Temperature real-time monitoring method and system based on mass concrete continuous pouring

Abstract

The invention discloses a temperature real-time monitoring method and system based on mass concrete continuous pouring, and the method comprises the steps: collecting data in a mass concrete continuous pouring process in real time through the arrangement of sensors, forming a global temperature set of temperature values at each collection moment, obtaining the temperature value of each layer at each collection moment according to pouring layering, and carrying out the real-time monitoring of the temperature of each layer; forming a layered temperature set of the temperature values of each layer; determining a concrete pouring abnormal index; determining a dynamic early warning threshold value in the concrete continuous pouring process, determining a multi-stage early warning and response mechanism according to comparison between the concrete abnormal index and the threshold value, and generating an early warning control signal; and based on the early warning control signal, controlling the water cooling equipment and carrying out real-time monitoring feedback. According to the method, the specific temperature conditions of each layer at different acquisition moments can be precisely known, the positions of the layers with abnormal temperature can be more precisely positioned, structural cracks caused by temperature incoordination between the layers are avoided, and real-time monitoring of the temperature of mass concrete continuous pouring is achieved.

Description

Technical Field

[0001] This invention relates to the field of temperature monitoring technology, and more specifically, to a method and system for real-time temperature monitoring based on continuous pouring of large-volume concrete. Background Technology

[0002] After concrete is poured, the cement hydration reaction generates heat of hydration, causing the internal temperature of the concrete to rise. Due to the large volume of concrete, heat is not easily dissipated, and the internal temperature may become excessively high. Furthermore, a large temperature difference between the inside and outside of the concrete can easily lead to temperature cracks, affecting structural quality and safety. Real-time monitoring of the internal temperature of the concrete provides a basis for temperature control during construction, allowing for timely measures such as adjusting cooling water flow and adding insulation layers to prevent cracking.

[0003] In existing technologies, temperature monitoring based on continuous pouring of large-volume concrete cannot provide real-time continuous monitoring. The temperature measurement frequency is low, making it easy to miss key temperature change points. The number of temperature measurement points is limited, making it difficult to fully reflect the temperature distribution inside the large-volume concrete. For large and complex concrete projects, the measurement is difficult and cannot meet the high-precision requirements of temperature monitoring for large-volume concrete. Insufficient temperature measurement accuracy due to large temperature differences inside the concrete may lead to misjudgment of the actual temperature condition of the concrete. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for real-time temperature monitoring based on continuous pouring of large-volume concrete, so as to solve the above-mentioned problems existing in the prior art.

[0005] The application is as follows:

[0006] A method for real-time temperature monitoring based on continuous pouring of large-volume concrete is provided, including the following steps:

[0007] Step 1: Collect temperature datasets in real time by deploying sensors to collect internal temperature, surface temperature, mold entry temperature and ambient temperature of different concrete layers during the continuous pouring of large-volume concrete, and obtain basic data of the concrete pouring process.

[0008] Step 2: Preprocess the acquired data, calculate the temperature value at each acquisition moment to form a global temperature set of the temperature values ​​at each acquisition moment, and obtain the temperature values ​​of each layer at each acquisition moment according to the pouring layer to form a layer temperature set of the temperature values ​​of each layer; calculate the correlation coefficient of each layer temperature set with all other layer temperature sets to determine the average temperature correlation of each layer temperature set; analyze the Euclidean distance difference of the instantaneous temperature rise rate of each layer set, and statistically analyze the relative deviation of the temperature fluctuation amplitude of each layer set to generate a layer temperature coordination index.

[0009] Step 3: Divide the global temperature set into multiple temperature subsets according to time windows. Based on shape clustering of cross-correlation coefficients, obtain the high-temperature change cluster and the low-temperature stability cluster for concrete. Calculate the distribution entropy of the mean temperature difference between adjacent subsets within each cluster to generate the intra-cluster change index. Statistically analyze the overall differences in the representation of subsets between two clusters. Combine the intra-cluster change index to obtain the temperature risk index. Based on the dispersion of the coordination index and the temperature risk index, determine the concrete pouring anomaly index.

[0010] Step 4: Obtain the mean and standard deviation of the concrete anomaly index during the continuous concrete pouring process to set the initial threshold. Adjust the mean and standard deviation in real time according to the progress of the pouring construction using the sliding window method to determine the dynamic early warning threshold during the continuous concrete pouring process. Based on the comparison between the concrete anomaly index and the threshold, determine the multi-level early warning and response mechanism and generate an early warning control signal.

[0011] Step 5: Based on the early warning control signal, obtain the initial cooling index of the concrete water cooling equipment and confirm the water supply requirement of the concrete water cooling equipment; if there is no water supply requirement, use the initial cooling index as the control cooling index; if there is a water supply requirement, confirm the control cooling index of the concrete water cooling equipment, start the concrete water cooling equipment for cooling and conduct real-time monitoring and feedback.

[0012] The calculation of the temperature value at each acquisition moment, forming a global temperature set of the temperature values ​​at each acquisition moment, includes:

[0013] The temperature value at each acquisition moment is calculated from the core temperature value and the surface temperature value. The core temperature value is the internal temperature measured by the embedded sensor, and the surface temperature value is the surface temperature measured by the surface infrared sensor or the patch sensor. The temperature value is calculated by weighted average of the core temperature value and the surface temperature value.

