Concrete curing environment control system based on intelligent temperature control

Abstract

The application discloses a concrete curing environment control system based on intelligent temperature control and belongs to the technical field of concrete curing. The system comprises the following modules: a temperature sensing module that collects the real-time temperature distribution field of the core area of concrete; an abnormality identification module that identifies the abnormal section position whose temperature gradient exceeds the set threshold; a twin modeling module that uses a laser scanner to obtain the surface topographic features of the abnormal section position and combines the temperature distribution field to construct a thermodynamic digital twin model; a simulation deduction module that virtually simulates and deduces a plurality of preset curing temperature curves to obtain an internal stress prediction cloud picture; a curve optimization module that extracts the preset curing temperature curve with the minimum global maximum tensile stress value as the target curing temperature curve; and a partition temperature control module that controls the curing equipment to perform sectional differentiated heating or cooling on the surface and the interior of the concrete member according to the target curing temperature curve.

Description

Technical Field

[0001] This invention relates to the field of concrete curing technology, specifically to a concrete curing environment control system based on intelligent temperature control. Background Technology

[0002] During the curing process of concrete components, the cement hydration reaction releases a large amount of heat, causing a sharp rise in the core temperature of the component while the surface dissipates heat more quickly. This temperature gradient creates tensile stress within the concrete. Once this tensile stress exceeds the early tensile strength of the concrete, cracking occurs, seriously threatening structural safety. To control temperature cracking, current curing techniques typically rely on experience or simple temperature control logic to set a uniform curing temperature curve, using heating or cooling devices to regulate the overall temperature of the entire component. This approach ignores the non-uniform temperature distribution within the concrete and the resulting localized stress concentrations. Especially in large-volume or irregularly shaped components, the temperature gradient at certain cross-sectional locations can be significantly higher than in other areas, forming potential weak points for cracking. Overall temperature control cannot specifically address these abnormal cross-sectional locations.

[0003] While some monitoring systems can collect discrete temperature data within concrete, they lack the ability to automatically identify and extract abnormal cross-sectional locations. The detected temperature information cannot be directly translated into adjustments to curing strategies. Even when using simulation software to model the curing process, it's difficult to efficiently couple the actual surface morphology with the internal temperature field. The simulation model deviates from reality, leading to distorted stress predictions and failing to provide reliable input for selecting the optimal curing temperature curve. Furthermore, conventional curing equipment performs heating and cooling actions holistically, failing to implement differentiated energy regulation based on the actual needs of different sections of the component surface and different depths within the structure. Surface temperature control and core temperature control are coupled and interfere with each other, failing to collaboratively mitigate temperature gradients at abnormal cross-sections and making it difficult to control tensile stress within a safe range throughout the curing process.

[0004] How to accurately formulate a curing temperature curve that minimizes internal tensile stress for abnormal temperature gradient areas that appear in real time inside concrete components, and how to carry out segmented and differentiated temperature control on the surface and interior associated with such abnormal areas, are problems that have not yet been effectively solved. Summary of the Invention

[0005] The purpose of this invention is to provide a concrete curing environment control system based on intelligent temperature control, in order to solve the technical problems in the prior art that it is impossible to select the curing temperature curve according to the abnormal cross section of the temperature gradient, and that it is impossible to achieve differentiated zone temperature control of the surface and the interior.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a concrete curing environment control system based on intelligent temperature control, including a temperature sensing module, an anomaly identification module, a twin modeling module, a simulation and deduction module, a curve optimization module, and a zoned temperature control module. The temperature sensing module collects the real-time temperature distribution field of the core area of ​​the concrete through a temperature sensing network embedded inside the concrete component, thereby accurately grasping the temperature gradient changes from the inside to the surface of the concrete component, avoiding monitoring blind spots, and providing a high-precision temperature data foundation for subsequent anomaly identification and modeling. The anomaly identification module identifies the locations of abnormal sections where the temperature gradient exceeds a set threshold based on the real-time temperature distribution field of the core area of ​​the concrete, enabling the timely identification of locations in the system prone to cracking without relying on manual experience judgment, thus improving the timeliness and accuracy of anomaly detection.

[0007] The digital twin modeling module utilizes a laser scanner to acquire the surface morphology features of concrete corresponding to abnormal cross-section locations, and combines this with the real-time temperature distribution field of the concrete core area to construct a thermodynamic digital twin model of the concrete. By fusing real surface geometric information with internal temperature data, the constructed digital twin model can realistically reflect the geometric state and thermodynamic boundary conditions of the concrete component at the current curing stage, providing a reliable basis for subsequent stress simulation and deduction. Preferably, when the laser scanner performs a mesh scan on the outer surface of the concrete corresponding to the abnormal cross-section location, the scanning resolution increases with the increase of the temperature gradient value at the abnormal cross-section location, so that areas with larger temperature gradients can obtain more detailed surface morphology descriptions, improving the modeling accuracy of the digital twin model in high-risk areas.

[0008] The simulation module uses a digital twin model of concrete thermodynamics to virtually simulate multiple preset curing temperature curves, obtaining predicted internal stress cloud maps corresponding to each preset curing temperature curve. By applying different curing temperature control strategies to the high-fidelity digital twin model, the system can predict the potential internal stress responses of various curves before actual curing is carried out, avoiding trial and error on actual components, thereby reducing the risk of temperature cracks caused by improper curing regimes and shortening the curing scheme determination cycle.

[0009] The curve selection module extracts the maximum tensile stress value from the internal stress prediction cloud map and selects the preset curing temperature curve with the minimum maximum tensile stress value as the target curing temperature curve. Since the maximum tensile stress value is used as the evaluation index for quantitative comparison, the system can automatically select the curing temperature control scheme that is most beneficial to the crack resistance of concrete, so as to avoid excessive temperature difference stress during the curing process and improve the stability of the curing quality of concrete components.

[0010] The zoned temperature control module controls the curing equipment to perform differentiated heating or cooling of the surface and interior of concrete components according to the target curing temperature curve. By implementing independent temperature control in different areas, the temperature difference between the component surface and core can be actively adjusted, thereby suppressing abnormal temperature gradients and preventing localized thermal shock, meeting the needs of large-volume or irregularly shaped concrete components for precise temperature control.

[0011] As a technical solution of this invention, the process of acquiring the real-time temperature distribution field of the core area of ​​concrete through a temperature sensing network embedded inside the concrete component includes: acquiring the identification codes and spatial coordinates of multiple fiber Bragg grating temperature sensors pre-embedded at different depths in the concrete component; sequentially polling and reading the wavelength offset of the multiple fiber Bragg grating temperature sensors at preset time intervals; calculating the measured temperature value at the location of each fiber Bragg grating temperature sensor based on the wavelength offset; associating and storing the measured temperature value and the corresponding spatial coordinates to generate discrete temperature point cloud data; and performing three-dimensional interpolation fitting on the discrete temperature point cloud data to obtain the real-time temperature distribution field of the continuously distributed core area of ​​concrete. This method can utilize the characteristics of fiber Bragg grating sensors—anti-electromagnetic interference, suitability for long-term monitoring, and ease of networking—to achieve large-scale, multi-depth temperature information acquisition. Simultaneously, the three-dimensional interpolation fitting transforms discrete points into a continuous temperature distribution field, providing a continuous and smooth temperature field data foundation for the accurate identification of abnormal sections.

[0012] As a technical solution of the present invention, the process of identifying the location of an abnormal section where the temperature gradient exceeds a set threshold based on the real-time temperature distribution field of the concrete core region includes: extracting a vertical temperature profile line along the thickness direction of the concrete member from the continuously distributed real-time temperature distribution field of the concrete core region; calculating the local temperature difference between two adjacent depth positions on the vertical temperature profile line; dividing the local temperature difference by the spatial distance between the two depth positions to obtain the temperature gradient value corresponding to each depth interval; comparing the temperature gradient value with the set threshold and recording all depth intervals where the temperature gradient value exceeds the set threshold; determining the geometric location of the abnormal section based on the spatial coordinates of the depth interval and marking it as the location of the abnormal section. By scanning the temperature gradient segment by segment along the thickness direction, the location of the most drastic temperature change can be automatically located, preventing the problem of stress concentration caused by uneven local temperature transitions from going undetected.

[0013] As a technical solution of this invention, the process of using a laser scanner to acquire the surface morphology features of concrete corresponding to the abnormal cross-section and constructing a concrete thermodynamic digital twin model by combining the real-time temperature distribution field of the concrete core region includes: controlling the laser scanner to perform a gridded scan on the outer surface of the concrete corresponding to the abnormal cross-section, acquiring the three-dimensional coordinates and surface reflection intensity of each grid node; generating a triangular mesh model of the concrete surface based on the three-dimensional coordinates of each grid node; extracting a temperature distribution subfield from the real-time temperature distribution field of the concrete core region adjacent to the abnormal cross-section; mapping the temperature values ​​in the temperature distribution subfield to the corresponding nodes of the concrete surface triangular mesh model to generate a surface geometric model with temperature labels; and calling a thermodynamic simulation engine to construct a concrete thermodynamic digital twin model using the surface geometric model with temperature labels as boundary conditions. By mapping the real surface morphology acquired by laser scanning with the internal temperature field at the node level, the digital twin model not only has a realistic geometric shape but also a thermal load input basis consistent with actual conditions, thereby improving the credibility of transient heat conduction and thermal stress simulation results.

