A high-altitude concrete foundation maintenance temperature control method and system based on multi-sensor data

Abstract

This invention discloses a method and system for temperature control during high-altitude concrete foundation curing based on multi-sensor data, belonging to the field of concrete engineering curing temperature control. The method includes: acquiring data from the target area; injecting the data as input parameters into a preset multi-field coupled state space data structure, performing forward differential iterative calculation to generate a rolling temperature gradient prediction matrix covering a preset future time window; traversing the rolling temperature gradient prediction matrix and comparing it with a preset dynamic safety temperature difference threshold; if a risk state exceeding the dynamic safety temperature difference threshold is detected at the target forward time node, extracting the pre-calibrated thermodynamic hysteresis time parameter of the concrete foundation curing actuator and performing pre-emptive defensive thermodynamic regulation. This invention covers the entire life cycle of mechanical property evolution and realizes adaptive dynamic adjustment of the temperature control threshold with the age of the concrete.

Description

Technical Field

[0001] This invention relates to the field of temperature control for concrete curing, and in particular to a method and system for temperature control of high-altitude concrete foundation curing based on multi-sensor data. Background Technology

[0002] With the accelerated construction of infrastructure such as wind farms, ultra-high-voltage transmission lines, and plateau bridges in high-altitude areas of western my country, an increasing number of large-volume concrete foundation pouring and curing operations need to be carried out in extreme environments above 3,000 meters in altitude. Large-volume concrete foundations release a large amount of heat during cement hydration. If the temperature gradient between internal heat storage and external heat dissipation is not effectively controlled, through-cracks can easily occur due to thermal stress exceeding the tensile strength of the concrete at its current age, seriously threatening structural safety and durability. Compared to plains areas, high-altitude construction environments exhibit a series of significantly different climatic and physical characteristics: the thinner atmosphere leads to a significant increase in solar ultraviolet radiation intensity, resulting in more drastic fluctuations in radiative heat input to the concrete surface; the low-pressure environment alters the hydration reaction kinetics and thermal properties of concrete materials, causing significant deviations in empirical formulas calibrated under standard atmospheric pressure conditions; and large diurnal temperature variations and frequent sudden strong winds and snowfalls cause rapid changes in the rate of heat loss from the concrete surface within a short period. The combination of these unique environmental factors at high altitudes results in a highly nonlinear and time-varying temperature field evolution process in concrete foundations, placing far higher demands on curing and temperature control technologies than in plain environments.

[0003] However, existing concrete curing temperature control technologies suffer from several systemic defects when dealing with the aforementioned high-altitude extreme environments: First, existing temperature control methods generally use fixed empirical temperature difference thresholds as control criteria, failing to adapt to the objective law of dynamic changes in concrete mechanical properties over time. Second, existing temperature control systems generally employ a feedback-based passive adjustment strategy based on the current measured temperature difference, failing to consider the inherent physical response lag of the curing actuator, resulting in adjustment behavior always lagging behind the actual occurrence of temperature difference risks. Third, existing temperature control schemes lack effective forward-looking prediction capabilities for the temperature field. Fourth, existing schemes lack effective fault-tolerant mechanisms for situations where some nodes of the sensor array fail in harsh high-altitude environments. Fifth, existing temperature control systems lack effective coordinated control logic when facing complex coupled situations where both excessive internal heat accumulation and excessive surface heat loss occur simultaneously in concrete. Sixth, existing temperature control systems lack a pre-verification mechanism for control effects before issuing control commands, failing to effectively prevent secondary risks caused by overcompensation.

[0004] Therefore, there is an urgent need for a concrete foundation curing and temperature control technology solution that can comprehensively solve the above-mentioned technical defects and is specifically suitable for high-altitude extreme climate conditions. Summary of the Invention

[0005] One of the objectives of this invention is to provide a method and system for temperature control during the curing of high-altitude concrete foundations based on multi-sensor data, in order to solve the problem in the prior art where the temperature control threshold is fixed and does not change with age, resulting in excessive conservatism in the early stages and insufficient protection in the later stages.

[0006] This invention is achieved through the following technical solution: a method for temperature control during curing of high-altitude concrete foundations based on multi-sensor data, comprising the following steps: acquiring high-altitude meteorological forecast time series data for the target area, and simultaneously collecting data on cumulative solar radiation and hydration heat release rate within each time step using a three-dimensional sensor array deployed on and inside the concrete foundation; using the high-altitude meteorological forecast time series data, cumulative solar radiation, and hydration heat release rate data as input parameters, injecting them into a preset multi-field coupled state space data structure, performing forward differential iterative calculation, and generating a rolling temperature gradient prediction matrix covering a preset future time window, wherein the rolling temperature gradient prediction matrix includes... The system includes a first temperature difference sequence and a second temperature difference sequence. It iterates through the first and second temperature difference sequences within the rolling temperature gradient prediction matrix and compares them with a preset dynamic safety temperature difference threshold. The dynamic safety temperature difference threshold dynamically changes with the age of the concrete pouring. If a risk state exceeding the dynamic safety temperature difference threshold is detected at a target look-ahead time node, the pre-calibrated thermodynamic hysteresis time parameter of the concrete foundation curing execution mechanism is extracted. At the physical advance triggering time obtained by subtracting the pre-calibrated thermodynamic hysteresis time parameter from the target look-ahead time node, a reverse time compensation temperature control command is output to the concrete foundation curing execution mechanism to perform pre-emptive defensive thermal regulation.

[0007] Furthermore, the process of obtaining the cumulative solar radiation includes: establishing a time-domain integral accumulator for solar irradiance; within each time step, accumulating multiple instantaneous solar radiation flux sampling values ​​collected by the high-frequency photosensitive unit in the three-dimensional sensor array using trapezoidal integration calculation in the time domain, and introducing the dynamic albedo of the concrete surface for correction, thereby generating the cumulative solar radiation; wherein, the specific method of the trapezoidal integration calculation is as follows: the areas of each trapezoid obtained by multiplying the arithmetic mean of the instantaneous solar radiation flux sampling values ​​of two adjacent sampling times within the time step by the sampling interval are accumulated one by one, and then multiplied by a net absorption ratio coefficient obtained by subtracting the dynamic albedo of the concrete surface, thereby obtaining the total net solar radiation energy actually absorbed by the concrete surface within the time step.

[0008] Furthermore, the process of acquiring the hydration heat release rate data includes: based on the temperature rise rate distribution collected by the temperature sensors embedded in the three-dimensional sensor array, and the predefined concrete specific heat capacity and density constant, the hydration heat release rate data is calculated and output in real time through an energy conservation inverse derivation operator; wherein, the calculation logic of the energy conservation inverse derivation operator is: multiplying the product of density and specific heat capacity at each spatial node by the temperature change rate at that node to obtain the total enthalpy change per unit volume of that node; then subtracting the net heat exchange with surrounding nodes caused by the heat conduction effect, characterized by the product of the equivalent thermal conductivity at that node and the sum of the second-order partial derivatives of the temperature field in three-dimensional space; the resulting difference is the hydration heat release rate at that node.

[0009] Furthermore, the first temperature difference sequence is a time series composed of the difference between the average predicted temperature of the concrete center node group and the average predicted temperature of the surface node group at each forward time node, and the second temperature difference sequence is a time series composed of the difference between the average predicted temperature of the surface node group at each forward time node and the predicted environmental temperature value at the corresponding time in the high-altitude meteorological forecast time series data.

[0010] Furthermore, the generation process of the dynamic temperature gradient prediction matrix includes: mapping the physical model of the concrete foundation into a three-dimensional spatial grid array defined by discrete three-dimensional node coordinates, and initializing the initial temperature state vector of each grid node at the current moment; constructing the internal heat source forcing vector of the three-dimensional spatial grid array based on the hydration heat release rate data, and constructing the convection and radiation boundary condition vectors of the surface grid nodes using the high-altitude meteorological forecast time series data and the accumulated solar radiation; establishing a multivariable nonlinear state-space extrapolation equation set, and substituting the internal heat source forcing vector, the convection and radiation boundary condition vectors, and the pre-calibrated heat conduction weighting coefficient matrix and global heat capacity matrix into the multivariable nonlinear state-space extrapolation equation set. In the future, within a preset time window, forward difference calculations are performed using the multivariable nonlinear state-space extrapolation equations with a fixed time advance step, iteratively updating the temperature state vector of each grid node at the end of each step; at each time advance step, the difference between the average predicted temperature of the central node group and the average predicted temperature of the surface node group is extracted as the current time value of the first temperature difference sequence, and the difference between the average predicted temperature of the surface node group and the corresponding ambient temperature forecast value in the high-altitude meteorological forecast time series data is extracted as the current time value of the second temperature difference sequence. The first temperature difference sequence values ​​and the second temperature difference sequence values ​​at each time advance step are arranged in chronological order to generate the rolling temperature gradient prediction matrix.

[0011] Furthermore, the multivariable nonlinear state-space extrapolation equations adopt a semi-implicit difference scheme. The solution logic is as follows: the sum of the global heat capacity matrix and the heat conduction weight coefficient matrix multiplied by half the time step size is used as the left-hand coefficient matrix, and the temperature state vector of the next time step is used as the left-hand unknown. The product of the difference between the global heat capacity matrix and the heat conduction weight coefficient matrix multiplied by half the time step size and the temperature state vector of the current time step is superimposed with the product of the time step size and the sum of the internal heat source forcing vector and the convection and radiation boundary condition vectors at the midpoint between the current step and the next step, and is used as the right-hand known. The left-hand coefficient matrix is ​​a constant matrix after mesh generation and material parameter determination. The system performs a one-time matrix decomposition on it in advance, and each subsequent iteration only needs to perform one previous-to-back substitution operation to obtain the temperature state vector of the next time step.

[0012] Furthermore, the construction of the convection and radiation boundary condition vector also includes a dynamic correction mechanism based on the wind chill equivalent reduction operator: before each forward difference calculation, the wind speed prediction component and humidity prediction component in the high-altitude meteorological forecast time series data are analyzed; when the wind speed prediction component is greater than the preset critical wind speed threshold, and the probability of snowfall is determined to be high according to the snowfall probability indicator function, the convective thermal resistance weight coefficient in the convection and radiation boundary condition vector is automatically reduced in an exponential decay form; wherein, the corrected convective thermal resistance weight coefficient is equal to the baseline thermal resistance weight value multiplied by an exponential decay factor; the exponent of the exponential decay factor is the negative value of the product of the empirical loss coefficient, the amount by which the wind speed exceeds the critical wind speed threshold, and the output value of the snowfall probability indicator function; when the wind speed does not exceed the critical wind speed threshold, the amount by which the wind speed exceeds the critical wind speed threshold is zero, the exponential decay factor is one, and no correction is performed.

[0013] Furthermore, after acquiring the accumulated solar radiation and the hydration heat release rate data, and before injecting the data into the multi-field coupled state-space data structure, the method further includes: performing deterministic data self-healing anti-blocking logic processing on the data acquisition channels in the three-dimensional sensor array, so that when some sensor nodes fail due to harsh environments, alternative data is automatically generated based on a deterministic physical interpolation algorithm to fill data gaps and maintain the integrity and continuity of the data stream; the process of deterministic data self-healing anti-blocking logic processing includes: real-time monitoring of the signal integrity flag bits of all independent data acquisition channels in the three-dimensional sensor array; freezing the interruption request caused by the failed node when a communication node fails to return a valid data packet for a consecutive preset number of polling cycles; and extracting the data related to the failed node. The temperature rise gradient vectors of the nearest number of physically adjacent valid nodes within a predetermined historical time period are combined and corrected by cosine projection of solar irradiation angle. A replacement data sequence is generated by a three-dimensional inverse distance weighted interpolation algorithm and injected into the multi-field coupled state space data structure. In the three-dimensional inverse distance weighted interpolation algorithm, the weight of each adjacent valid node is inversely proportional to the square of its Euclidean distance to the failed node. The solar irradiation angle cosine projection correction is performed by multiplying the measured data value of each adjacent valid node by the ratio between the cosine of the angle between the sunlight and the surface normal at the failed node and the cosine of the angle between the sunlight and the surface normal at the adjacent valid node, so as to eliminate the systematic deviation of the light receiving conditions caused by the difference in the surface orientation of each node.

[0014] Furthermore, the logic for generating the dynamic safe temperature difference threshold includes: establishing a lookup table based on the elastic modulus growth curve and tensile strength evolution curve corresponding to different pouring ages; within each time calculation step, querying the tensile strength value at the current age, and combining it with the thermal expansion coefficient, Poisson's ratio, and creep relaxation coefficient of concrete to calculate the ultimate temperature gradient difference that the current structure can withstand; wherein, the ultimate temperature gradient difference is equal to the quotient obtained by dividing the tensile strength at the current age by the product of the elastic modulus at the current age, the thermal expansion coefficient, the difference between the current age and the Poisson's ratio, and the creep relaxation coefficient; multiplying the ultimate temperature gradient difference by a safety margin coefficient to obtain the dynamic safe temperature difference threshold at the current age, and dynamically updating the threshold sequence covering the future preset time window.

