An intelligent temperature control curing method for ultra-high performance concrete

Abstract

This invention relates to the field of ultra-high performance concrete (UHVPC) curing technology, and more particularly to an intelligent temperature-controlled curing method for UHVPC. The method includes the following steps: generating preliminary temperature-controlled curing data for concrete based on UHVPC mix proportion parameters and temperature-controlled curing requirements data; analyzing the distributed temperature field characteristics of the pouring nodes using UHVPC pouring node data to generate distributed temperature field characteristic data for the pouring nodes; analyzing the temperature evolution and hydration state of the pouring node temperature field based on the distributed temperature field characteristic data and the preliminary temperature-controlled curing data to generate temperature evolution and hydration regulation state data; and performing dynamic temperature-controlled curing analysis on the preliminary temperature-controlled curing data to generate dynamic temperature-controlled curing data for the concrete. This invention achieves intelligent temperature-controlled curing by combining the application of UHVPC.

Description

Technical Field

[0001] This invention relates to the field of ultra-high performance concrete curing technology, and in particular to an intelligent temperature-controlled curing method for ultra-high performance concrete. Background Technology

[0002] High-performance concrete (UHVPC), with its excellent mechanical properties, durability, and impermeability, is widely used in the construction of major infrastructure projects such as bridges, high-rise buildings, and marine engineering. Temperature-controlled chamber curing, as a core method of standardized curing for UHVPC, provides a relatively sealed and controllable temperature environment, which is crucial for ensuring its strength development and performance stability. However, during curing in a temperature-controlled chamber, the large amount of heat released during the hydration process of UHVPC can easily lead to localized heat accumulation within the chamber. This results in significant temperature gradients between the concrete's interior and surface, as well as between different pouring points, potentially causing temperature stress cracks. This severely impacts the safety and service life of the concrete structure, necessitating precise temperature-controlled curing based on the temperature-controlled chamber environment to improve the curing quality of UHVPC. However, existing temperature control curing methods for ultra-high performance concrete are mostly based on experience to set fixed temperature control parameters, such as constant cooling rate and heat preservation time. They fail to fully consider the impact of individual factors such as differences in concrete mix proportions and spatial distribution of pouring nodes on temperature control curing. Furthermore, most of them are static temperature control methods, which cannot dynamically adjust the temperature control parameters according to the temperature evolution state during the concrete hydration process. This results in low accuracy of temperature control curing and makes it difficult to effectively suppress the generation of temperature cracks. Summary of the Invention

[0003] Based on this, the present invention provides an intelligent temperature-controlled curing method for ultra-high performance concrete to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for intelligent temperature-controlled curing of ultra-high performance concrete includes the following steps:

[0005] Step S1: Obtain the mix proportion parameters and temperature control curing requirements of ultra-high performance concrete; perform preliminary temperature control curing analysis of concrete based on the mix proportion parameters and temperature control curing requirements of ultra-high performance concrete, and generate preliminary temperature control curing data of concrete.

[0006] Step S2: Obtain ultra-high performance concrete pouring node data; perform temperature field characteristic analysis on the pouring node using the ultra-high performance concrete pouring node data to generate temperature field characteristic data of the pouring node.

[0007] Step S3: Based on the temperature field characteristic data of the pouring node distribution and the preliminary temperature control curing data of concrete, perform temperature control evolution and hydration state analysis and processing to regulate the temperature field of the pouring node, and generate temperature control evolution and hydration regulation state data.

[0008] Step S4: Based on the temperature control evolution hydration regulation state data, perform dynamic temperature control curing analysis on the preliminary temperature control curing data of concrete to generate dynamic temperature control curing data of concrete.

[0009] Furthermore, step S1 includes the following steps:

[0010] Step S11: Obtain the mix proportion parameters of ultra-high performance concrete and the temperature control curing requirements of ultra-high performance concrete;

[0011] Step S12: Analyze the matching characteristics of the hydration heat effect of the ultra-high performance concrete mix proportion parameters based on the mix proportion parameters, and generate hydration heat effect matching characteristic data of the mix proportion parameters.

[0012] Step S13: Analyze the influence factors of the thermal properties of concrete mix proportion parameters by matching characteristic data of hydration heat effect of mix proportion parameters, and generate the influence factors of the thermal properties of concrete mix proportion parameters.

[0013] Step S14: Based on the influencing factors of thermal properties of concrete mix proportion parameters, perform thermal property analysis on the ultra-high performance concrete mix proportion parameters to generate thermal property data of concrete mix proportion parameters;

[0014] Step S15: Analyze the heat release of concrete hydration based on the thermal property data of concrete mix proportion parameters, generate concrete hydration heat release data, and analyze the concrete hydration heat reaction curve based on the concrete hydration heat release data to generate concrete hydration heat reaction curve data.

[0015] Step S16: Based on the concrete hydration heat reaction curve data, perform temperature control curing attribute characteristic analysis on the concrete mix proportion, and generate temperature control curing attribute characteristic data of the concrete mix proportion.

[0016] Step S17: Conduct preliminary temperature control curing analysis of concrete using the temperature control curing requirement data of ultra-high performance concrete and the temperature control curing attribute characteristic data of concrete mix proportion, and generate preliminary temperature control curing data of concrete.

[0017] Furthermore, the concrete mix proportioning parameter thermal property influence factor mentioned in step S13 includes the concrete hydration rate influence weight parameter and the concrete thermal effect lag influence weight parameter.

[0018] Furthermore, step S16 includes the following steps:

[0019] Step S161: Analyze the key temperature range of concrete temperature-controlled curing using concrete hydration heat reaction curve data, and generate key temperature range data for concrete temperature-controlled curing.

[0020] Step S162: Analyze the temperature control curing attribute characteristics of concrete mix design based on the key temperature range data for concrete temperature control curing, and generate temperature control curing attribute characteristic data for concrete mix design.

[0021] Furthermore, the key temperature range data for concrete temperature control curing mentioned in step S161 includes the range of rapid rise in concrete temperature, the range of stable peak concrete temperature, and the range of gradual decrease in concrete temperature.

[0022] Furthermore, step S2 includes the following steps:

[0023] Step S21: Obtain the pouring node data of ultra-high performance concrete;

[0024] Step S22: Collect the distribution temperature of the pouring nodes based on the ultra-high performance concrete pouring node data to obtain the distribution temperature data of the pouring nodes.

[0025] Step S23: Based on the temperature distribution data of the casting nodes, perform temperature stability quality assessment of the spatial environment of the casting nodes to generate temperature stability quality data of the casting nodes.

[0026] Step S24: Analyze the distribution temperature field characteristics of the casting nodes using the temperature stability quality data of the casting nodes, and generate the distribution temperature field characteristic data of the casting nodes.

[0027] Furthermore, the temperature field characteristic data of the casting node distribution mentioned in step S24 includes longitudinal temperature field gradient data of the casting node, temperature field gradient data of spatially adjacent nodes, and temperature field trend data of the casting node distribution.

[0028] Furthermore, step S3 includes the following steps:

[0029] Step S31: Based on the temperature field characteristics data of the pouring node distribution and the concrete hydration heat reaction curve data, analyze the influencing factors of pouring adjustment for concrete temperature control curing, and generate data on the influencing factors of pouring adjustment for temperature control curing.

[0030] Step S32: Analyze the temperature control curing change characteristics of concrete by adjusting the initial temperature control curing data of concrete pouring through the data of factors affecting temperature control curing pouring, and generate concrete temperature control curing change characteristic data.

[0031] Step S33: Analyze the hydration state of the concrete under preliminary temperature control curing based on the preliminary temperature control curing parameters, generate preliminary temperature control curing hydration state data, and perform hydration state evolution processing on the preliminary temperature control curing hydration state data to generate preliminary temperature control curing evolution hydration state data.

[0032] Step S34: Based on the concrete temperature control curing change characteristic data, perform temperature control evolution hydration state adjustment processing on the preliminary temperature control curing evolution hydration state data to generate temperature control evolution hydration adjustment state data.

[0033] Furthermore, step S34 includes the following steps:

[0034] Step S341: The fuzzy influence transformation of the hydration state data of the initial temperature-controlled curing evolution is processed by the concrete temperature-controlled curing change characteristic data to generate fuzzy influence data of the evolution hydration state. The fuzzy influence data of the evolution hydration state includes fuzzy influence data of the temperature-controlled stable section, fuzzy influence data of the temperature-sensitive section, and fuzzy influence data of the heat release peak section.

[0035] Step S342: Perform evolutionary temperature control latency regulation characteristic analysis on the fuzzy impact data of the stable evolutionary temperature control segment to generate evolutionary temperature control latency regulation characteristic data;

[0036] Step S343: Perform fuzzy impact analysis on the evolutionary warming sensitive section data to generate evolutionary warming acceleration period regulation characteristic data.

[0037] Step S344: Perform evolutionary heat release peak segment fuzzy impact analysis on the evolutionary heat release inhibition period regulation characteristics data to generate evolutionary heat release inhibition period regulation characteristic data;

[0038] Step S345: Analyze the evolutionary hydration regulation state of temperature control changes based on the evolutionary temperature control latency period regulation characteristic data, the evolutionary temperature rise acceleration period regulation characteristic data, and the evolutionary heat release inhibition period regulation characteristic data, and generate temperature control evolutionary hydration regulation state data.

[0039] Furthermore, step S4 includes the following steps:

[0040] Step S41: Based on the temperature-controlled evolutionary hydration regulation state data, perform a short-cycle evolutionary hydration state temperature control risk analysis to generate short-cycle evolutionary hydration state temperature control risk data;

[0041] Step S42: Analyze the temperature control adjustment parameters of concrete in the short-term evolution period using the temperature control risk data of the hydration state in the short-term evolution period, and generate the temperature control adjustment parameters of concrete in the short-term evolution period.

[0042] Step S43: Perform dynamic temperature control target analysis on concrete based on the evolutionary short-cycle concrete temperature control adjustment parameters, and generate dynamic temperature control target data for concrete.

[0043] Step S44: Based on the concrete dynamic temperature control target data, perform concrete dynamic temperature control curing analysis on the preliminary concrete temperature control curing data to generate concrete dynamic temperature control curing data.

[0044] The beneficial effects of this application are as follows: This invention achieves precise control over the release law of concrete hydration heat by first acquiring mix proportion parameters and curing requirements data, and then through a progressively detailed process including hydration heat effect matching characteristic analysis, extraction of thermophysical property influencing factors (including hydration rate and thermal effect lag weight parameters), thermophysical property data calculation, and analysis of hydration heat release and reaction curves. In particular, by analyzing the hydration heat reaction curves to locate key temperature ranges such as rapid temperature rise, peak stability, and slow decline, the characteristics of temperature-controlled curing attributes are extracted. This allows the generated preliminary temperature-controlled curing data to fully match the individual thermophysical property differences of concrete with different mix proportions, providing a basis for subsequent dynamic temperature control adjustment and ensuring the targeted nature and initial accuracy of temperature-controlled curing. By acquiring pouring node data through the system, first collecting node distribution temperature, and then filtering effective temperature data through temperature stability quality assessment, characteristic data including longitudinal temperature field gradient, spatial adjacent node temperature field gradient, and temperature field trend are generated, achieving a comprehensive and multi-dimensional accurate characterization of the temperature field at pouring nodes. Among them, longitudinal temperature field gradient data can accurately reflect the temperature difference in the direction of concrete depth, spatial adjacent node gradient data can capture the problem of uneven temperature distribution in the plane, and temperature field trend data can predict the trend of temperature change. These data together provide the core basis for identifying the temperature control requirements of different pouring nodes and formulating targeted control measures, effectively avoiding the problem of local overheating or insufficient heat dissipation caused by overall temperature control, and further improving the accuracy and comprehensiveness of temperature control curing. Based on the temperature field characteristic data of pouring nodes and preliminary temperature control curing data, the influencing factors of pouring regulation are first identified, and then the characteristics of temperature control curing changes are analyzed. At the same time, the evolution analysis of hydration state is carried out, and the influence of different temperature control sections on hydration state is accurately quantified through fuzzy influence transformation processing, generating temperature control evolution hydration regulation state data in stages (latent period, temperature rise acceleration period, and heat release inhibition period). This progressive analytical logic not only enables precise control over the evolution of concrete hydration state but also allows for the targeted extraction of adjustment characteristics at each stage. This provides a forward-looking and refined basis for the subsequent formulation of dynamic temperature control parameters, effectively avoiding the lag and blindness of temperature control adjustments and ensuring the dynamic adaptation of temperature control strategies to the hydration state. Based on the hydration adjustment state data of the temperature control evolution, short-cycle hydration state temperature control risk analysis accurately identifies the temperature control risk level at different curing stages. Then, targeted short-cycle temperature control adjustment parameters and dynamic temperature control targets are formulated, ultimately optimizing and iterating the initial temperature control curing data to generate dynamic temperature control curing data.The dynamic control logic, with short-cycle risk management at its core, can respond in real time to the evolution of concrete hydration state and the dynamic characteristics of temperature field at pouring nodes. By adapting differentiated control measures to risk classification, it not only ensures the real-time optimization of temperature control parameters but also effectively avoids temperature control risks (such as temperature cracks and abnormal hydration). The generated dynamic temperature control curing data can directly guide actual curing construction, realizing the transformation from passive control to proactive and precise control, and providing a core guarantee for the stable improvement of ultra-high performance concrete curing quality.