[0014] As a technical solution of the present invention, the process of virtually simulating and extrapolating multiple preset curing temperature curves using a concrete thermodynamic digital twin model to obtain the internal stress prediction cloud map corresponding to each preset curing temperature curve includes: acquiring multiple preset curing temperature curves pre-stored in a curing database, each preset curing temperature curve consisting of multiple time nodes and the target surface temperature and target core temperature corresponding to each time node; sequentially loading the multiple preset curing temperature curves into the boundary conditions of the concrete thermodynamic digital twin model; for each preset curing temperature curve, driving the concrete thermodynamic digital twin model to perform transient heat conduction simulation calculations in a progressive manner according to the time step, obtaining the transient temperature distribution inside the concrete at each time step; calling the thermoelastic constitutive equation at the end of each time step, calculating the thermal strain of each finite element based on the transient temperature distribution and the linear expansion coefficient of the concrete; converting the thermal strain into equivalent nodal forces and then solving the finite element equilibrium equations to obtain the internal stress distribution at each time step; stacking the internal stress distributions at all time steps in chronological order to form an internal stress prediction cloud map corresponding to a preset curing temperature curve. Preferably, the time step for transient heat conduction simulation is adaptively adjusted according to the rate of temperature change of the concrete; the larger the rate of temperature change, the smaller the time step. This adaptive time step mechanism allows for the capture of temperature peaks and troughs with a denser temporal resolution during periods of rapid temperature change, preventing the omission of stress peaks due to excessively large calculation steps. Conversely, during periods of stable temperature, the time step is appropriately increased to improve simulation efficiency, thus ensuring both the accuracy of stress prediction and the reasonable utilization of simulation resources.

[0015] As a technical solution of the present invention, the process of extracting the maximum tensile stress value from the internal stress prediction cloud map and selecting the preset curing temperature curve with the minimum maximum tensile stress value as the target curing temperature curve includes: traversing the stress tensor of each spatial location in the internal stress prediction cloud map at each time step; solving for the principal stresses of the stress tensor at each spatial location to obtain three principal stress values; selecting the largest positive value from the three principal stress values ​​as the maximum tensile stress value at that spatial location at that time step; summarizing the maximum tensile stress values ​​of all spatial locations at all time steps, and taking the maximum value as the global maximum tensile stress value of the internal stress prediction cloud map; comparing the global maximum tensile stress values ​​corresponding to each preset curing temperature curve, and selecting the preset curing temperature curve with the minimum global maximum tensile stress value as the target curing temperature curve. Using the maximum tensile stress in the entire time and space domain as the selection criterion ensures that the selected curing temperature curve has the lowest tensile stress level at the most unfavorable location and the most unfavorable time, thereby minimizing the possibility of concrete cracking.

[0016] As a technical solution of the present invention, the process of controlling the curing equipment to perform segmented differentiated heating or cooling of the surface and interior of a concrete component according to the target curing temperature curve includes: analyzing the target surface temperature and target core temperature corresponding to each time node from the target curing temperature curve; obtaining the layout position of the surface heating pad array and the layout path of the internal circulating water pipe in the curing equipment; dividing the surface of the concrete component into multiple surface temperature control sections according to the layout position of the surface heating pad array, and dividing the interior of the concrete component into multiple internal temperature control sections according to the layout path of the internal circulating water pipe; controlling the surface heating pad array in each surface temperature control section to independently adjust its power according to the target surface temperature; and controlling the valve opening of the internal circulating water pipe in each internal temperature control section to independently adjust its flow rate according to the target core temperature. Through the segmented and independent control of the surface heating pad array and the internal circulating water pipe in space, the heating or cooling intensity of different areas on the same component can be configured differently, and targeted local compensation or suppression treatment can be performed on areas with abnormal temperature gradients. Preferably, when the surface of a concrete component is divided into multiple surface temperature control zones, the boundary of each surface temperature control zone is aligned with the projection outline of the abnormal section on the concrete surface, so that the temperature control zone is accurately aligned with the temperature abnormal area, further improving the targeting of temperature difference control.

[0017] As a technical solution of this invention, during the process of controlling the curing equipment to perform segmented differentiated heating or cooling of the surface and interior of concrete components according to the target curing temperature curve, a feedback correction step is also performed: In each control cycle, the real-time temperature distribution field of the core area of ​​the concrete is re-acquired to obtain the current measured temperature distribution field; the current measured temperature distribution field is compared point-by-point with the expected temperature distribution field at the corresponding time node in the target curing temperature curve to calculate the temperature deviation value of each temperature control segment; when the temperature deviation value of a certain temperature control segment exceeds the allowable deviation range, the power compensation direction and power compensation amount of that temperature control segment are determined according to the sign and magnitude of the temperature deviation value; the output parameters of the heating or cooling actuator corresponding to that temperature control segment are adjusted in real time according to the power compensation amount. This forms a closed-loop control from temperature sensing to compensation execution, enabling any temperature deviation caused by environmental changes or equipment response lag during the curing process to be quickly corrected, maintaining the temperature change of the concrete component always following the target curing temperature curve.

[0018] Furthermore, the process of adjusting the output parameters of the heating or cooling actuator corresponding to the temperature control section in real time according to the power compensation amount includes: identifying whether the current temperature control section is a surface temperature control section or an internal temperature control section; if the current temperature control section is a surface temperature control section and the temperature deviation value is negative, increasing the power supply duty cycle of the surface heating pad array within the surface temperature control section, so that the heating power of the surface heating pad array is increased according to the power compensation amount; if the current temperature control section is a surface temperature control section and the temperature deviation value is positive, decreasing the power supply of the surface heating pad array within the surface temperature control section. The power supply duty cycle is adjusted or some surface heating pads are shut down, causing the heating power of the surface heating pad array to decrease according to the power compensation amount. If the current temperature control section is an internal temperature control section and the temperature deviation value is negative, the pump speed or valve opening of the internal circulating water pipe in that internal temperature control section is increased, causing the heat exchange of the internal circulating water pipe to increase according to the power compensation amount. If the current temperature control section is an internal temperature control section and the temperature deviation value is positive, the pump speed or valve opening of the internal circulating water pipe in that internal temperature control section is decreased, causing the heat exchange of the internal circulating water pipe to decrease according to the power compensation amount. This differentiated compensation method can take corresponding adjustment measures for different actuators on the surface and inside, so that the feedback correction matches the actual physical execution capability and ensures that the compensation process converges quickly.

[0019] As a technical solution of this invention, after the curing equipment performs segmented differentiated heating or cooling on the surface and interior of the concrete component according to the target curing temperature curve, a curing end determination step is also performed: During the curing process, concrete core test blocks are collected at preset intervals, and rebound and ultrasonic wave velocity tests are conducted on the concrete core test blocks to obtain rebound and ultrasonic wave velocity values; the actual compressive strength of the concrete core test block is calculated based on the rebound and ultrasonic wave velocity values; when the actual compressive strength reaches the standard curing strength value corresponding to the design strength grade, a curing end signal is triggered; the time length between the start of curing and the triggering of the curing end signal is recorded as the actual curing cycle, and the actual curing cycle and the corresponding target curing temperature curve are associated and stored in a historical curing case database. Using the combined rebound and ultrasonic method for on-site strength assessment can accurately determine whether the concrete has reached the design maturity without damaging the component, avoiding premature removal of curing equipment or resource waste caused by over-curing. Simultaneously, the accumulated historical curing cases can provide data reference for the pre-selection of curing curves for similar future projects.

[0020] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0021] A temperature sensing network embedded within the concrete structure is used to collect the temperature distribution field of the core area in real time, automatically identifying abnormal cross-sectional locations where the temperature gradient exceeds a set threshold. For the identified abnormal cross-sectional locations, a laser scanner is used to acquire the corresponding concrete surface morphology features, which are then combined with temperature subfields of adjacent areas extracted from the continuous temperature distribution field to construct a thermodynamic digital twin model with realistic surface geometry and temperature labels. A thermodynamic simulation engine is then invoked, using this digital twin model as boundary conditions, to perform virtual simulations on multiple preset curing temperature curves stored in the database. The simulation process employs transient heat conduction and thermoelastic constitutive equations to generate internal stress prediction cloud maps for each curve. The stress tensors at all spatial locations and all time steps in the cloud maps are traversed to solve for the principal stresses and extract the global maximum tensile stress value. The preset curing temperature curve with the smallest global maximum tensile stress value is automatically selected as the target curing temperature curve. This approach deeply binds the development of the maintenance curve with the actual temperature gradient anomaly characteristics of the component, and only builds a refined twin model for the key section to ensure that the stress prediction results are closer to the real dangerous area. By optimizing the candidate curves, the peak tensile stress at the abnormal section location is directly reduced, thus inhibiting the initiation of temperature cracks.

[0022] The control and curing equipment performs differentiated heating or cooling on the surface and interior of concrete components according to the target curing temperature curve, divided into sections. The target surface temperature and target core temperature at each time point are analyzed from the target curing temperature curve. The surface of the concrete component is divided into multiple surface temperature control sections according to the placement of the heating pad array, with the boundaries of these sections aligned with the projected contours of abnormal cross-sections on the concrete surface. The interior of the component is divided into multiple internal temperature control sections according to the routing of the circulating water pipes. The power duty cycle or start / stop of the heating pad array is independently adjusted for each surface temperature control section, and the valve opening or pump speed of the circulating water pipe is independently adjusted for each internal temperature control section. Simultaneously, the measured temperature distribution field is re-acquired and compared with the expected temperature distribution field within each control cycle. The temperature deviation value of each temperature control section is calculated, and the power compensation direction and amount are determined accordingly, allowing for real-time adjustment of the output parameters of the heating or cooling actuators. By implementing independent control and feedback correction for different sections, precise temperature control with surface-internal coordination centered on abnormal cross sections is achieved. This dynamically eliminates unfavorable temperature gradients, prevents local tensile stress in components from exceeding limits, and ensures uniform strength growth of concrete during curing. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0024] Figure 1 This is a schematic diagram of a concrete curing environment control system based on intelligent temperature control.