[0015] Furthermore, the safety margin coefficient ranges from 0.8 to 0.85.

[0016] Furthermore, the pre-calibrated thermodynamic hysteresis time parameter consists of two independent hysteresis queues, including: a first hysteresis time constant for the cooling water pipe network system, which covers the sum of valve mechanical opening delay, water pump pressurization time, and the flow delay time of the cooling medium in the pipe network to the target area; and a second hysteresis time constant for the surface electric heat tracing insulation system, which covers the sum of contactor closing delay and the thermal inertia time for the heating element surface to reach the working temperature.

[0017] Furthermore, the output of the reverse time-compensated temperature control command and the execution of the pre-emptive defensive thermal regulation include mutually exclusive and hierarchical control logic, specifically including the following three operating condition branches: Operating condition one, if only the first temperature difference sequence exceeds the dynamic safe temperature difference threshold at the target look-ahead time node, then the flow regulation signal is sent to the cooling water pipe network system to increase the internal cooling circulation flow by advancing the time corresponding to the first hysteresis time constant; Operating condition two, if only the second temperature difference sequence exceeds the dynamic safe temperature difference threshold at the target look-ahead time node, then the control array bit mask is sent to the surface electric heat tracing insulation system to selectively activate the insulation heating module in the corresponding area by advancing the time corresponding to the second hysteresis time constant; Operating condition three, if the first temperature difference sequence and the second temperature difference sequence simultaneously face the risk of exceeding their respective thresholds within a similar time window, then the execution priority of the internal cooling regulation is set to lower than that of the surface insulation regulation, and the maximum allowable flow increment of the internal cooling is limited according to the severity of the first temperature difference sequence exceeding the limit.

[0018] Furthermore, the control quantity of the flow regulation signal sent to the cooling water pipe network system in operating condition one is determined as follows: the maximum excess amount of the first temperature difference sequence exceeding the dynamic safe temperature difference threshold within a preset prediction window width extended backward from the target look-ahead time node is used as the input deviation amount, and the output valve control quantity is calculated by the proportional-integral-derivative controller function; the valve control quantity is also multiplied by a factor obtained by subtracting the surface supercooling mutual exclusion mask value; wherein, when the prediction matrix shows that there is an extreme risk of heat loss on the concrete surface, the surface supercooling mutual exclusion mask value is one, so that the valve control quantity is forced to zero to fundamentally block the start of internal cooling; when there is no risk of surface supercooling, the surface supercooling mutual exclusion mask value is zero, and the valve control quantity is output normally.

[0019] Furthermore, in the coupled adjustment branch of operating condition three: firstly, the heat preservation and heating module in the windward area is selectively activated through the control array bit mask to perform surface heat preservation adjustment; then, a flow rate upper limit constraint is applied to the internal cooling adjustment. The flow rate upper limit constraint is determined proportionally based on the magnitude of the overscalar amount of the first temperature difference sequence exceeding the dynamic safe temperature difference threshold, in order to prevent the superposition of excessive internal cooling and excessive surface heat loss from causing a secondary collapse of the temperature field gradient structure.

[0020] Furthermore, after generating the reverse time-compensated temperature control command and before formally issuing it through the output port, the process includes executing a look-ahead verification loop: performing state cloning backup on the multi-field coupled state space data structure; injecting the control quantity to be issued as an additional input condition into the boundary conditions and heat source terms of the state cloning backup; using the forward differential iterative calculation to perform a secondary extrapolation of the future evolution of the system after applying the control quantity, generating a verification temperature gradient sequence; determining whether the maximum temperature difference value of the verification temperature gradient sequence does not exceed the dynamic safe temperature difference threshold at the corresponding time throughout the entire time period from the current moment to the prediction endpoint; if yes, then formally issuing the reverse time-compensated temperature control command through the output port; if no, then performing a reduction adjustment on the control quantity and re-performing the secondary extrapolation verification until the verification temperature gradient sequence falls back to within the range of the dynamic safe temperature difference threshold.

[0021] Furthermore, in the energy conservation inverse derivation operator: the temperature change rate is approximated by the temperature difference between adjacent time moments; the sum of the second-order partial derivatives of the temperature field in three-dimensional space is obtained by calculating the temperature values ​​of the target node and its adjacent nodes in three orthogonal spatial directions using a central difference scheme; and the time derivative of the temperature is taken as the value at the current time moment, and the second-order partial derivative of the space is taken as the value at the previous time moment, forming a semi-implicit time discretization scheme.

[0022] Another aspect of the present invention provides a high-altitude concrete foundation curing and temperature control system based on multi-sensor data, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements any of the high-altitude concrete foundation curing and temperature control methods based on multi-sensor data as described above.

[0023] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0024] 1. This invention calculates the maximum temperature gradient difference that the current structure can withstand in real time by combining the elastic modulus growth curve and tensile strength evolution curve corresponding to different pouring ages with the coefficient of thermal expansion, Poisson's ratio, and creep relaxation coefficient. A safety margin coefficient is applied to generate a dynamic safety temperature difference threshold. In the early stage of pouring, when the elastic modulus of concrete is low and the deformation capacity is strong, the threshold is automatically widened to avoid unnecessary consumption of cooling water and electric heating energy. In the later stage, when the concrete hardens and becomes brittle and the elastic modulus increases significantly, the threshold is automatically tightened to ensure that the temperature control standard is accurately matched with the current mechanical state. This covers the entire life cycle of mechanical performance evolution and realizes the adaptive dynamic adjustment of the temperature control threshold with the age of concrete.

[0025] 2. This invention uses a rolling temperature gradient prediction matrix generated by forward differential iterative calculation to identify the target look-ahead time node for future temperature difference exceeding limits. Then, by subtracting the pre-calibrated thermodynamic hysteresis parameter of the maintenance actuator, the physical early triggering time is determined. At this moment, a reverse time-compensated temperature control command is issued in advance. This ensures that when the temperature difference risk actually arrives at the target look-ahead time node, the actuator's temperature control effect has already been fully established and reached the design power, effectively reducing the system's response delay to near zero. This eliminates the uncontrolled window period caused by hysteresis in traditional control, and simultaneously incorporates the high-altitude weather forecast time sequence. The data, the cumulative solar radiation processed by time-domain integration, and the hydration heat release rate data obtained in real time from the measured temperature field through the energy conservation inverse derivation operator are injected into the multi-field coupled state space data structure. On this basis, deterministic forward differential iterative calculation is performed. Since the iterative process strictly follows the discretization solution format of the heat conduction partial differential equation, its prediction results naturally satisfy the energy conservation law. Even under extreme weather change conditions that have never appeared in the training data, it will not output abnormal prediction values ​​that violate physical laws, thus eliminating the calibration deviation of empirical formulas in high-altitude and low-pressure environments.

[0026] 3. This invention detects node failure status by real-time monitoring of signal integrity flags in each data acquisition channel. After freezing the interruption request of a failed node, it extracts data from the nearest valid node and generates replacement data using a three-dimensional spatial inverse distance weighted interpolation algorithm corrected by the cosine projection of the solar irradiation angle. This interpolation method not only considers the influence of spatial distance but also eliminates systematic deviations caused by differences in light conditions due to different surface orientations through cosine ratio correction. This ensures that the repaired data strictly follows the heat source light distribution law in three-dimensional space. This fault-tolerant mechanism ensures that even if some sensor nodes fail due to harsh conditions in high-altitude unattended environments, the overall prediction system will not stagnate due to data loss, maintaining uninterrupted operation of the curing temperature control process and guaranteeing the integrity of the data stream and the continuity of system operation. Attached Figure Description

[0027] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0028] Figure 1 The above is a flowchart of the overall method provided in Embodiment 1 of the present invention.

[0029] Figure 2 This is a schematic diagram of the solar radiation flux and trapezoidal integral smoothing effect provided in Embodiment 1 of the present invention.

[0030] Figure 3 The diagram shows the impact analysis of the calculation of cumulative solar radiation provided in Embodiment 1 of the present invention.

[0031] Figure 4 This is a schematic diagram of the heat of hydration release rate provided in Embodiment 1 of the present invention.

[0032] Figure 5 This is a space-time thermogram of the rolling temperature gradient prediction matrix provided in Embodiment 1 of the present invention.

[0033] Figure 6 This is a comparison chart of surface heat loss rates provided in Embodiment 1 of the present invention.

[0034] Figure 7 This is a schematic diagram of the evolution of a three-dimensional spatial thermal map of the temperature field provided in Embodiment 1 of the present invention.

[0035] Figure 8 This is an adaptive evolution curve provided in Embodiment 1 of the present invention.

[0036] Figure 9 This is a schematic diagram comparing the temperature difference control effect provided in Embodiment 1 of the present invention.

[0037] Figure 10 This is a composition decomposition and timing relationship diagram provided in Embodiment 1 of the present invention.

[0038] Figure 11 This is a schematic diagram illustrating the effect of the mutual exclusion mask provided in Embodiment 1 of the present invention on suppressing internal cooling.

[0039] Figure 12 This is a time axis linkage diagram of temperature difference, control quantity and energy consumption provided in Embodiment 1 of the present invention.

[0040] Figure 13 This is a schematic diagram of error comparison provided in Embodiment 1 of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0042] Example 1

[0043] This embodiment discloses a method for temperature control during curing of high-altitude concrete foundations based on multi-sensor data. Figure 1 The overall method flowchart of this embodiment is shown. As can be seen from the figure, this embodiment includes the following steps:

[0044] Step 1: Acquire time series data of high-altitude meteorological forecasts for the target area, and simultaneously collect data on the cumulative solar radiation and the real-time hydration heat release rate of the concrete at each time step using a three-dimensional sensor array deployed on and inside the concrete foundation.

[0045] The target area refers to a high-altitude construction area where concrete foundation pouring and curing operations are currently underway. For example, the target area could be a wind power foundation pouring site at an altitude of over 3,000 meters, a high-altitude transmission line tower foundation construction area, or a high-altitude bridge abutment pouring area. This target area typically exhibits extreme climate characteristics unique to high altitudes, such as high ultraviolet radiation intensity, low air pressure, large temperature differences, and frequent strong winds and snowfall.

[0046] High-altitude meteorological forecast time series data refers to a set of meteorological parameter predictions covering a target area for a future period. This data set is organized in the form of a discrete time series, containing multidimensional meteorological parameter predictions for each future forecast time point. For example, high-altitude meteorological forecast time series data includes, but is not limited to, predicted ambient temperature, wind speed, wind direction, relative humidity, precipitation (snowfall) probability, and cloud cover for each future time point. This data can be output from regional numerical weather prediction models of meteorological departments or generated by automatic weather stations deployed at construction sites in conjunction with short-term forecast algorithms.

[0047] A three-dimensional sensor array refers to a cluster of various types of sensors distributed and deployed on the surface and at different depths within a concrete foundation structure according to a pre-defined three-dimensional spatial topology. The sensors in this array include, but are not limited to, high-frequency photosensitive units (such as thermopile radiation sensors and silicon-based photodiode radiation sensors) for measuring instantaneous solar radiation flux, embedded temperature sensors (such as platinum resistance temperature sensors and thermocouple sensors) for measuring the internal temperature distribution of concrete, and non-contact infrared temperature sensors for measuring surface temperature.

[0048] Understandably, three-dimensional means that sensor nodes are not only arranged in a grid pattern on the horizontal surface of the concrete foundation, but also have multiple depth layers (such as surface layer, shallow layer, central layer, and deep layer) set in the vertical direction along the thickness of the concrete, thus forming a three-dimensional sensing network that can capture the three-dimensional spatial temperature field distribution inside the concrete structure.

[0049] Synchronous acquisition refers to the system ensuring that the data reading operations of all types of sensors are strictly aligned in time within each predefined time step through a hard real-time interrupt mechanism or a synchronous clock trigger mechanism, so as to avoid data time registration errors caused by the deviation of the acquisition time of each channel.

[0050] Cumulative solar radiation refers to the integral cumulative value of solar radiation energy actually absorbed by the surface of a concrete foundation within a set collection time window. This value is not a radiation flux reading at a single instant, but rather the total heat input value obtained by time-domain integration of the continuously changing radiation flux curve within that time window.