[0045] Therefore, the intelligent temperature-controlled curing method for ultra-high performance concrete of this invention can specifically solve the problems of existing technologies, such as setting fixed temperature control parameters based on experience, not fully considering personalized factors, and low temperature control accuracy caused by static control. It has significant technical advantages and practical value. By deeply analyzing the matching characteristics of the liquefaction heat effect and the influencing factors of thermal properties of ultra-high performance concrete mix proportions, and generating preliminary temperature control data in combination with temperature-controlled curing requirements, it fully takes into account the personalized thermal property differences of different concrete mix proportions, avoiding the problem of insufficient adaptability of fixed temperature control parameters to concretes of different mix proportions. This makes the temperature control strategy more in line with the characteristics of the concrete itself, laying the foundation for precise curing. By introducing the temperature field characteristic analysis of the pouring nodes, and by collecting temperature data of the pouring nodes and evaluating the quality of temperature stability, it accurately captures the influence of the spatial distribution differences of different pouring nodes on the temperature field. It can formulate targeted differentiated temperature control adjustment measures, effectively solving the problem of local temperature control imbalance caused by neglecting the spatial distribution differences of pouring nodes in existing technologies. Overcoming the limitations of static temperature control, this method dynamically tracks the temperature evolution during concrete hydration through temperature evolution and liquefaction state analysis. Combined with short-cycle liquefaction state temperature control risk analysis, it adjusts temperature control parameters in real time, forming an adaptive dynamic temperature control strategy that significantly improves the real-time performance and accuracy of temperature-controlled curing. Through multi-dimensional precise control and dynamic adaptation, it effectively reduces the temperature gradient between the concrete interior and surface, and between different pouring nodes, significantly lowering the risk of temperature stress cracking. This ensures the mechanical properties and durability of ultra-high performance concrete, thereby improving the safety and service life of concrete structures and providing strong support for the reliable application of ultra-high performance concrete in various major projects. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the steps of an intelligent temperature-controlled curing method for ultra-high performance concrete according to the present invention.

[0047] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S1.

[0048] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0049] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0050] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. Functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods. The term "and / or" as used herein includes any and all combinations of one or more of the associated items listed.

[0051] To achieve the above objectives, please refer to Figures 1 to 2 This invention provides a method for intelligent temperature-controlled curing of ultra-high performance concrete. In the embodiments of this invention, please refer to... Figure 1 The diagram shown is a flowchart illustrating the steps of an intelligent temperature-controlled curing method for ultra-high performance concrete according to the present invention. The intelligent temperature-controlled curing method for ultra-high performance concrete includes the following steps:

[0052] To achieve the above objectives, a method for intelligent temperature-controlled curing of ultra-high performance concrete includes the following steps:

[0053] Step S1: Obtain the mix proportion parameters and temperature control curing requirements of ultra-high performance concrete; perform preliminary temperature control curing analysis of concrete based on the mix proportion parameters and temperature control curing requirements of ultra-high performance concrete, and generate preliminary temperature control curing data of concrete.

[0054] In this embodiment of the invention, the mix proportion parameters of ultra-high performance concrete are obtained through concrete mix design documents. For example, the total amount of cementitious materials is 780 kg / m³, including 480 kg / m³ of P·O52.5 cement, 120 kg / m³ of silica fume, and 180 kg / m³ of slag powder, with a water-cement ratio of 0.18. The aggregate used is 5-16 mm basalt crushed stone, the dosage of polycarboxylate-based high-efficiency water-reducing agent is 1.1%, and the fiber is end-hooked steel fiber (volume dosage 2%). The temperature control curing requirements of ultra-high performance concrete are obtained through engineering construction technical specifications, specifically including a target 28-day strength of 120 MPa, a curing period of 14 days, a maximum allowable temperature difference of 20℃, and an ambient temperature control range of 20-35℃. Based on the obtained data, a preliminary temperature control curing analysis of the concrete is conducted. The temperature control chamber has a temperature range of -10~200℃, dimensions of 4m×3m×2.5m, and has a closed constant temperature control capability. The heating power is 15kW, the cooling power is 10kW, and three sets of circulating air ducts are arranged inside. First, a differential scanning calorimeter was used to quantitatively analyze the hydration heat effect of each component in the mix design. By measuring the heat flow curves of each component at different temperatures, the peak temperature, peak time, and cumulative heat release of each component were determined. This established a matching relationship between each component and the overall hydration heat effect of the concrete, generating hydration heat effect matching characteristic data for the mix design parameters. Next, a weighted analysis model was used to process this matching characteristic data. The influence of each mix design parameter on the hydration heat release rate was calculated to determine the weighted parameter for the concrete hydration rate, and the lag influence of each mix design parameter on the peak hydration heat release time was calculated to determine the weighted parameter for the hysteresis effect of the concrete thermal effect. Both of these factors together constitute the thermal property influence factor of the concrete mix design parameters. Based on this thermal property influence factor, a heat conduction model was used to numerically simulate the mix design parameters of ultra-high performance concrete, yielding a thermal conductivity of 1.8 W / (m·K), a specific heat capacity of 1000 J / (kg·K), and a volumetric expansion coefficient of 1.2 × 10⁻⁶. -5The thermal properties of concrete mix design parameters, such as / ℃, were collected. Continuous heat release data was monitored on concrete samples corresponding to these parameters using an isothermal calorimeter. The heat release rate at different curing time points was recorded to generate concrete hydration heat release data. Based on this data, a curve was plotted with curing time on the x-axis and cumulative heat release on the y-axis to generate concrete hydration heat response curve data. By dividing the concrete hydration heat response curve data into segments, the rapid temperature rise range of concrete from 0-12 hours after pouring and the peak stable temperature range of concrete from 12-48 hours were identified. The data on the key temperature ranges for concrete temperature control curing, including the 48-336 hour interval between intervals, constitutes the critical temperature range data for concrete temperature control curing. By combining the temperature change rate and duration of each range, the corresponding temperature control thresholds and adjustment priorities are extracted, generating characteristic data of concrete mix temperature control curing attributes. Finally, the target strength, allowable temperature difference, and other parameters in the temperature control curing requirements data of ultra-high performance concrete in the temperature control chamber are correlated and matched with the characteristic data of concrete mix temperature control curing attributes to determine the preliminary insulation measures, initial temperature control temperature, and cooling rate benchmark values, generating preliminary concrete temperature control curing data.

[0055] Step S2: Obtain ultra-high performance concrete pouring node data; perform temperature field characteristic analysis on the pouring node using the ultra-high performance concrete pouring node data to generate temperature field characteristic data of the pouring node.

[0056] In this embodiment of the invention, data on ultra-high performance concrete pouring nodes are obtained. For example, based on the dimensions of the main beam pouring area (50m long, 12m wide, and 80cm thick), the pouring area is divided into 150 pouring nodes using a 2m×2m grid division standard. At the same time, spatial characteristic parameters such as the longitudinal pouring depth, steel reinforcement distribution density, and surrounding shading of each node are obtained. Based on the data from the pouring nodes, a temperature field characteristic analysis was conducted. Resistance temperature sensors were deployed at each node according to the defined pouring node locations, with depths of 1 / 4, 1 / 2, and 3 / 4 of the pouring thickness, forming a three-dimensional temperature acquisition array. Temperature data at different depths of each node was continuously collected at 30-minute intervals, yielding temperature distribution data for each pouring node including spatial coordinates, acquisition time, and temperature values. A temperature stability evaluation method was used to process the temperature distribution data, calculating the fluctuation range of the temperature from three consecutive acquisitions at each node and the temperature difference between adjacent nodes at the same acquisition time. A stability criterion was set: a temperature fluctuation range not exceeding 3℃ and a temperature difference between adjacent nodes not exceeding 8℃. The temperature stability of each node was evaluated individually, generating a stability rating for each node. The data includes stable temperature quality data for pouring nodes during stable periods and with varying fluctuation ranges. Based on this data, node temperature data meeting stability standards are selected. Temperature field gradient calculation methods are used to analyze the selected data. The longitudinal temperature field gradient data for pouring nodes is obtained by calculating the ratio of temperature difference at different depths to depth differences at the same node. Spatially adjacent node temperature field gradient data is obtained by calculating the ratio of temperature difference at the same depth at adjacent nodes to the node spacing. Trend fitting of temperature data collected at different times for each node yields the rising, stabilizing, or falling temperature variation patterns of each node over time, forming the temperature field trend data for the pouring nodes. The aforementioned longitudinal temperature field gradient data, spatially adjacent node temperature field gradient data, and pouring node temperature field trend data together constitute the characteristic data of the temperature field distribution for pouring nodes.

[0057] Step S3: Based on the temperature field characteristic data of the pouring node distribution and the preliminary temperature control curing data of concrete, perform temperature control evolution and hydration state analysis and processing to regulate the temperature field of the pouring node, and generate temperature control evolution and hydration regulation state data.

[0058] In this embodiment of the invention, an influencing factor weighting method is used to perform correlation analysis on the longitudinal temperature field gradient, spatial adjacent node temperature field gradient, temperature field trend, and the peak value and peak occurrence time of the heat release rate in the concrete hydration heat response curve data of the temperature field characteristics data of the pouring node distribution. By calculating the correlation coefficient between each parameter and the temperature control curing effect, the weights are determined as follows: longitudinal temperature field gradient 0.4, spatial adjacent node temperature field gradient 0.3, and temperature field trend and heat release characteristic matching degree 0.3. The weights are determined based on the correlation analysis results of parallel experiments, generating a temperature control curing pouring adjustment effect data containing the name of each influencing factor and its corresponding weight. Influencing factor data; Based on the data on influencing factors of temperature-controlled curing pouring adjustment, a temperature control parameter sensitivity analysis method is used to couple the initial temperature control temperature and cooling rate benchmark values ​​in the preliminary temperature-controlled curing data with each influencing factor. The variation range of the temperature control parameters and their impact on the curing effect under different influencing factor values ​​are calculated, generating concrete temperature-controlled curing change characteristic data containing the variation range and trend of each temperature control parameter under different influencing factor conditions. Using a hydration state evaluation index system, based on the temperature control range and curing cycle in the preliminary concrete temperature-controlled curing parameters, and combined with the concrete hydration heat response curve data, the water content at different curing stages is calculated. Preliminary hydration state data for temperature-controlled curing was generated by analyzing the hydration reaction progress and heat release rate. Then, time-series evolution analysis was used to predict the trend of this data over the next 12 hours at 2-hour intervals, generating preliminary hydration state evolution data for temperature-controlled curing that included predicted values ​​of hydration reaction progress and heat release rate at each time point. A fuzzy comprehensive evaluation model was then used to process this data. This model uses a triangular membership function to divide each hydration state of temperature control evolution into three evaluation dimensions: a stable temperature-controlled evolution zone, a temperature-sensitive evolution zone, and a peak heat release evolution zone. Each dimension has a weighted distribution. The values ​​are set to 0.2, 0.3, and 0.5 respectively. Fuzzy influence values ​​for each segment are calculated using the model to generate fuzzy influence data for the evolutionary hydration state. For the fuzzy influence data of the stable temperature-controlled segment, a latent period regulation feature extraction method is used to analyze the correlation between temperature fluctuations and the hydration reaction initiation time in this segment. The latent period temperature control accuracy of ±1℃ and the triggering conditions for adjusting insulation measures are determined, generating evolutionary temperature control latent period regulation feature data. For the fuzzy influence data of the temperature-sensitive segment, a temperature acceleration period regulation feature extraction method is used to analyze the matching relationship between the temperature rise rate and the heat release rate in this segment, determining the critical value of 1 for the cooling rate during the temperature acceleration period.Using a 5℃ / h rate and a cooling measure activation temperature threshold, regulatory characteristic data for the accelerated warming phase of evolution were generated. For the fuzzy influence data in the peak heat release segment, a regulatory characteristic extraction method for the heat release inhibition phase was employed to analyze the correlation between the peak temperature and the completeness of the hydration reaction in this segment. The peak temperature control range for the heat release inhibition phase was determined to be 75-80℃, and the heat release rate inhibition threshold was set, generating regulatory characteristic data for the heat release inhibition phase. A multi-dimensional regulatory characteristic integration method was used to fuse the regulatory characteristic data for the latent temperature control phase, the accelerated warming phase, and the heat release inhibition phase, determining the core regulatory objectives, parameter ranges, and execution priorities for each maintenance stage, generating temperature-controlled evolutionary hydration regulation status data.