[0014] Calculate the correlation coefficients between each temperature set and all other temperature sets, and determine the mean temperature correlation of each temperature set, including:

[0015] Through the correlation coefficient ρ ij The mean temperature correlation of each temperature set is determined, and the formula for calculating the mean temperature correlation is as follows:

[0016]

[0017] Among them, M it This represents the mean temperature correlation value, and m represents the number of measurement points.

[0018] Analyzing the Euclidean distance differences in the instantaneous temperature rise rates of each layer and statistically analyzing the relative deviations in the temperature fluctuation amplitudes of each layer, a layered temperature compatibility index is generated, including:

[0019] Calculate the instantaneous temperature rise rate of each layer set, obtain the Euclidean distance difference between the instantaneous temperature rise rates of each layer set and all other layer sets based on the instantaneous temperature rise rate of each layer set, and take the average value to obtain the average difference in temperature change rate.

[0020] Extract the peak temperature of each layer set, calculate the difference between the peak temperature of each layer set and the peak temperature of all other layer sets, and obtain the relative deviation of the temperature fluctuation amplitude of each layer set;

[0021] The stratified temperature coordination index is generated based on the mean temperature correlation of each layer set, the mean difference in temperature change rate, and the relative deviation of temperature fluctuation amplitude.

[0022] Calculate the instantaneous temperature rise rate of each layer set. Based on the instantaneous temperature rise rate of each layer set, obtain the Euclidean distance difference between the instantaneous temperature rise rates of each layer set and all other layer sets. Take the average value to obtain the average difference in temperature change rate, including:

[0023] The mean difference in the rate of temperature change is:

[0024]

[0025] Among them, D1 it d represents the mean difference in the rate of temperature change. ij The Euclidean distance difference between the instantaneous temperature rise rate of each layer set and the instantaneous temperature rise rate of all other layers set is obtained, where m represents the number of measurement points.

[0026] Extract the peak temperature of each layer set, calculate the difference between the peak temperature of each layer set and the peak temperatures of all other layer sets, and obtain the relative deviation of the temperature fluctuation amplitude of each layer set, including:

[0027]

[0028] Among them, D2 it A represents the relative deviation of the temperature fluctuation amplitude of each layer. it A represents the temperature fluctuation amplitude at time t in the i-th layer. jt A represents the temperature fluctuation amplitude at time t in the j-th layer; it =max(T) it )-min(T it ), T it This represents the temperature value at time t in the i-th layer.

[0029] Based on the average temperature correlation of each layer, the average difference in temperature change rate, and the relative deviation of temperature fluctuation amplitude, a layered temperature compatibility index is generated, expressed as follows:

[0030] C it =αMit +βD1 it +γD2 it

[0031] Where α, β, and γ represent weights, and M it D1 represents the mean of temperature correlation. it D2 represents the mean difference in the rate of temperature change. it This indicates the relative deviation of the temperature fluctuation amplitude of each layer.

[0032] Calculate the mean temperature difference distribution entropy of adjacent subsets within each cluster to generate an intra-cluster variation index. Statistically analyze the overall differences in the representation of subsets between two clusters. Combine this with the intra-cluster variation index to obtain a temperature risk index, including:

[0033] The mean temperature difference between adjacent time points is calculated for subsets in the high-temperature change cluster and the low-temperature stable cluster of concrete, and the mean temperature difference μΔ between adjacent time points of each subset within the cluster is obtained.

[0034] Divide the mean temperature difference between adjacent subsets within a cluster into several intervals, calculate the probability of the mean temperature difference between adjacent subsets within each interval, and obtain the distribution entropy value E of the mean temperature difference between adjacent subsets within the cluster. C ;

[0035] Based on the mean temperature difference and distribution entropy of adjacent clusters, an intra-cluster variation index is generated. The similarity between two cluster subsets is calculated to obtain the overall difference in the representation of the subsets between the two clusters. The temperature risk index V is obtained based on the intra-cluster variation index and the overall difference in the representation of the subsets between the two clusters.

[0036] Based on the dispersion of the coordination index and the temperature risk index, the concrete pouring anomaly index is determined as follows:

[0037] The standard deviation is used to represent the dispersion of the harmony index. The abnormality index of concrete pouring, represented by A, is as follows:

[0038]

[0039] Where median() represents the median function, and η represents the environmental correction factor. The standard deviation of the stratified temperature compatibility index is represented by V, and the temperature risk index is represented by C. it This indicates the stratified temperature compatibility index.

[0040] A real-time temperature monitoring system based on continuous pouring of large-volume concrete is provided to implement the above-mentioned real-time temperature monitoring method based on continuous pouring of large-volume concrete. The system includes: a data acquisition module, a data processing module, an anomaly judgment module, a monitoring and early warning module, and a cooling water circulation module.

[0041] The data acquisition module collects temperature datasets in real time through sensor deployment, including the internal temperature, surface temperature, mold entry temperature, and ambient temperature of different concrete layers during the continuous pouring of large-volume concrete, and obtains basic data during the concrete pouring process.