[0051] Understandably, at high altitudes, due to the thinner atmosphere and faster cloud movement, instantaneous solar radiation flux exhibits significant high-frequency fluctuations. Directly using instantaneous radiation values ​​as the boundary input to a heat transfer model would lead to severe numerical oscillations in the prediction results. Therefore, it is necessary to accumulate the discrete instantaneous radiation flux within a defined time window through time-domain integration to obtain a smooth value representing the total actual heat input during that period. This eliminates the interference of instantaneous radiation spikes caused by rapid cloud cover and dissipation on the boundary conditions of the heat transfer model.

[0052] Real-time hydration heat release rate data for concrete refers to the real-time quantitative data of heat released per unit time and per unit volume by the chemical reaction of cement hydration at each observation node within the concrete structure at the current moment. This data reflects the intensity of the chemical reaction in the concrete at its current age and serves as a direct basis for constructing the internal heat source forcing vector.

[0053] Understandably, traditional methods typically rely on empirical formulas to estimate the heat release process of hydration. However, these empirical formulas are often calibrated under standard atmospheric pressure and specific mix proportions, making them difficult to adapt to high-altitude, low-pressure environments and variations caused by different admixture ratios. Therefore, the heat release rate data for hydration in this embodiment is not based on a pre-set empirical formula, but is obtained in real-time from measured temperature field data through reverse derivation. The specific calculation process will be explained in detail later.

[0054] In this embodiment, to ensure that the cumulative solar radiation accurately reflects the actual net radiant heat absorbed by the concrete foundation surface and to eliminate the pulsating noise caused by rapid cloud changes in the high-frequency sampling data, the process of obtaining the cumulative solar radiation includes:

[0055] A time-domain integral accumulator for solar radiation intensity is established. Within each set acquisition time window, multiple instantaneous solar radiation flux samples collected by the high-frequency photosensitive units in the array are accumulated in the time domain using the trapezoidal rule. The dynamic albedo of the concrete surface is introduced for correction, generating an array of accumulated solar radiation representing the heat boundary input.

[0056] Among them, the solar radiation intensity time-domain integral accumulator refers to a numerical integration calculation module that runs continuously in the system. This module is periodically started according to a preset acquisition time window, performs numerical integration calculation on the instantaneous radiation data continuously acquired within the window, and outputs a scalar cumulative result at the end of each window.

[0057] A high-frequency photosensitive unit refers to a solar radiation flux sensor with high temporal resolution deployed on the surface of a concrete foundation. Its sampling frequency is sufficient to capture transient changes in radiation flux caused by rapid cloud movement at high altitudes. For example, the sampling interval of a high-frequency photosensitive unit can be from 0.5 seconds to 5 seconds.

[0058] Trapezoid rule integration is a classic numerical integration method. Its basic idea is to take the arithmetic mean of the radiative flux values ​​of two adjacent sampling times, multiply it by the time interval between the two sampling times, and then sum all such products to approximately calculate the area enclosed by the continuous radiation curve within the time window, that is, the total radiative energy within that period.

[0059] The dynamic albedo of concrete surface refers to the proportion of incident solar radiation reflected by the concrete surface. This coefficient is not a fixed constant, but rather varies dynamically with factors such as the wetness of the concrete surface, its age, and whether or not it is covered with a curing film. For example, the albedo of freshly poured, wet concrete is relatively low, while the albedo of dried concrete is relatively high.

[0060] For example, in this embodiment, the cumulative solar radiation can be calculated using the following formula:

[0061]

[0062] in, In the first The cumulative amount of solar radiation at the end of a time window, its physical meaning is the total net solar radiation energy actually absorbed by the concrete surface within that time window. For the first time window The instantaneous solar radiation flux at each sampling moment was directly measured by the high-frequency photosensitive unit, and the unit is watts per square meter; The total number of sampling points within this time window is determined by the ratio of the window duration to the high-frequency sampling interval; The high-frequency sampling interval is the time span between two adjacent sampling points, measured in seconds. The dynamic albedo of the concrete surface represents the proportion of radiation energy reflected by the surface but not absorbed to the total incident radiation energy, and its value ranges from 0 to 1. Figure 2 This diagram illustrates the relationship between instantaneous solar radiation flux at high altitudes and the trapezoidal integral smoothing effect in this embodiment. Figure 2The X-axis represents time (minutes), and the Y-axis represents radiant flux (W / m²). Figure 2 The upper subplot shows the original signal of high-frequency pulsations in instantaneous radiative flux caused by rapid cloud movement in a high-altitude environment; the lower subplot shows the cumulative solar radiation curve after time-domain integration using the trapezoidal rule, which shows a smooth increasing trend. It can be seen that time-domain integration using the trapezoidal rule can effectively eliminate radiation spike pulsations caused by cloud cover at high altitudes, providing a stable heat flux input at the energy conservation level for the heat transfer model, thereby avoiding numerical oscillations in the prediction results. Figure 3 This embodiment shows a graph illustrating the impact of different albedo rates on the calculation of cumulative solar radiation. Figure 3 The left figure shows a comparison of three sets of bar charts, including: the cumulative amount without considering albedo, the cumulative amount under the albedo of wet concrete surface, and the cumulative amount under the albedo of dry concrete surface. The error band in the right figure shows the percentage deviation of heat input caused by improper selection of albedo.

[0063] It should be noted that in the above formula This is the basic computational unit of the composite trapezoidal numerical integration scheme. Its physical meaning is: the radiant flux values ​​at two adjacent sampling times are considered as the heights of two parallel sides of a trapezoid, with the sampling interval... If we consider the width of the trapezoid as the value, then the product represents the area of ​​that smaller trapezoid, signifying the radiative energy input within that tiny time interval. Summing up the areas of the trapezoids across all these tiny time intervals completes the approximate calculation of the area of ​​the continuous radiation curve over the entire time window. Multiplying by a factor... This ensures that only the net radiant energy actually absorbed by the concrete surface is included in the calculation, excluding the radiant energy reflected back to the atmosphere, strictly adhering to the principle of energy conservation. Through this time-domain integration, high-frequency instantaneous radiative fluctuations are smoothed into the total heat input over a time period, thus providing a stable heat flux input at the energy conservation level for subsequent heat transfer models.

[0064] In this embodiment, in order to break away from the dependence on empirical hydration heat release curves and achieve real-time, adaptive quantification of the intensity of internal chemical reactions in concrete, the process of obtaining real-time hydration heat release rate data of concrete includes:

[0065] Based on the temperature rise rate distribution collected by the internally embedded temperature sensor, as well as the predefined concrete specific heat capacity and density constant, the real-time hydration heat release rate data of concrete, which reflects the intensity of the current chemical reaction, is calculated and output in real time by the energy conservation inverse derivation operator.

[0066] The temperature rise rate distribution refers to the spatial distribution data at the current moment, consisting of the rate of temperature change over time recorded by multiple embedded temperature sensors located at different spatial positions within the concrete structure. This distribution data can be approximated by taking the time difference of the temperature values ​​of each sensor node at adjacent sampling times.

[0067] Predefined concrete specific heat capacity and density constants refer to the thermal properties of concrete determined before concrete pouring based on its mix design and material test reports, and considered as known constants during the current curing cycle. For example, for conventional C40 concrete, its density typically ranges from 2300 to 2500 kg per cubic meter, and its specific heat capacity typically ranges from 0.9 to 1.0 kJ per kilogram per degree Celsius.

[0068] The energy conservation inverse derivation operator is a mathematical operation module established based on the energy conservation principle of the Fourier heat conduction equation. Its core logic is: the enthalpy change corresponding to the temperature change rate of a spatial node within concrete is equal to the sum of the heat received by that node from or dissipated to surrounding nodes through heat conduction and the heat generated by the internal heat source of that node. Therefore, if the temperature change rate (obtained through time difference) and the Laplace operator of the spatial temperature distribution (obtained through the central difference scheme of the temperature values ​​of adjacent nodes) are known, the intensity of the internal heat source, i.e., the hydration heat release rate, can be solved in reverse.

[0069] For example, in this embodiment, the real-time heat release rate of concrete hydration can be calculated using the following formula:

[0070]

[0071] in, For in space nodes place, time The hydration heat release rate at a given time is, in physical terms, the heat released per unit volume per unit time by the cement hydration reaction at that node location, expressed in watts per cubic meter. This refers to the density of concrete, expressed in kilograms per cubic meter. Specific heat capacity of concrete, expressed in joules per kilogram per degree Celsius; For nodes The partial derivative of temperature with respect to time at a given point, i.e., the rate of temperature change, can be obtained by the temperature difference between adjacent time points. Approximate values ​​are obtained, in degrees Celsius per second. The equivalent thermal conductivity of concrete reflects the ability of concrete to conduct heat, and is measured in watts per meter per degree Celsius. For the previous moment The Laplace operator of the temperature field, i.e., the sum of the second-order partial derivatives of temperature in three-dimensional space, can be obtained from the central difference scheme at the nodes. Rather than , , The temperature values ​​of adjacent nodes in three directions are calculated. Figure 4 A schematic diagram of the heat of hydration release rate in this embodiment is shown. Figure 4 This demonstrates the difference between the predicted values ​​from traditional empirical formulas (calibrated under standard atmospheric pressure) and the actual values ​​derived from the measured temperature field using the inverse derivation operator based on energy conservation in this scheme. Figure 4 It can be seen that there is a significant deviation between the two under high altitude and low pressure conditions. This indicates that under high altitude and low pressure conditions, traditional empirical formulas will systematically overestimate the peak value of heat release of hydration and misjudge the time of peak occurrence, while the reverse derivation method can capture the real hydration process in real time and adaptively without the need to preset the heat release curve.

[0072] It should be noted that the physical meaning of the above formula can be interpreted as: the hydration heat release rate on the left side of the equation. It is the unknown quantity to be determined; the first term on the right side of the equation. This represents the total enthalpy change per unit volume of concrete at that node per unit time, i.e., the total heat change corresponding to the actual observed temperature change; the second term on the right side of the equation... This represents the contribution of net heat exchange between the node and its surrounding nodes due to thermal conduction. Subtracting the contribution of thermal conduction from the total enthalpy change yields the intensity of the heat source contributed by internal chemical reactions. The time derivative of temperature in the above equation is taken at the current moment. The spatial Laplace operator takes the previous time step. This constitutes a semi-implicit time-discrete scheme that balances accuracy and stability in numerical calculations. Through this reverse derivation mechanism, the system can infer the cause of the hydration reaction in real time from the results of temperature field observations, without relying on any preset empirical exothermic curves, thus achieving deterministic quantification of the intensity of the current chemical reaction.

[0073] Step 2: Input the high-altitude meteorological forecast time series data, cumulative solar radiation and real-time concrete hydration heat release rate data as input parameters into the preset multi-field coupled state space data structure, perform customized forward differential iterative logic calculation, and generate a rolling temperature gradient prediction matrix for the next 3 to 6 hours.

[0074] The multi-field coupled state-space data structure refers to a unified data organization architecture established in computing memory to carry and manage all state variables, physical property parameters, and boundary conditions related to temperature field prediction. This data structure integrates information on the internal heat conduction process (temperature field), the external convective and radiative heat transfer process (environmental field), and the exothermic process of internal hydration chemical reactions (chemical field) into a single data framework accessible for iterative calculations, hence the name multi-field coupling.

[0075] Understandably, the purpose of this data structure is to provide a structured, efficiently indexable, and updatable data base for subsequent forward differential iterative calculations, enabling each iterative calculation to quickly read the required state vectors, thermal property parameters, boundary conditions, and heat source terms from the structure, and write the calculation results back into the structure for use in the next iteration.

[0076] Forward differential iterative logic computation refers to a spatiotemporal discretization numerical solution method based on physical laws (i.e., partial differential equations of heat conduction). This method discretizes the continuous time dimension into several equally spaced time steps and the continuous spatial dimension into several grid nodes. Then, starting from the known temperature state at the current moment, it uses the discretized control equations to progressively calculate the predicted temperature value of each spatial node at the end of each future time step (i.e., along the forward time direction).

[0077] Understandably, this embodiment abandons black-box prediction methods based on machine learning because data-driven models may output predictions that violate physical laws (e.g., predicting negative heat capacity effects or violating energy conservation) when faced with extreme weather events not present in the training data (such as sudden blizzards or drastic diurnal temperature variations in high-altitude environments). Instead, a rigorous partial differential equation discretization method is used for deterministic forward inference, ensuring that each calculation strictly adheres to the fundamental laws of thermodynamics.

[0078] The rolling temperature gradient prediction matrix is ​​the final output of the forward differential iterative logic calculation. It is a two-dimensional data matrix with time as the row index and temperature gradient index as the column index. This matrix covers all discrete time nodes within the prediction time window of 3 to 6 hours from the current moment, and explicitly records two key temperature gradient indices at each time node: the first temperature difference sequence and the second temperature difference sequence.