[0059] Step S4: Based on the temperature control evolution hydration regulation state data, perform dynamic temperature control curing analysis on the preliminary temperature control curing data of concrete to generate dynamic temperature control curing data of concrete.

[0060] In this embodiment of the invention, a short-cycle risk assessment system is adopted, dividing the maintenance cycle into two-hour evolutionary short cycles. Combining the adjustment targets and characteristic parameters of each stage in the temperature-controlled evolutionary hydration regulation state data, risk assessment indicators are established, including temperature deviation rate, hydration reaction progress deviation rate, and temperature change rate deviation rate. Threshold ranges are set for each indicator, where a temperature deviation rate exceeding 5%, a hydration reaction progress deviation rate exceeding 10%, and a temperature change rate deviation rate exceeding 20% ​​are considered high-risk, corresponding to deviation rates of 2%-5%, 5%-10%, and 10%, respectively. -20% is considered medium risk, while below 2%, 5%, and 10% are considered low risk. The risk level of each short-cycle is determined by calculating the deviation between the actual values ​​and thresholds of each assessment indicator within that short-cycle. This generates short-cycle hydration state temperature control risk data containing the short-cycle number, risk level, risk indicator, and deviation value. Based on this short-cycle hydration state temperature control risk data, a risk level-parameter adaptation method is used. For high-risk short cycles, the following parameters are applied: cooling rate increased by 0.5℃ / h, temperature control accuracy increased to ±0.5℃, and monitoring frequency increased. The adjustment parameters are determined as follows: For medium-risk short cycles, the adjustment parameters are determined according to the principle of increasing the cooling rate by 0.2℃ / h and the temperature control accuracy to ±0.8℃; for low-risk short cycles, the initial temperature control curing parameters are kept unchanged, and evolutionary short-cycle concrete temperature control adjustment parameters containing each short cycle number, corresponding adjustment parameters, and parameter adjustment ranges are generated; using the dynamic target decomposition method, the evolutionary short-cycle concrete temperature control adjustment parameters are matched with the adjustment targets of each stage in the temperature control evolution hydration adjustment state data, and the specific temperature control target value, temperature change rate range, and hydration reaction progress target value of each short cycle are obtained, generating concrete dynamic temperature control target data; finally, using the temperature control data optimization and integration method, the concrete dynamic temperature control target data is compared with the concrete initial temperature control curing data, the parameters in the initial temperature control data that do not match the dynamic targets are replaced, and the execution conditions of adjustment measures and the priority of monitoring nodes for each short cycle in the ultra-high performance concrete temperature control chamber are supplemented, forming concrete dynamic temperature control curing data containing temperature control values, adjustment parameters, monitoring requirements, and execution measures for each curing stage and each short cycle.

[0061] Furthermore, as an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart illustrating the implementation steps of step S1 is provided in this embodiment. Step S1 includes:

[0062] Step S11: Obtain the mix proportion parameters of ultra-high performance concrete and the temperature control curing requirements of ultra-high performance concrete;

[0063] In this embodiment of the invention, the mix proportion parameters of ultra-high performance concrete were obtained by consulting the official concrete mix design documents. The specific parameters are: total cementitious material amount 780 kg / m³, including 480 kg / m³ of P·O52.5 cement, 120 kg / m³ of silica fume, and 180 kg / m³ of slag powder; water-cement ratio 0.18; aggregates of 5-16 mm basalt crushed stone; polycarboxylate-based high-efficiency water-reducing agent dosage 1.1%; and end-hooked steel fiber (volume dosage 2%). The temperature control curing requirements of ultra-high performance concrete were obtained by retrieving the engineering construction technical disclosure documents and curing quality acceptance standards. Specifically, the target 28-day compressive strength is 120 MPa, the curing period is 12 days, the maximum allowable temperature difference during the curing process is 18℃, the ambient temperature control range is 25-32℃ for the first 3 days after pouring, the ambient temperature control range is 22-28℃ after 3 days, and the cooling rate does not exceed 1℃ / h. The mixing parameters are based solely on the mixing ratio notification issued by the laboratory. The dosage and proportion of each component are checked and recorded one by one to ensure that the parameters are completely consistent with the actual poured concrete. The curing requirements data are combined with the temperature control range, air speed adjustment capability and component size of the temperature control box. For example, the temperature control box has a temperature range of -10~200℃, a size of 4m×3m×2.5m, and has a closed constant temperature control capability. The heating power is 15kW, the cooling power is 10kW, and there are 3 sets of circulating air ducts inside. Targeted temperature control indicators are extracted from the construction specifications to clarify the control boundaries of the temperature control box at each stage, ensuring that the requirements data are accurately matched with the curing scenario inside the temperature control box.

[0064] Step S12: Analyze the matching characteristics of the hydration heat effect of the ultra-high performance concrete mix proportion parameters based on the mix proportion parameters, and generate hydration heat effect matching characteristic data of the mix proportion parameters.

[0065] In this embodiment of the invention, the hydration heat effect matching characteristics of the ultra-high performance concrete mix proportion parameters are analyzed. Single-component tests clarify the hydration heat release characteristics of each component, determining that cement exhibits a heat flux peak at 55℃, silica fume at 62℃, and slag powder at 48℃. Through testing the mixed cementitious material system, the interaction of the hydration processes of each component is analyzed, determining that cement hydration heat release accounts for 68% of the total release in the mixed system, silica fume 20%, and slag powder 12%, with a 3-4 hour time difference between the peak heat flux times of each component. Based on the above test results, a quantitative matching relationship between each mix proportion component and the overall hydration heat effect of the concrete is established, clarifying the contribution of each component to the overall hydration heat effect at different temperature stages, and generating mix proportion parameter hydration heat effect matching characteristic data that includes the heat flux characteristic parameters of each component, the heat release law of the mixed system, and the synergistic effect coefficient between components.

[0066] Step S13: Analyze the influence factors of the thermal properties of concrete mix proportion parameters by matching characteristic data of hydration heat effect of mix proportion parameters, and generate the influence factors of the thermal properties of concrete mix proportion parameters.

[0067] In this embodiment of the invention, the thermal property influence factors of concrete mix proportion parameters are analyzed by matching characteristic data of hydration heat effect of mix proportion parameters. The weighted analysis model is used to quantify the matching characteristic data of hydration heat effect of mix proportion parameters. The model is configured as the analytic hierarchy process, with the degree of influence of concrete hydration heat effect as the target layer, each mix proportion component as the criterion layer, and the peak heat flow, cumulative heat release, and peak occurrence time of each component as the indicator layer. The weights of each indicator on the criterion layer are calculated by the model, and then the weights of the criterion layer on the target layer are superimposed to obtain the influence weights of each proportion parameter on the concrete hydration rate and thermal effect lag. The calculation basis for the weight parameter of the concrete hydration rate influence parameter is the degree to which each proportion parameter increases or decreases the hydration heat release rate. The influence weight of cement hydration rate is determined to be 0.65, silica fume 0.2, slag powder 0.1, and water-reducing agent 0.05. The calculation basis for the weight parameter of the concrete thermal effect lag influence parameter is the degree to which each proportion parameter delays the occurrence time of the peak hydration heat release. The influence weight of slag powder thermal effect lag influence parameter is determined to be 0.4, silica fume 0.3, cement 0.25, and water-reducing agent 0.05. The above two types of weight parameters are integrated to generate the concrete proportion parameter thermophysical property influence factor, which includes the specific weight values ​​of each proportion parameter and the basis for weight calculation.

[0068] Step S14: Based on the influencing factors of thermal properties of concrete mix proportion parameters, perform thermal property analysis on the ultra-high performance concrete mix proportion parameters to generate thermal property data of concrete mix proportion parameters;

[0069] In this embodiment of the invention, a numerical simulation model of heat conduction is used to analyze the thermal properties of ultra-high performance concrete mix proportions. The model is configured as a three-dimensional steady-state heat conduction model, with boundary conditions set at an ambient temperature of 25℃ and a heat exchange coefficient of 15W / (m²·K). The thermal property influence factors of the concrete mix proportions are used as input parameters of the model and coupled with parameters such as aggregate gradation, water-cement ratio, and fiber content in the ultra-high performance concrete mix proportions for calculation. The hydration heat release rate parameter of the cementitious material in the model is corrected based on the hydration rate influence weight parameter, and the heat conduction delay coefficient in the model is corrected based on the thermal effect lag influence weight parameter. The thermal properties of the concrete at different curing stages are obtained through model calculation, including thermal conductivity, specific heat capacity, and volume expansion coefficient. The calculated thermal conductivity is 1.75W / (m·K), the specific heat capacity is 980J / (kg·K), and the volume expansion coefficient is 1.1×10⁻⁶. -5 / ℃; The calculation results are verified by comparing them with the measured thermophysical property data of concrete samples with the same mix proportion to ensure that the calculation error is controlled within 3%. Finally, the thermophysical property data of concrete mix proportion parameters containing the values ​​of each thermophysical parameter, calculation conditions and verification results are generated.

[0070] Step S15: Analyze the heat release of concrete hydration based on the thermal property data of concrete mix proportion parameters, generate concrete hydration heat release data, and analyze the concrete hydration heat reaction curve based on the concrete hydration heat release data to generate concrete hydration heat reaction curve data.

[0071] In this embodiment of the invention, an isothermal calorimeter is used to continuously monitor the hydration heat release of concrete samples corresponding to the thermal property data of concrete mix proportion parameters. The monitoring temperature is kept constant at 25°C, the monitoring time lasts for 72 hours, and the monitoring interval is set to 10 minutes. The instantaneous heat release rate and cumulative heat release amount at each monitoring time point are recorded in real time to generate concrete hydration heat release data. The data includes two sets of correspondences: time-instantaneous heat release rate and time-cumulative heat release amount. Based on the monitored data of concrete hydration heat release, a curve was plotted using a linear fitting method with curing time as the x-axis (unit: hours) and cumulative heat release as the y-axis (unit: kJ / kg). The curve analysis determined three key stages of hydration heat release. During curve plotting, the monitored data was smoothed to remove abnormal fluctuations, ensuring the continuity and accuracy of the curve. The curve clearly shows that 0-10 hours after pouring is the slow growth stage of heat release, 10-36 hours is the rapid growth stage, and 36-72 hours is the decay stage. Finally, a concrete hydration heat response curve data was generated, containing complete curve characteristic parameters (start point, inflection point, peak point, and end point) and the heat release patterns at each stage.