[0025] Figure 2 This is a flowchart of the identification of abnormal sections of temperature gradient during concrete curing based on fiber Bragg grating sensor networks.

[0026] Figure 3 This is a flowchart of the zone temperature control module's workflow;

[0027] Figure 4 This is a flowchart for the closed-loop feedback correction and curing end determination of temperature control in concrete components. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] See Figure 1 This invention provides a concrete curing environment control system based on intelligent temperature control, including a temperature sensing module, an anomaly identification module, a twin modeling module, a simulation and deduction module, a curve optimization module, and a zoned temperature control module. The temperature sensing module collects the real-time temperature distribution field of the concrete core area through a temperature sensing network embedded inside the concrete component. The anomaly identification module identifies the locations of abnormal sections where the temperature gradient exceeds a set threshold based on the real-time temperature distribution field of the concrete core area. The twin modeling module uses a laser scanner to obtain the concrete surface morphology features corresponding to the abnormal section locations and constructs a concrete thermodynamic digital twin model based on the real-time temperature distribution field of the concrete core area. The simulation and deduction module performs virtual simulation and deduction on multiple preset curing temperature curves using the concrete thermodynamic digital twin model, obtaining internal stress prediction cloud maps corresponding to each preset curing temperature curve. The curve optimization module extracts the maximum tensile stress value from the internal stress prediction cloud map and selects the preset curing temperature curve with the smallest maximum tensile stress value as the target curing temperature curve. The zoned temperature control module controls the curing equipment to perform differentiated heating or cooling of the surface and interior of the concrete component according to the target curing temperature curve.

[0030] Example 1

[0031] In specific implementation, please refer to Figure 2 In a concrete curing environment control system based on intelligent temperature control, the temperature sensing module and the anomaly identification module work together. The temperature sensing module acquires the real-time temperature distribution field of the core area of ​​the concrete, reflecting the distribution of the heat of hydration, through a temperature sensing network pre-embedded inside the concrete component. The anomaly identification module receives the real-time temperature distribution field of the core area of ​​the concrete, traverses all spatial points in the distribution field, and identifies the locations of abnormal sections where the temperature gradient exceeds a set threshold.

[0032] In practice, the process of collecting the real-time temperature distribution field of the core area of ​​concrete through a temperature sensing network embedded inside the concrete component includes five implementation steps: obtaining sensor identification codes and spatial coordinates, polling and reading wavelength offsets, calculating measured temperature values, associating and storing discrete temperature point cloud data, and generating a continuous distribution field through three-dimensional interpolation fitting.

[0033] In acquiring the identification codes and spatial coordinates of multiple fiber Bragg grating temperature sensors pre-embedded at different depths in concrete components, during the concrete pouring stage, these sensors are bound and fixed to the reinforcing steel frame at different depths according to a pre-defined spatial layout scheme. Each fiber Bragg grating temperature sensor is assigned a unique identification code at the factory, using an alphanumeric string format to uniquely distinguish each sensor within the data acquisition network. Using a total station, the three-dimensional spatial coordinates of each fiber Bragg grating temperature sensor in the concrete component coordinate system are measured. A mapping relationship is established between each spatial coordinate and its corresponding fiber Bragg grating temperature sensor identification code, forming a sensor layout information table. This table is stored in the non-volatile memory of the temperature sensing module.

[0034] In the implementation of sequentially polling and reading the wavelength offset of multiple fiber Bragg grating temperature sensors at preset time intervals, the temperature sensing module has a built-in timer that generates a polling trigger signal at the preset time interval. The preset time interval is set based on the changing characteristics of the concrete hydration reaction rate. In the early curing stage when the hydration reaction is intense, the preset time interval is set to a fixed value between 5 and 15 seconds; in the later curing stage when the hydration reaction slows down, the preset time interval is set to a fixed value between 30 and 60 seconds. After each polling trigger signal is generated, the temperature sensing module sends a sequential reading command to the fiber Bragg grating demodulator. The fiber Bragg grating demodulator, according to the identification code sequence recorded in the sensor deployment information table, emits a broadband light source signal to each fiber Bragg grating temperature sensor, receives the narrowband reflected light signal from each fiber Bragg grating temperature sensor, demodulates the center wavelength value of the reflected light signal, and subtracts the real-time measured center wavelength value from the initial center wavelength value of each fiber Bragg grating temperature sensor before installation to obtain the wavelength offset of each fiber Bragg grating temperature sensor in the current polling cycle.

[0035] In the process of calculating the measured temperature value at the location of each fiber Bragg grating temperature sensor based on the wavelength offset, the measured temperature value T for each fiber Bragg grating temperature sensor is calculated as follows:

[0036]

[0037] in: This represents the wavelength shift measured by the fiber Bragg grating temperature sensor within the current polling period, in picometers. This represents the temperature sensitivity coefficient of the fiber Bragg grating temperature sensor. The value was obtained through constant temperature water bath calibration experiments on this batch of fiber grating temperature sensors. During the calibration experiment, the wavelength shift corresponding to the known standard temperature was recorded, and the slope value was obtained using least-squares linear fitting. The value of , The typical value range is 9.8 picometers per degree Celsius to 10.2 picometers per degree Celsius; This represents the measured temperature value at the location of the fiber Bragg grating temperature sensor, obtained through inverse calculation, in degrees Celsius.

[0038] In the process of associating and storing measured temperature values ​​with their corresponding spatial coordinates to generate discrete temperature point cloud data, the temperature sensing module creates a discrete temperature record in memory. Each discrete temperature record contains four data fields: the identifier code of the fiber Bragg grating temperature sensor, the current polling timestamp, the back-calculated measured temperature value, and the spatial coordinates corresponding to the fiber Bragg grating temperature sensor. All discrete temperature records corresponding to the fiber Bragg grating temperature sensors are aggregated into a discrete temperature data set, which constitutes the discrete temperature point cloud data. Each record in the discrete temperature point cloud data represents a temperature scalar value at a discrete point in space.

[0039] In the process of obtaining a real-time temperature distribution field of the continuously distributed concrete core area by performing three-dimensional interpolation fitting on discrete temperature point cloud data, the temperature sensing module calls a three-dimensional interpolation algorithm library. The discrete temperature point cloud data is used as input, and a regular three-dimensional grid covering the entire area of ​​the concrete component is set. The grid spacing in the length, width, and thickness directions is selected according to the interpolation accuracy requirements. A radial basis function interpolation algorithm is used, with the spatial coordinates in each discrete temperature point cloud data point as the center and the variable influence radius as the parameter, to construct a set of radial basis functions. The weight coefficients of each radial basis function are determined by solving a system of linear equations. After solving for the weight coefficients, the temperature value is interpolated at each grid node position in the regular three-dimensional grid. The interpolation results of all grid nodes are arranged according to their spatial positions to form a real-time temperature distribution field of the continuously distributed concrete core area in the form of a three-dimensional matrix.

[0040] In practice, the process of identifying the location of abnormal sections where the temperature gradient exceeds the set threshold based on the real-time temperature distribution field of the concrete core area includes five implementation steps: extracting vertical temperature profile lines, calculating local temperature differences, calculating temperature gradient values, comparing with the set threshold and recording the depth range, and marking the geometric location of abnormal sections.

[0041] In the process of extracting vertical temperature profiles along the thickness of concrete components from the real-time temperature distribution field of a continuously distributed concrete core region, the anomaly detection module selects a fixed planar coordinate position along the length and width directions of the concrete component within the real-time temperature distribution field. It then extracts the temperature values ​​of all grid nodes corresponding to that planar coordinate position from top to bottom along the thickness direction of the concrete component in a regular three-dimensional grid. The extracted temperature values ​​are arranged in ascending order of depth to form a one-dimensional vertical temperature profile. Within the planar range of the concrete component, the planar coordinate position is moved along the length and width directions at a preset sampling interval, and the above extraction operation is repeated to obtain multiple vertical temperature profiles covering the entire planar range of the concrete component.

[0042] In the implementation of calculating the local temperature difference between two adjacent depth positions on a vertical temperature profile, for each vertical temperature profile, the i-th depth position on the vertical temperature profile is... The temperature value at that location is denoted as , set the (i+1)th depth position The temperature value at that location is denoted as Calculate the local temperature difference Local temperature difference The calculation method is as follows ,in and Let be the coordinates of two adjacent depth positions on the vertical temperature profile. In the step of dividing the local temperature difference by the spatial distance between the two depth positions to obtain the temperature gradient value corresponding to each depth interval, for the depth interval formed by the i-th depth position and the (i+1)-th depth position on the vertical temperature profile, the spatial distance between the two depth positions is... The calculation method is as follows Temperature gradient value The calculation method is as follows Temperature gradient value The unit is degrees Celsius per meter. By traversing all adjacent depth pairs along the vertical temperature profile using the above calculation method, a set of temperature gradient values ​​corresponding to the vertical temperature profile is obtained.