[0042] The data processing module preprocesses the acquired data, calculates the temperature value at each acquisition moment, forms a global temperature set of the temperature values ​​at each acquisition moment, acquires the temperature values ​​of each layer at each acquisition moment according to the pouring layer, forms a layered temperature set of the temperature values ​​of each layer; calculates the correlation coefficient of each layer temperature set with all other layered sets, determines the average temperature correlation of each layer temperature set; analyzes the Euclidean distance difference of the instantaneous temperature rise rate of each layer set, statistically analyzes the relative deviation of the temperature fluctuation amplitude of each layer set, and generates a layered temperature coordination index.

[0043] The anomaly detection module divides the global temperature set into multiple temperature subsets according to time windows, and obtains high-temperature change clusters and low-temperature stable clusters for concrete based on shape clustering of cross-correlation coefficients; calculates the distribution entropy of the mean temperature difference between adjacent subsets within each cluster, generates an intra-cluster change index, statistically analyzes the overall differences in the representation of subsets between two clusters, and obtains a temperature risk index by combining the intra-cluster change index; and determines the concrete pouring anomaly index based on the dispersion of the coordination index and the temperature risk index.

[0044] The monitoring and early warning module acquires the mean and standard deviation of the concrete anomaly index during the continuous concrete pouring process to set an initial threshold. Based on the progress of the pouring construction, the mean and standard deviation are adjusted in real time using the sliding window method to determine the dynamic early warning threshold during the continuous concrete pouring process. Based on the comparison between the concrete anomaly index and the threshold, a multi-level early warning and response mechanism is determined, and an early warning control signal is generated.

[0045] The cooling water supply module obtains the initial cooling index of the concrete water supply cooling equipment based on the early warning control signal and confirms the water supply requirement of the concrete water supply cooling equipment. If there is no water supply requirement, the initial cooling index is used as the control cooling index. If there is a water supply requirement, the control cooling index of the concrete water supply cooling equipment is confirmed, the concrete water supply cooling equipment is started for cooling, and real-time monitoring and feedback are performed.

[0046] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects:

[0047] 1. In the continuous pouring of large-volume concrete, this invention acquires the core temperature and surface temperature values ​​in real time during the operation, and calculates the temperature value at each acquisition moment using a weighted average, forming a global temperature set of temperature values ​​at each acquisition moment; this can more accurately reflect the actual temperature level of the concrete at that acquisition moment, providing a reliable basis for accurately judging the temperature change of the concrete, and after forming the global temperature set, it can clearly and intuitively present the dynamic change trend of the concrete temperature throughout the pouring process;

[0048] 2. This invention acquires the temperature values ​​of each layer at each sampling time according to the pouring layer, forming a layer temperature set of each layer's temperature values; generates a layer temperature coordination index, divides the global temperature set into multiple temperature subsets according to time windows, and determines the concrete pouring anomaly index based on the dispersion of the coordination index and the temperature risk index; enabling construction personnel to have a detailed understanding of the specific temperature conditions of each layer at different sampling times, more accurately locate the layer with temperature anomalies, avoid structural cracks caused by temperature incoordination between layers, provide more detailed information for targeted temperature control measures, and consider the influence of temperature between different parts of the concrete to obtain a more accurate concrete pouring anomaly index;

[0049] 3. This invention determines a multi-level early warning and response mechanism based on the comparison of concrete anomaly index and threshold, and generates an early warning control signal. The cooling water module obtains the initial cooling index of the concrete water cooling equipment based on the early warning control signal, and confirms the water supply requirements of the concrete water cooling equipment. It can accurately determine when to start the cooling water equipment and adjust the cooling index such as water flow rate, so that the internal temperature of the concrete is always controlled within a reasonable range, avoiding adverse effects caused by excessive temperature or large temperature difference, and effectively preventing quality problems such as concrete cracks. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the real-time temperature monitoring method based on continuous pouring of large-volume concrete provided in an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the structure of a real-time temperature monitoring system based on continuous pouring of large-volume concrete provided in an embodiment of the present invention. Detailed Implementation

[0052] The present invention will now be described in detail with reference to the accompanying drawings.

[0053] Example 1

[0054] like Figure 1 As shown, this invention provides a method for real-time temperature monitoring based on continuous pouring of large-volume concrete, comprising the following steps:

[0055] Step 1: Collect temperature datasets in real time by deploying sensors to collect internal temperature, surface temperature, mold entry temperature and ambient temperature of different concrete layers during the continuous pouring of large-volume concrete, and obtain basic data of the concrete pouring process.

[0056] Step 2: Preprocess the acquired data, calculate the temperature value at each acquisition moment to form a global temperature set of the temperature values ​​at each acquisition moment, and obtain the temperature values ​​of each layer at each acquisition moment according to the pouring layer to form a layer temperature set of the temperature values ​​of each layer; calculate the correlation coefficient of each layer temperature set with all other layer temperature sets to determine the average temperature correlation of each layer temperature set; analyze the Euclidean distance difference of the instantaneous temperature rise rate of each layer set, and statistically analyze the relative deviation of the temperature fluctuation amplitude of each layer set to generate a layer temperature coordination index.