[0072] Step S16: Based on the concrete hydration heat reaction curve data, perform temperature control curing attribute characteristic analysis on the concrete mix proportion, and generate temperature control curing attribute characteristic data of the concrete mix proportion.

[0073] In this embodiment of the invention, the temperature-controlled curing property characteristics of concrete mix design are analyzed based on the data of the concrete hydration heat reaction curve. The time interval corresponding to the temperature change is determined by the change in the growth rate of the cumulative heat release in the curve. When the growth rate of the cumulative heat release is greater than 0.8 kJ / (kg·h), it corresponds to the rapid rise in concrete temperature, which is determined to be 10-36 hours after pouring, with the temperature rising from 25℃ to 76℃. When the growth rate of the cumulative heat release is between 0.1-0.8 kJ / (kg·h), it corresponds to the stable peak temperature range of concrete, which is determined to be 36-60 hours after pouring, with the temperature maintained at 76-78℃. When the growth rate of the cumulative heat release is less than 0.1 kJ / (kg·h), it corresponds to the slow decrease in concrete temperature, which is determined to be 60-288 hours after pouring, with the temperature dropping from 78℃ to 25℃. This generates the key temperature range data for concrete temperature-controlled curing. Based on the key temperature range data, the attribute feature extraction method was used to analyze the temperature control requirements of each range. The temperature control accuracy for the rapidly rising temperature range was determined to be ±1℃, and the cooling rate threshold was 0.8℃ / h; the temperature control accuracy for the stable peak temperature range was ±0.5℃, with a fluctuation range not exceeding 2℃; and the temperature control accuracy for the gradually decreasing temperature range was ±1℃, with a cooling rate threshold of 0.5℃ / h. At the same time, the temperature control adjustment priority for each range was clarified, with the rapidly rising temperature range and the stable peak temperature range as the first priority, and the gradually decreasing temperature range as the second priority. Finally, concrete mix temperature control curing attribute feature data containing temperature control indicators and adjustment priorities for each range was generated.

[0074] Step S17: Conduct preliminary temperature control curing analysis of concrete using the temperature control curing requirement data of ultra-high performance concrete and the temperature control curing attribute characteristic data of concrete mix proportion, and generate preliminary temperature control curing data of concrete.

[0075] In this embodiment of the invention, preliminary temperature control curing analysis of concrete is conducted using temperature control curing requirement data for ultra-high performance concrete and temperature control curing attribute characteristic data for concrete mix design. The target strength (120 MPa), maximum allowable temperature difference (18℃), and curing period (12 days) in the curing requirement data are matched with the temperature control parameters for each key temperature range in the attribute characteristic data. For the rapid temperature rise range, combined with the maximum allowable temperature difference requirement, the cooling rate threshold is adjusted from 0.8℃ / h to 0.6℃ / h to ensure that the temperature difference between the interior and surface of the concrete does not exceed 18℃. For the stable temperature peak range, combined with... To meet the target strength requirements, the temperature control range was set at 76-77℃, with the stabilization time extended to 26 hours to ensure sufficient hydration reaction. For the temperature slow-descent range, combined with the curing cycle requirements, the cooling rate threshold was set at 0.5℃ / h to ensure a smooth drop to ambient temperature within the 12-day curing cycle. Furthermore, based on the adjustment priority of each range, the first-priority range was covered with an intelligent temperature-controlled shed for insulation, while the second-priority range was covered with insulation cotton. This resulted in the generation of preliminary concrete temperature control curing data, including the temperature control range, cooling rate, type of insulation measures, and execution period for each curing stage.

[0076] Furthermore, the concrete mix proportioning parameter thermal property influence factor mentioned in step S13 includes the concrete hydration rate influence weight parameter and the concrete thermal effect lag influence weight parameter.

[0077] Furthermore, step S16 includes the following steps:

[0078] Step S161: Analyze the key temperature range of concrete temperature-controlled curing using concrete hydration heat reaction curve data, and generate key temperature range data for concrete temperature-controlled curing.

[0079] In this embodiment of the invention, the hydration heat reaction curve data is preprocessed to remove abnormal data points caused by instrument fluctuations during monitoring. A moving average method is used to smooth the curve, ensuring a continuous and stable trend. Based on the preprocessed curve, the cumulative heat release data corresponding to each time point is extracted, and the instantaneous heat release rate is obtained through differential calculation. The instantaneous heat release rate is set as the core criterion for interval division, and three threshold standards are defined: an instantaneous heat release rate greater than 0.8 kJ / (kg·h) is the threshold for rapid temperature rise; an instantaneous heat release rate between 0.1 and 0.8 kJ / (kg·h) is the threshold for peak stability; and an instantaneous heat release rate less than 0.1 kJ / (kg·h) is the threshold for gradual temperature decrease. Based on these thresholds, the heat release rate changes throughout the entire curing cycle are matched time-by-time, determining that the rapid temperature rise interval for concrete is 10-36 hours after pouring, during which the instantaneous heat release rate increases from 0.8 kJ / (kg·h). The instantaneous heat release rate (J / (kg·h)) rises to a peak of 2.3 kJ / (kg·h) and then falls back to 0.8 kJ / (kg·h), corresponding to a concrete temperature increase from 25℃ to 76℃. The peak stable range of concrete temperature is 36-60 hours after pouring, during which the instantaneous heat release rate stabilizes between 0.1-0.8 kJ / (kg·h), with fluctuations not exceeding 0.05 kJ / (kg·h), corresponding to a concrete temperature maintained at 76-78℃. The range of gradual temperature decrease is 60-288 hours after pouring, during which the instantaneous heat release rate gradually decreases from 0.1 kJ / (kg·h) to 0.02 kJ / (kg·h), corresponding to a concrete temperature steadily decreasing from 78℃ to 25℃. Finally, the start time, end time, temperature range, and heat release rate characteristics of each range are integrated to generate key temperature range data for concrete temperature control curing. The data format strictly corresponds to the quantitative indicators of each range, ensuring that it can be directly used for subsequent analysis.

[0080] Step S162: Analyze the temperature control curing attribute characteristics of concrete mix design based on the key temperature range data for concrete temperature control curing, and generate temperature control curing attribute characteristic data for concrete mix design.

[0081] In this embodiment of the invention, for the rapid temperature rise range of concrete, the focus is on analyzing the correlation between the temperature rise rate and structural stress. Thermal stress simulation calculations determine that the temperature control accuracy in this range needs to reach ±1℃ to avoid excessive temperature stress caused by rapid temperature increases. Simultaneously, the cooling rate threshold for this range is defined as 0.6℃ / h; when the monitored temperature rise rate exceeds 0.6℃ / h, active cooling measures must be initiated. For the peak stable temperature range of concrete, the core control objective is to reduce the impact of temperature fluctuations on the uniformity of the hydration reaction. Through correlation analysis between the degree of hydration and temperature fluctuations, it is determined that the temperature fluctuation amplitude in this range should not exceed 2℃, and the temperature control range is locked at 76-77℃. The temperature control adjustment priority in this range is set to the highest, requiring temperature monitoring and confirmation every 30 minutes. For the slow temperature decline range of concrete, the core focus is on ensuring the stable development of concrete strength, combined with… Based on the heat dissipation characteristics of the temperature control chamber structure, the cooling rate threshold for this range is determined to be 0.5℃ / h, with a temperature control accuracy of ±1℃. When the temperature drop rate exceeds the threshold, the insulation effect needs to be enhanced. The temperature control adjustment priority for this range is set as the second highest, with a monitoring frequency of once every 60 minutes. In addition, considering the common needs of the three ranges, the applicable types of temperature control measures for each range are clarified. The rapid temperature rise range and the peak stable range adopt a closed and precise control using an intelligent temperature control shed, while the slow temperature drop range adopts a control method combining insulation cotton covering with natural heat dissipation. The parameters such as the temperature control accuracy, cooling rate threshold, fluctuation range limit, adjustment priority, and applicable temperature control measures for each range are systematically integrated to form concrete mix temperature control curing attribute characteristic data that includes the personalized temperature control requirements of each range. The data must clearly correspond to each key temperature range to ensure accurate matching with the parameters of the subsequent preliminary temperature control curing analysis.

[0082] Furthermore, the key temperature range data for concrete temperature control curing mentioned in step S161 includes the range of rapid rise in concrete temperature, the range of stable peak concrete temperature, and the range of gradual decrease in concrete temperature.

[0083] Furthermore, step S2 includes the following steps:

[0084] Step S21: Obtain the pouring node data of ultra-high performance concrete;

[0085] In this embodiment of the invention, a three-dimensional spatial grid division method is used to obtain the pouring node data of ultra-high performance concrete. For example, the dimensions of the tunnel lining pouring area are 100m in length, 12m in width, and 60cm in thickness. The grid division standard is set to 2m×2m×0.2m, with 50 nodes along the length direction, 6 nodes along the width direction, and 3 nodes along the thickness direction, forming a total of 900 three-dimensional pouring nodes. First, the three-dimensional boundary coordinates of the pouring area are determined based on the tunnel lining design drawings. A spatial rectangular coordinate system is established with the boundary coordinates as the reference, with the X-axis along the tunnel length, the Y-axis along the width, and the Z-axis along the thickness. Nodes in each direction are located one by one according to the set grid standard, and the three-dimensional coordinate values ​​of each node are recorded. Simultaneously, the reinforcement design drawings of the tunnel lining are retrieved to obtain the steel reinforcement distribution density data corresponding to each pouring node. The steel reinforcement distribution density of the surface layer (Z-axis 0-20cm) nodes is 120kg / m³, the middle layer (Z-axis 20-40cm) is 110kg / m³, and the bottom layer (Z-axis 40-60cm) is 100kg / m³. At the same time, the shading situation around each node is recorded to clarify the spatial environment characteristics of the top nodes of the lining being unshaded and the side wall nodes being shaded by the tunnel wall. The three-dimensional coordinates, steel reinforcement distribution density, and surrounding shading conditions of all nodes are systematically integrated to generate ultra-high performance concrete pouring node data containing complete spatial feature information of 900 nodes.

[0086] Step S22: Collect the distribution temperature of the pouring nodes based on the ultra-high performance concrete pouring node data to obtain the distribution temperature data of the pouring nodes.

[0087] In this embodiment of the invention, a resistance temperature sensor array is used to collect the distributed temperature of the casting node. The sensor accuracy level is set to Class A, the measurement range is -50℃ to 150℃, and the error range does not exceed ±0.1℃. The implementation logic is as follows: Based on the three-dimensional coordinates of the pouring nodes obtained in step S21, one temperature sensor is deployed at each node's Z-axis positions of 15cm (1 / 4 of the thickness), 30cm (1 / 2 of the thickness), and 45cm (3 / 4 of the thickness) to form a three-dimensional temperature acquisition network. After deployment, all sensors are uniformly calibrated using a standard constant temperature chamber with three calibration points of 25℃, 50℃, and 75℃ to ensure accurate sensor measurement data. Temperature data at different depths of each node are continuously collected at a fixed interval of 30 minutes. During the collection process, the node's three-dimensional coordinates, collection time, and sensor deployment depth corresponding to each data point are recorded simultaneously. Wired transmission is used when collecting data to avoid signal interference from wireless transmission and ensure the stability and integrity of data transmission. All collected temperature data are grouped and organized by node to obtain pouring node distribution temperature data containing node coordinates, collection time, deployment depth, and temperature value.

[0088] Step S23: Based on the temperature distribution data of the casting nodes, perform temperature stability quality assessment of the spatial environment of the casting nodes to generate temperature stability quality data of the casting nodes.