[0043] In the process of comparing temperature gradient values ​​with a set threshold and recording the depth range where all temperature gradient values ​​exceed the set threshold, the anomaly detection module pre-stores a set threshold. This threshold is determined based on the results of thermal cracking sensitivity tests on concrete materials. Under laboratory conditions, a known temperature gradient is applied to concrete specimens with the same mix proportion, and the critical temperature gradient corresponding to the moment surface cracking is monitored. This critical temperature gradient is multiplied by a safety factor, which is then used as the set threshold. The safety factor ranges from 0.7 to 0.9. The anomaly detection module iterates through all temperature gradient value sequences corresponding to all vertical temperature profiles, comparing each temperature gradient value with the set threshold. When a temperature gradient value exceeds the set threshold, the module records the start and end depth coordinates of the corresponding depth range in the thickness direction.

[0044] In the process of determining the geometric location of the abnormal section based on its spatial coordinates within a depth interval and marking it as the abnormal section location, the anomaly identification module combines the start and end depth coordinates of the depth interval exceeding a set threshold with the planar coordinates of the corresponding vertical temperature profile line extracted from that depth interval to obtain the spatial location range of the abnormal section in the three-dimensional space of the concrete member. The abnormal section is a spatial region with a finite thickness, the thickness of which is equal to the length of the depth interval exceeding the set threshold. The planar range of the abnormal section is jointly determined by the planar coordinates of multiple adjacent vertical temperature profile lines corresponding to that depth interval. The anomaly identification module packages the spatial location range of the abnormal section and the temperature gradient values ​​corresponding to each depth interval within the abnormal section into abnormal section location data, which is then sent to the twin modeling module via the data bus.

[0045] Example 2

[0046] In practice, the twin modeling module receives the abnormal section location data sent by the anomaly identification module, uses a laser scanner to obtain the concrete surface morphology features corresponding to the abnormal section location, and combines the real-time temperature distribution field of the core area of ​​the concrete provided by the temperature sensing module to construct a digital twin model of concrete thermodynamics.

[0047] In the process of controlling the laser scanner to perform a gridded scan of the concrete outer surface corresponding to the abnormal section location, and to obtain the three-dimensional coordinates and surface reflection intensity of each grid node, the twin modeling module extracts the spatial range of the abnormal section location from the abnormal section location data. The spatial range of the abnormal section location consists of a planar range and start and end depth coordinates along the thickness direction. The twin modeling module selects the boundary plane closest to the outer surface of the concrete component within the spatial range of the abnormal section location, and defines the surface area corresponding to this boundary plane on the outer surface of the concrete component as the scanning target area for the laser scanner. The twin modeling module sends a scanning command to the laser scanner, which includes the boundary contour coordinates of the scanning target area.

[0048] After receiving a scanning command, the laser scanner adjusts the pointing angle of its scanning head so that the optical axis of the scanning head is perpendicular to the center of the target area. The laser scanner uses a line scanning method to scan the target area line by line, with each line consisting of multiple scanning line segments. The scanning resolution is controlled by two parameters: the line spacing and the sampling point spacing on the same line. The line spacing refers to the vertical distance between two adjacent parallel scanning lines, while the sampling point spacing refers to the horizontal distance between two adjacent sampling points on the same scanning line.

[0049] In practice, when the laser scanner performs a gridded scan, the scanning resolution increases with the temperature gradient value at the location of the anomalous section. The twin modeling module extracts the temperature gradient values ​​corresponding to each depth interval within the anomalous section location from the anomalous section location data, selecting the maximum value among all temperature gradient values ​​as the representative temperature gradient value. (Scan line spacing...) The calculation method is as follows:

[0050]

[0051] in: This indicates the spacing between the reference scan lines, which is 5 mm. This indicates that a threshold value is set. The threshold value is used in the anomaly detection module to determine whether the temperature gradient exceeds the limit. This represents the temperature gradient value, which is the maximum value among all temperature gradient values ​​within the location of the abnormal section. When the calculated scan line spacing is less than the minimum allowable scan line spacing, the minimum allowable scan line spacing is used, which is 1 mm. The spacing between sampling points on the scan line is the same as the scan line spacing. Using this method, the larger the temperature gradient, the smaller the scan line spacing and the spacing between sampling points on the scan line, resulting in higher scanning resolution.

[0052] The laser scanner emits a laser pulse at each sampling point and receives the laser echo signal reflected from the outer surface of the concrete. It measures the distance from the sampling point to the laser scanner using the time-of-flight method and, combined with the current pointing angle of the laser scanner head, obtains the three-dimensional coordinates of the sampling point in the coordinate system of the concrete component through trigonometric transformation. Simultaneously, the laser scanner records the intensity value of the reflected laser echo signal, using this intensity value as the surface reflection intensity. The laser scanner packages the three-dimensional coordinates and surface reflection intensity of each sampling point into a grid node data entry. All grid node data entries constitute a grid node set, which the twin modeling module receives as the result of the meshed scanning.

[0053] In the implementation of generating a triangular mesh model of the concrete surface based on the 3D coordinates of each mesh node, the twin modeling module uses the 3D coordinates of all mesh nodes in the mesh node set as the input point cloud. It then denoises the input point cloud, removing outliers that are significantly deviated from the outer surface of the concrete. The Delaunay triangulation algorithm is used to divide the denoised input point cloud into triangular meshes. The Delaunay triangulation algorithm generates a set of triangular facets based on maximizing the minimum angle, with each facet consisting of the indices of three mesh nodes. The twin modeling module combines all the triangular facets together to form a closed triangular mesh model of the concrete surface. This model accurately describes the 3D geometry of the outer surface of the concrete corresponding to the location of abnormal cross-sections.

[0054] In the implementation of extracting a temperature distribution subfield from the real-time temperature distribution field of the concrete core region, adjacent to the location of the abnormal section, the twin modeling module, based on the spatial location range of the abnormal section, extends outward by a preset extension distance along the thickness direction of the concrete member and by the same preset extension distance along the plane direction of the concrete member, resulting in an expanded cuboid spatial range. This expanded cuboid spatial range is defined as the region adjacent to the abnormal section location. The preset extension distance is 0.2 times the thickness of the concrete member section. The twin modeling module retrieves the temperature values ​​of all grid nodes located within the region adjacent to the abnormal section location from the continuously distributed real-time temperature distribution field of the concrete core region provided by the temperature sensing module, according to their coordinate range. These temperature values, along with their spatial coordinates, are extracted to form the temperature distribution subfield.

[0055] In the process of mapping temperature values ​​from the temperature distribution subfield to corresponding nodes of the concrete surface triangular mesh model to generate a surface geometry model with temperature labels, for each mesh node in the concrete surface triangular mesh model, the twin modeling module takes the 3D coordinates of that mesh node, finds the temperature sampling point in the temperature distribution subfield that is closest to the 3D coordinate space, and assigns the temperature value of that sampling point to the mesh node as a temperature label. If a mesh node in the concrete surface triangular mesh model is located outside the spatial range of the temperature distribution subfield, the boundary temperature value of the temperature distribution subfield closest to that mesh node is used as the temperature label. After assigning temperature labels to all mesh nodes, each mesh node of the concrete surface triangular mesh model carries a temperature label value, forming a surface geometry model with temperature labels.

[0056] In the implementation of building a digital twin model of concrete thermodynamics using a surface geometry model with temperature labels as boundary conditions, the twin modeling module initiates the thermodynamic simulation engine and imports the surface geometry model with temperature labels into the engine's preprocessing module. Based on the surface geometry model with temperature labels, the preprocessing module of the thermodynamic simulation engine offsets inward to generate a three-dimensional solid geometry model of the concrete component. The offset thickness is equal to the thickness of the concrete component corresponding to the abnormal cross-section. The preprocessing module then performs finite element mesh generation on the three-dimensional solid geometry model, generating tetrahedral or hexahedral volume element meshes. The spatial coordinates of the nodes in the volume element mesh at the surface coincide with the spatial coordinates of the mesh nodes in the triangular mesh model of the concrete surface.

[0057] Example 3

[0058] In practical implementation, the simulation module receives the concrete thermodynamic digital twin model constructed by the twin modeling module, reads multiple preset curing temperature curves from the curing database, and sequentially loads each preset curing temperature curve into the concrete thermodynamic digital twin model for virtual simulation, outputting the internal stress prediction cloud map corresponding to each preset curing temperature curve. The curve selection module receives all internal stress prediction cloud maps, extracts the global maximum tensile stress value from each internal stress prediction cloud map, compares the global maximum tensile stress values ​​corresponding to all preset curing temperature curves, and selects the preset curing temperature curve with the smallest global maximum tensile stress value as the target curing temperature curve.

[0059] In the implementation of acquiring multiple preset curing temperature curves pre-stored in the curing database, the curing database is a relational database built on a non-volatile storage medium, pre-storing multiple preset curing temperature curves. Each preset curing temperature curve consists of multiple time nodes and the target surface temperature and target core temperature corresponding to each time node. The time nodes are arranged in ascending order at fixed time intervals, with the curing start time as zero. The time interval between adjacent time nodes is 1 hour. The target surface temperature refers to the set temperature value that the outer surface of the concrete component should reach at the corresponding time node, and the target core temperature refers to the set temperature value that the center of the concrete component should reach at the corresponding time node. The differences between the multiple preset curing temperature curves are reflected in the different rates of rise and fall of the target surface temperature and target core temperature over time, the duration of constant temperature maintenance, and the upper limit value of the maximum temperature, which correspond to different curing strategies, including rapid heating curing strategy, slow heating curing strategy, and segmented constant temperature curing strategy.