[0057] Step 3: Divide the global temperature set into multiple temperature subsets according to time windows. Based on shape clustering of cross-correlation coefficients, obtain the high-temperature change cluster and the low-temperature stability cluster for concrete. Calculate the distribution entropy of the mean temperature difference between adjacent subsets within each cluster to generate the intra-cluster change index. Statistically analyze the overall differences in the representation of subsets between two clusters. Combine the intra-cluster change index to obtain the temperature risk index. Based on the dispersion of the coordination index and the temperature risk index, determine the concrete pouring anomaly index.

[0058] Step 4: Obtain the mean and standard deviation of the concrete anomaly index during the continuous concrete pouring process to set the initial threshold. Adjust the mean and standard deviation in real time according to the progress of the pouring construction using the sliding window method to determine the dynamic early warning threshold during the continuous concrete pouring process. Based on the comparison between the concrete anomaly index and the threshold, determine the multi-level early warning and response mechanism and generate an early warning control signal.

[0059] Step 5: Based on the early warning control signal, obtain the initial cooling index of the concrete water cooling equipment and confirm the water supply requirement of the concrete water cooling equipment; if there is no water supply requirement, use the initial cooling index as the control cooling index; if there is a water supply requirement, confirm the control cooling index of the concrete water cooling equipment, start the concrete water cooling equipment for cooling and conduct real-time monitoring and feedback.

[0060] The basic data mentioned in step one includes: concrete thermal conductivity, specific heat capacity, density, heat of hydration, cooling water flow rate, cooling water temperature, cooling water density, and specific heat capacity;

[0061] The preprocessing of the acquired data in step two includes cleaning, noise reduction, and time alignment.

[0062] The calculation of the temperature value at each acquisition moment, forming a global temperature set of the temperature values ​​at each acquisition moment, includes:

[0063] The temperature value at each acquisition moment is calculated from the core temperature value and the surface temperature value. The core temperature value is the internal temperature measured by the embedded sensor, and the surface temperature value is the surface temperature measured by the surface infrared sensor or the patch sensor. The temperature value is calculated by weighted average of the core temperature value and the surface temperature value.

[0064] Specifically, the formula for calculating the temperature value is: T t =ω1T t1 +ω2T t2 , among which, T t T represents the temperature value at time t, ω1 and ω2 represent the weights, and T t1 T represents the internal temperature at time t. t2 This represents the surface temperature at time t; simultaneously, the weighting coefficients are dynamically adjusted when an abnormality in internal or surface temperature is detected.

[0065] The temperature values ​​of each layer at each sampling time are obtained according to the pouring layer, forming a layer temperature set of the temperature values ​​of each layer, including:

[0066] The temperature values ​​of each layer at each sampling time represent T. it The temperature values ​​are measured by embedded sensors arranged in each layer; the layered temperature set of the temperature values ​​of each layer is represented as {T}. i1 T i2 ...T it}, calculate the temperature T of each layer and all other layers. jt correlation coefficient ρ ij Where j≠i, the correlation coefficient ρ ij The calculation formula is:

[0067]

[0068] Where n represents the length of the time set, This represents the average temperature of the i-th layer at time t; This represents the average temperature of the j-th layer at time t;

[0069] Through the correlation coefficient ρ ij The mean temperature correlation of each temperature set is determined, and the formula for calculating the mean temperature correlation is as follows:

[0070]

[0071] Among them, M it This represents the mean temperature correlation value, and m represents the number of measurement points.

[0072] Analyzing the Euclidean distance differences in the instantaneous temperature rise rates of each layer and statistically analyzing the relative deviations in the temperature fluctuation amplitudes of each layer, a layered temperature compatibility index is generated, including:

[0073] Calculate the instantaneous temperature rise rate of each layer set, obtain the Euclidean distance difference between the instantaneous temperature rise rates of each layer set and all other layer sets based on the instantaneous temperature rise rate of each layer set, and take the average value to obtain the average difference in temperature change rate.

[0074] Extract the peak temperature of each layer set, calculate the difference between the peak temperature of each layer set and the peak temperature of all other layer sets, and obtain the relative deviation of the temperature fluctuation amplitude of each layer set;

[0075] A layered temperature compatibility index is generated based on the mean temperature correlation of each layer set, the mean difference in temperature change rate, and the relative deviation of temperature fluctuation amplitude.

[0076] Calculate the instantaneous temperature rise rate of each layer set. Based on the instantaneous temperature rise rate of each layer set, obtain the Euclidean distance difference between the instantaneous temperature rise rates of each layer set and all other layer sets. Take the average value to obtain the average difference in temperature change rate, including:

[0077] The instantaneous temperature rise rate of each layer is expressed as:

[0078] ΔT it =T i(t+1) -T it

[0079] Where, ΔT it T represents the instantaneous temperature rise rate of the i-th layer at time t. i(t+1) T represents the temperature value of the i-th layer at time t+1. it This represents the temperature value of the i-th layer at time t;

[0080]

[0081] Where, d ij The Euclidean distance ΔT represents the difference between the instantaneous temperature rise rate of each layer set and the instantaneous temperature rise rates of all other layers set. it Let ΔT represent the instantaneous temperature rise rate at time t in the i-th layer. jt Let represent the instantaneous temperature rise rate at time t in the j-th layer, where j ≠ i, and m represent the number of measuring points;

[0082] The mean difference in the rate of temperature change is:

[0083]

[0084] Among them, D1 it This represents the mean difference in the rate of temperature change.