[0079] The first temperature difference sequence refers to the time series consisting of the difference between the average predicted temperature value of the node group in the central region of the concrete structure and the average predicted temperature value of the node group in the surface region at each look-ahead time node of the rolling temperature gradient prediction matrix. This sequence reflects the magnitude of the internal and external temperature gradient generated by the accumulation of heat released during hydration inside the concrete and its evolution trend over time.

[0080] The second temperature difference sequence refers to the time series consisting of the difference between the average predicted temperature value of the nodal group on the surface of the concrete structure and the predicted temperature value of the external environment at each look-ahead time node of the rolling temperature gradient prediction matrix. This sequence reflects the magnitude of the driving force for heat dissipation from the concrete surface to the external environment and its evolution trend over time, directly related to the risk of excessive or slow heat loss from the surface.

[0081] In this embodiment, the specific steps for generating the rolling temperature gradient prediction matrix through forward differential iterative logic may include steps S2.1 to S2.5, which will be described below.

[0082] S2.1: Map the physical model of the concrete foundation to a three-dimensional spatial grid array defined by discrete three-dimensional node coordinates, and initialize the initial temperature state vector of each grid node at the current moment.

[0083] The three-dimensional spatial grid array refers to the set of computational grids formed by discretizing the continuous physical spatial domain of the concrete foundation structure in the length, width, and depth directions according to a preset grid spacing. Each grid node represents the center position of a tiny volumetric unit in the concrete structure, and the temperature value at that node is used to approximate the average temperature of that tiny volumetric unit.

[0084] Understandably, the mesh density (i.e., the mesh spacing) needs to be determined based on a balance between the required prediction accuracy and available computing resources. A denser mesh results in higher spatial resolution and better prediction accuracy, but the computational cost also increases exponentially; a sparser mesh results in faster computation, but may miss important details of local temperature gradients.

[0085] The initial temperature state vector refers to the vector formed by all The composition of the measured or optimal temperature values ​​of each spatial discrete node at the current time (i.e., the start time of the prediction calculation) 3D column vector, denoted as This vector is the starting condition for the entire forward difference iteration calculation, and its accuracy directly affects the reliability of the prediction results for all subsequent time steps.

[0086] Understandably, for nodes with temperature sensors directly installed, their initial temperature is taken from the current reading of that sensor; for nodes without sensors directly installed (i.e., when the number of grid nodes is greater than the number of sensors), their initial temperature needs to be obtained by spatial interpolation using data from neighboring sensor nodes.

[0087] S2.2: Construct the internal heat source forcing vector of the spatial grid based on the real-time hydration heat release rate data of concrete, and construct the convection and radiation boundary condition matrix of the surface grid nodes using high-altitude meteorological forecast time series data and solar radiation accumulation.

[0088] Among them, the internal heat source forcing vector refers to the vector formed by all the heat sources. The hydration heat release rate values ​​at each spatial discrete node constitute 3D column vector, denoted as Each element in this vector corresponds to a grid node, and its value is the real-time hydration heat release rate at that node location, calculated by the energy conservation inverse derivation operator in step 100. For nodes located inside the concrete, the hydration heat release rate is positive (releasing heat); for nodes located on the concrete surface, the hydration heat release rate is typically low or close to zero (depending on the depth of the node from the cast surface). This vector acts as an internal driving force in the forward difference iterative equation, driving the continuous increase in the internal temperature of the concrete.

[0089] The convection and radiation boundary condition matrix (vector) is a vectorized representation of the heat exchange conditions applied to all surface nodes in a spatial grid, constructed from high-altitude meteorological forecast time series data (providing parameters such as ambient temperature, wind speed, and humidity at future times) and accumulated solar radiation (providing surface radiative heat input). It is denoted as... Each element in this vector corresponds to a surface mesh node, and its value reflects the net rate of heat gain or loss at that node at a current or future moment through convective heat transfer (heat exchange with ambient air) and radiative heat transfer (absorption of solar radiation and long-wave radiation into the sky). This vector acts as an external boundary constraint in the forward difference iterative equation, determining the rate of heat inflow and outflow from the concrete surface.

[0090] S2.3: Establish a multivariable nonlinear state-space extrapolation equation system, and substitute the internal heat source forcing vector, boundary condition matrix, and a set of heat conduction weight coefficient matrices pre-calibrated based on historical high-altitude maintenance data into the equation system.

[0091] Among them, the multivariable nonlinear state-space extrapolation equation system refers to the mathematical equation system describing how the system state vector (i.e., the temperatures of all grid nodes) evolves from the current time step to the next time step. This equation system is the algebraic expression of the partial differential equations of heat conduction after spatial and temporal discretization. "Multivariable" refers to the fact that the equation system contains... The node temperature is considered as an unknown variable in the coupling. Nonlinearity refers to the fact that certain thermophysical parameters (such as thermal conductivity and convective heat transfer coefficient) may change with temperature.

[0092] The heat conduction weighting coefficient matrix is ​​a coefficient matrix determined during spatial discretization by the geometric relationships between adjacent grid nodes (such as node spacing) and material thermal properties (such as thermal conductivity). It is denoted as... Each non-zero element in this matrix reflects the amount of heat transfer between two adjacent nodes.

[0093] Understandably, the matrix was pre-calibrated based on historical high-altitude curing data, meaning that key parameters such as thermal conductivity in the matrix are not simply taken from the standard values ​​in the material handbook, but are calibrated through inversion analysis (parameter identification) of measured temperature field data in historical engineering cases, so as to fully reflect the actual impact of factors such as high-altitude low-pressure environment and special mix proportion admixtures on the thermal properties of concrete.

[0094] In addition, the system also maintains a global heat capacity matrix. This matrix characterizes the ability of each grid node to store heat in a tiny volume unit, and its diagonal elements are determined by the density, specific heat capacity, and allocated volume of the concrete at that node.

[0095] For example, in this embodiment, the extrapolation equation for the forward difference state space can be expressed as follows:

[0096]

[0097] in, In the first At each time step, by all The system state vector, composed of the temperature values ​​of discrete spatial nodes, represents the complete temperature field distribution information of the entire concrete structure at that moment. The temperature state vector to be solved in the next time step is the unknown quantity in the iterative calculation; The global heat capacity matrix has a size of [value missing]. Its physical meaning lies in characterizing the ability of each node to store heat, that is, the amount of heat absorbed or released by each node and the tiny volume unit it represents when the temperature changes by one unit. This is the thermal conductivity matrix with dimensions of . Its physical meaning lies in characterizing the ability of heat transfer between adjacent nodes. The non-zero elements in the matrix reflect the thermal coupling strength between spatially adjacent node pairs. The set time step size is the time interval between two adjacent calculation time nodes. Its value needs to be determined based on the requirements of numerical stability and prediction accuracy. The internal hydration heat source forcing vector is at the first Step and the first The intermediate time value between steps, by taking the first step Step and the first The heat source items in the step (estimated value) are approximated by averaging; The value of the boundary condition vector for convective and radiative heat transfer in the external environment at the midpoint of the time step is approximated by averaging the boundary conditions over the two time steps.

[0098] It should be noted that the above equation uses the Crank-Nicolson semi-implicit difference scheme, the core of which is that the left side of the equals sign includes the unknown temperature vector at the next time step. (pass The term is reflected in the equation, and the right side of the equation contains the known temperature vector at the current moment. (also through) The heat conduction term is weighted equally between the current and next time steps. This semi-implicit approach gives the scheme a crucial numerical property: for linear heat conduction problems, the scheme is unconditionally stable, meaning it remains stable regardless of the time step. Regardless of the value, the numerical solution will not diverge or oscillate. This characteristic is crucial for engineering applications that require rapid multi-step forward inference (covering the next 3 to 6 hours) in computational memory, as it allows for appropriately increasing the time step size to reduce computational load without sacrificing numerical stability, thus ensuring that prediction calculations can be completed within the time constraints required by real-time control. In the calculation of each time step, the coefficient matrix on the left side of the equals sign... Once the mesh and material parameters are determined, the matrix becomes a constant and can be pre-decomposed once (e.g., LU decomposition or Cholesky decomposition). Subsequent iterations only require one previous-to-back substitution operation for efficient solution. .

[0099] S2.4: Within a time window of 3 to 6 hours in the future, set a fixed time advance step size, use the system of equations to perform forward difference calculations, and iteratively update the state vector of each grid node at the end of each step size.

[0100] Here, a fixed time advance step size refers to a discrete time interval that remains constant throughout the entire forward prediction process. The selection of this step size needs to consider the following factors: an excessively large step size may lead to insufficient numerical accuracy in regions with drastic changes in spatial temperature gradients (although the Crank-Nicolson scheme guarantees numerical stability, accuracy is still related to the step size); an excessively small step size will significantly increase the number of iterations and the total computational cost. For example, in practical engineering, the time advancement step size can be between 1 minute and 10 minutes, requiring 18 to 360 iterations for a prediction window of 3 to 6 hours in the future.

[0101] Understandably, in each iterative calculation, the system reads the state vector of the current time step from the multi-field coupled state-space data structure. Internal hydration heat source forced vector and the value at intermediate time Substituting into the above Crank-Nicolson equation and solving, we obtain... And then Write it back into the data structure as the initial state for the next iteration, and repeat this process until the entire prediction time window is covered.

[0102] S2.5: At each time step, extract the average predicted values ​​of the center node group and the surface node group to calculate the first temperature difference between the center and the surface, extract the surface node group and the environmental forecast value to calculate the second temperature difference between the surface and the environment, and arrange them in time order to generate the rolling temperature gradient prediction matrix.

[0103] The central node group refers to a set of mesh nodes located at the geometric center of the concrete foundation structure within a three-dimensional mesh array. These nodes are located at significant distances from all external surfaces, and their temperature is primarily influenced by internal hydration heat release and internal heat conduction, with relatively weak direct influence from external environmental boundary conditions. The average predicted temperature of the central node group is denoted as... .

[0104] A surface node set refers to a collection of mesh nodes located on the outer surface of a concrete foundation structure within a three-dimensional mesh array. These nodes are directly exposed to the external environment, and their temperature is affected by both internal heat conduction and external convective and radiative heat transfer. The average predicted temperature of the surface node set is denoted as... .

[0105] Understandably, the first temperature difference is calculated as follows: This reflects the magnitude of the temperature gradient within the concrete and is a core indicator for assessing the risk of internal thermal stress. The second temperature difference is calculated as follows: ,in The predicted ambient temperature value at the corresponding moment in the high-altitude meteorological forecast time series data reflects the rate at which the concrete surface dissipates heat to the external environment and is a core indicator for measuring the risk of surface heat loss. The rolling temperature gradient prediction matrix is ​​formed by arranging the first and second temperature differences calculated at each time step in chronological order. Figure 5 The space-time heatmap of the rolling temperature gradient prediction matrix in this embodiment is shown. Figure 5 The rolling temperature gradient prediction matrix is ​​visualized in the form of a heat map. The X-axis represents the forward prediction time node (0-6 hours), and the Y-axis represents the spatial node number (arranged from the center to the surface). The color intensity represents the predicted temperature value of each node, showing the gradient distribution of the high-temperature region at the center and the low-temperature region on the surface and its evolution trend over time. It can be seen that the full-time and space-time temperature field prediction results output by the forward differential iterative model can completely capture the time evolution law of the three-dimensional temperature distribution inside the concrete structure, providing comprehensive information support for risk prediction.

[0106] In addition, in this embodiment, to ensure that the forward prediction model can accurately reflect the drastic acceleration of heat loss from the concrete surface caused by sudden weather changes such as strong winds and snowfall unique to high altitudes, the calling logic of the heat conduction weight coefficient matrix in step S2.3 also includes a dynamic correction mechanism, which can be:

[0107] The system has a built-in wind chill equivalent reduction operator. Before each iteration, it analyzes the wind speed prediction component and humidity prediction component in the high-altitude weather forecast time series data. When the wind speed prediction component is greater than the preset critical wind speed threshold and the humidity component comprehensively indicates a high probability of snowfall, it triggers the nonlinear heat loss intensification condition. The system automatically lowers the convective thermal resistance weight coefficient in the boundary condition matrix according to the preset exponential compensation formula to force the matching of the boundary rapid heat loss physical process caused by the strong wind cooling unique to high altitudes.

