BIM-based concrete dynamic curing system and method

Abstract

The application discloses a kind of concrete dynamic maintenance system and method based on BIM, system includes BIM central control platform, edge sensor gateway and microfluidic intelligent maintenance film, integrated with indium tin oxide addressing electrode array in microfluidic intelligent maintenance film, the method is generated virtual pixel matrix by BIM to three-dimensional geometric model voxelization, and mapping relationship is established with the physical coordinates of addressing electrode array.System alternately executes detection phase and driving phase, in detection phase, the system obtains the equivalent complex impedance of micro-interface, and reconstructs superpixel node using clustering algorithm to update logical control boundary, in driving phase, the system outputs direct current driving voltage to specific node, triggers dielectric wetting effect to change the solid-liquid interface contact angle in micropore, so that maintenance liquid overcomes capillary resistance and accurately releases to local concrete surface.The application realizes real-time dynamic perception and microfluidic self-adaptive on-demand directional maintenance of concrete heterogeneous surface.

Description

Technical Field

[0001] This invention relates to the field of building construction and intelligent control technology, specifically to a BIM-based dynamic curing system and method for concrete. Background Technology

[0002] Curing concrete after it has been formed is a crucial step in building construction. During the hydration and hardening process of concrete, it is necessary to maintain suitable temperature and humidity conditions to ensure the full progress of the internal hydration reaction, thereby ensuring the final strength and structural durability of the concrete and preventing shrinkage cracks caused by excessively rapid evaporation of moisture or excessive temperature differences between the inside and outside.

[0003] Currently, existing dynamic curing methods for concrete mainly employ a combination of covering with thermal insulation and moisture-retaining materials and spraying water through a pipe network. This method is technically mature, easy to operate, and can achieve large-area macroscopic cooling and moisturizing. However, in the construction of large or complex concrete structures, a network of temperature sensors is usually pre-embedded inside the concrete, and the collected internal temperature and external ambient temperature and humidity data are connected to a monitoring system for visualization. When the system detects that the temperature at the pre-embedded points exceeds a set threshold, the control backend triggers the opening of an external electromagnetic water valve, which sprays water onto the entire concrete surface through pipes and nozzles located outside the component to cool and moisturize it.

[0004] However, existing curing systems rely on mechanical pipelines for overall watering when performing curing operations. Due to the significant spatial asymmetry in the hydration reaction rate and heat dissipation of various local areas of complex concrete components, this spraying method based on the entire area cannot make precise local adaptive control according to the heterogeneity of the actual temperature and humidity state of the surface. As a result, some curing water is lost before it reaches dry or high-temperature areas, causing waste of water resources. In addition, it is difficult to achieve precise moisture control by directly spraying cold water over a large area, and frequent spraying with large temperature differences may cause sudden temperature gradient changes on the local surface, which may induce temperature cracks on the concrete surface. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that the traditional spray curing method in the prior art adopts the overall covering method, which cannot adjust the moisture retention according to the actual humidity of the local area of ​​the concrete surface, resulting in insufficient or uneven curing in some areas; at the same time, the rough control of the spray water volume can easily cause local water accumulation or excessively fast drying on the surface, affecting the consistency of curing quality.

[0006] To address the aforementioned technical problems, the present invention is described below:

[0007] The first aspect of the present invention provides a BIM-based dynamic concrete curing system, which includes: a BIM central control platform, an edge sensing gateway, and a microfluidic intelligent curing membrane.

[0008] The BIM central control platform establishes a two-way communication connection with the edge sensing gateway to send addressing coordinates, timing control commands and electrical signals.

[0009] The microfluidic intelligent curing membrane is configured to cover the outer surface of the concrete to be cured and formed, including an upper surface layer, a middle reservoir layer, and a lower base layer. The middle reservoir layer contains a capillary network structure and is configured as a reservoir space to contain the curing fluid. The lower base layer has microporous channels distributed inside and an indium tin oxide (ITO) addressing electrode array is disposed inside. The ITO addressing electrode array is wrapped with a dielectric insulating layer. The edge sensing gateway is used to receive the analog electrical response signal returned by the microfluidic intelligent curing membrane and convert it into a digital signal to be uploaded to the BIM central control platform. The microfluidic intelligent curing membrane is used to receive the voltage control signal output by the BIM central control platform to trigger and execute the dielectric wetting fluid release action.