[0085] Extract the peak temperature of each layer set, calculate the difference between the peak temperature of each layer set and the peak temperatures of all other layer sets, and obtain the relative deviation of the temperature fluctuation amplitude of each layer set, including:

[0086]

[0087] Among them, D2 it A represents the relative deviation of the temperature fluctuation amplitude of each layer. it A represents the temperature fluctuation amplitude at time t in the i-th layer. jt A represents the temperature fluctuation amplitude at time t in the j-th layer; it =max(T) it )-min(T it );

[0088] Based on the average temperature correlation of each layer, the average difference in temperature change rate, and the relative deviation of temperature fluctuation amplitude, a layered temperature compatibility index is generated, expressed as follows:

[0089] C it =αM it +βD1 it +γD2 it

[0090] Where α, β, and γ represent weights, and M it D1 represents the mean of temperature correlation. it D2 represents the mean difference in the rate of temperature change. it This indicates the relative deviation of the temperature fluctuation amplitude of each layer.

[0091] In step three, the global temperature set is divided into multiple temperature subsets according to time windows. Based on shape clustering of cross-correlation coefficients, the high-temperature change cluster and low-temperature stability cluster of concrete are obtained, including:

[0092] The global temperature set is represented as {T1, T2, ..., T...} n Starting from the initial time of the global temperature set, subsets are sequentially extracted according to the length of the time window. For all the resulting temperature subsets, the cross-correlation coefficient between each pair of subsets is calculated, where the cross-correlation coefficient is expressed as: Where σX and σY represent the standard deviations of sets X and Y, respectively, and Cov(X,Y) represents the covariance of sets X and Y. Based on the magnitude of the cross-correlation coefficient, a threshold is set to determine the shape similarity clustering of subsets, and the temperature subsets are clustered into a concrete high-temperature variation cluster C1 and a low-temperature stable cluster C2. The concrete high-temperature variation cluster contains subsets that exhibit fluctuating characteristics in temperature variation; the low-temperature stable cluster contains subsets that exhibit stable characteristics in temperature variation.

[0093] Calculate the mean temperature difference distribution entropy of adjacent subsets within each cluster to generate an intra-cluster variation index. Statistically analyze the overall differences in the representation of subsets between two clusters. Combine this with the intra-cluster variation index to obtain a temperature risk index, including:

[0094] For subsets within the high-temperature change cluster and the low-temperature stable cluster of concrete, the mean temperature difference between adjacent time points is calculated to obtain the mean temperature difference μΔ between adjacent subsets within each cluster. Where s represents the length of the subset, ΔT k This represents the rate of change of concrete temperature at time k.

[0095] Divide the mean temperature difference between adjacent subsets within a cluster into several intervals, calculate the probability of the mean temperature difference between adjacent subsets within each interval, and obtain the distribution entropy value E of the mean temperature difference between adjacent subsets within the cluster. C , Where Q represents the number of intervals, p q This represents the probability that the mean temperature difference μΔ between adjacent elements of each subset falls within the q-th interval;

[0096] Based on the mean temperature difference and distribution entropy of adjacent clusters, an intra-cluster variation index is generated. The similarity between two cluster subsets is calculated to obtain the overall difference in characterization between the two cluster subsets. Based on the intra-cluster variation index and the overall difference in characterization between the two cluster subsets, the temperature risk index V is obtained. Where D represents the overall difference in representation between subsets of two clusters, which is obtained by the average similarity of the feature vectors of all subsets; The intra-cluster variation index of cluster C1 representing the high-temperature variation of concrete is... The index representing the intra-cluster variation of low-temperature stable concrete cluster C2;

[0097] The similarity between the two subsets can be calculated using Euclidean distance, cosine similarity, or Manhattan distance; no specific limitation is made here.

[0098] Based on the dispersion of the coordination index and the temperature risk index, the concrete pouring anomaly index is determined as follows:

[0099] The standard deviation is used to represent the dispersion of the harmony index. The abnormality index of concrete pouring, represented by A, is as follows:

[0100]

[0101] Where median() represents the median function, and η represents the environmental correction factor. The standard deviation of the stratified temperature compatibility index is represented by V, and the temperature risk index is represented by C. it This indicates the stratified temperature compatibility index;

[0102] The degree of dispersion reflects the distribution of the coordination index of the hierarchical temperature sets in which the temperature values ​​of each layer with the same position are located in all temperature subsets in the cluster.

[0103] Step four: Obtain the mean and standard deviation of the concrete anomaly index during the continuous concrete pouring process to set an initial threshold. Adjust the mean and standard deviation in real time using the sliding window method according to the progress of the pouring construction to determine the dynamic early warning threshold during the continuous concrete pouring process. Based on the comparison between the concrete anomaly index and the threshold, determine a multi-level early warning and response mechanism and generate an early warning control signal.