[0108] The wind chill equivalent reduction operator is a mathematical function built into the boundary condition calculation module. This function dynamically adjusts the equivalent heat exchange intensity between the concrete surface and the external environment based on current or future forecasted wind speed and snowfall conditions. Under standard windless or light wind conditions, the convective heat transfer coefficient of the concrete surface is relatively stable and can be taken as a standard value. However, during extreme weather events such as strong winds and snowfall, strong winds increase the surface air displacement rate, and factors such as snowflakes condensing on the surface and releasing latent heat of phase change lead to a sharp increase in the surface heat loss rate. If this is not corrected, the prediction model will underestimate the surface heat loss rate, resulting in overly optimistic temperature difference predictions and missed early warning opportunities.

[0109] The critical wind speed threshold is a pre-set wind speed reference value. When the wind speed is below this threshold, the effect of wind speed on convective heat transfer is considered negligible and the system does not make any additional corrections. When the wind speed is above this threshold, the effect of wind speed becomes significant and the system initiates correction logic.

[0110] The convective thermal resistance weighting coefficient is a key weighting parameter used in calculating convective heat transfer on concrete surfaces. This parameter reflects the magnitude of the thermal resistance between the surface and the environment. The larger the thermal resistance (i.e., the larger the coefficient), the more difficult it is for the surface to dissipate heat, and the slower the heat loss rate; the smaller the thermal resistance (i.e., the smaller the coefficient), the easier it is for the surface to dissipate heat, and the faster the heat loss rate.

[0111] For example, in this embodiment, the dynamic correction of the convection thermal resistance weighting coefficient can be calculated by the following formula:

[0112]

[0113] in, This is the convective thermal resistance weighting coefficient after correction by the wind-cold equivalent reduction operator. This value will be directly substituted into the boundary condition vector. In the calculation; This is the baseline thermal resistance weight value under standard conditions (no wind, no snowfall), which is usually calibrated based on the static natural convection conditions of the concrete surface. The wind speed value is the forecast wind speed at the current time or the corresponding forecast time step, in meters per second; This is the critical wind speed threshold, measured in meters per second. When the actual wind speed does not exceed this threshold... The output of is zero, making the exponent term 1, that is... No further modifications were made; This is a snowfall probability indicator function, which uses ambient relative humidity as a metric. The temperature condition is taken as input. When the overall probability of snowfall is determined to be high, the output value is 1 (activating correction); otherwise, the output value is 0 (not activating correction). This is the experience loss coefficient, a positive coefficient that controls the steepness of the exponential decay curve. The larger the value, the greater the reduction in thermal resistance caused by the same wind speed increment exceeding the critical wind speed.

[0114] It should be noted that the above formula uses an exponential decay form. And nonlinear correction form The reason for this is that the physical relationship between wind speed and enhanced surface convection heat transfer is inherently nonlinear. As wind speed increases, the enhanced effect of convection heat transfer does not increase linearly but rather accelerates (this is closely related to the physical process of boundary layer flow transitioning from laminar to turbulent). The exponential decay function precisely reflects this nonlinear acceleration characteristic where the higher the wind speed, the faster the thermal resistance decreases. Meanwhile, The introduction of the indicator function ensures that this correction is activated only under snowfall conditions. This is because the condensation process of snowflakes on concrete surfaces during snowfall involves a complex latent heat exchange mechanism of phase change (water vapor condenses into snowflakes, releasing latent heat, but the snowflakes melt and absorb latent heat after landing on the concrete surface). Its heat exchange characteristics are fundamentally different from pure dry wind convection, requiring additional correction intensity for accurate simulation. Through this mechanism, the forward prediction model can automatically increase the estimation of boundary heat loss after detecting meteorological signals of strong winds and snowfall, avoiding overly optimistic temperature difference predictions and ensuring that the control system can obtain accurate risk warnings in advance.

[0115] Figure 6 This diagram shows a comparison of surface heat loss rates before and after the wind chill equivalent reduction correction in this embodiment. Figure 6 Comparing the two sets of curves, the surface temperature prediction without correction is too optimistic; however, after exponential wind chill reduction correction, the surface temperature prediction is significantly reduced and closer to the measured value. The exponential wind chill equivalent reduction operator in this embodiment can accurately capture the nonlinear boundary heat loss aggravation effect under high altitude, strong wind and snowfall conditions, avoid the prediction model's overly optimistic estimate of surface temperature, and ensure the timeliness of risk warning. Figure 7 This illustration shows a schematic diagram of the evolution of the three-dimensional spatial thermogram of the temperature field under a 24-hour rolling prediction window in this embodiment. Figure 7 A 2×3 subplot matrix is ​​used to display the temperature distribution heatmap of the concrete cross section at six typical time points (0h, 2h, 4h, 8h, 16h, 24h). Figure 7 The X-axis represents the position in the width direction of the cross section, the Y-axis represents the position in the depth direction of the cross section, and the color temperature represents the temperature value. It shows the panoramic view of the spatial distribution of the temperature field generated by the forward differential iterative model evolving over time, presenting the physical process of the hydration heat peak spreading from the inside to the surface, and the effect of the temperature gradient being effectively suppressed after the intervention of the temperature control strategy.

[0116] Step 3: Traverse the first and second temperature difference sequences within the rolling temperature gradient prediction matrix and perform a step-wise comparison analysis with the preset dynamic safe temperature stress threshold table; if a risk state exceeding the threshold is detected at the target look-ahead time node, extract the pre-calibrated thermodynamic hysteresis time parameter of the concrete foundation curing actuator, and at the physical advance triggering time obtained by subtracting the thermodynamic hysteresis time from the target look-ahead time node, output a reverse time compensation temperature control command to the concrete foundation curing actuator to perform pre-emptive defensive thermal regulation.

[0117] The dynamic safe temperature stress threshold table is a lookup table of safe temperature difference limits that dynamically change with the age of concrete pouring. The thresholds in this table are not fixed empirical constants set by humans, but are generated in real time based on the mechanical properties (elastic modulus, tensile strength) and thermodynamic parameters (thermal expansion coefficient) of concrete at different ages through the elastic mechanical thermal stress criterion.

[0118] Understandably, traditional temperature control methods typically use fixed empirical values ​​(e.g., the internal and external temperature difference should not exceed 25 degrees Celsius) as control thresholds. However, this approach ignores the fact that the mechanical properties of concrete change significantly with age. In the early stages of pouring, concrete is still in the plastic stage, with a very low modulus of elasticity. The material has a strong ability to withstand deformation and can tolerate larger temperature differences without generating destructive thermal stress. However, as the age increases, the cement hydration process progresses, the concrete gradually hardens and becomes brittle, and the modulus of elasticity increases significantly. The same temperature difference will generate much greater thermal stress than in the early stages, thus requiring stricter temperature difference control. Using the same fixed threshold throughout the entire age period can lead to overly conservative practices in the early stages (wasting cooling water and electricity) and insufficient protection in the later stages (the threshold is too lenient and fails to effectively prevent cracking).

[0119] The target look-ahead time point refers to the earliest future time point identified, after stepwise comparative analysis, as the first occurrence of a temperature difference exceeding the dynamic safety threshold on the time axis of the rolling temperature gradient prediction matrix. This time point represents the most imminent predicted moment when the temperature gradient risk will occur.

[0120] The concrete foundation curing execution agency refers to the hardware equipment cluster that actually undertakes the physical operation of temperature control on site, including but not limited to the internal cooling water pipeline system (including servo valves, circulating water pumps, cooling water pipelines, etc.) and the surface electric heat tracing insulation system (including contactors, heating cables or heating films, etc.).

[0121] The pre-calibrated thermodynamic hysteresis time parameter refers to a time constant that is determined in advance through experimental measurement and system calibration, reflecting the physical response delay between receiving a control command and actually producing an effective temperature control effect for various maintenance actuators.

[0122] Understandably, the response of any physical device cannot be instantaneous. For example, after receiving a valve opening command, a cooling water piping system needs to go through several physical stages, including the mechanical opening delay of the valve, the pump start-up and pressurization time, the flow delay of cooling water from the cooling station to the target area inside the concrete, and the time required to establish a stable heat exchange between the cooling water and the surrounding concrete. Before all these stages are completed, the cooling system cannot effectively regulate the temperature field of the concrete. If a control command is issued only when the temperature difference is about to exceed the limit, the temperature difference will continue to worsen during the lag time before the regulation effect is established, leading to a passive situation of delayed regulation.

[0123] The physical advance triggering moment refers to the precise time point obtained by subtracting the thermodynamic lag time of the corresponding maintenance actuator from the absolute time value of the target look-ahead time node on the timeline. Issuing control commands in advance at this moment ensures that when the temperature difference risk actually arrives at the target look-ahead time node, the temperature control effect of the maintenance actuator has been fully established and has reached the design power.

[0124] The reverse time compensation temperature control command refers to the control signal generated after the above reverse time offset calculation. The reverse means that the control command is issued at a time that is determined by offsetting the predicted future risk moment along the time axis by a hysteresis time, rather than being triggered based on the actual temperature difference exceeding the standard that has already occurred.

[0125] Preemptive defensive thermal regulation refers to temperature control behavior that is initiated before the actual occurrence of temperature difference risks, achieved through the aforementioned reverse time compensation mechanism. Its core concept is to shift the control strategy from traditional reactive response to proactive prevention.

[0126] In this embodiment, in order to achieve adaptive updating of the dynamic safe temperature stress threshold table with the age of concrete and ensure that the temperature control threshold can automatically adjust the trigger boundary when the stiffness of the concrete structure changes, the generation logic of the dynamic safe temperature stress threshold table includes:

[0127] A lookup table was established based on the growth curves of the elastic modulus and the evolution curves of the tensile strength of concrete at different pouring ages.

[0128] Within each time calculation step, the tensile strength limit value at the current age is queried, and combined with the thermal expansion coefficient of concrete, the limit temperature gradient difference that the current structure can withstand is calculated through the inverse mechanics derivation formula.

[0129] Multiply the extreme temperature gradient difference by a safety margin factor of 0.8 to 0.85 to dynamically update the dynamic safe temperature stress threshold table covering a time window of 3 to 6 hours.

[0130] Among them, the elastic modulus growth curve refers to the curve describing the elastic modulus of concrete. With the age of pouring The curve represents a monotonically increasing function. In the initial stage of pouring, the elastic modulus is close to zero. Subsequently, as cement hydration products are continuously generated and the microstructure of the concrete becomes denser, the elastic modulus rapidly increases and eventually stabilizes. This curve can be obtained through mechanical tests on standard laboratory specimens or by fitting an empirical model (such as the CEB-FIP model).

[0131] The tensile strength evolution curve refers to the curve describing the tensile strength of concrete. With the age of pouring The tensile strength is a monotonically increasing function curve. It also gradually increases with age, but its rate of increase and absolute value are usually much smaller than that of the compressive strength. For temperature-controlled crack prevention, tensile strength is the most critical mechanical indicator because if the thermal stress caused by temperature differences exceeds the current tensile strength of the concrete, cracks will form.

[0132] The inverse mechanics derivation formula refers to the mathematical formula established based on the thermal stress analysis principle of elasticity, which deduces the temperature difference limit that the material can withstand from the mechanical strength index.

[0133] The safety margin factor is a positive coefficient less than 1, which is between 0.8 and 0.85. The purpose of this factor is to apply a conservative reduction to the theoretically calculated limit safety temperature difference to ensure that the actual control threshold is lower than the theoretical limit value, and to reserve a safety margin for uncertainties such as model error, material inhomogeneity, and temperature field measurement error.

[0134] For example, in this embodiment, the dynamic safety temperature difference threshold can be calculated using the following formula:

[0135]

[0136] in, In order to at the current pouring age The dynamic safety temperature difference threshold, in degrees Celsius, is the maximum temperature gradient difference that a structure can safely withstand under the current mechanical state of concrete. For concrete at age The tensile strength at that time, measured in Pascals, increases gradually with age and represents the maximum tensile stress that concrete can withstand at that moment. For concrete at age The elastic modulus at age 20, measured in Pascals, also increases rapidly with age and represents the stiffness of a material against deformation. is the linear thermal expansion coefficient of concrete, expressed in degrees Celsius, representing the linear strain produced by concrete for every one degree Celsius change in temperature; Poisson's ratio of concrete is a dimensionless parameter that represents the ratio of transverse strain to longitudinal strain. The creep relaxation coefficient is a dimensionless parameter used to reflect the characteristic that the stress of concrete gradually relaxes (i.e. decreases) over time under continuous load. Since concrete is not a perfectly elastic body, its actual stress under continuous deformation constraint will be less than the stress value calculated according to pure elastic theory. The relaxation coefficient is a quantitative correction of this effect. This is the safety margin coefficient. Figure 8 The graph shows the adaptive evolution curve of the dynamic safe temperature difference threshold with age in this embodiment. Figure 8 The X-axis represents the casting age (hours), the left Y-axis represents the safe temperature difference threshold (°C), and the right Y-axis represents the elastic modulus (GPa) and tensile strength (MPa). The three curves in the figure are the elastic modulus growth curve, the tensile strength growth curve, and the dynamic safe temperature difference threshold curve derived from the two. Meanwhile, the traditional fixed temperature difference limit (e.g., 25°C) is marked with a horizontal dashed line. This indicates that the dynamic threshold of this scheme allows for a larger temperature difference in the early stages (reducing unnecessary cooling consumption) and automatically tightens in the later stages (matching the high stiffness characteristics after hardening), achieving adaptive coverage throughout the entire life cycle compared to the traditional fixed threshold.