[0060] In the implementation step of sequentially loading multiple preset curing temperature curves into the boundary conditions of the concrete thermodynamic digital twin model, the simulation derivation module establishes a simulation task queue, adding multiple preset curing temperature curves from the curing database to the queue in storage order. The simulation derivation module retrieves the first preset curing temperature curve from the queue and, in the boundary condition setting interface of the concrete thermodynamic digital twin model, binds the target surface temperature value sequence from the preset curing temperature curve to the temperature boundary conditions of all surface nodes in the concrete thermodynamic digital twin model, and binds the target core temperature value sequence from the preset curing temperature curve to the node temperature constraint conditions of the internal core region in the concrete thermodynamic digital twin model.

[0061] In the simulation process of obtaining the transient temperature distribution inside the concrete at each time step by driving the concrete thermodynamic digital twin model to perform transient heat conduction simulation calculations for each preset curing temperature curve in a progressively increasing time step manner, the simulation derivation module divides the total time length of the preset curing temperature curve into multiple simulation time steps. The simulation time step is adaptively adjusted according to the concrete temperature change rate; the larger the temperature change rate, the smaller the simulation time step. The temperature change rate is calculated as follows: at the beginning of each simulation time step, the absolute value of the difference between the target surface temperature value at the beginning of the current simulation time step and the target surface temperature value at the beginning of the previous simulation time step is obtained. This absolute value of the difference is divided by the time interval between the beginning of the two simulation time steps to obtain the temperature change rate corresponding to the current simulation time step. The simulation derivation module pre-stores a mapping table between the temperature change rate and the simulation time step. The mapping table divides the temperature change rate into five intervals from small to large, corresponding to simulation time step values ​​of 60 seconds, 30 seconds, 15 seconds, 5 seconds, and 1 second, respectively. When the rate of temperature change falls into the highest range, the simulation time step is 1 second; when the rate of temperature change falls into the lowest range, the simulation time step is 60 seconds.

[0062] The simulation module calls the transient heat conduction solver of the thermodynamic simulation engine. It uses the internal temperature distribution of the concrete at the start of the current simulation time step as the initial condition, and the target surface temperature and target core temperature at the end of the current simulation time step as the boundary conditions. It solves the transient heat conduction governing equation, which considers the internal heat source term of the concrete's hydration heat. The heat generation rate of this term is calculated by interpolation using the equivalent age method based on the adiabatic temperature rise experimental data of the concrete. After solving, the transient temperature distribution inside the concrete at the end of the current simulation time step is obtained. This transient temperature distribution is represented as the nodal temperature value of each finite element in the concrete thermodynamic digital twin model. The simulation module saves the transient temperature distribution of the current simulation time step and uses it as the initial condition for the next simulation time step. It iteratively executes the simulation in a progressive manner according to the simulation time step until all time nodes of the preset curing temperature curve are covered, resulting in a sequence of transient temperature distributions for all simulation time steps corresponding to the preset curing temperature curve.

[0063] At the end of each simulation time step, the thermoelastic constitutive equation is invoked to calculate the thermal strain of each finite element based on the transient temperature distribution and the linear expansion coefficient of concrete. In this implementation, for each finite element in the concrete thermodynamic digital twin model, the simulation derivation module obtains the temperature difference between the finite element at the end of the current simulation time step and at the beginning of the current simulation time step. The temperature difference is calculated by subtracting the arithmetic mean of the temperatures at the beginning of the current simulation time step from the arithmetic mean of the temperatures at all nodes of the finite element at all nodes. The thermal strain is calculated by multiplying the temperature difference by the linear expansion coefficient of concrete, which is 1.0 × 10^(-5) degrees Celsius. The thermal strain of each finite element is a third-order tensor, with the three normal strain components having equal values ​​equal to the product of the temperature difference and the linear expansion coefficient, and the three shear strain components having zero values.

[0064] In the process of converting thermal strain into equivalent nodal forces and solving the finite element equilibrium equations to obtain the internal stress distribution at each simulation time step, the simulation derivation module calls the static solver of the thermodynamic simulation engine. The static solver reads the thermal strain of each finite element, substitutes the thermal strain as the initial strain into the elastic constitutive matrix of each finite element, and calculates the equivalent nodal force vector generated by the thermal strain of each finite element. The equivalent nodal force vector represents the force vector that needs to be applied at each node of the finite element when the thermal strain constraint of the finite element is restored to zero. The static solver assembles the equivalent nodal force vectors of all finite elements according to the node number to form a global equivalent nodal force matrix, assembles the global stiffness matrix with the displacement constraint conditions of the concrete thermodynamic digital twin model, and solves the linear equations to obtain the nodal displacement vector at the current simulation time step. The static solver inversely calculates the strain tensor and stress tensor of each finite element based on the nodal displacement vector. The stress tensor contains three normal stress components and three shear stress components, thus obtaining the internal stress distribution at the current simulation time step. The internal stress distribution is represented in the form of the stress tensor of each finite element in the concrete thermodynamic digital twin model.

[0065] In the implementation of stacking the internal stress distributions under all simulation time steps in chronological order to form an internal stress prediction cloud map corresponding to a preset curing temperature curve, the simulation derivation module creates a four-dimensional data container. The four dimensions of the four-dimensional data container are the three coordinate components of the spatial position of the finite element in the length, width, and thickness directions, and the time dimension of the simulation time step. The simulation derivation module stores the stress tensor of each finite element in the internal stress distribution under each simulation time step into the corresponding spatial grid position of the four-dimensional data container according to the spatial position of the finite element, arranged frame by frame in the time dimension according to the chronological order of the simulation time steps. After storage, the four-dimensional data container constitutes an internal stress prediction cloud map corresponding to a preset curing temperature curve. The internal stress prediction cloud map records the stress tensor information of all spatial positions of the concrete thermodynamic digital twin model at all times under the action of the preset curing temperature curve. The simulation derivation module sends the internal stress prediction cloud map to the curve selection module, and then retrieves the next preset curing temperature curve from the simulation task queue, repeating the above virtual simulation derivation process until all preset curing temperature curves in the simulation task queue have completed simulation calculations.

[0066] In the process of traversing the internal stress prediction cloud map at each spatial location under each simulation time step to realize the stress tensor, the curve optimization module receives the internal stress prediction cloud map corresponding to a preset curing temperature curve sent by the simulation derivation module. The internal stress prediction cloud map contains... Spatial location and Each simulation time step, total × Each stress tensor data unit. The curve optimization module establishes a double loop structure, with the outer loop traversing all... Each spatial location, the inner loop iterates through all locations. For each simulation time step, the stress tensor at each spatial location is extracted sequentially at each simulation time step.

[0067] In the process of solving for the principal stresses of the stress tensor at each spatial location to obtain the three principal stress values, for a stress tensor selected during the traversal, the stress tensor matrix is ​​a 3×3 symmetric matrix, with the diagonal elements representing normal stress components and the off-diagonal elements representing shear stress components. The curve optimization module calls the eigenvalue solving algorithm to solve for the three eigenvalues ​​of the stress tensor matrix, and labels the three eigenvalues ​​as the first principal stress value, the second principal stress value, and the third principal stress value, respectively, arranged in descending order of their algebraic values.

[0068] In the process of selecting the largest positive value from the three principal stress values ​​as the maximum tensile stress value at the given spatial location within the simulation time step, the curve optimization module checks the sign of the largest first principal stress value. When the first principal stress value is positive, it is taken as the maximum tensile stress value at the given spatial location within the simulation time step; when the first principal stress value is zero or negative, the maximum tensile stress value is recorded as zero, indicating that there is no tensile stress at the given spatial location within the simulation time step. This approach aligns with the engineering requirements where concrete has relatively low tensile strength and only tensile stress failure is a concern.

[0069] In the process of summarizing the maximum tensile stress values ​​at all spatial locations across all simulation time steps and selecting the maximum value as the global maximum tensile stress value for the internal stress prediction contour map, the curve optimization module will use all the values ​​recorded during the aforementioned double loop traversal. × A one-dimensional array is formed from the maximum tensile stress values. This array is sorted in descending order, and the first element with the largest value is taken as the global maximum tensile stress value of the internal stress prediction contour map. The curve optimization module establishes a correlation record between the curve number of the currently processed preset curing temperature curve and the global maximum tensile stress value.

[0070] In the process of comparing the global maximum tensile stress values ​​corresponding to each preset curing temperature curve and selecting the preset curing temperature curve with the smallest global maximum tensile stress value as the target curing temperature curve, the curve selection module maintains a minimum tensile stress recording variable and an optimal curve number recording variable. After the internal stress prediction cloud map of the first preset curing temperature curve is processed, the global maximum tensile stress value corresponding to the first preset curing temperature curve is stored in the minimum tensile stress recording variable, and the curve number of the first preset curing temperature curve is stored in the optimal curve number recording variable. After the internal stress prediction cloud map of each subsequent preset curing temperature curve is processed, the global maximum tensile stress value corresponding to the current preset curing temperature curve is compared with the value in the minimum tensile stress recording variable. If the global maximum tensile stress value corresponding to the current preset curing temperature curve is less than the value in the minimum tensile stress recording variable, then the minimum tensile stress recording variable is updated to the global maximum tensile stress value corresponding to the current preset curing temperature curve, and the optimal curve number recording variable is updated to the curve number of the current preset curing temperature curve. Once all the internal stress prediction cloud maps corresponding to the preset curing temperature curves have been processed, the preset curing temperature curve corresponding to the curve number recorded in the optimal curve number recording variable is selected as the target curing temperature curve. The curve selection module then sends the target curing temperature curve to the zone temperature control module.