[0104] Specifically, during the continuous concrete pouring process, the concrete anomaly index at each moment is continuously recorded and calculated. The mean and standard deviation of the concrete anomaly index are calculated, and an initial threshold is set based on the calculated mean and standard deviation. The mean and standard deviation are updated in real time using a sliding window method. The size of the sliding window is selected to contain the most recent z concrete anomaly index data. At each time point, when new concrete anomaly index data is generated, the new concrete anomaly index data is added to the sliding window, and the oldest data is removed from the sliding window. The mean and standard deviation of the data in the sliding window are then recalculated.

[0105] Based on the real-time adjusted mean and standard deviation, and the set thresholds, a multi-level early warning and response mechanism is established. When the concrete anomaly index exceeds the first-level early warning threshold, a first-level early warning is triggered, reminding relevant personnel to closely monitor the concrete pouring process and check the operating status of the pouring equipment and the performance of the concrete. When the concrete anomaly index exceeds the second-level early warning threshold, a second-level early warning is triggered, increasing the frequency of reminders to relevant personnel to monitor the concrete pouring process and to check and adjust the concrete pouring technique. When the concrete anomaly index exceeds the third-level early warning threshold, a third-level early warning is triggered, and pouring construction is suspended. According to the early warning level, corresponding early warning control signals are generated to control the operation of the cooling equipment.

[0106] Step 5: Based on the early warning control signal, obtain the initial cooling index of the concrete water cooling equipment and confirm the water supply requirement of the concrete water cooling equipment; if there is no water supply requirement, use the initial cooling index as the control cooling index; if there is a water supply requirement, confirm the control cooling index of the concrete water cooling equipment, start the concrete water cooling equipment for cooling and conduct real-time monitoring and feedback.

[0107] Based on different levels of early warning and control signals, the initial cooling indicators of the concrete water cooling equipment are obtained, including water flow rate, water temperature, and water supply time.

[0108] Specifically, the initial cooling index is determined based on a comprehensive consideration of concrete temperature changes, pouring process, and concrete material properties. When a level one warning is issued, the initial water flow rate is determined as Q1, the water temperature as T1, and the water flow time as t1 according to preset rules or empirical formulas. The concrete temperature changes, water flow rate, and water temperature are monitored in real time, and the monitoring data is fed back to the water cooling equipment control system. Based on the feedback data, the cooling index is adjusted in a timely manner.

[0109] Example 2

[0110] like Figure 2 As shown, the present invention also provides a real-time temperature monitoring system based on continuous pouring of large-volume concrete, including: a data acquisition module, a data processing module, an anomaly judgment module, a monitoring and early warning module, and a cooling water circulation module.

[0111] The data acquisition module collects temperature datasets in real time through sensor deployment, including the internal temperature, surface temperature, mold entry temperature, and ambient temperature of different concrete layers during the continuous pouring of large-volume concrete, and obtains basic data during the concrete pouring process.

[0112] The data processing module preprocesses the acquired data, calculates the temperature value at each acquisition moment, forms a global temperature set of the temperature values ​​at each acquisition moment, acquires the temperature values ​​of each layer at each acquisition moment according to the pouring layers, forms a layered temperature set of the temperature values ​​of each layer; calculates the correlation coefficient of each layer temperature set with all other layered sets, analyzes the Euclidean distance difference of the instantaneous temperature rise rate of each layer set, statistically analyzes the relative deviation of the temperature fluctuation amplitude of each layer set, and generates a layered temperature coordination index.

[0113] The anomaly detection module divides the global temperature set into multiple temperature subsets according to time windows, and obtains high-temperature change clusters and low-temperature stable clusters for concrete based on shape clustering of cross-correlation coefficients; calculates the distribution entropy of the mean temperature difference between adjacent subsets within each cluster, generates an intra-cluster change index, statistically analyzes the overall differences in the representation of subsets between two clusters, and obtains a temperature risk index by combining the intra-cluster change index; and determines the concrete pouring anomaly index based on the dispersion of the coordination index and the temperature risk index.

[0114] The monitoring and early warning module acquires the mean and standard deviation of the concrete anomaly index during the continuous concrete pouring process to set an initial threshold. Based on the progress of the pouring construction, the mean and standard deviation are adjusted in real time using the sliding window method to determine the dynamic early warning threshold during the continuous concrete pouring process. Based on the comparison between the concrete anomaly index and the threshold, a multi-level early warning and response mechanism is determined, and an early warning control signal is generated.

[0115] The cooling water supply module obtains the initial cooling index of the concrete water supply cooling equipment based on the early warning control signal and confirms the water supply requirement of the concrete water supply cooling equipment. If there is no water supply requirement, the initial cooling index is used as the control cooling index. If there is a water supply requirement, the control cooling index of the concrete water supply cooling equipment is confirmed, the concrete water supply cooling equipment is started for cooling, and real-time monitoring and feedback are performed.