[0137] It should be noted that the denominator in the above formula... The product of these represents the actual effective thermal stress generated per unit temperature difference under the mechanical conditions at the current age. Among them, Corresponding to the nominal thermal stress generated per unit temperature difference under free constraint conditions; multiplied by The factor is a correction for thermal stress under simplified constraint conditions, reflecting the influence of the Poisson effect on constraint stress—when concrete is constrained by thermal expansion in one direction, due to the Poisson effect, additional stress will also be generated in the direction perpendicular to it. This is a simplified representation of the three-dimensional constraint effect; multiplied by This takes into account the reduction in effective stress due to creep relaxation. The numerator represents the tensile strength at the current age. , represents the maximum tensile stress limit that concrete can withstand. The ratio of the two is the maximum temperature difference that concrete can safely withstand under the current mechanical conditions and constraints.

[0138] Understandably, in the early stages of pouring, The value is very low (the material is soft and has low stiffness), while Although it is also relatively low, its growth rate is relatively fast, therefore A larger ratio indicates a larger allowable safe temperature difference threshold in the early stages—this aligns with engineering practice, as concrete is in its plastic stage at this point, exhibiting strong deformation capacity and reducing the likelihood of cracking. However, in the later stages... As the temperature rises significantly (due to material hardening and increased stiffness), this ratio decreases rapidly, and the safe temperature difference threshold automatically tightens—this also aligns with engineering practice, as concrete becomes brittle at this point, and even a small temperature difference can generate significant thermal stress. Through this dynamically changing threshold mechanism, the system achieves adaptive coverage of the entire lifecycle of concrete's mechanical evolution: reducing unnecessary consumption of cooling water and electric heating energy in the early stages, and automatically tightening control standards in the later stages to ensure structural safety.

[0139] In this embodiment, after determining the dynamic safety temperature difference threshold, the system searches in the rolling temperature gradient prediction matrix for the earliest time point when the temperature difference first exceeds the dynamic safety threshold, and determines the critical risk time point.

[0140] For example, in this embodiment, the critical risk time point and the control trigger time can be determined by the following formula:

[0141]

[0142]

[0143] in, The target look-ahead time point is the time point in the rolling temperature gradient prediction matrix at which the core-surface temperature difference (i.e., the value in the first temperature difference sequence) first exceeds the dynamic safety threshold. This is the infimum operator, which takes the earliest of all future moments that satisfy the condition. For the current moment; For time intervals extracted from the prediction matrix Predicted center-surface temperature difference at the location; For corresponding time Dynamic safe temperature difference threshold at different concrete ages; It is the first hysteresis time constant of the cooling water pipe network system, which includes the sum of all physical delays such as valve mechanical response delay, pipe network water flow transmission delay, and heat exchange establishment delay; This refers to the physical advance triggering time at which the cooling water system should actually issue a control command. Figure 9 This diagram illustrates a comparison of the temperature difference control effects of reverse time compensation and traditional hysteresis regulation in this embodiment. Figure 9The X-axis represents time (hours), and the Y-axis represents the center-surface temperature difference (°C). The three sets of curves are the dynamic safety threshold line, the temperature difference evolution curve where the traditional method only starts adjustment after the temperature difference actually exceeds the limit (with a clear exceeding range), and the temperature difference evolution curve triggered in advance by the reverse time compensation in this scheme (always controlled below the threshold). This shows that the reverse time compensation mechanism in this embodiment effectively reduces the system response delay to near zero, eliminates the temperature difference exceeding window caused by physical hysteresis in the traditional method, and achieves true pre-emptive defensive control. Figure 10 The diagram shows the composition and timing relationship of the physical lag time of the cooling water system in this embodiment. Figure 10 The X-axis represents time (seconds), and the Y-axis represents the normalized system response (0~1). This figure shows that the two types of actuators have drastically different physical response characteristics, illustrating the necessity of using two independent hysteresis queues for separate calibration and compensation.

[0144] Understandably, the aforementioned reverse time compensation mechanism is the core solution to the lag problem in traditional temperature control systems. Traditional control algorithms typically react when the actual temperature difference exceeds the limit. The alarm is only triggered and adjustments are initiated at the exact moment, but there is a subsequent physical delay. Inside, the temperature difference will continue to worsen while the cooling system has not yet established effective cooling capacity, creating an uncontrolled and dangerous window period. The algorithm in this embodiment identifies risk points in advance within the prediction matrix and reverses the issuance time of control commands to... Execution should be carried out in advance to ensure that the risk of temperature difference is mitigated. When the moment actually arrives, the peak cooling power of the cooling water system has been fully established and offsets the temperature rise peak, effectively reducing the system's response delay to near zero.

[0145] In this embodiment, the pre-calibrated thermodynamic hysteresis time parameter is composed of two independent hysteresis queues. The system maintains a first hysteresis time constant for the cooling water pipe network system and a second hysteresis time constant for the surface electric heat tracing insulation system, respectively.

[0146] The first hysteresis constant encompasses the sum of valve mechanical opening delay, pump pressurization time, and refrigerant flow delay in the pipeline network to the target area.

[0147] The second hysteresis constant encompasses the sum of the contactor closing delay and the thermal inertia time for the surface of the heating element to reach its operating temperature.

[0148] In this context, valve mechanical opening delay refers to the time required for a servo valve's mechanical actuator (such as an electric actuator or pneumatic actuator) to move from a stationary state to the valve core reaching a specified opening position after receiving an electrical signal. This delay depends on the valve type, drive method, and target opening degree.

[0149] The pump pressure build-up time refers to the time required for the pressure inside the pump chamber to rise to the design operating condition from receiving the start-up command. During this period, the cooling water in the pipeline network has not yet reached the design flow rate.

[0150] The flow delay time of refrigerant in the pipeline network to the target area refers to the time required for cooling water to travel from the cooling station (or cold water storage tank) through the pipeline system to the target control area in the cooling water pipes embedded inside the concrete foundation. This delay depends on the pipeline length, pipe diameter, and cooling water flow rate.

[0151] Contactor closing delay refers to the time required for the mechanical contacts of the power supply contactor in an electric heat tracing system to go from an open state to a fully closed state after receiving a control signal.

[0152] The thermal inertia time for the surface of a heating element to reach its operating temperature refers to the time required for the electric heating cable or film to rise from its initial temperature to the operating temperature that effectively heats and insulates the concrete surface after being energized. This time depends on the power density, heat capacity, and thermal contact conditions between the heating element and the concrete surface.

[0153] Understandably, the design purpose of the dual independent hysteresis queues in the system maintenance is that the cooling water pipe network system and the surface electric heat tracing insulation system are two physically independent and functionally complementary control devices, each with different physical response characteristics and delay times. The cooling system is mainly used to deal with excessively high internal temperatures (requiring active heat absorption from the concrete interior), while the electric heat tracing system is mainly used to deal with excessively rapid surface heat loss (requiring active heat replenishment to the concrete surface). Since the hysteresis times of the two systems may differ significantly (for example, the hysteresis time of the cooling water pipe network is usually longer, while the hysteresis time of the electric heat tracing system is usually shorter), when calculating the physical advance triggering time, their respective corresponding hysteresis time constants must be used to ensure that each system is activated in advance at the correct time.

[0154] In this embodiment, the output of the reverse time-compensated temperature control command and the execution of the pre-emptive defensive thermal regulation include mutually exclusive and hierarchical control logic, specifically including the following three operating condition branches:

[0155] Condition 1: Only the first temperature difference sequence exceeds the threshold.

[0156] If only the first temperature difference sequence exceeds the dynamic safe temperature difference threshold at the look-ahead time node (indicating that the temperature at the center of the concrete is too high relative to the surface, and there is too much internal heat storage), the system sends the first PWM duty cycle signal to the flow control module of the cooling water pipe network system in advance by the amount of time corresponding to the first hysteresis time constant. This drives the servo valve to increase the circulation flow of the internal cooling water pipe by a specific opening, thereby actively absorbing excess heat from the inside of the concrete and reducing the center-surface temperature difference.

[0157] The first PWM duty cycle signal refers to a control signal encoded using pulse width modulation (PWM). By adjusting the duty cycle of the pulse (i.e., the proportion of the high-level duration to the signal period), the opening degree of the servo valve is linearly controlled. The larger the duty cycle, the larger the valve opening, the greater the flow rate through the cooling water pipe, and the stronger the cooling effect.

[0158] For example, in this embodiment, the cooling water valve control quantity can be calculated using the following formula:

[0159]

[0160] in, This is the PWM control output of the cooling water valve, i.e., the actual control signal value sent to the servo valve driver; This is a PID controller function. Based on the input deviation (temperature difference exceeding the limit) and the three pre-calibrated proportional, integral, and derivative gain parameters, this function calculates the corresponding control output, thereby establishing a reasonable mapping relationship between the control quantity and the deviation quantity. To predict the width of the window, i.e., from A period of time extending backwards; To ensure that the control quantity is determined based on the most severe exceedance within the prediction window, rather than just the first exceedance point, the operator is designed to take the extreme value of the temperature difference exceeding the limit within the prediction window. This ensures that the control force is sufficient to cope with the worst operating conditions within the entire window. This is a surface undercooling mutual exclusion mask, and its value logic will be explained in detail in Case 3. Figure 11 This diagram illustrates the effect of the mutual exclusion mask on suppressing internal cooling in this embodiment. Figure 11 In the graph, the X-axis represents the prediction time (in hours), and the Y-axis contains three subplots. Figure 11 (a) shows a comparison between the first temperature difference and the threshold. Figure 11 (b) shows a comparison between the second temperature difference and the threshold. Figure 11 (c) represents the PWM control quantity of the cooling water. This figure shows the range in which the cooling water control quantity is forced to zero after the mutual exclusion mask is activated when internal overheating and surface overcooling occur simultaneously. It is compared with the output of the cooling water control quantity when there is no mutual exclusion protection.

[0161] Condition 2: Only the second temperature difference sequence exceeds the threshold.

[0162] If only the second temperature difference sequence exceeds the dynamic safe temperature difference threshold at the look-ahead time node (indicating that the temperature difference between the concrete surface and the ambient temperature is too large, i.e., the surface heat is lost to the environment too quickly, which may cause the surface temperature to drop sharply and cause surface cracks), the system sends the control array bit mask to the power distribution execution unit in advance according to the time amount corresponding to the second hysteresis time constant, activates the surface heat preservation and heating module of the corresponding windward area, actively replenishes heat to the concrete surface, and slows down the rate of surface heat loss.

[0163] In this system, the control array bitmask refers to a set of binary bit-coded control signals, where each binary bit corresponds to a physically independent surface insulation heating module (such as a section of heating cable loop or heating film unit). A bit set to 1 in the bitmask indicates that the corresponding heating module should be activated and powered on, while a bit set to 0 indicates that the corresponding heating module should remain off. Through this bitmask control method, the system can precisely and selectively activate the heating modules in the windward area (the area with the most severe heat loss) without having to activate all heating modules simultaneously, thus effectively maintaining heat while avoiding energy waste.

[0164] Operating Condition 3: The first and second temperature differences simultaneously face the risk of exceeding limits within a similar time window.

[0165] If the prediction matrix shows that the first and second temperature difference sequences simultaneously exceed their respective thresholds within similar time windows—that is, while the internal heat storage of the concrete is too high, the surface also faces the risk of excessive heat loss—then the system enters the coupled regulation branch. The core control logic of this branch is:

[0166] Set the execution priority of the internal cooling command (cooling water system) to be lower than that of the surface insulation command (electric heat tracing system).

[0167] The maximum permissible flow increment of the internal cooling water pipes is limited based on the severity of the first temperature difference exceeding the limit (i.e., the magnitude of the excess).