[0071] Example 4

[0072] In specific implementation, please refer to Figure 3 The zone temperature control module receives the target curing temperature curve sent by the curve optimization module, and at the same time reads the pre-stored curing equipment configuration information to complete the zoned differentiated heating or cooling control of the surface and interior of the concrete component.

[0073] In the process of parsing the target surface temperature and target core temperature corresponding to each time node from the target curing temperature curve, the target curing temperature curve is stored in the form of a structured data table. Each row of the data table corresponds to a time node and includes a time offset field, a target surface temperature field, and a target core temperature field. The zone temperature control module reads the entire data table of the target curing temperature curve into a two-dimensional array in memory, traverses the two-dimensional array row by row, extracts the target surface temperature value and target core temperature value corresponding to each time node, and generates a target surface temperature sequence and a target core temperature sequence. Each element in the target surface temperature sequence is a key-value pair of a time offset and a corresponding target surface temperature value, and each element in the target core temperature sequence is a key-value pair of a time offset and a corresponding target core temperature value.

[0074] In the process of obtaining the placement location of the surface heating pad array and the layout path of the internal circulating water pipes in the curing equipment, the curing equipment configuration information is stored in a curing equipment configuration database, which includes a surface heating pad placement table and an internal circulating water pipe placement table. Each record in the surface heating pad placement table includes a heating pad identifier, the plane coordinates of the heating pad's geometric center in the coordinate system of the concrete component surface, the effective heating area of ​​the heating pad, and the maximum rated power of the heating pad. The zone temperature control module reads all records in the surface heating pad placement table to obtain the placement location information of the surface heating pad array. Each record in the internal circulating water pipe placement table includes a valve identifier for a water pipe control valve, the three-dimensional coordinates of the pipe node where the valve is located, the length of the upstream and downstream pipe sections controlled by the valve, and the maximum flow rate when the valve is fully open. The zone temperature control module reads all records in the internal circulating water pipe placement table to obtain the layout path information of the internal circulating water pipes. The layout path of the internal circulating water pipes is formed by connecting the three-dimensional coordinates of all water pipe control valve pipe nodes sequentially according to the water flow direction.

[0075] In the implementation of dividing the surface of a concrete component into multiple surface temperature control zones based on the placement of the surface heating pad array, the zone temperature control module uses the geometric center of each surface heating pad in the array as a reference, combined with the effective heating area of ​​the heating pad, to divide the initial surface temperature control zone on the geometric model of the concrete component surface corresponding to that surface heating pad. The boundary of each initial surface temperature control zone is determined by Voronoi division according to the perpendicular bisector of the line connecting the geometric centers of adjacent heating pads. After the initial division is completed, the zone temperature control module superimposes the projected contour line of the abnormal section location on the concrete surface onto the geometric model of the concrete component surface. The projection contour line is obtained as follows: the zone temperature control module obtains the spatial location range data of the abnormal section location from the abnormality identification module, and projects the abnormal section location vertically along the thickness direction of the concrete component onto the outer surface of the concrete component, obtaining a closed planar curve, which is the projection contour line. The zone temperature control module checks whether the boundary of each initial surface temperature control zone intersects with the projection contour line. When an intersection exists, the zone temperature control module uses the projected contour line as the new boundary of the initial surface temperature control segment, dividing the initial surface temperature control segment into two independent surface temperature control segments. The boundary of each segment is aligned with the projected contour line. When no intersection exists, the initial surface temperature control segment is directly used as the final determined surface temperature control segment. During the segmentation process, the surface heating pad corresponding to the segmented initial surface temperature control segment is split into two independently controlled heating units, each configured with a different heating pad identifier, and assigned to the two segmented surface temperature control segments.

[0076] In the implementation of dividing the interior of a concrete component into multiple internal temperature-controlled zones based on the layout path of the internal circulating water pipes, the zone temperature control module uses the valve identifier of each water pipe control valve along the internal circulating water pipe layout path as the basic unit for segment division. For each water pipe control valve, the zone temperature control module extends along the internal circulating water pipe layout path, centering on the three-dimensional coordinates of the pipe node where the water pipe control valve is located, and extends upwards and downwards by half the length of the upstream and downstream pipe segments controlled by the water pipe control valve, obtaining a pipe segment spatial range. This pipe segment spatial range and the concrete volume it encloses are defined as an internal temperature-controlled zone. The internal temperature-controlled zones corresponding to two adjacent water pipe control valves share a boundary at the interface. After the division is completed, the entire internal area of ​​the concrete component is divided into multiple non-overlapping and completely covered internal temperature-controlled zones, with each internal temperature-controlled zone uniquely corresponding to a water pipe control valve and the internal circulating water pipe segment it controls.

[0077] In the implementation of independently adjusting the power of the surface heating pad array within each surface temperature control zone according to the target surface temperature, the zone temperature control module starts a periodic timer with a period set to 30 seconds. At the beginning of each cycle, the zone temperature control module retrieves the target surface temperature value at the corresponding time offset from the target surface temperature sequence based on the difference between the current system time and the curing start time. For each surface temperature control zone, the zone temperature control module reads the current measured surface temperature fed back by the temperature sensing module embedded below the corresponding surface of the surface temperature control zone. The zone temperature control module calculates the temperature deviation value of the current surface temperature control zone, which is the target surface temperature value corresponding to the current surface temperature control zone minus the current measured surface temperature. Based on the temperature deviation value, the zone temperature control module calculates the power supply duty cycle adjustment of the surface heating pads within the current surface temperature control zone. The power supply duty cycle adjustment is linearly proportional to the temperature deviation value, and the proportionality coefficient is predetermined through calibration experiments of the thermal response characteristics of the surface heating pads. The zone temperature control module applies the calculated power duty cycle adjustment to the power controller of the surface heating pad corresponding to the current surface temperature control zone, thereby achieving independent power adjustment.

[0078] In the implementation of independent flow regulation of the valve opening of the internal circulating water pipes within each internal temperature control zone according to the target core temperature, within each cycle, the zone temperature control module retrieves the target core temperature value at the corresponding time offset from the target core temperature sequence based on the difference between the current system time and the curing start time. For each internal temperature control zone, the zone temperature control module reads the current measured core temperature fed back by the temperature sensing module embedded in the corresponding concrete core area of ​​the internal temperature control zone. The zone temperature control module calculates the core temperature deviation value of the current internal temperature control zone, which is the target core temperature value corresponding to the current internal temperature control zone minus the current measured core temperature. Based on the core temperature deviation value, the zone temperature control module calculates the valve opening increment of the water pipe control valve corresponding to the current internal temperature control zone. The valve opening increment is calculated as follows: When the core temperature deviation is positive, it indicates that the measured core temperature is too low and requires heating. The control water pipe reduces the valve opening to slow down the refrigerant circulation, and the valve opening increment is negative. When the core temperature deviation is negative, it indicates that the measured core temperature is too high and requires cooling. The control water pipe increases the valve opening to accelerate the refrigerant circulation, and the valve opening increment is positive. The valve opening increment is linearly proportional to the absolute value of the core temperature deviation, and the proportionality coefficient is predetermined through a calibration experiment of the internal circulating water pipe's heat exchange characteristics. The zone temperature control module sends the calculated valve opening increment to the stepper motor driver of the corresponding water pipe control valve. The stepper motor driver drives the water pipe control valve to rotate by the corresponding angle, completing the independent flow regulation of the valve opening.

[0079] Example 5

[0080] In specific implementation, please refer to Figure 4 The zoned temperature control module controls the curing equipment to perform differentiated heating or cooling of the surface and interior of concrete components according to the target curing temperature curve. Simultaneously, a feedback correction step is executed to form a closed-loop temperature regulation mechanism. The feedback correction step includes four implementation stages: re-acquiring the real-time temperature distribution field of the concrete core area, calculating the temperature deviation value, determining the power compensation direction and amount, and adjusting the output parameters of the heating or cooling actuators in real time. After the curing process is completed, the system also executes a curing end determination step, which includes four implementation stages: collecting concrete core test blocks and performing rebound and ultrasonic wave velocity tests, converting the actual compressive strength, triggering the curing end signal, and associating and storing the actual curing cycle.

[0081] In the feedback correction step, the real-time temperature distribution field of the concrete core area is re-acquired within each control cycle to obtain the current measured temperature distribution field. In this process, the zone temperature control module has a built-in control cycle timer. The cycle setting of the control cycle timer is synchronized with the cycle setting of the periodic timer in Example 4, both being 30 seconds. At each control cycle timer trigger moment, the zone temperature control module sends a re-acquisition command to the temperature sensing module. Upon receiving the re-acquisition command, the temperature sensing module executes the same acquisition process as in Example 1: it sequentially polls and reads the wavelength offsets of multiple fiber optic temperature sensors embedded at different depths in the concrete component according to a preset time interval. Based on the wavelength offsets, it calculates the measured temperature value at the location of each fiber optic temperature sensor. It then associates and stores the measured temperature value with the corresponding spatial coordinates to generate discrete temperature point cloud data. Three-dimensional interpolation fitting is performed on the discrete temperature point cloud data to obtain the continuously distributed real-time temperature distribution field of the concrete core area under the current control cycle. This real-time temperature distribution field is the current measured temperature distribution field. The temperature sensing module sends the current measured temperature distribution field back to the zone temperature control module via the data bus.