[0089] In this embodiment of the invention, the temperature stability quality assessment of the spatial environment of the pouring node is performed based on the temperature distribution data of the pouring node. Two core evaluation indicators are set: the temperature fluctuation amplitude of a single node and the temperature difference between adjacent nodes. The criterion for judging the temperature fluctuation amplitude of a single node is that the difference between the maximum and minimum temperature values ​​collected in three consecutive data collections does not exceed 3°C. The criterion for judging the temperature difference between adjacent nodes is that the temperature difference between adjacent nodes does not exceed 8°C under the same data collection time and the same deployment depth. First, extract three consecutive data collections for each node from the temperature distribution data of the casting nodes, calculate the temperature fluctuation amplitude and thermal conductivity characteristics of each node, and compare them with the judgment criteria to determine the temperature stability of a single node. Then, extract the temperature data of adjacent nodes at the same collection time and the same deployment depth, calculate the temperature difference between each pair of adjacent nodes, and compare them with the judgment criteria to determine the temperature distribution stability of adjacent nodes. Combining the results of the two evaluation indicators, the temperature stability quality of the casting nodes is divided into three levels: both indicators meet the requirements for a stable level, one indicator meets the requirements for a basically stable level, and neither indicator meets the requirements for an unstable level. At the same time, record the specific location of the unstable level nodes, the collection time period when instability occurs, and the specific value of the fluctuation / difference. Integrate this information with the stable level data to generate casting node temperature stability quality data that includes node number, stability level, unstable period, and fluctuation amplitude / temperature difference value.

[0090] Step S24: Analyze the distribution temperature field characteristics of the casting nodes using the temperature stability quality data of the casting nodes, and generate the distribution temperature field characteristic data of the casting nodes.

[0091] In this embodiment of the invention, the temperature distribution data of the casting nodes is analyzed and processed using the stable quality data of the casting node temperature. For the filtered temperature data, the longitudinal temperature gradient calculation method is used to calculate the temperature difference between different deployment depths (15cm, 30cm, 45cm) of the same node, and then divided by the corresponding depth difference (15cm) to obtain the longitudinal temperature field gradient data of each node, thus clarifying the temperature change law in the lining thickness direction. The spatial adjacent node temperature gradient calculation method is used to calculate the temperature difference between adjacent nodes at the same acquisition time and the same deployment depth, and divided by the node spacing (2m) to obtain the spatial adjacent node temperature gradient. Temperature gradient data of adjacent nodes were used to clarify the temperature distribution differences within the lining plane. A linear trend fitting method was employed to fit the temperature data collected continuously for 24 hours at each node. The slope of the fitted curve was used to determine the temperature change trend of the node over time: a slope greater than 0 indicates an upward trend, a slope close to 0 indicates a stable trend, and a slope less than 0 indicates a downward trend, thus forming the temperature field trend data of the casting nodes. The longitudinal temperature gradient data, the temperature gradient data of spatially adjacent nodes, and the temperature field trend data of the casting nodes were systematically integrated, and the collection time and node location of each data point were labeled to generate complete characteristic data of the temperature field distribution of the casting nodes.

[0092] Furthermore, the temperature field characteristic data of the casting node distribution mentioned in step S24 includes longitudinal temperature field gradient data of the casting node, temperature field gradient data of spatially adjacent nodes, and temperature field trend data of the casting node distribution.

[0093] Furthermore, step S3 includes the following steps:

[0094] Step S31: Based on the temperature field characteristics data of the pouring node distribution and the concrete hydration heat reaction curve data, analyze the influencing factors of pouring adjustment for concrete temperature control curing, and generate data on the influencing factors of pouring adjustment for temperature control curing.

[0095] In this embodiment of the invention, the factors influencing the pouring adjustment of concrete temperature control curing are analyzed based on the temperature field characteristic data of the pouring nodes and the concrete hydration heat response curve data. First, the dimensions of the core influencing factors are identified. Then, combined with the preset concrete hydration heat response curve data, a dynamic assessment of the hydration process during pouring is completed, clarifying core characteristics such as the hydration reaction initiation rate, early heat release intensity, and the synergistic evolution law of the temperature field and hydration heat during pouring. Based on this assessment result, the dimensions of the core influencing factors are identified. Combined with the characteristics of the sealed environment of the temperature control box, six factors are determined: longitudinal temperature field gradient of the pouring nodes, temperature field gradient of spatially adjacent nodes, temperature field trend of the pouring nodes, peak value of the heat release rate of the hydration heat response curve, time of occurrence of the heat release peak, and air velocity in the air duct of the temperature control box. The first three factors are directly related to the temperature field-hydration heat synergistic characteristics assessed during pouring, while the latter two originate from the core parameters of hydration heat release during pouring. The air duct velocity is a regulatory factor adapted to the temperature control box environment. Correlation analysis was used to determine the degree of association between each factor and the effect of temperature-controlled curing. A hierarchical analysis model was configured to quantify the weights. The model takes the effect of temperature-controlled curing in the temperature-controlled chamber as the target layer, the six influencing factors as the criterion layer, and the evaluation parameters during pouring (such as gradient value, peak value, wind speed, and hydration initiation rate) corresponding to each factor as the index layer. The calculated weights were: longitudinal temperature field gradient weight 0.3, spatial adjacent node temperature field gradient weight 0.25, temperature field trend weight 0.15, peak heat release rate weight 0.12, peak occurrence time weight 0.08, and duct wind speed weight 0.1. These weights were determined based on the correlation analysis results of parallel experiments. By simultaneously analyzing the hydration process assessment results during pouring, the mechanism of action of various factors within the temperature control chamber was determined. It was found that when the longitudinal temperature gradient assessed during pouring exceeds 0.5℃ / cm, the accumulation of hydration heat exacerbates internal stress in the component; when the temperature difference between adjacent nodes exceeds 6℃, it leads to an imbalance in the local hydration reaction rate, requiring adjustment of the local air duct outlet angle; during the period when the temperature rise trend overlaps with the peak heat release (10-36 hours after pouring), the set heating rate of the temperature control chamber needs to be reduced to avoid excessive accumulation of hydration heat; when the air duct velocity is below 1.2m / s, it cannot match the heat release intensity requirement assessed during pouring, necessitating an increase in air velocity to enhance heat dissipation. By integrating the weight values ​​of each factor, the mechanism of action based on the pouring hydration assessment, and critical thresholds, data on the influencing factors of temperature-controlled curing pouring regulation, containing a complete system of influencing factors, was generated.

[0096] Step S32: Analyze the temperature control curing change characteristics of concrete by adjusting the initial temperature control curing data of concrete pouring through the data of factors affecting temperature control curing pouring, and generate concrete temperature control curing change characteristic data.

[0097] In this embodiment of the invention, based on the data of factors affecting the temperature control curing pouring adjustment, the temperature control curing change characteristics of the concrete preliminary temperature control curing data are analyzed. The analysis process also incorporates the control capability of the temperature control box (temperature control accuracy ±0.3℃, wind speed adjustment range 0.8-2.0m / s). Using the core parameters (temperature control chamber set temperature range, cooling rate, and duct wind speed baseline value) from the preliminary temperature control curing data as the analysis object, each influencing factor of pouring adjustment was coupled with the core parameters in descending order of weight. The gradient setting of the coupling analysis directly referenced the key parameter range obtained from the hydration process assessment during pouring: based on the actual distribution range of the longitudinal temperature field gradient assessed during pouring, three gradient levels of 0.3℃ / cm, 0.4℃ / cm, and 0.5℃ / cm were set; combined with the temperature difference monitoring results of adjacent nodes in space during pouring, three gradient levels of 3℃ / m, 5℃ / m, and 6℃ / m were set; referring to the adaptation test data of heat release rate and wind speed control during pouring, three levels of duct wind speed were set: 1.0m / s, 1.5m / s, and 2.0m / s. The variation range of the core temperature control parameters under different gradient combinations was calculated respectively. Based on the positive correlation between temperature field gradient and hydration rate derived from the hydration process assessment during pouring, it is determined that for every 0.1℃ / cm increase in the longitudinal temperature field gradient, the cooling rate of the temperature control box needs to be reduced by 0.1℃ / h, and the lower limit of the set temperature range needs to be increased by 1℃ to suppress the risk of thermal cracking caused by excessively rapid hydration. Combined with the assessment results of the hydration uniformity of adjacent nodes during pouring, it is determined that for every 2℃ / m increase in the temperature field gradient of adjacent nodes, the angle of the corresponding area's air duct outlet needs to be adjusted by 30° to increase local airflow and balance the local hydration environment. Based on the heat dissipation adaptation assessment of the heat release rate and wind speed during pouring, it is determined that for every 0.5m / s decrease in the air duct wind speed, the cooling rate needs to be further reduced by 0.05℃ / h. Simultaneously, considering the matching relationship between the temperature field trend and the heat release stage assessed during pouring, it is determined that when the temperature field upward trend overlaps with the rapid growth stage of heat release, the temperature control box needs to activate the cooling preheating mode in advance, with the precooling temperature set 2℃ lower than the current set temperature to offset the peak impact of hydration heat in advance. By integrating information such as the range and trend of temperature control parameters under the influence of various factors, and the adjustment basis based on the hydration assessment during pouring, data on the characteristics of temperature control curing changes in concrete are generated.

[0098] Step S33: Analyze the hydration state of the concrete under preliminary temperature control curing based on the preliminary temperature control curing parameters, generate preliminary temperature control curing hydration state data, and perform hydration state evolution processing on the preliminary temperature control curing hydration state data to generate preliminary temperature control curing evolution hydration state data.

[0099] In this embodiment of the invention, the hydration process evaluation data of ultra-high performance concrete during pouring is used as the basis for analysis, combined with the preliminary temperature control curing parameters of the concrete, while also taking into account the constant temperature and sealed environment characteristics inside the temperature control chamber. First, a hydration state evaluation index system is established with the hydration process evaluation index during pouring as the core. The core indicators include the hydration reaction progress, the rate of change of heat release rate, and the deviation value between the internal temperature of the component and the set temperature of the temperature control chamber. The hydration reaction progress is calculated by correcting the proportion of the cumulative heat release amount monitored in real time during pouring to the total heat release amount. The rate of change of heat release rate is calculated based on the trend continuity of the difference in heat release rate between adjacent time periods during pouring. The temperature deviation value is the difference between the actual temperature at the pouring node and the set temperature of the temperature control chamber, and the threshold value of the difference is set with reference to the hydration heat tolerance temperature range evaluated during pouring. Based on the temperature range (25-32℃, 22-28℃) and cooling rate (0.6℃ / h) of the temperature control chamber in the preliminary temperature control curing parameters, and combined with the real-time hydration data from 0 to 24 hours during pouring, the hydration state indicators for three time periods after pouring (24-48 hours and 48-72 hours) are supplemented and calculated to generate preliminary temperature control curing hydration state data that includes the measured data during pouring and the data for subsequent time periods. Based on this, the hydration state evolution processing is carried out. This evolution process takes the development trend of the hydration process during pouring as the core guide and adopts the time series evolution analysis method. With a 2-hour time interval, an evolution model is constructed based on the historical hydration state data during pouring and the preliminary temperature control curing parameters. The model is configured as an autoregressive integral moving average model, with a lag order of 3 (matching the 3 key hydration stages during pouring), a difference order of 1, and a moving average order of 2. It predicts the hydration reaction progress, heat release rate, and internal temperature change trend of the component at each time node in the next 24 hours. The prediction error is controlled within 5% through model verification. At the same time, the prediction results are corrected by combining the control response speed of the temperature control box (temperature adjustment response time ≤ 10 minutes) and the sensitivity of the hydration process assessed during pouring (such as the response coefficient of the heat release rate to temperature changes during pouring). Finally, preliminary temperature control curing evolution hydration state data containing measured data during pouring, subsequent time period data, and future prediction data are generated.

[0100] Step S34: Based on the concrete temperature control curing change characteristic data, perform temperature control evolution hydration state adjustment processing on the preliminary temperature control curing evolution hydration state data to generate temperature control evolution hydration adjustment state data.