[0168] Understandably, the design of this coupled regulation branch is based on the following physical safety considerations: If the surface is already experiencing excessive heat loss due to factors such as strong winds, and the internal cooling water pipes are opened without restriction for significant internal cooling, the internal temperature of the concrete will decrease rapidly from the center to the surface. This not only fails to alleviate the surface heat loss problem but also creates a more pronounced gradient distribution in the overall temperature field across the center, surface, and environment levels—rapid cooling of the interior due to cooling water and rapid heat loss of the surface due to strong winds. The combined effect of these two factors could cause the entire temperature gradient structure to collapse completely in a short period, leading to simultaneous excessive tensile stress in multiple directions, resulting in a so-called secondary collapse of the temperature gradient. Therefore, the coupled regulation branch prioritizes surface insulation (supplementing surface heat through electric heat tracing) while limiting the intensity of internal cooling to ensure that the two control measures do not produce contradictory effects.

[0169] It should be noted that in the above formula This is the surface supercooling mutual exclusion mask. When the prediction shows that the concrete surface also has an extreme risk of heat loss, this mask is set to 1. In this case, the value at the end of the formula is... The factor becomes 0, completely limiting the control increment of the cooling water to zero, fundamentally preventing the initiation of internal cooling under surface supercooling conditions. When there is no risk of surface supercooling, this mask value is 0. When the factor becomes 1, the cooling water control output is normal and unrestricted. This mutual exclusion mask mechanism essentially constitutes a hard safety interlock, ensuring that the control strategy will not fail to address all issues under extremely complex operating conditions.

[0170] Step 4: After sending the reverse time compensation temperature control command, the system enters the look-ahead verification loop.

[0171] In this embodiment, in order to ensure that the final temperature control command will not cause new thermal stress risks due to overcompensation, after the control command is generated in step 3 and before it is actually released and issued through the I / O port, the system also performs a virtual sandbox verification calculation process.

[0172] The look-ahead verification loop is a safety verification layer built into the control decision-making process. Its core function is to use the forward differential iterative model established in step 2 to perform a complete secondary simulation (virtual sandbox simulation) in the computational memory before the control command is actually issued to the physical actuator. This simulation assumes how the system will evolve after the control command is applied, and verifies whether the simulation results meet the safety requirements. Only control commands that pass the verification are allowed to be officially issued and executed; if the verification fails, the control variables need to be adjusted and re-verified.

[0173] Understandably, due to the numerous nonlinear influencing factors in high-altitude environments (such as the time-varying nature of hydration heat, the nonlinearity of wind chill effects, and the coupling of multidimensional heat conduction), the control quantity initially calculated in step 3 may be at risk of overcompensation. For example, an excessively large calculated value for the cooling water valve opening could cause a sudden drop in the internal temperature of the concrete, forming a reverse temperature gradient in the central region and inducing new tensile stress, i.e., the so-called reverse thermal stress caused by excessive cooling. This new risk may not be fully foreseen in the one-time PID calculation in step 3. Therefore, it is necessary to treat the control quantity to be sent as a new known condition and re-substitute it into the forward prediction model for a complete full-time domain extrapolation to verify whether the temperature difference remains within the safe envelope throughout the entire prediction period after the control quantity is applied.

[0174] In this embodiment, the specific execution process of the look-ahead verification loop includes:

[0175] By utilizing the state cloning backup in the multi-field coupled state-space data structure, the servo valve opening increment and surface heating module power to be sent are injected into the boundary conditions and internal heat source forcing vectors, and a single fast verification calculation is performed again.

[0176] If the verification calculation results show that the regenerated verification temperature gradient sequence has completely fallen back to within the range of the dynamic safe temperature stress threshold table, then the temperature control command will be officially released through the I / O port.

[0177] State cloning backup refers to the system making a complete memory copy of all state vectors, boundary condition parameters, and heat source data in the current multi-field coupled state space data structure before performing verification calculations. Verification and deduction calculations are then performed on this cloned copy to avoid data modifications during the verification process affecting the original data in the main data structure.

[0178] Injecting the servo valve opening increment into the boundary conditions and internal heat source forcing vector means that the cooling effect of the cooling water pipe inside the concrete can be equivalent to applying a negative heat source term (endothermic) to the mesh nodes through which the cooling pipe passes, or modifying the boundary conditions of these nodes (using the cooling water temperature as the internal boundary condition). The heating effect of the electric heat tracing system can be equivalent to adding a positive heat source term to the surface nodes or modifying the surface boundary conditions. Therefore, injecting the control quantity into the model means reflecting the physical effects corresponding to the control command in the forward inference model by modifying the heat source vector and boundary condition vector.

[0179] The verification temperature gradient sequence refers to a new temperature gradient time series, which is derived by a forward differential iterative model after applying the control variable to be verified, and consists of the first and second temperature differences at each future time step.

[0180] For example, in this embodiment, the virtual sandbox verification logic can be represented by the following formula:

[0181]

[0182] in, This is the system state transition operator, which internally encapsulates the complete forward difference iterative model established in step 200 (including the Crank-Nicolson equation solver, boundary condition update logic, heat source term update logic, etc.). Its input is the temperature state vector at the current moment. Environmental boundary conditions Hydration heat source forced vector and the control sequence to be verified (Including valve opening and heating power, etc.), its output is the temperature difference distribution sequence at each predicted time under given initial state, boundary conditions, heat source and control input; In order to be from the current moment To the predicted endpoint The calculation of the maximum temperature difference in the simulation results over the entire time period; This represents the dynamic safety temperature difference threshold at the corresponding moment. Indicates the control sequence The data is officially sent to the executing mechanism through the I / O port.

[0183] It should be noted that the above formula means that only when the virtual simulation shows that a control sequence has been applied... Later, in the future from arrive The control sequence is deemed safe and executable and formally issued only when the maximum temperature difference at any given moment does not exceed the corresponding dynamic safe temperature difference threshold throughout the entire prediction period. If the verification fails—that is, the simulation reveals that the temperature difference at certain moments exceeds the threshold after control is applied (e.g., reverse temperature difference caused by excessive cooling water)—the system needs to reduce or adjust the control quantity according to the preset backoff strategy and re-verify until it passes. This mechanism essentially constitutes a safety verification layer for closed-loop feedforward control, ensuring that every defensive control command ultimately issued to the physical actuator undergoes rigorous full-time virtual simulation verification, guaranteeing that no new thermal stress risks will be caused by overcompensation, and that the entire control process is completely converged within the safety envelope. Figure 12 This embodiment demonstrates the overall temperature control effect throughout the entire cycle, showing a time-axis correlation graph of temperature difference, control parameters, and energy consumption. Figure 12 The X-axis represents the age after pouring (in hours, 0~72h). This figure includes four sub-figures, among which... Figure 12 (a) Comparison of center-surface temperature difference with dynamic threshold. Figure 12(b) Comparison of surface-ambient temperature difference with threshold value. Figure 12 (c) represents the timing sequence of control quantities for cooling water flow rate and electric heat tracing power. Figure 12 (d) shows the cumulative energy consumption comparison. This figure illustrates that while the temperature difference is always controlled below the safety threshold throughout the entire maintenance cycle, the cumulative consumption of cooling and heating resources is significantly lower than that of the traditional scheme because the dynamic threshold allows for a larger temperature difference in the early stages, thus achieving a dual optimization of safety and economy.

[0184] Step 5: In addition, in this embodiment, in order to cope with the hardware challenges of extreme communication failures that may occur in high-altitude environments, after obtaining the original sensor data (Step 1) and before injecting the data into the multi-field coupled state space data structure, a deterministic data self-healing anti-blocking logic can also be executed to ensure that the overall prediction system will not stop or disconnect due to data loss when some sensor nodes fail due to the harsh environment.

[0185] Specifically, performing deterministic data self-healing and anti-blocking logic processing on the data acquisition channels in the 3D sensor array includes the following:

[0186] Deterministic data self-healing anti-blocking logic refers to a real-time fault tolerance mechanism built into the data preprocessing layer. This mechanism can autonomously detect the failure state of sensor nodes and automatically generate alternative data based on deterministic physical interpolation algorithms without relying on manual intervention, thereby filling the data gaps left by the failed nodes and maintaining the integrity and continuity of the data flow.

[0187] Understandably, in high-altitude construction environments, strong ultraviolet radiation accelerates the aging and degradation of sensor housings and communication cables, while extreme low temperatures at high altitudes can cause icing at sensor connectors or degrade the performance of electronic components. Strong winds and dust can also obscure the photosensitive surface of the photosensitive unit. These harsh conditions can easily lead to the shutdown or communication interruption of some sensor nodes in the array. If the system simply stops operating or waits for manual repair after detecting missing data, in unattended or manpower-scarce scenarios at high altitudes, this will result in an unacceptably long period of stagnation in the curing temperature control logic, causing uncontrolled internal temperature in the concrete and leading to structural cracks.

[0188] In this embodiment, the specific execution process of the deterministic data self-healing anti-blocking logic includes:

[0189] Real-time monitoring of the signal integrity flag bits of all independent data acquisition channels in the three-dimensional sensor array;

[0190] When a surface communication node fails to return a heartbeat packet or data packet for N consecutive polling cycles due to high altitude, strong ultraviolet radiation, or low temperature icing, the system freezes the interruption request triggered by the failed node.

[0191] Extract the temperature rise gradient vector combination of the four nearest valid nodes in terms of physical distance to the failed node in the past hour, and combine it with the cosine projection relationship of the current solar irradiation angle. Then, generate a replacement data sequence through a three-dimensional spatial inverse distance weighted interpolation algorithm and inject it into the multi-field coupled state space data structure.

[0192] The signal integrity flag is a set of binary status bits maintained by the system for each independent data acquisition channel to indicate the current communication health status of that channel. For example, when a channel successfully returns a complete data packet and heartbeat packet within a predetermined polling cycle, its signal integrity flag is set to valid (logic 1); conversely, if no valid packet is received for several consecutive polling cycles, the flag is set to invalid (logic 0).

[0193] The N consecutive polling cycles refer to the minimum number of consecutive unresponsive cycles required for the system to determine that a sensor node has failed. This parameter N is a preset fault tolerance threshold, and its setting needs to balance the false positive rate and detection sensitivity: if N is too small, it may lead to the misjudgment of normal nodes as failed nodes due to instantaneous communication jitter; if N is too large, it may delay the detection of real faults. For example, in actual engineering, N can be set to 3 to 5 polling cycles.

[0194] Freezing the interrupt request triggered by the failed node means that after the system determines that a node has failed, it temporarily removes or blocks the data acquisition channel corresponding to that node from the system's interrupt service routine. This avoids the system's processor time and communication bus bandwidth being occupied due to continuous timeout waiting or retry operations, and prevents the abnormal state of a single failed node from spreading to the entire system.

[0195] The temperature gradient vector combination refers to the set of temperature change rate data recorded by the four normally functioning neighboring nodes that are physically closest to the failed node over a period of time (e.g., one hour). This combination provides a spatial reference data source for subsequent interpolation calculations.

[0196] The cosine projection relationship of solar irradiance angle refers to a correction factor based on the geometric position of the sun introduced during spatial interpolation repair. Its purpose is to take into account that the surface normal direction may be different at different locations on a concrete structure. Therefore, even if two nodes are spatially adjacent, if they are located on different orientation surfaces of the structure (e.g., one on the sunlit side and one on the shaded side), the solar radiation heat they receive will be significantly different. If the mean of the neighboring nodes is simply used as a substitute without considering this orientation difference, a systematic bias will be introduced.

[0197] The three-dimensional spatial inverse distance weighted interpolation algorithm is a deterministic spatial interpolation method. Its principle is that the closer the effective neighbor node is to the failed node, the greater the contribution weight of its data to the repair value; the farther the node is, the smaller the weight, and the weight is inversely proportional to the square of the distance.

[0198] For example, in this embodiment, the data repair value of the faulty node can be calculated using the following formula:

[0199]

[0200] in, Faulty node At any moment The estimated repair value will be injected into the multi-field coupled state space data structure, replacing the measured data that should have been provided by the failed node. ( These are the four normally functioning neighboring nodes closest to the faulty node; neighboring nodes With faulty nodes The Euclidean physical distance between them, in meters; The inverse distance squared weighting function is designed so that the closer the neighboring node is, the greater its contribution to the repair value, and the farther the neighboring node is, the smaller its contribution, which is consistent with the local correlation characteristics of the spatial temperature field. For the first The neighboring nodes at time... The measured data values; For a moment The angle between the sunlight and the normal to the surface where the fault node is located; For a moment Sunlight and the The angle between the surface normals of the neighboring nodes.