[0082] In the process of comparing the current measured temperature distribution field with the expected temperature distribution field at the corresponding time node in the target curing temperature curve point by point and calculating the temperature deviation value of each temperature control segment, the zone temperature control module finds the target surface temperature and target core temperature at the corresponding time node in the target curing temperature curve based on the difference between the current system time and the curing start time. The zone temperature control module constructs the expected temperature distribution field for the corresponding time node based on the target surface temperature and target core temperature. The construction method of the expected temperature distribution field is as follows: in the surface area of ​​the concrete component, the temperature value of each spatial location point in the expected temperature distribution field is assigned as the target surface temperature; in the core area of ​​the concrete component, the temperature value of each spatial location point in the expected temperature distribution field is assigned as the target core temperature; in the transition area between the surface area and the core area, the temperature value in the expected temperature distribution field gradually changes from the target surface temperature to the target core temperature using a linear interpolation method.

[0083] The zone temperature control module spatially aligns the current measured temperature distribution field with the expected temperature distribution field, ensuring that both distribution fields use the same three-dimensional mesh division method and the same spatial coordinate system. During point-by-point comparison, the zone temperature control module traverses each mesh node in the three-dimensional mesh, extracts the measured temperature value of that mesh node from the current measured temperature distribution field, extracts the expected temperature value of that mesh node from the expected temperature distribution field, and calculates the individual node temperature deviation value of that mesh node. The individual node temperature deviation value is the measured temperature value minus the expected temperature value.

[0084] Based on the boundary information of the multiple surface temperature control sections and multiple internal temperature control sections defined in Example 4, the zone temperature control module assigns and statistically analyzes all grid nodes within each temperature control section. For each surface temperature control section, the zone temperature control module calculates the arithmetic mean of the individual node temperature deviation values ​​of all grid nodes within the surface temperature control section to obtain the temperature deviation value of that surface temperature control section. For each internal temperature control section, the zone temperature control module calculates the arithmetic mean of the individual node temperature deviation values ​​of all grid nodes within the internal temperature control section to obtain the temperature deviation value of that internal temperature control section. Surface temperature control sections and internal temperature control sections are collectively referred to as temperature control sections, and each temperature control section corresponds to a temperature deviation value.

[0085] When the temperature deviation of a certain temperature control zone exceeds the allowable deviation range, the power compensation direction and amount for that temperature control zone are determined based on the sign and magnitude of the temperature deviation. The zone temperature control module pre-stores an allowable deviation range, consisting of a positive upper limit and a negative lower limit. The positive upper limit is +2 degrees Celsius, and the negative lower limit is -2 degrees Celsius. The zone temperature control module compares the temperature deviation of each temperature control zone with the allowable deviation range. When the temperature deviation of a temperature control zone is greater than the positive upper limit, the temperature of that zone is determined to be too high, and the power compensation direction is to reduce heating power or increase cooling power (i.e., the power compensation direction is negative). When the temperature deviation is less than the negative lower limit, the temperature of that zone is determined to be too low, and the power compensation direction is to increase heating power or decrease cooling power (i.e., the power compensation direction is positive). When the temperature deviation of a temperature control zone is within the allowable deviation range, power compensation is not triggered.

[0086] The power compensation amount is calculated as follows: the power compensation amount equals the absolute value of the temperature deviation multiplied by the power compensation ratio coefficient. The power compensation ratio coefficient is predetermined through a power-temperature response characteristic calibration experiment of the curing equipment. In the calibration experiment, different levels of power input are applied to the surface heating pad array and the internal circulating water pipe, and the temperature change in the corresponding area of ​​the concrete component is measured. A linear regression relationship between the power input and the temperature change is established, and the slope of the regression line is taken as the power compensation ratio coefficient. The unit of the power compensation ratio coefficient for the surface heating pad array is watts per degree Celsius, and the unit of the power compensation ratio coefficient for the internal circulating water pipe is flow rate per degree Celsius. In the implementation of real-time adjustment of the output parameters of the heating or cooling actuator corresponding to the temperature control section according to the power compensation amount, the zone temperature control module first identifies whether the current temperature control section is a surface temperature control section or an internal temperature control section, and then performs the corresponding output parameter adjustment operation according to the type.

[0087] In the process of identifying whether the current temperature control zone is a surface temperature control zone or an internal temperature control zone, the zone temperature control module maintains a temperature control zone type identifier table. This table stores the mapping relationship between the zone identifier and the zone type for each temperature control zone, with the zone type being either surface or internal. The zone temperature control module queries the temperature control zone type identifier table for the corresponding zone type based on the zone identifier of the temperature control zone to be adjusted.

[0088] If the current temperature control zone is a surface temperature control zone and the temperature deviation value is negative, it indicates that the measured temperature of this surface temperature control zone is lower than the expected temperature, and the heating power needs to be increased. The zone temperature control module increases the power supply duty cycle of the surface heating pad array within this surface temperature control zone. The increment of the power supply duty cycle is calculated based on the rated power of the surface heating pad according to the power compensation amount. The specific method for increasing the heating power of the surface heating pad array according to the power compensation amount is as follows: the zone temperature control module sends a new power supply duty cycle command to the power controller of the surface heating pad corresponding to this surface temperature control zone. The power controller adjusts the duty cycle of the pulse width modulation signal according to the new power supply duty cycle command, so that the effective heating power of the surface heating pad within this control cycle is increased by the value corresponding to the power compensation amount.

[0089] If the current temperature control zone is a surface temperature control zone and the temperature deviation value is positive, it indicates that the measured temperature of this surface temperature control zone is higher than the expected temperature, and the heating power needs to be reduced. The zone temperature control module reduces the power supply duty cycle of the surface heating pad array within this surface temperature control zone or shuts down some surface heating pads, causing the heating power of the surface heating pad array to decrease according to the power compensation amount. When the power compensation amount is less than or equal to the heating power corresponding to the current power supply duty cycle, the zone temperature control module only reduces the power supply duty cycle; when the power compensation amount is greater than the heating power corresponding to the current power supply duty cycle, the zone temperature control module switches some surface heating pads within the current surface temperature control zone to a closed state. The number of closed surface heating pads is the minimum integer required to satisfy the requirement that the cumulative closed heating power is greater than or equal to the excess portion. The zone temperature control module sends a zero duty cycle command to the power controller of the closed surface heating pads and sends a corresponding reduced power supply duty cycle command to the power controllers of the remaining surface heating pads.

[0090] If the current temperature control zone is an internal temperature control zone and the temperature deviation value is negative, it indicates that the measured temperature of this internal temperature control zone is lower than the expected temperature, requiring increased heating or reduced heat dissipation. The zone temperature control module increases the pump speed or valve opening of the internal circulating water pipe within this internal temperature control zone, increasing the heat exchange capacity of the internal circulating water pipe according to the power compensation amount. Increasing the valve opening is achieved by the zone temperature control module sending a positive stepping pulse sequence to the stepper motor driver of the water pipe control valve corresponding to the internal temperature control zone. The stepper motor driver drives the water pipe control valve to rotate in the opening direction, and the angle value of the valve opening increment is calculated based on the power compensation amount and the valve flow characteristic curve. Increasing the pump speed is achieved by the zone temperature control module sending a frequency increase command to the pump inverter of the internal circulating water pipe. The increase ratio of the pump speed is determined based on the power compensation amount and the hydraulic characteristics of the circulating water pipe system.

[0091] If the current temperature control zone is an internal temperature control zone and the temperature deviation value is positive, it indicates that the measured temperature of this internal temperature control zone is higher than the expected temperature, requiring increased heat dissipation. The zone temperature control module reduces the pump speed or valve opening of the internal circulating water pipe within this internal temperature control zone, thereby reducing the heat exchange of the internal circulating water pipe according to the power compensation amount. The reduction of valve opening is achieved by the zone temperature control module sending a reverse stepping pulse sequence to the stepper motor driver of the water pipe control valve corresponding to the internal temperature control zone, causing the stepper motor driver to drive the water pipe control valve to rotate in the closing direction. The reduction of pump speed is achieved by the zone temperature control module sending a frequency reduction command to the pump inverter of the internal circulating water pipe.

[0092] In the curing completion determination step, concrete core samples are collected at preset intervals during the curing process, with the preset interval being 2 hours. The zone temperature control module has a built-in curing completion determination timer, with its period set to the preset interval. At each time the curing completion determination timer is triggered, the zone temperature control module sends a sampling command to the concrete core sampler. The sampler drills a cylindrical concrete core sample (50 mm in diameter and 100 mm in length) from a pre-drilled sampling hole in the concrete component, marks the sampling timestamp on the sample, and delivers it to the testing station. In the rebound and ultrasonic velocity tests on the concrete core sample, a digital rebound hammer is used. The hammer selects 16 impact points evenly on the side of the concrete core sample, with a minimum spacing of 20 mm between each impact point and a minimum distance of 30 mm between each impact point and the edge of the concrete core sample. The digital rebound hammer was used to strike 16 impact points in sequence, and the rebound value reading of each impact point was recorded. After removing the three maximum values ​​and three minimum values, the arithmetic mean of the remaining ten rebound value readings was calculated as the rebound value of the concrete core test block.

[0093] Ultrasonic wave velocity testing utilizes an ultrasonic testing instrument, which includes a transmitting transducer and a receiving transducer. The transmitting transducer is placed against one end face of the concrete core specimen, and the receiving transducer is placed against the other end face. A coupling agent is applied between the transmitting and receiving transducers to ensure effective acoustic coupling. The ultrasonic testing instrument transmits a 50 kHz ultrasonic pulse wave into the concrete core specimen through the transmitting transducer. The ultrasonic pulse wave propagates along the length of the concrete core specimen and is received by the receiving transducer. The ultrasonic testing instrument records the propagation time of the ultrasonic pulse wave from transmission to reception. The ultrasonic wave velocity value is calculated as follows:

[0094]

[0095] in: This indicates the ultrasonic wave velocity, measured in meters per second. This indicates the length of the concrete core test block, taken as 0.1 meters; This indicates the propagation time of an ultrasonic pulse wave from transmission to reception, measured in seconds.