[0010] A second aspect of the present invention provides a BIM-based dynamic curing method for concrete, implemented based on the BIM-based dynamic curing system for concrete described in the first aspect of the present invention. The method includes the following steps:

[0011] A three-dimensional geometric model of the concrete structure to be poured is acquired and spatially discretized to generate a three-dimensional voxel mesh set. Surface voxels are extracted and unfolded in two dimensions to form a virtual pixel matrix. An initial addressing mapping relationship is established between the virtual pixel matrix and the physical coordinates of the indium tin oxide (ITO) addressing electrode array within the microfluidic intelligent curing membrane. The system operation cycle is divided into alternating detection and driving phases. During the detection phase, the edge sensing gateway injects a high-frequency AC excitation voltage into each physical coordinate node in the ITO addressing electrode array. The response current signal is collected to calculate the equivalent complex impedance of each physical coordinate node. The real and imaginary feature data of the equivalent complex impedance are extracted, and the feature distance between adjacent physical coordinate nodes is calculated. A clustering algorithm is called to merge consecutive physical coordinate nodes with a feature distance less than a set threshold, reconstructing them into superpixel nodes. The logic control boundary is updated using the superpixel node as an independent computing unit. Entering the driving phase, the transient heat source generation rate and the real part feature data are combined to calculate and determine when the temperature gradient or humidity decrease rate of a certain superpixel node reaches a set threshold. Then, a DC driving voltage is output to the indium tin oxide addressing electrode array associated with the superpixel node. Under the action of the DC driving voltage, the microporous solid-liquid interface in the microfluidic intelligent curing membrane undergoes a dielectric wetting effect. The interface contact angle decreases, causing the capillary pressure in the micropores to change. This allows the curing liquid to overcome physical viscous resistance and be released to the local concrete surface. The detection phase and driving phase are continuously executed in a loop. The real part time derivative of the equivalent complex impedance is calculated synchronously. When the real part time derivative approaches zero and the real part value of the equivalent complex impedance is greater than the set resistance threshold, the time-division multiplexing control timing is terminated and the operation ends.

[0012] This invention transplants dielectric wetting (EWOD) technology from the field of microfluidics to the concrete curing scenario. It utilizes the core capabilities of EWOD technology for pixel-level addressing of droplets and precise control of micro-fluids, which meets the engineering requirements of 'local on-demand fixed-point water replenishment' due to the spatial asymmetry of hydration heat on the surface of complex concrete components.

[0013] Furthermore, the innovative principles of the above method are explained as follows: When establishing the initial addressing mapping relationship, the system uses an affine transformation matrix model to establish spatial registration between virtual pixel coordinates and physical coordinates. The actual physical coordinates are confirmed by acquiring the reference alignment electrical signal of the intersection nodes of array edge pixels, and the mapping coefficient and translation coefficient are solved simultaneously to complete the hard mapping binding of the coordinates. During the complex impedance measurement of the probe phase, the system acquires the initial response current signal superimposed with high-frequency electrical noise and calls the mathematical morphology filtering module to perform opening and closing combination operations to filter out the noise. Subsequently, the real and imaginary parts of the complex impedance are calculated using the amplitude and phase difference of the excitation voltage and the target response current, respectively used to evaluate the regional water content and determine the material phase composition. When reconstructing superpixel nodes to update the logical control boundary, the system selects core seed nodes based on the feature matrix containing spatial location and complex impedance component data, and extracts neighboring nodes based on the eight-connected topology to perform boundary expansion operations. Nodes with consistent physical states are iteratively expanded to generate a cluster set, and the convex hull algorithm is called to extract the outer contour coordinates to update the logical control boundary, effectively avoiding the computational fragmentation of continuous hydration feature regions by the fixed grid. During the addressing and fluid release execution of the driving phase, the system applies voltages to the target row scan lines and column data lines respectively. When the absolute value of the formed local DC driving voltage is greater than or equal to the dielectric wetting threshold voltage, the dielectric wetting effect is triggered. At this time, the capillary pressure in the micropores reverses, and the amount of capillary pressure change is proportional to the gas-liquid interfacial tension and positively correlated with the cosine difference of the contact angle. This change acts as an active driving force to promote the directional seepage of the curing fluid, while the area not affected by the target driving voltage remains hydrophobic to block leakage. In addition, during the cyclic execution, the system determines the hydration heating and cooling periods by acquiring the time derivative data of the transient internal heat source generation rate. Based on the actual temperature gradient difference of the superpixel nodes, it dynamically shortens or extends the total physical duration of the global reference cycle and adaptively adjusts the duty cycle of the driving phase time window and the amplitude of the DC driving voltage. At the same time, the system combines the proportion of global hardening convergence rate nodes and the real part time derivative to perform a composite determination of adaptive termination of curing, realizing safe and orderly shutdown of dynamic curing throughout the entire life cycle.

