherein The spatial tension parameter field is calculated 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, specifically: The geometric model of the concrete structure is used to calculate the shortest distance from any point in the structure to the surface of the structure; The tension is calculated using the shortest distance, and for the spatial points required for interpolation calculation, the specific tension parameter value of each spatial point is obtained using the shortest distance and the tension, thereby forming a spatially varying tension parameter field.

5. The method according to claim 1, wherein The three-dimensional temperature field of the entire concrete structure is constructed by a tension spline interpolation method based on the spatially varying tension parameter field, specifically: A three-dimensional interpolation grid is set within the concrete structure volume, and the governing equations of the three-dimensional tension spline interpolation are established; Substituting the filtered temperature values ​​of each sensor into the control equation as known boundary conditions; Solving the control equations using a numerical method to obtain coefficients of a three-dimensional tension spline function; The obtained coefficients and spline basis functions are used to calculate the temperature value of each node on the three-dimensional interpolation grid to obtain the three-dimensional temperature field of the entire concrete structure.

6. A temperature monitoring system for mass concrete construction, characterized in that: The system includes the following modules: An acquisition module is used to obtain real-time temperature series data from multiple temperature sensors distributed inside the concrete structure; a filtering module for determining the scale of volume point distribution based on the current temperature change rate for the real-time temperature series data of the temperature sensor, and obtaining a filtered temperature series of each temperature sensor using a volumetric Kalman filter using a simplified heat conduction model in state prediction; A temperature field construction module is used to calculate a 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, and to construct a three-dimensional temperature field of the entire concrete structure using a tension spline interpolation method based on the spatial tension parameter field; The temperature monitoring module is used to calculate the maximum temperature difference between the core area and the surface area of ​​the structure and the average cooling rate of a specific position on the surface of the structure within a preset time period based on the three-dimensional temperature field.

7. The system according to claim 6, wherein: The scale of volume point distribution is determined based on the current temperature change rate, specifically: Calculate the difference between the current real-time temperature value of each temperature sensor and the filtered temperature value at the previous moment to obtain the current temperature change; According to the absolute value of the current temperature change, a scale for generating the volume point at the current moment is calculated by a preset function, wherein a scale parameter in the functional relationship is positively correlated with the absolute value; The scale is used to generate a volume point set required for performing volumetric Kalman filtering at the current moment.

8. The system according to claim 6, wherein: The temperature sequence of each temperature sensor after filtering is obtained by using a volumetric Kalman filter with a simplified heat conduction model in state prediction, specifically: In the state prediction of the volumetric Kalman filter, the hydration heat release per unit time and the equivalent heat dissipation coefficient of the simplified heat conduction model are obtained, and the state transfer function is constructed using the hydration heat release per unit time and the equivalent heat dissipation coefficient; Propagating the volume point set at the previous moment through the state transfer function to obtain the predicted state mean and predicted state covariance at the current moment; In the state update, the predicted state mean and predicted state covariance are corrected in combination with the real-time temperature data at the current moment to obtain the filtered temperature value at the current moment, and then obtain the filtered temperature sequence.

9. The system according to claim 6, wherein: The spatial tension parameter field is calculated 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, specifically: The geometric model of the concrete structure is used to calculate the shortest distance from any point in the structure to the surface of the structure; The tension is calculated using the shortest distance, and for the spatial points required for interpolation calculation, the specific tension parameter value of each spatial point is obtained using the shortest distance and the tension, thereby forming a spatially varying tension parameter field.

10. The system according to claim 6, wherein: The three-dimensional temperature field of the entire concrete structure is constructed by a tension spline interpolation method based on the spatially varying tension parameter field, specifically: A three-dimensional interpolation grid is set within the concrete structure volume, and the governing equations of the three-dimensional tension spline interpolation are established; Substituting the filtered temperature values ​​of each sensor into the control equation as known boundary conditions; Solving the control equations using a numerical method to obtain coefficients of a three-dimensional tension spline function; The obtained coefficients and spline basis functions are used to calculate the temperature value of each node on the three-dimensional interpolation grid to obtain the three-dimensional temperature field of the entire concrete structure.

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