[0116] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0117] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

Claims

1. A method for real-time temperature monitoring during continuous pouring of large-volume concrete, characterized in that, Includes the following steps: Step 1: Collect temperature datasets in real time by deploying sensors to collect internal temperature, surface temperature, mold entry temperature and ambient temperature of different concrete layers during the continuous pouring of large-volume concrete, and obtain basic data of the concrete pouring process. Step 2: Preprocess the acquired data, calculate the temperature value at each acquisition moment to form a global temperature set of the temperature values ​​at each acquisition moment, and obtain the temperature values ​​of each layer at each acquisition moment according to the pouring layer to form a layer temperature set of the temperature values ​​of each layer; calculate the correlation coefficient of each layer temperature set with all other layer temperature sets to determine the average temperature correlation of each layer temperature set; analyze the Euclidean distance difference of the instantaneous temperature rise rate of each layer set, and statistically analyze the relative deviation of the temperature fluctuation amplitude of each layer set to generate a layer temperature coordination index. Step 3: Divide the global temperature set into multiple temperature subsets according to the time window, and obtain the high temperature change cluster and low temperature stability cluster of concrete based on the shape clustering of cross-correlation coefficients; calculate the distribution entropy value of the mean temperature difference between adjacent subsets in each cluster, generate the intra-cluster change index, statistically analyze the overall difference in the representation of subsets between two clusters, and obtain the temperature risk index by combining the intra-cluster change index. Based on the dispersion of the coordination index and the temperature risk index, the concrete pouring anomaly index is determined. Step 4: Obtain the mean and standard deviation of the concrete anomaly index during the continuous concrete pouring process to set the initial threshold. Adjust the mean and standard deviation in real time according to the progress of the pouring construction using the sliding window method to determine the dynamic early warning threshold during the continuous concrete pouring process. Based on the comparison between the concrete anomaly index and the threshold, determine the multi-level early warning and response mechanism and generate an early warning control signal. Step 5: Based on the early warning control signal, obtain the initial cooling index of the concrete water cooling equipment and confirm the water supply requirement of the concrete water cooling equipment; if there is no water supply requirement, use the initial cooling index as the control cooling index; if there is a water supply requirement, confirm the control cooling index of the concrete water cooling equipment, start the concrete water cooling equipment for cooling and conduct real-time monitoring and feedback.

2. The method for real-time temperature monitoring based on continuous pouring of large-volume concrete according to claim 1, characterized in that, The calculation of the temperature value at each acquisition moment, forming a global temperature set of the temperature values ​​at each acquisition moment, includes: The temperature value at each acquisition moment is calculated from the core temperature value and the surface temperature value. The core temperature value is the internal temperature measured by the embedded sensor, and the surface temperature value is the surface temperature measured by the surface infrared sensor or the patch sensor. The temperature value is calculated by weighted average of the core temperature value and the surface temperature value.

3. The method for real-time temperature monitoring based on continuous pouring of large-volume concrete according to claim 1, characterized in that, Calculate the correlation coefficients between each temperature set and all other temperature sets, and determine the mean temperature correlation of each temperature set, including: Through the correlation coefficient ρ ij The mean temperature correlation of each temperature set is determined, and the formula for calculating the mean temperature correlation is as follows: Among them, M it This represents the mean temperature correlation value, and m represents the number of measurement points.

4. The method for real-time temperature monitoring based on continuous pouring of large-volume concrete according to claim 1, characterized in that, Analyzing the Euclidean distance differences in the instantaneous temperature rise rates of each layer and statistically analyzing the relative deviations in the temperature fluctuation amplitudes of each layer, a layered temperature compatibility index is generated, including: Calculate the instantaneous temperature rise rate of each layer set, obtain the Euclidean distance difference between the instantaneous temperature rise rates of each layer set and all other layer sets based on the instantaneous temperature rise rate of each layer set, and take the average value to obtain the average difference in temperature change rate. Extract the peak temperature of each layer set, calculate the difference between the peak temperature of each layer set and the peak temperature of all other layer sets, and obtain the relative deviation of the temperature fluctuation amplitude of each layer set; The stratified temperature coordination index is generated based on the mean temperature correlation of each layer set, the mean difference in temperature change rate, and the relative deviation of temperature fluctuation amplitude.

5. The method for real-time temperature monitoring based on continuous pouring of large-volume concrete according to claim 4, characterized in that, Calculate the instantaneous temperature rise rate of each layer set. Based on the instantaneous temperature rise rate of each layer set, obtain the Euclidean distance difference between the instantaneous temperature rise rates of each layer set and all other layer sets. Take the average value to obtain the average difference in temperature change rate, including: The mean difference in the rate of temperature change is: Among them, D1 it d represents the mean difference in the rate of temperature change. ij The Euclidean distance difference between the instantaneous temperature rise rate of each layer set and the instantaneous temperature rise rate of all other layers set is obtained, where m represents the number of measurement points.