[0201] It should be noted that in the above formula This cosine ratio term is a crucial spatial geometric constraint correction factor. Its physical meaning lies in the fact that, according to Lambert's cosine law, the intensity of solar radiation received by a surface is proportional to the cosine of the angle between the surface normal and the incident direction of sunlight. Therefore, at the same moment, the solar radiation heat density received by surfaces facing different orientations on a concrete structure is different. If a neighboring node is located on a surface with a smaller angle to sunlight (i.e., more directly facing the sun), while the faulty node is located on a surface with a larger angle (i.e., more obliquely facing the sun), then directly using the neighboring node's temperature data for interpolation will overestimate the actual temperature of the faulty node. By multiplying by this cosine ratio, the data of the neighboring node is projected from the heating conditions of its facing surface to the heating conditions of the faulty node's facing surface, thereby eliminating the systematic bias caused by the difference in surface orientation. This physically constrained interpolation method ensures that the repair data strictly follows the light distribution law of the heat source in three-dimensional space, rather than simply performing uniform interpolation, thus preventing local communication failures from causing deviations or collapses in the state-space calculation of the entire prediction system. Figure 13 This diagram illustrates a comparison of errors between inverse-range weighted interpolation with solar declination correction and simple mean interpolation in this embodiment. Figure 13 The X-axis represents time (minutes), and the Y-axis represents temperature error (°C). By introducing a correction factor for the cosine ratio of the solar irradiation angle, the interpolation repair accuracy is significantly better than that of simple mean substitution. This effectively avoids the systematic deviation introduced by ignoring the heating differences of different orientation surfaces and ensures the numerical stability of the state matrix.

[0202] Example 2

[0203] This embodiment discloses a high-altitude concrete foundation curing temperature control system based on multi-sensor data. This system can be integrated into electronic devices such as on-site industrial control computers, edge computing nodes, or remote monitoring servers. The on-site industrial control computer can be a programmable logic controller (PLC) based on an embedded real-time operating system, an industrial server, or an integrated industrial control computer with hard real-time interrupt capabilities. The remote monitoring server can be a single server or a server cluster consisting of multiple servers, establishing data synchronization with the on-site equipment through a high-altitude base station communication link.

[0204] In some embodiments, the high-altitude concrete foundation curing temperature control system based on multi-sensor data can also be integrated into multiple electronic devices. For example, the field acquisition layer is responsible for high-frequency acquisition and preprocessing of sensor signals by an embedded microcontroller, the calculation and inference layer is responsible for executing forward differential iteration and temperature control decisions by an edge server or industrial computer with strong floating-point computing power, and the execution layer is responsible for driving cooling water valves and electric heat tracing modules by a controller with relay output or analog output interface. Multiple devices work together through an industrial real-time bus or wired Ethernet to collaboratively implement the solution in Embodiment 1 of this application.

[0205] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for temperature control during curing of high-altitude concrete foundations based on multi-sensor data, characterized in that, The maintenance and temperature control methods include: Acquire time series data of high-altitude meteorological forecasts for the target area, and simultaneously collect data on cumulative solar radiation and hydration heat release rate at each time step using a three-dimensional sensor array deployed on and inside the concrete foundation. The high-altitude meteorological forecast time series data, cumulative solar radiation, and hydration heat release rate data are used as input parameters and injected into a preset multi-field coupled state-space data structure. Forward differential iterative calculation is then performed to generate a rolling temperature gradient prediction matrix covering a preset future time window. The rolling temperature gradient prediction matrix includes a first temperature difference sequence and a second temperature difference sequence; The first and second temperature difference sequences within the rolling temperature gradient prediction matrix are traversed and compared with a preset dynamic safety temperature difference threshold. The dynamic safety temperature difference threshold varies dynamically with the age of the concrete pouring process. If a risk state exceeding the dynamic safety temperature difference threshold is detected at the target look-ahead time node, the pre-calibrated thermodynamic hysteresis time parameter of the concrete foundation curing actuator is extracted, and at the physical advance triggering time obtained by subtracting the pre-calibrated thermodynamic hysteresis time parameter from the target look-ahead time node, a reverse time compensation temperature control command is output to the concrete foundation curing actuator to perform pre-emptive defensive thermal regulation.

2. The method for temperature control and curing of high-altitude concrete foundations based on multi-sensor data according to claim 1, characterized in that, The first temperature difference sequence is a time series consisting of the difference between the average predicted temperature of the concrete center node group and the average predicted temperature of the surface node group at each look-ahead time node. The second temperature difference sequence is a time series consisting of the difference between the average predicted temperature of the surface node group at each look-ahead time node and the predicted environmental temperature value at the corresponding time in the high-altitude meteorological forecast time series data.

3. The method for temperature control and curing of high-altitude concrete foundations based on multi-sensor data according to claim 2, characterized in that, The process of generating the rolling temperature gradient prediction matrix includes: The physical model of the concrete foundation is mapped to a three-dimensional spatial grid array defined by discrete three-dimensional node coordinates, and the initial temperature state vector of each grid node at the current time is initialized. Based on the hydration heat release rate data, the internal heat source forcing vector of the three-dimensional spatial grid array is constructed, and the convection and radiation boundary condition vector of the surface grid node is constructed using the high-altitude meteorological forecast time series data and the solar radiation accumulation. A multivariable nonlinear state-space extrapolation equation system is established, and the internal heat source forcing vector, the convection and radiation boundary condition vectors, the pre-calibrated heat conduction weighting coefficient matrix, and the global heat capacity matrix are substituted into the multivariable nonlinear state-space extrapolation equation system. Within a future preset time window, forward difference calculations are performed using the multivariable nonlinear state-space extrapolation equations with a fixed time advance step size, and the temperature state vector of each grid node is iteratively updated at the end of each step size. At each time step, the difference between the average predicted temperature of the central node group and the average predicted temperature of the surface node group is extracted as the current time value of the first temperature difference sequence. The difference between the average predicted temperature of the surface node group and the corresponding ambient temperature forecast value in the high-altitude meteorological forecast time series data is extracted as the current time value of the second temperature difference sequence. The current time values ​​of the first temperature difference sequence and the second temperature difference sequence at each time step are arranged in chronological order to generate the rolling temperature gradient prediction matrix.

4. The method for temperature control and curing of high-altitude concrete foundations based on multi-sensor data according to claim 1, characterized in that, After acquiring the accumulated solar radiation and the hydration heat release rate data, and before injecting the data into the multi-field coupled state-space data structure, the process also includes: Deterministic data self-healing and anti-blocking logic processing is performed on the data acquisition channels in the three-dimensional sensor array. When some sensor nodes fail due to harsh environments, alternative data is automatically generated based on a deterministic physical interpolation algorithm to fill the data gaps and maintain the integrity and continuity of the data stream. The process of the deterministic data self-healing and anti-blocking logic processing includes: Real-time monitoring of the signal integrity flag bits of all independent data acquisition channels in the three-dimensional sensor array; When a communication node fails to return a valid data packet for a preset number of consecutive polling cycles, the interruption request triggered by that communication node is frozen, and the communication node is designated as a failed node. Extract the temperature rise gradient vector combination of the preset number of adjacent valid nodes that are closest to the failed node in terms of physical distance within a preset historical period, combine it with the cosine projection correction of the solar irradiation angle, generate a replacement data sequence through a three-dimensional spatial inverse distance weighted interpolation algorithm, and inject the replacement data sequence into the multi-field coupled state space data structure; In the three-dimensional spatial inverse distance weighted interpolation algorithm, the weight of each adjacent valid node is inversely proportional to the square of its Euclidean distance to the failed node; the solar irradiation deflection angle cosine projection correction is: multiplying the measured data value of each adjacent valid node by the ratio between the cosine of the angle between the sunlight and the surface normal at the failed node and the cosine of the angle between the sunlight and the surface normal at the adjacent valid node, so as to eliminate the systematic deviation of the light receiving conditions caused by the difference in the surface orientation of each node.

5. The method for temperature control and curing of high-altitude concrete foundations based on multi-sensor data according to claim 1, characterized in that, The logic for generating the dynamic safety temperature difference threshold includes: A lookup table was established based on the growth curves of elastic modulus and the evolution curves of tensile strength corresponding to different casting ages; Within each time calculation step, the tensile strength value at the current age is queried, and combined with the thermal expansion coefficient, Poisson's ratio, and creep relaxation coefficient of concrete, the ultimate temperature gradient difference that the current structure can withstand is calculated; wherein, the ultimate temperature gradient difference is equal to the quotient obtained by dividing the tensile strength at the current age by the product of the elastic modulus at the current age, the thermal expansion coefficient, the difference between the current age and the Poisson's ratio, and the creep relaxation coefficient. The dynamic safe temperature difference threshold for the current age is obtained by multiplying the extreme temperature gradient difference by the safety margin coefficient, and the threshold sequence covering the future preset time window is dynamically updated.

6. The method for temperature control and curing of high-altitude concrete foundations based on multi-sensor data according to claim 1, characterized in that, The pre-calibrated thermodynamic hysteresis time parameter consists of two independent hysteresis queues, including: The first hysteresis time constant of the cooling water pipeline system, which includes the sum of valve mechanical opening delay, water pump pressurization time, and the flow delay time of the cooling medium in the pipeline to the target area; The second hysteresis constant of the surface electric heat tracing insulation system, which covers the sum of the contactor closing delay and the thermal inertia time for the surface of the heating element to reach the working temperature.

7. The method for temperature control and curing of high-altitude concrete foundations based on multi-sensor data according to claim 6, characterized in that, The output of the reverse time-compensated temperature control command and the execution of the pre-emptive defensive thermal regulation include mutually exclusive and hierarchical control logic, specifically including the following three operating condition branches: Condition 1: If only the first temperature difference sequence exceeds the dynamic safety temperature difference threshold at the target look-ahead time node, then the flow control signal is sent to the cooling water pipeline system to increase the internal cooling circulation flow by advancing the time corresponding to the first hysteresis time constant. Condition 2: If only the second temperature difference sequence exceeds the dynamic safety temperature difference threshold at the target look-ahead time node, then the control array bit mask is sent to the surface electric heat tracing insulation system in advance by the amount of time corresponding to the second hysteresis time constant to selectively activate the insulation heating module of the corresponding area. Condition 3: If the first temperature difference sequence and the second temperature difference sequence simultaneously face the risk of exceeding their respective thresholds within a similar time window, the execution priority of internal cooling regulation will be set to lower than that of surface insulation regulation, and the maximum allowable flow rate increment of internal cooling will be limited according to the severity of the first temperature difference sequence exceeding the limit.

8. The method for temperature control during curing of high-altitude concrete foundations based on multi-sensor data according to claim 1, characterized in that, After generating the reverse time-compensated temperature control command and before officially issuing it through the output port, a look-ahead verification loop is also included: Perform state cloning backup on the multi-field coupled state space data structure; The control quantity to be issued is injected as an additional input condition into the boundary conditions and heat source terms of the state clone backup. The forward differential iterative calculation is used to perform a secondary deduction of the future evolution of the system after the control quantity is applied, and a verification temperature gradient sequence is generated. Determine whether the maximum temperature difference value of the verification temperature gradient sequence does not exceed the dynamic safe temperature difference threshold at the corresponding time throughout the entire period from the current time to the prediction endpoint; If yes, then the reverse time compensation temperature control command is officially issued through the output port; if no, then the control quantity is reduced and adjusted, and a second deduction and verification is performed until the verification temperature gradient sequence falls back to within the dynamic safety temperature difference threshold range.

9. The method for temperature control during curing of high-altitude concrete foundations based on multi-sensor data according to claim 1, characterized in that, The process of obtaining the hydration heat release rate data includes: Based on the temperature rise rate distribution collected by the temperature sensors embedded in the three-dimensional sensor array, as well as the predefined concrete specific heat capacity and density constant, the hydration heat release rate data is calculated and output in real time through the energy conservation inverse derivation operator. The calculation logic of the energy conservation inverse derivation operator is as follows: multiply the product of density and specific heat capacity at each spatial node by the temperature change rate at that node to obtain the total enthalpy change per unit volume of that node; then subtract the net heat exchange with surrounding nodes caused by the heat conduction effect, which is characterized by the product of the equivalent thermal conductivity at that node and the sum of the second-order partial derivatives of the temperature field in three-dimensional space. The difference obtained is the hydration heat release rate at that node; In the energy conservation inverse derivation operator: The rate of temperature change is approximated by the temperature difference between adjacent time points; the sum of the second-order partial derivatives of the temperature field in three-dimensional space is obtained by calculating the temperature values ​​of the target node and its adjacent nodes in three orthogonal spatial directions using a central difference scheme. Furthermore, the time derivative of temperature is taken as the value at the current moment, and the second-order partial derivative of space is taken as the value at the previous moment, forming a semi-implicit time discrete scheme.

10. A high-altitude concrete foundation curing and temperature control system based on multi-sensor data, characterized in that, The maintenance temperature control system includes: processor; The memory stores a computer program that, when executed by a processor, implements the high-altitude concrete foundation curing and temperature control method based on multi-sensor data as described in any one of claims 1 to 9.

Images