[0096] In the process of calculating the actual compressive strength of the concrete core specimen based on the rebound value and ultrasonic velocity value, the zone temperature control module calls a pre-stored strength conversion curve. This curve uses the rebound value and ultrasonic velocity value as joint independent variables and the concrete compressive strength as the dependent variable. The function form of the strength conversion curve is a bivariate quadratic polynomial. The coefficients of the bivariate quadratic polynomial are determined by conducting joint calibration experiments of rebound-ultrasonic-compressive strength on multiple sets of standard specimens made under the same concrete mix design conditions, using the least squares surface fitting method. The zone temperature control module substitutes the currently measured rebound value and ultrasonic velocity value into the bivariate quadratic polynomial to calculate the actual compressive strength of the concrete core specimen, with the unit of actual compressive strength being megapascals (MPa).

[0097] In the process of triggering the curing end signal when the actual compressive strength reaches the standard curing strength value corresponding to the design strength grade, the standard curing strength value corresponding to the design strength grade is the characteristic value of the compressive strength that concrete should reach under the 28-day standard curing conditions specified by the design strength grade. The zone temperature control module pre-stores a design strength mapping table, which records the correspondence between different concrete strength grades and their corresponding standard curing strength values. The zone temperature control module looks up the corresponding standard curing strength value in the design strength mapping table based on the current design strength grade of the concrete component. The zone temperature control module compares the calculated actual compressive strength of the concrete core specimen with the standard curing strength value. When the actual compressive strength is greater than or equal to the standard curing strength value, the zone temperature control module generates a curing end signal, sending a stop command to all actuators of the curing equipment, and the curing equipment stops heating and cooling operations. When the actual compressive strength is less than the standard curing strength value, the curing process continues, waiting for the next curing end determination timer to trigger before re-executing the sampling and testing process.

[0098] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A concrete curing environment control system based on intelligent temperature control, characterized in that, include: The temperature sensing module collects the real-time temperature distribution field of the core area of ​​the concrete through a temperature sensing network embedded inside the concrete component. The anomaly identification module identifies the location of abnormal sections where the temperature gradient exceeds a set threshold based on the real-time temperature distribution field of the concrete core area. The twin modeling module uses a laser scanner to obtain the surface morphology features of the concrete corresponding to the abnormal cross-section, and combines the real-time temperature distribution field of the core area of ​​the concrete to construct a digital twin model of concrete thermodynamics. The simulation and deduction module uses the concrete thermodynamic digital twin model to perform virtual simulation and deduction on multiple preset curing temperature curves, and obtains the internal stress prediction cloud map corresponding to each preset curing temperature curve. The curve optimization module extracts the maximum tensile stress value from the internal stress prediction cloud map and selects the preset curing temperature curve with the minimum maximum tensile stress value as the target curing temperature curve. The zoned temperature control module controls the curing equipment to perform differentiated heating or cooling of the surface and interior of the concrete component according to the target curing temperature curve.

2. The concrete curing environment control system based on intelligent temperature control according to claim 1, characterized in that, The real-time temperature distribution field of the core area of ​​the concrete is collected through a temperature sensing network embedded inside the concrete component. Specifically, this includes: Obtain the identification codes and spatial coordinates of multiple fiber optic temperature sensors pre-embedded at different depths in concrete components; The wavelength offset of the plurality of fiber Bragg grating temperature sensors is sequentially polled and read at preset time intervals. The measured temperature value at the location of each fiber grating temperature sensor is calculated based on the wavelength offset. The measured temperature values ​​and their corresponding spatial coordinates are associated and stored to generate discrete temperature point cloud data; The discrete temperature point cloud data is subjected to three-dimensional interpolation fitting to obtain the real-time temperature distribution field of the continuously distributed concrete core area.

3. The concrete curing environment control system based on intelligent temperature control according to claim 2, characterized in that, The locations of abnormal cross-sections where the temperature gradient exceeds a set threshold are identified based on the real-time temperature distribution field of the concrete core area. Specifically, these include: Extract the vertical temperature profile line along the thickness direction of the concrete component from the real-time temperature distribution field of the continuously distributed concrete core area. Calculate the local temperature difference between two adjacent depth positions on the vertical temperature profile line; Divide the local temperature difference by the spatial distance between the two depth locations to obtain the temperature gradient value corresponding to each depth interval. The temperature gradient value is compared with the set threshold, and the depth range of all temperature gradient values ​​exceeding the set threshold is recorded; The geometric location of the abnormal section is determined based on the spatial coordinates of the depth range and marked as the location of the abnormal section.

4. The concrete curing environment control system based on intelligent temperature control according to claim 1, characterized in that, Using a laser scanner to acquire the surface morphology features of the concrete corresponding to the abnormal cross-section, and combining this with the real-time temperature distribution field of the core region of the concrete to construct a digital twin model of concrete thermodynamics, specifically includes: The laser scanner is controlled to perform a grid-based scan of the outer surface of the concrete corresponding to the abnormal cross-section location, and the three-dimensional coordinates and surface reflection intensity of each grid node are obtained; A triangular mesh model of the concrete surface is generated based on the three-dimensional coordinates of each mesh node; Extract a temperature distribution subfield from the real-time temperature distribution field of the concrete core region, within the region adjacent to the location of the abnormal section; The temperature values ​​in the temperature distribution subfield are mapped one by one to the corresponding nodes of the triangular mesh model of the concrete surface to generate a surface geometric model with temperature labels. A thermodynamic digital twin model of concrete is constructed by calling a thermodynamic simulation engine and using the surface geometry model with temperature labels as boundary conditions.

5. The concrete curing environment control system based on intelligent temperature control according to claim 4, characterized in that, When the laser scanner performs a gridded scan on the outer surface of the concrete corresponding to the location of the abnormal section, the scanning resolution increases as the temperature gradient value at the location of the abnormal section increases.

6. The concrete curing environment control system based on intelligent temperature control according to claim 4, characterized in that, The concrete thermodynamic digital twin model is used to virtually simulate and extrapolate multiple preset curing temperature curves, resulting in predicted internal stress cloud maps corresponding to each preset curing temperature curve. Specifically, these include: Obtain multiple preset curing temperature curves stored in the curing database. Each preset curing temperature curve consists of multiple time points and the target surface temperature and target core temperature corresponding to each time point. The multiple preset curing temperature curves are sequentially loaded into the boundary conditions of the concrete thermodynamic digital twin model; For each preset curing temperature curve, the concrete thermodynamic digital twin model is driven to perform transient heat conduction simulation calculations in a progressive manner according to the time step, so as to obtain the transient temperature distribution inside the concrete at each time step. At the end of each time step, the thermoelastic constitutive equation is invoked to calculate the thermal strain of each finite element based on the transient temperature distribution and the linear expansion coefficient of concrete. After converting the thermal strain into equivalent nodal forces, the finite element equilibrium equations are solved to obtain the internal stress distribution at each time step. The internal stress distributions at all time steps are stacked in chronological order to form an internal stress prediction cloud map corresponding to a preset curing temperature curve.

7. The concrete curing environment control system based on intelligent temperature control according to claim 6, characterized in that, The time step of the transient heat conduction simulation calculation is adaptively adjusted according to the temperature change rate of the concrete; the larger the temperature change rate, the smaller the time step.

8. The concrete curing environment control system based on intelligent temperature control according to claim 6, characterized in that, Extracting the maximum tensile stress value from the internal stress prediction cloud map, and selecting the preset curing temperature curve with the minimum maximum tensile stress value as the target curing temperature curve specifically includes: Traverse the stress tensor at each time step for each spatial location in the internal stress prediction cloud map; The principal stresses at each spatial location are solved by calculating the stress tensor to obtain three principal stress values. The largest positive value among the three principal stress values ​​is selected as the maximum tensile stress value at that spatial location at that time step. The maximum tensile stress values ​​at all spatial locations over all time steps are summarized, and the maximum value among them is taken as the global maximum tensile stress value of the internal stress prediction cloud map. The global maximum tensile stress value corresponding to each preset curing temperature curve is compared, and the preset curing temperature curve with the smallest global maximum tensile stress value is selected as the target curing temperature curve.

9. The concrete curing environment control system based on intelligent temperature control according to claim 8, characterized in that, The controlled curing equipment performs segmented differentiated heating or cooling of the surface and interior of the concrete component according to the target curing temperature curve, specifically including: The target surface temperature and target core temperature at each time point are analyzed from the target curing temperature curve. Obtain the placement location of the surface heating pad array and the layout path of the internal circulating water pipes in the maintenance equipment; The surface of the concrete component is divided into multiple surface temperature control zones according to the arrangement of the surface heating pad array, and the interior of the concrete component is divided into multiple internal temperature control zones according to the arrangement path of the internal circulating water pipe. The power of the surface heating pad array within each surface temperature control zone is independently adjusted according to the target surface temperature. The valve opening of the internal circulating water pipe in each internal temperature control zone is controlled to adjust the flow rate independently according to the target core temperature.

10. The concrete curing environment control system based on intelligent temperature control according to claim 9, characterized in that, When the surface of a concrete member is divided into multiple surface temperature control zones, the boundary of each surface temperature control zone is aligned with the projected contour line of the abnormal section location on the concrete surface.

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