[0101] In this embodiment of the invention, the hydration state data of the initial temperature-controlled curing evolution is adjusted based on the concrete temperature-controlled curing change characteristic data. First, by evaluating the core characteristics such as the hydration initiation rate, the peak heat release period, and the peak intensity during pouring, the basis for dividing the three key hydration zones of temperature-controlled evolution is clarified. Then, the preliminary temperature-controlled curing evolution hydration state data is adjusted based on the concrete temperature-controlled curing change characteristic data. First, a fuzzy comprehensive evaluation model was used to process the fuzzy influence transformation of each hydration state during temperature control evolution. The model was configured with triangular membership functions, dividing the hydration state into three evaluation dimensions: the temperature control latency period, the temperature rise sensitive zone, and the heat release peak zone. The weights for each dimension were set to 0.2, 0.3, and 0.5, respectively, with the weight allocation determined by referencing the heat release contribution ratio of each hydration zone during pouring (latency period 10%, temperature rise acceleration period 60%, heat release peak period 30%). The input temperature control curing change characteristic data (including cooling rate adjustment, temperature control range offset, and duct wind speed adjustment) and initial... The hydration state data (including hydration reaction progress and predicted heat release rate) of the temperature-controlled curing process were standardized and preprocessed into dimensionless values ​​in the 0-1 range. The preprocessing standard was set with reference to the benchmark range of each parameter evaluated during pouring. The fuzzy membership degree of each data point to the three evaluation dimensions was calculated using a triangular membership function. The membership degree calculation focused on the matching between the core characteristics of each hydration section during pouring (slow hydration initiation during the latent period, rapid growth of heat release during the temperature acceleration period, and stable heat release during the heat release peak period) and the temperature control chamber regulation. Then, the fuzzy influence values ​​of each evolution period were obtained by weighted summation according to the weights (0.1-0.1 in the stable temperature control section of the evolution). 3. Evolutionary temperature rise sensitive zone (0.4-0.7) and evolutionary heat release peak zone (0.6-0.9) are used to generate fuzzy influence data on the evolutionary hydration state, containing fuzzy influence data for these three zones. Subsequently, for the fuzzy influence data of the evolutionary temperature control stable zone, combined with the latent hydration characteristics assessed during pouring (0-10 hours after pouring, hydration reaction progress less than 10%, heat release rate less than 0.8 kJ / (kg·h)), the latent period regulation feature extraction method is used to determine the regulation settings. For the fuzzy influence data of the evolutionary temperature rise sensitive zone, combined with the heat release characteristics of the temperature rise acceleration period assessed during pouring (10-36 hours after pouring, heat release... The rate of temperature rise increased from 0.8 kJ / (kg·h) to a peak of 2.3 kJ / (kg·h). The regulation setting was determined by the feature extraction method during the temperature acceleration period. For the fuzzy influence data of the heat release peak section, the regulation setting was determined by the feature extraction method during the heat release inhibition period, combined with the heat release peak characteristics evaluated during the pouring process (peak temperature 76-78℃ 36-60 hours after pouring). Finally, a multi-dimensional feature integration method was used to sort the regulation feature data of the three sections according to the time development order of the hydration process during pouring, and to carry out smoothing processing and priority setting of the transition period. After system integration, temperature control evolution hydration regulation state data was generated.

[0102] Furthermore, step S34 includes the following steps:

[0103] Step S341: The fuzzy influence transformation of the hydration state data of the initial temperature-controlled curing evolution is processed by the concrete temperature-controlled curing change characteristic data to generate fuzzy influence data of the evolution hydration state. The fuzzy influence data of the evolution hydration state includes fuzzy influence data of the temperature-controlled stable section, fuzzy influence data of the temperature-sensitive section, and fuzzy influence data of the heat release peak section.

[0104] In this embodiment of the invention, the fuzzy influence transformation of each hydration state in the initial temperature-controlled curing evolution is processed by using concrete temperature-controlled curing change characteristic data. A fuzzy comprehensive evaluation model is used to carry out the fuzzy influence transformation of each hydration state in the temperature-controlled evolution. The model is configured with a triangular membership function to divide the temperature-controlled evolution hydration state into three evaluation dimensions: the temperature-controlled stable evolution section, the temperature-sensitive evolution section, and the heat release peak evolution section. The dimension division directly corresponds to the three core hydration stages evaluated during pouring (initiation latency period, temperature acceleration period, and heat release peak period). First, the input data is standardized and preprocessed. The preprocessing baseline value is determined based on the measured range of parameters at each hydration stage during pouring: parameters such as the cooling rate adjustment range, temperature control range offset, and duct wind speed adjustment in the temperature control change characteristic data are uniformly converted into dimensionless values ​​in the 0-1 range according to the safety parameter range assessed during pouring; the predicted values ​​of hydration reaction progress and heat release rate in the evolution hydration state data are normalized to the same standard according to the measured parameter range of each stage during pouring. Based on the preprocessed data, the fuzzy membership degree of each data point to the three evaluation dimensions is calculated using a triangular membership function. The membership degree calculation for the evolutionary temperature-controlled stable zone focuses on the matching between the temperature fluctuation amplitude inside the temperature control chamber and the hydration reaction initiation rate assessed during pouring, with a key focus on verifying whether the temperature environment is suitable for the slow initiation requirement determined during pouring. The evolutionary temperature-sensitive zone focuses on the synergy between the temperature rise rate and the heat release rate assessed during pouring, while also considering the cooling response capability of the temperature control chamber to ensure that the cooling rhythm matches the heat release growth rhythm. The evolutionary heat release peak zone focuses on the correlation between the peak temperature and the degree of hydration completeness assessed during pouring, while also considering the thermal load-bearing capacity of the temperature control chamber to avoid the peak temperature exceeding the thermal tolerance threshold assessed during pouring. Based on the set dimensional weights (referring to the contribution ratio of heat release at each stage during pouring being set to 0.2, 0.3, and 0.5), the membership degrees are weighted and summed to obtain the fuzzy influence values ​​for the three segments corresponding to each evolution period. The fuzzy influence value ranges from 0.1 to 0.3 for the temperature-controlled stable segment (corresponding to low hydration activity during the latent period of pouring), from 0.4 to 0.7 for the temperature-sensitive segment (corresponding to high heat release activity during the temperature rise period of pouring), and from 0.6 to 0.9 for the peak heat release segment (corresponding to stable heat release during the peak period of pouring). The fuzzy influence values ​​for each period, the corresponding evaluation dimensions, and the calculation basis based on the pouring hydration assessment are integrated to generate fuzzy influence data for the evolutionary hydration state, including fuzzy influence data for the temperature-controlled stable segment, the temperature-sensitive segment, and the peak heat release segment.

[0105] Step S342: Perform evolutionary temperature control latency regulation characteristic analysis on the fuzzy impact data of the stable evolutionary temperature control segment to generate evolutionary temperature control latency regulation characteristic data;

[0106] In this embodiment of the invention, the latent period regulation characteristics of evolutionary temperature control are analyzed for the fuzzy impact data of the stable temperature control zone. A latent period regulation feature extraction method is used to conduct the latent period regulation characteristic analysis for the fuzzy impact data of the stable temperature control zone, while also considering the sealed constant temperature characteristics of the temperature-controlled chamber and preliminary temperature control curing parameters. First, based on the real-time monitoring of hydration heat release data and temperature data during pouring, the time range of the evolutionary temperature control latent period is assessed and determined: combined with the hydration heat reaction curve data during pouring, the 0-10 hours after pouring is identified as the evolutionary temperature control latent period. During this stage, the hydration reaction progress is less than 10% and the heat release rate is less than 0.8 kJ / (kg·h) as assessed during pouring. The hydration reaction is mainly the initial hydration of cement, with low reactivity and sensitivity to temperature environment, requiring precise temperature control to ensure balanced hydration initiation. The fuzzy influence data of the temperature-controlled stable zone during evolution were correlated with the hydration state parameters (hydration reaction progress and heat release rate) assessed during pouring to clarify the correspondence between the fuzzy influence values ​​and the temperature control accuracy of the temperature control box: when the fuzzy influence value is 0.1-0.2, corresponding to the low activity latency assessed during pouring, the temperature control accuracy of the temperature control box needs to reach ±0.8℃ to avoid the hydration start-up delay caused by excessively low temperature; when the fuzzy influence value is 0.2-0.3, corresponding to the activity start-up critical period assessed during pouring, the temperature control accuracy needs to be improved to ±0.6℃ to prevent the early hydration rate imbalance caused by excessively high local temperature. The latent period temperature control chamber's triggering conditions were determined through thermal stress simulation calculations. Simulation parameters referenced the early mechanical properties of concrete assessed during pouring (compressive strength < 2 MPa). It was determined that when the actual temperature at a component node deviates from the set temperature of the temperature control chamber by more than 0.5℃ and lasts for more than 30 minutes, it leads to localized differences in hydration initiation. In this case, the heating power of the temperature control chamber needs to be increased from 5kW to 8kW, while the duct velocity is reduced from 1.0 m / s to 0.8 m / s. Temperature uniformity is ensured through increased heating and reduced ventilation. Furthermore, considering the hydration uniformity requirements of adjacent nodes assessed during pouring, the temperature difference between adjacent pouring nodes within the temperature control chamber must not exceed 3℃. If this threshold is exceeded, the opening of the corresponding area's duct outlet needs to be adjusted to 80% to increase local airflow circulation and eliminate temperature differences, preventing asynchronous hydration initiation at adjacent nodes. The parameters determined based on the pouring hydration assessment, including temperature control accuracy, triggering conditions, temperature uniformity requirements, and applicable time range, are integrated to generate evolutionary temperature control latent period regulation characteristic data encompassing complete latent period regulation requirements.

[0107] Step S343: Perform fuzzy impact analysis on the evolutionary warming sensitive section data to generate evolutionary warming acceleration period regulation characteristic data.

[0108] In this embodiment of the invention, the fuzzy influence data of the sensitive section of temperature rise is analyzed for the adjustment characteristics of the accelerated temperature rise period. A method for extracting adjustment characteristics during the accelerated temperature rise period is used. Based on the fuzzy influence data of the sensitive section of temperature rise, the analysis focuses on the temperature control requirements during the rapid rise of concrete temperature in the temperature control chamber, and the requirements are directly matched with the heat release characteristics assessed during pouring. First, based on the real-time monitoring data of the hydration heat release rate and reaction progress during pouring, the time range of the accelerated temperature rise period is determined: 10-36 hours after pouring is defined as the accelerated temperature rise period, corresponding to the rapid rise in concrete temperature. During this stage, the hydration reaction progress is assessed to increase from 10% to 60%, and the heat release rate increases from 0.8 kJ / (kg·h) to a peak of 2.3 kJ / (kg·h). Cement hydration and microsilica secondary hydration occur synergistically, resulting in high heat release intensity and a rapid temperature rise rate, making this a critical stage for temperature control and crack prevention. The fuzzy influence data of the temperature rise sensitive section was coupled with the core parameters of the accelerated temperature rise period (temperature rise rate, heat release rate, longitudinal temperature field gradient, and cooling response speed of the temperature control box) assessed during pouring. The matching rules between the fuzzy influence values ​​and the cooling adjustment parameters of the temperature control box were clarified: when the fuzzy influence value is 0.4-0.5, corresponding to the initial stage of rapid heat release growth assessed during pouring, the critical value of the cooling rate is set to 0.5℃ / h; when the fuzzy influence value is 0.5-0.6, corresponding to the high-speed growth period of heat release assessed during pouring, the critical value of the cooling rate is adjusted to 0.4℃ / h; when the fuzzy influence value is 0.6-0.7, corresponding to the period approaching the peak of heat release assessed during pouring, the critical value of the cooling rate is reduced to 0.3℃ / h, and the heat release intensity is adapted to the changes through graded cooling. The activation temperature threshold for cooling measures was calculated by combining numerical simulation of heat conduction with the cooling capacity of the temperature control box. The simulation parameters referenced the thermal conductivity coefficient of concrete assessed during pouring (1.75 W / (m·K)). It was determined that when the component node temperature rises to 70℃, the cooling system of the temperature control box needs to be activated, with the cooling power set at 8kW. At the same time, the airflow velocity in the duct was increased to 1.8m / s to quickly remove the accumulated heat of hydration. In addition, based on the heat release fluctuation characteristics assessed during pouring, the monitoring frequency requirements for the accelerated heating period were determined: when the fuzzy influence value exceeds 0.5 (corresponding to the high-speed growth period of heat release), the monitoring frequency was shortened to once every 20 minutes; when it is below 0.5 (corresponding to the initial stage of heat release growth), it was maintained at once every 30 minutes to ensure real-time capture of the synergistic relationship between heat release and temperature changes. Furthermore, for component areas where the longitudinal temperature field gradient assessed during pouring exceeds 0.4℃ / cm (prone to thermal cracking risk), the airflow direction was adjusted by local guide vanes inside the temperature control box to increase the local wind speed to 2.0m / s, thereby enhancing local heat dissipation and balancing the temperature gradient. By integrating parameters such as the critical value of the cooling rate of the temperature control box, the temperature threshold for the start of cooling measures, the monitoring frequency, and the local airflow adjustment scheme determined based on the hydration assessment of the pouring, the characteristic data of the regulation during the accelerated warming period are generated.