6. The method for real-time temperature monitoring based on continuous pouring of large-volume concrete according to claim 4, characterized in that, Extract the peak temperature of each layer set, calculate the difference between the peak temperature of each layer set and the peak temperatures of all other layer sets, and obtain the relative deviation of the temperature fluctuation amplitude of each layer set, including: Among them, D2 it A represents the relative deviation of the temperature fluctuation amplitude of each layer. it A represents the temperature fluctuation amplitude at time t in the i-th layer. jt A represents the temperature fluctuation amplitude at time t in the j-th layer; it =max(T) it )-min(T it ), T it This represents the temperature value at time t in the i-th layer.

7. The method for real-time temperature monitoring based on continuous pouring of large-volume concrete according to claim 4, characterized in that, Based on the average temperature correlation of each layer, the average difference in temperature change rate, and the relative deviation of temperature fluctuation amplitude, a layered temperature compatibility index is generated, expressed as follows: C it =αM it +βD1 it +γD2 it Where α, β, and γ represent weights, and M it D1 represents the mean of temperature correlation. it D2 represents the mean difference in the rate of temperature change. it This indicates the relative deviation of the temperature fluctuation amplitude of each layer.

8. The method for real-time temperature monitoring based on continuous pouring of large-volume concrete according to claim 1, characterized in that, Calculate the mean temperature difference distribution entropy of adjacent subsets within each cluster to generate an intra-cluster variation index. Statistically analyze the overall differences in the representation of subsets between two clusters. Combine this with the intra-cluster variation index to obtain a temperature risk index, including: The mean temperature difference between adjacent time points is calculated for subsets in the high-temperature change cluster and the low-temperature stable cluster of concrete, and the mean temperature difference μΔ between adjacent time points of each subset within the cluster is obtained. Divide the mean temperature difference between adjacent subsets within a cluster into several intervals, calculate the probability of the mean temperature difference between adjacent subsets within each interval, and obtain the distribution entropy value E of the mean temperature difference between adjacent subsets within the cluster. C ; Based on the mean temperature difference and distribution entropy of adjacent clusters, an intra-cluster variation index is generated. The similarity between two cluster subsets is calculated to obtain the overall difference in the representation of the subsets between the two clusters. The temperature risk index V is obtained based on the intra-cluster variation index and the overall difference in the representation of the subsets between the two clusters.

9. The method for real-time temperature monitoring based on continuous pouring of large-volume concrete according to claim 1, characterized in that, Based on the dispersion of the coordination index and the temperature risk index, the concrete pouring anomaly index is determined as follows: The standard deviation is used to represent the dispersion of the harmony index. The abnormality index of concrete pouring, represented by A, is as follows: Where median() represents the median function, and η represents the environmental correction factor. The standard deviation of the stratified temperature compatibility index is represented by V, and the temperature risk index is represented by C. it This indicates the stratified temperature compatibility index.

10. A real-time temperature monitoring system based on continuous pouring of large-volume concrete, characterized in that, The system is used to implement the real-time temperature monitoring method based on continuous pouring of large-volume concrete as described in any one of claims 1-9. The system includes: a data acquisition module, a data processing module, an anomaly judgment module, a monitoring and early warning module, and a cooling water circulation module. The data acquisition module collects temperature datasets in real time through sensor deployment, including the internal temperature, surface temperature, mold entry temperature, and ambient temperature of different concrete layers during the continuous pouring of large-volume concrete, and obtains basic data during the concrete pouring process. The data processing module preprocesses the acquired data, calculates the temperature value at each acquisition moment, forms a global temperature set of the temperature values ​​at each acquisition moment, acquires the temperature values ​​of each layer at each acquisition moment according to the pouring layer, forms a layered temperature set of the temperature values ​​of each layer; calculates the correlation coefficient of each layer temperature set with all other layered sets, determines the average temperature correlation of each layer temperature set; analyzes the Euclidean distance difference of the instantaneous temperature rise rate of each layer set, statistically analyzes the relative deviation of the temperature fluctuation amplitude of each layer set, and generates a layered temperature coordination index. The anomaly detection module divides the global temperature set into multiple temperature subsets according to time windows, and obtains high-temperature change clusters and low-temperature stable clusters for concrete based on shape clustering of cross-correlation coefficients; calculates the distribution entropy of the mean temperature difference between adjacent subsets within each cluster, generates an intra-cluster change index, statistically analyzes the overall differences in the representation of subsets between two clusters, and obtains a temperature risk index by combining the intra-cluster change index; and determines the concrete pouring anomaly index based on the dispersion of the coordination index and the temperature risk index. The monitoring and early warning module acquires the mean and standard deviation of the concrete anomaly index during the continuous concrete pouring process to set an initial threshold. Based on the progress of the pouring construction, the mean and standard deviation are adjusted in real time using the sliding window method to determine the dynamic early warning threshold during the continuous concrete pouring process. Based on the comparison between the concrete anomaly index and the threshold, a multi-level early warning and response mechanism is determined, and an early warning control signal is generated. The cooling water supply module obtains the initial cooling index of the concrete water supply cooling equipment based on the early warning control signal and confirms the water supply requirement of the concrete water supply cooling equipment. If there is no water supply requirement, the initial cooling index is used as the control cooling index. If there is a water supply requirement, the control cooling index of the concrete water supply cooling equipment is confirmed, the concrete water supply cooling equipment is started for cooling, and real-time monitoring and feedback are performed.

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