[0014] The present invention, by adopting the above technical solution, can bring the following beneficial effects:

[0015] 1. This invention generates a virtual pixel matrix by spatial voxelizing the concrete structure to be poured, and establishes a mapping and binding relationship with the indium tin oxide addressing electrode array inside the microfluidic intelligent curing membrane. Then, it accurately outputs DC driving voltage in the driving phase according to the regional temperature gradient. By using the dielectric wetting effect to break the capillary resistance of the solid-liquid interface in the local microporous channels, it achieves the effect of on-demand directional penetration and release of curing liquid into specific local areas, thereby avoiding the resource waste and temperature stress impact on the surface concrete caused by traditional macro spraying.

[0016] 2. This invention introduces time-division multiplexing control to alternately execute the detection phase and the driving phase. In the detection phase, an AC excitation voltage is injected into the electrode array and the response current signal is collected to calculate the equivalent complex impedance of each node. At the same time, a spatial constraint clustering module is used to merge continuous nodes with feature distance less than a threshold into superpixel nodes to update the control boundary. This invention can perceive the heterogeneous hydration state of the concrete surface in real time and dynamically reconstruct the control boundary, effectively avoiding the calculation fragmentation defect of static fixed mesh for continuous hydration feature regions.

[0017] 3. This invention encapsulates the indium tin oxide (ITO) addressing electrode array with a composite dielectric insulating layer of nano-alumina and parylene, and incorporates a pre-placed low-conductivity moisturizing agent within the intermediate reservoir. This effectively solves the problems of electrode corrosion and electric field shielding failure in the highly alkaline environment of concrete. Simultaneously, it uses adaptive termination and composite determination based on the physical evolution states such as the real part time derivative of the equivalent complex impedance and the global hardening convergence rate, achieving safe and orderly shutdown for dynamic curing throughout the entire lifecycle. It should be noted that this solution primarily targets localized moisture control on the concrete surface; for controlling the heat of hydration within large-volume concrete, traditional internal cooling measures are still required. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the BIM-based dynamic concrete curing system of the present invention;

[0019] Figure 2 This is a schematic diagram of the microfluidic intelligent curing membrane of the present invention;

[0020] Figure 3 This is a physical topology diagram of the indium tin oxide addressing electrode array of the present invention;

[0021] Figure 4 This is a schematic diagram of the dielectric wetting microfluidic dynamics mechanism of the present invention;

[0022] Figure 5 This is the main flowchart of the macroscopic closed-loop control method of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0024] Please refer to the appendix. Figure 1-2The present invention provides a BIM-based dynamic concrete curing system, comprising: a BIM central control platform 100, an edge sensing gateway 200, and a microfluidic intelligent curing membrane 300.

[0025] The BIM central control platform 100 is configured as the core of the system's data processing and logical decision-making. The BIM central control platform 100 contains a three-dimensional geometric model of the building structure and a finite difference calculation module 110. The finite difference calculation module 110 is used to perform transient heat source generation rate calculations for spatial voxels. The formula for the transient internal heat source generation rate is:

[0026] In the formula: The transient internal heat source generation rate; This represents the limiting heat release from hydration per unit volume of concrete. For reaction rate parameters (dimension controlled) Constraints, having (dimensions). is a dimensionless shape parameter; For hydration time (with time dimension), the BIM central control platform 100 establishes a two-way communication connection with the edge sensing gateway 200 to send addressing coordinates, timing control commands and alternating or direct current signals.

[0027] The edge sensing gateway 200 is arranged on the edge side of the construction site. The edge sensing gateway 200 includes an analog-to-digital conversion circuit 210 and a signal conditioning circuit 220. The edge sensing gateway 200 receives the analog electrical response signal returned by the microfluidic intelligent curing membrane 300, converts the analog electrical response signal into a digital signal and uploads it to the BIM central control platform 100.

[0028] The microfluidic intelligent curing membrane 300 is made of a flexible polymer substrate to ensure good conformal fit with the outer surface of the concrete to be cured, which has a certain roughness. As the physical sensing and execution unit of the system, the microfluidic intelligent curing membrane 300 integrates an indium tin oxide addressing electrode array 310. The microfluidic intelligent curing membrane 300 receives the voltage control signal output by the BIM central control platform 100, and performs electrical signal feedback of the physical state of the local surface concrete and release of dielectric wetting fluid.

[0029] As the core physical sensing and execution layer of the aforementioned BIM-based concrete dynamic curing system, the microfluidic intelligent curing membrane 300 includes: an upper surface layer 320, a middle reservoir layer 330, and a lower base layer 340.