[0109] Step S344: Perform evolutionary heat release peak segment fuzzy impact analysis on the evolutionary heat release inhibition period regulation characteristics data to generate evolutionary heat release inhibition period regulation characteristic data;

[0110] In this embodiment of the invention, the fuzzy impact data of the evolutionary heat release peak segment is analyzed for the adjustment characteristics of the evolutionary heat release inhibition period. An extraction method for the adjustment characteristics of the heat release inhibition period is used, combined with the fuzzy impact data of the evolutionary heat release peak segment, to conduct adjustment characteristic analysis on the evolutionary heat release peak segment. The focus is on the heat release inhibition requirements during the stable peak stage of concrete temperature in the temperature control chamber, and the inhibition strategy matches the peak heat release characteristics assessed during pouring. Firstly, based on the real-time monitored heat release peak data and temperature data during pouring, the time range of the evolutionary heat release peak segment is assessed and determined: 36-60 hours after pouring is identified as the evolutionary heat release peak segment, corresponding to the stable peak concrete temperature range. During this stage, the hydration reaction progress is maintained at 60%-85%, the heat release rate is stable at 0.1-0.8 kJ / (kg·h), and the peak temperature is 76-78℃. The hydration reaction is mainly secondary hydration of silica fume and slag powder. The heat release is stable but prolonged, requiring precise temperature control to ensure sufficient hydration and avoid thermal cracking. The fuzzy influence data of the heat release peak segment during evolution were correlated with the core indicators (peak temperature, heat release rate fluctuation amplitude, and thermal load capacity of the temperature control box) assessed during pouring. This clarified the matching rules between the fuzzy influence values ​​and the temperature control box parameters: when the fuzzy influence value was 0.6-0.7, corresponding to the initial stage of the heat release peak assessed during pouring, the temperature control range of the temperature control box was locked at 75-76℃, and the heat release rate suppression threshold was 0.3 kJ / (kg·h); when the fuzzy influence value was... When the value is 0.7-0.8, corresponding to the stable period of the heat release peak assessed during pouring, the temperature control range is tightened to 75.5-76℃, and the heat release rate suppression threshold is reduced to 0.25kJ / (kg·h). When the fuzzy influence value is 0.8-0.9, corresponding to the later stage of the heat release peak assessed during pouring (heat release rate fluctuations increase), the temperature control range is further tightened to 75.5-76℃, and the secondary cooling mode of the temperature control box is activated, with the cooling power increased to 10kW to enhance heat release suppression. Temperature fluctuation limits were determined through hydration reaction uniformity analysis. Based on the hydration uniformity requirements assessed during casting, the analysis clarified that regardless of the range of fuzzy influence values, the temperature fluctuation within the temperature control box during the peak phase must not exceed 1℃. When the fluctuation exceeds the threshold, the frequency converter module of the temperature control box is adjusted to improve the temperature response accuracy to ±0.3℃. The control logic of the temperature control box during the heat release inhibition period was also clarified. When the heat release rate is lower than the inhibition threshold, it indicates that the hydration reaction is slowing down, requiring a reduction in cooling power to 3kW and a decrease in duct velocity to 1.2m / s to ensure a slow temperature decrease and prevent excessively rapid cooling that could lead to internal stress accumulation. The parameters determined based on the casting hydration assessment, including the temperature control range, heat release rate inhibition threshold, temperature fluctuation limits, and cooling power adjustment rules, were integrated to generate characteristic data for the evolution of the heat release inhibition period.

[0111] Step S345: Analyze the evolutionary hydration regulation state of temperature control changes based on the evolutionary temperature control latency period regulation characteristic data, the evolutionary temperature rise acceleration period regulation characteristic data, and the evolutionary heat release inhibition period regulation characteristic data, and generate temperature control evolutionary hydration regulation state data.

[0112] In this embodiment of the invention, the evolutionary hydration regulation state of temperature control changes is analyzed based on the evolutionary temperature control latency period regulation characteristic data, the evolutionary temperature rise acceleration period regulation characteristic data, and the evolutionary heat release inhibition period regulation characteristic data. First, the regulation characteristic data of the three sections are sorted according to the time development sequence of the hydration process assessed at the time of pouring, clarifying the time connection relationship of the evolutionary temperature control latency period (0-10 hours), the evolutionary temperature rise acceleration period (10-36 hours), and the evolutionary heat release peak period (36-60 hours). The sorting is strictly based on the time sequence characteristics of the hydration heat release curve monitored at the time of pouring, ensuring that the temperature control box control parameters at each time period do not conflict with the development rhythm of the hydration process. For the transition periods between adjacent sections (10 hours and 36 hours), a smooth transition analysis of adjustment parameters was conducted based on the transition characteristics of hydration parameters in adjacent stages assessed during pouring. For example, at the 10-hour node (transition from the latent period to the accelerated heating period), referring to the hydration reaction progress (10%) and heat release rate (0.8 kJ / (kg·h)) at this node during pouring, the temperature control accuracy of the temperature control box during the latent period was gradually transitioned from ±0.6℃ to ±0.8℃ during the accelerated heating period, the cooling rate was gradually increased from 0 to 0.3℃ / h, and the cooling power was gradually increased from 0kW to 3kW to avoid temperature fluctuations in the temperature control box caused by parameter abrupt changes, which could lead to abrupt changes in the hydration rate. At the 36-hour node (transition from the accelerated heating period to the peak heat release period), referring to the peak heat release characteristics at this node during pouring, the cooling rate was finely adjusted from 0.3℃ / h to 0.25℃ / h, and the cooling power was gradually adjusted from 8kW to 6kW to achieve a smooth transition. A weighted superposition method was adopted to integrate the adjustment targets of each section. The weight allocation was based on the proportion of heat release contribution and crack prevention risk level of each section during pouring. The adjustment priority of the accelerated heating period and the peak heat release period was set as the highest (corresponding to high heat release and high crack risk during pouring), and the temperature control latency period was set as the second highest priority (corresponding to the critical period of hydration initiation during pouring). When the adjustment requirements of different sections overlapped, the control requirements of the high-priority section were given priority. The temperature control box control components corresponding to each adjustment characteristic parameter were simultaneously labeled. For example, temperature control parameters correspond to heating / cooling modules, and wind speed adjustment corresponds to air duct fans and guide vanes. The labeling was based on the verification results of the impact of each parameter adjustment on the hydration process during pouring, ensuring that the control commands could be directly matched with the temperature control box hardware. Finally, the sorted time series, the smooth transition adjustment parameters based on the hydration assessment during pouring, the priority ranking, and the matching information of the temperature control box control components are systematically integrated to generate temperature control evolution hydration adjustment status data containing the precise control requirements for each time period. The evaluation logic of the hydration process during pouring is continued throughout the process, so as to achieve precise adaptation between temperature control and hydration process.

[0113] Furthermore, step S4 includes the following steps:

[0114] Step S41: Based on the temperature-controlled evolutionary hydration regulation state data, perform a short-cycle evolutionary hydration state temperature control risk analysis to generate short-cycle evolutionary hydration state temperature control risk data;

[0115] In this embodiment of the invention, a short-cycle hydration state temperature control risk analysis is conducted based on temperature-controlled evolution hydration regulation state data. First, the criteria for defining short-cycle evolution are clarified. Combining the time characteristics of concrete hydration state evolution and the control response speed of the temperature control chamber, the entire curing cycle is divided into 2-hour short-cycle evolutions, covering the evolution temperature control latency period (0-10 hours), the evolution temperature acceleration period (10-36 hours), the evolution heat release peak section (36-60 hours), and the subsequent slow descent stage (60-288 hours). Three core risk assessment indicators are established: temperature deviation rate, hydration reaction progress deviation rate, and temperature change rate deviation rate. The temperature deviation rate is the percentage of the difference between the actual temperature of the component node and the temperature set temperature in the temperature-controlled evolution hydration regulation state data relative to the set temperature. The hydration reaction progress deviation rate is the percentage of the difference between the actual hydration reaction progress and the predicted progress relative to the predicted progress. The deviation rate of temperature change is the percentage of the difference between the actual temperature change rate and the critical rate in the regulation characteristic data relative to the critical rate. Risk level thresholds are set: a temperature deviation rate exceeding 5%, a hydration reaction progress deviation rate exceeding 10%, and a temperature change rate deviation rate exceeding 20% ​​are considered high-risk; corresponding indicator deviation rates between 2%-5%, 5%-10%, and 10%-20% are considered medium-risk; and those below 2%, 5%, and 10% are considered low-risk. By calculating the actual values ​​of the three indicators within each short evolutionary cycle, the risk level of each short cycle is determined by comparing them with the thresholds. The component node positions, specific values ​​of risk indicators, and preliminary causes of deviations (such as blockage of the temperature control box air duct, response delay of the refrigeration module, etc.) corresponding to high- and medium-risk cycles are recorded simultaneously. This information is integrated to generate evolutionary short-cycle hydration state temperature control risk data containing short cycle number, risk level, risk indicator data, and risk node positions.

[0116] Step S42: Analyze the temperature control adjustment parameters of concrete in the short-term evolution period using the temperature control risk data of the hydration state in the short-term evolution period, and generate the temperature control adjustment parameters of concrete in the short-term evolution period.

[0117] In this embodiment of the invention, the temperature control adjustment parameters of concrete in the short-term evolution of hydration state are analyzed using short-term evolutionary temperature control risk data. Differentiated temperature control box control strategies are formulated for different risk levels. For high-risk cycles, the core strategy is to strengthen control intensity and quickly correct deviations; for medium-risk cycles, the core strategy is to moderately adjust parameters and stabilize the temperature control state; and for low-risk cycles, the core strategy is to maintain existing parameters and ensure stability. For high-risk cycles, such as short cycles with a temperature deviation rate of 6% during the accelerated heating phase, the cooling rate of the temperature control box is further reduced from 0.3℃ / h to 0.2℃ / h, a secondary cooling mode (cooling power 10kW) is activated, and the duct velocity is increased from 1.8m / s to 2.0m / s, while the monitoring frequency is shortened from once every 20 minutes to once every 15 minutes. For medium-risk cycles, such as short cycles with a temperature change rate deviation rate of 15% during the slow cooling phase, the temperature control box cooling rate is further reduced from 0.3℃ / h to 0.2℃ / h, a secondary cooling mode is activated (cooling power 10kW), and the duct velocity is increased from 1.8m / s to 2.0m / s, while the monitoring frequency is shortened from once every 20 minutes to once every 15 minutes. The cooling rate of the control box was adjusted from 0.5℃ / h to 0.4℃ / h, and the duct zone control mode was activated. The opening of the air outlet of the corresponding area duct was adjusted to 90%, and the monitoring frequency was maintained once every 30 minutes. For low-risk cycles, such as short cycles where the deviation rate of various indicators is less than 2% during the incubation period, the basic parameters in the evolutionary temperature control incubation period adjustment characteristic data were maintained, with the temperature control box heating power at 5kW and the wind speed at 0.8m / s, without any additional parameter adjustments. For the surface node areas of components with large longitudinal temperature field gradients, regardless of the risk level, an additional start command for the local heating / cooling module of the temperature control box was added to the adjustment parameters to ensure local temperature uniformity. The risk level of each short cycle, the corresponding temperature control box adjustment parameters (cooling rate, heating / cooling power, wind speed, monitoring frequency), parameter adjustment range, and basis were integrated to generate the evolutionary short-cycle concrete temperature control adjustment parameters.

[0118] Step S43: Perform dynamic temperature control target analysis on concrete based on the evolutionary short-cycle concrete temperature control adjustment parameters, and generate dynamic temperature control target data for concrete.

[0119] In this embodiment of the invention, dynamic temperature control target analysis of concrete is performed based on the short-cycle concrete temperature control adjustment parameters. First, the core adjustment requirements in the short-cycle concrete temperature control adjustment parameters are decomposed into specific temperature control targets for each short cycle. For the temperature control target, the temperature control range for each short cycle is calculated by combining the adjusted cooling / heating rate and the response time of the temperature control chamber. For example, in a high-risk short cycle, the temperature rise is set to not exceed 0.4℃ (2 hours × 0.2℃ / h), and the temperature control chamber's temperature control range is locked at 72-73℃. Regarding the temperature change rate target, the maximum allowable change rate within each short cycle is defined: 0.2℃ / h for high-risk cycles, 0.3℃ / h for medium-risk cycles, and 0.5℃ / h for low-risk cycles. Regarding the hydration reaction progress... The objectives are determined by considering the impact of adjustment parameters on the hydration process. For example, the target hydration reaction progress increment for each short cycle is 3%-4% during the low-risk phase of accelerated heating, and revised to 2%-3% for the high-risk phase after adjustment, to prevent excessive heat buildup due to rapid progress. For the transition between adjacent short cycles, target smoothing is implemented to ensure that the temperature deviation between the end temperature of the temperature control chamber in the previous cycle and the starting temperature in the next cycle does not exceed 0.3℃, and the adjustment range of heating / cooling power does not exceed 3kW, preventing drastic temperature fluctuations within the temperature control chamber caused by sudden parameter changes. Information such as the temperature control range, temperature change rate limit, hydration reaction progress target, and transition smoothing requirements for each short cycle are integrated to generate dynamic temperature control target data for concrete that includes precise target parameters for each short cycle.

[0120] Step S44: Based on the concrete dynamic temperature control target data, perform concrete dynamic temperature control curing analysis on the preliminary concrete temperature control curing data to generate concrete dynamic temperature control curing data.

[0121] In this embodiment of the invention, dynamic temperature control and curing analysis of concrete is performed on the preliminary temperature control and curing data of concrete based on the dynamic temperature control target data. First, the dynamic temperature control target data and the preliminary temperature control and curing data are compared parameter by parameter to identify mismatched parameters. For example, the preliminary data shows a uniform cooling rate of 0.5℃ / h during the accelerated heating period, while the dynamic target data shows a cooling rate of 0.2-0.4℃ / h for different risk periods. The mismatched parameters are replaced with the dynamic target data. The missing details of the temperature control box's control execution in the preliminary data are supplemented, clarifying the activation conditions for each short-cycle temperature control adjustment measure. For example, when the temperature of a component node in a short cycle reaches the upper limit of the control range, the corresponding cooling module and air duct enhancement mode of the temperature control box are immediately activated; when the temperature drops below the lower limit, the heating module and airflow reduction mode are activated. The execution of each control measure is clarified. Priority is set for the following: when there is a conflict between the cooling / heating measures and the fan speed adjustment measures in the temperature control box, the cooling / heating measures are executed first to control the core temperature deviation; when there is a conflict between the local control measures and the overall control measures, the local measures are executed first to solve the problem of differential temperature at component nodes. The hardware operation instructions of the temperature control box corresponding to each dynamic temperature control parameter are marked simultaneously, such as the current adjustment instruction of the heating module of the temperature control box corresponding to the heating power adjustment, and the frequency conversion instruction of the fan corresponding to the fan speed adjustment. Finally, the optimized temperature control parameters, the execution conditions of the temperature control box control measures, the execution priority, the hardware operation instructions and other information are integrated into the system to generate concrete dynamic temperature control curing data that includes the precise control requirements of each short cycle and each component node in the entire curing cycle. This ensures that the data can be directly used to guide the operation of the intelligent control system of the temperature control box and realize the adaptive dynamic adjustment of ultra-high performance concrete curing in the temperature control box.

[0122] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0123] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for intelligent temperature-controlled curing of ultra-high performance concrete, characterized in that, Includes the following steps: Step S1: Obtain the mix proportion parameters and temperature control curing requirements of ultra-high performance concrete; perform preliminary temperature control curing analysis of concrete based on the mix proportion parameters and temperature control curing requirements of ultra-high performance concrete, and generate preliminary temperature control curing data of concrete. Step S1 includes the following steps: Step S11: Obtain the mix proportion parameters of ultra-high performance concrete and the temperature control curing requirements of ultra-high performance concrete; Step S12: Analyze the matching characteristics of the hydration heat effect of the ultra-high performance concrete mix proportion parameters based on the mix proportion parameters, and generate hydration heat effect matching characteristic data of the mix proportion parameters. Step S13: Analyze the influence factors of the thermal properties of concrete mix proportion parameters by matching characteristic data of hydration heat effect of mix proportion parameters, and generate the influence factors of the thermal properties of concrete mix proportion parameters. Step S14: Based on the influencing factors of thermal properties of concrete mix proportion parameters, perform thermal property analysis on the ultra-high performance concrete mix proportion parameters to generate thermal property data of concrete mix proportion parameters; Step S15: Analyze the heat release of concrete hydration based on the thermal property data of concrete mix proportion parameters, generate concrete hydration heat release data, and analyze the concrete hydration heat reaction curve based on the concrete hydration heat release data to generate concrete hydration heat reaction curve data. Step S16: Based on the concrete hydration heat reaction curve data, perform temperature control curing attribute characteristic analysis of concrete mix proportions to generate temperature control curing attribute characteristic data of concrete mix proportions. Step S17: Conduct preliminary temperature control curing analysis of concrete using the temperature control curing requirement data of ultra-high performance concrete and the temperature control curing attribute characteristic data of concrete mix proportion, and generate preliminary temperature control curing data of concrete. Step S2: Obtain ultra-high performance concrete pouring node data; perform temperature field characteristic analysis on the pouring node using the ultra-high performance concrete pouring node data to generate temperature field characteristic data of the pouring node. Step S3: Based on the temperature field characteristic data of the pouring node distribution and the preliminary temperature control curing data of concrete, perform temperature control evolution and hydration state analysis and processing to regulate the temperature field of the pouring node, and generate temperature control evolution and hydration regulation state data. Step S3 includes the following steps: Step S31: Based on the temperature field characteristics data of the pouring node distribution and the concrete hydration heat reaction curve data, analyze the influencing factors of pouring adjustment for concrete temperature control curing, and generate data on the influencing factors of pouring adjustment for temperature control curing. Step S32: Analyze the temperature control curing change characteristics of concrete by adjusting the initial temperature control curing data of concrete pouring through the data of factors affecting temperature control curing pouring, and generate concrete temperature control curing change characteristic data. Step S33: Analyze the hydration state of the concrete under preliminary temperature control curing based on the preliminary temperature control curing data, generate preliminary temperature control curing hydration state data, and perform hydration state evolution processing on the preliminary temperature control curing hydration state data to generate preliminary temperature control curing evolution hydration state data. Step S34: Based on the concrete temperature control curing change characteristic data, perform temperature control evolution hydration state adjustment processing on the preliminary temperature control curing evolution hydration state data to generate temperature control evolution hydration adjustment state data. Step S4: Based on the temperature control evolution hydration regulation state data, perform dynamic temperature control curing analysis on the preliminary temperature control curing data of concrete to generate dynamic temperature control curing data of concrete.

2. The intelligent temperature-controlled curing method for ultra-high performance concrete according to claim 1, characterized in that, The concrete mix design parameters in step S13 include the weighted parameters for the influence of concrete hydration rate and the weighted parameters for the hysteresis effect of concrete thermal effect.

3. The intelligent temperature-controlled curing method for ultra-high performance concrete according to claim 1, characterized in that, Step S16 includes the following steps: Step S161: Analyze the key temperature range of concrete temperature-controlled curing using concrete hydration heat reaction curve data, and generate key temperature range data for concrete temperature-controlled curing. Step S162: Analyze the temperature control curing attribute characteristics of concrete mix design based on the key temperature range data for concrete temperature control curing, and generate temperature control curing attribute characteristic data for concrete mix design.

4. The intelligent temperature-controlled curing method for ultra-high performance concrete according to claim 3, characterized in that, The key temperature range data for concrete temperature control curing mentioned in step S161 includes the range of rapid rise in concrete temperature, the range of stable peak concrete temperature, and the range of gradual decrease in concrete temperature.

5. The intelligent temperature-controlled curing method for ultra-high performance concrete according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Obtain the pouring node data of ultra-high performance concrete; Step S22: Collect the distribution temperature of the pouring nodes based on the ultra-high performance concrete pouring node data to obtain the distribution temperature data of the pouring nodes. Step S23: Based on the temperature distribution data of the casting nodes, perform temperature stability quality assessment of the spatial environment of the casting nodes to generate temperature stability quality data of the casting nodes. Step S24: Analyze the distribution temperature field characteristics of the casting nodes using the temperature stability quality data of the casting nodes, and generate the distribution temperature field characteristic data of the casting nodes.

6. The intelligent temperature-controlled curing method for ultra-high performance concrete according to claim 5, characterized in that, The temperature field characteristic data of the casting node distribution mentioned in step S24 includes longitudinal temperature field gradient data of the casting node, temperature field gradient data of spatially adjacent nodes, and temperature field trend data of the casting node distribution.

7. The intelligent temperature-controlled curing method for ultra-high performance concrete according to claim 1, characterized in that, Step S34 includes the following steps: Step S341: The fuzzy influence transformation of the hydration state data of the initial temperature-controlled curing evolution is processed by the concrete temperature-controlled curing change characteristic data to generate fuzzy influence data of the evolution hydration state. The fuzzy influence data of the evolution hydration state includes fuzzy influence data of the temperature-controlled stable section, fuzzy influence data of the temperature-sensitive section, and fuzzy influence data of the heat release peak section. Step S342: Perform evolutionary temperature control latency regulation characteristic analysis on the fuzzy impact data of the stable evolutionary temperature control segment to generate evolutionary temperature control latency regulation characteristic data; Step S343: Perform fuzzy impact analysis on the evolutionary warming sensitive section data to generate evolutionary warming acceleration period regulation characteristic data. Step S344: Perform evolutionary heat release peak segment fuzzy impact analysis on the evolutionary heat release inhibition period regulation characteristics data to generate evolutionary heat release inhibition period regulation characteristic data; Step S345: Analyze the evolutionary hydration regulation state of temperature control changes based on the evolutionary temperature control latency period regulation characteristic data, the evolutionary temperature rise acceleration period regulation characteristic data, and the evolutionary heat release inhibition period regulation characteristic data, and generate temperature control evolutionary hydration regulation state data.

8. The intelligent temperature-controlled curing method for ultra-high performance concrete according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Based on the temperature-controlled evolutionary hydration regulation state data, perform a short-cycle evolutionary hydration state temperature control risk analysis to generate short-cycle evolutionary hydration state temperature control risk data; Step S42: Analyze the temperature control adjustment parameters of concrete in the short-term evolution period using the temperature control risk data of the hydration state in the short-term evolution period, and generate the temperature control adjustment parameters of concrete in the short-term evolution period. Step S43: Perform dynamic temperature control target analysis on concrete based on the evolutionary short-cycle concrete temperature control adjustment parameters, and generate dynamic temperature control target data for concrete. Step S44: Based on the concrete dynamic temperature control target data, perform concrete dynamic temperature control curing analysis on the preliminary concrete temperature control curing data to generate concrete dynamic temperature control curing data.

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