[0030] The upper surface layer 320 is made of polytetrafluoroethylene microporous filter membrane material. The upper surface layer 320 covers the top surface of the middle reservoir 330. The upper surface layer 320 is configured to prevent liquid water inside the middle reservoir 330 from leaking into the external environment of the microfluidic intelligent maintenance membrane 300. The upper surface layer 320 is configured to allow water vapor inside the middle reservoir 330 to diffuse unidirectionally into the external environment of the microfluidic intelligent maintenance membrane 300.

[0031] The intermediate reservoir 330 is disposed between the upper surface layer 320 and the lower layer 340. The intermediate reservoir 330 contains a capillary network structure 331. The intermediate reservoir 330 is configured as a storage space for containing the maintenance fluid. The intermediate reservoir 330 utilizes the capillary network structure 331 to generate capillary suction. The capillary network structure 331 maintains the uniform distribution of the maintenance fluid inside the intermediate reservoir 330.

[0032] The lower base layer 340 is configured as the physical interface between the microfluidic intelligent curing membrane 300 and the surface of the concrete to be cured. The lower base layer 340 is made of hydrophobic porous polymer material. Its internal pores generate capillary resistance in the opposite direction of curing liquid leakage in the initial state, forming a passive capillary micro-valve to prevent non-target leakage of curing liquid in the unenergized state. The lower base layer 340 has microporous channels 341 distributed inside, which connect the middle reservoir 330 with the surface of the concrete to be cured. The indium tin oxide addressing electrode array 310 is set inside the lower base layer 340. The indium tin oxide addressing electrode array 310 is wrapped with a dielectric insulating layer 342. The dielectric insulating layer 342 physically isolates the indium tin oxide addressing electrode array 310 from the fluid in the microporous channels 341. In order to solve the corrosion problem of concrete in a highly alkaline environment and take into account the high dielectric performance, the dielectric insulating layer 342 is a composite dense film of nano alumina (Al2O3) and parylene (Parylene-C). The dielectric insulating layer 342 is configured to prevent the fluid in the microporous channels 341 from undergoing electrolytic reaction under the current state, and also serves as an alkali-resistant and corrosion-resistant protective layer to ensure the long-term chemical stability of the indium tin oxide addressing electrode array 310 in the highly alkaline bleeding environment of concrete.

[0033] The microporous channel 341 within the lower layer 340 serves as the end release channel for microfluidics, and the curing solution inside is mainly supplied by the middle reservoir 330. To maintain the physical stability of the dielectric wetting effect and avoid the shielding effect of high-concentration alkaline ions on the electric field, the curing solution pre-placed in the middle reservoir 330 uses a special humectant with low conductivity.

[0034] Please refer to the appendix. Figure 3-5 The present invention also provides a BIM-based dynamic curing method for concrete. Based on the above system, in this process, the various modules of the system work together, and the specific steps are as follows:

[0035] In step S100, the BIM central control platform 100 acquires the three-dimensional geometric model of the concrete structure to be poured. The BIM central control platform 100 performs spatial discretization processing on the three-dimensional geometric model to generate a three-dimensional voxel mesh set. The BIM central control platform 100 calls the finite difference operation module 110 to calculate the transient heat source generation rate inside each voxel in the three-dimensional voxel mesh set. The BIM central control platform 100 extracts the surface voxels located outside the model in the three-dimensional voxel mesh set. The BIM central control platform 100 unfolds the surface voxels in two dimensions to form a virtual pixel matrix. The BIM central control platform 100 establishes the initial addressing mapping relationship between the virtual pixel matrix and the physical coordinates of the indium tin oxide addressing electrode array 310 inside the microfluidic intelligent curing membrane 300.

[0036] In step S200, the BIM central control platform 100 starts the internal time-division multiplexing control module 120, which divides the system operation cycle into alternating detection and driving phases.

[0037] Step S300: Entering the detection phase, the BIM central control platform 100 pauses the output of fluid drive commands. The BIM central control platform 100 controls the edge sensing gateway 200 to inject high-frequency AC excitation voltage into each physical coordinate node in the indium tin oxide addressing electrode array 310. The edge sensing gateway 200 collects the response current signal fed back from the concrete surface interface. The edge sensing gateway 200 converts the response current signal into digital format and uploads it to the BIM central control platform 100. The BIM central control platform 100 uses the high-frequency AC excitation voltage and response current signal to calculate the equivalent complex impedance of each physical coordinate node. The BIM central control platform 100 extracts the real part feature data and imaginary part feature data of the equivalent complex impedance.

[0038] In step S400, the BIM central control platform 100 calculates the feature distance between adjacent physical coordinate nodes, calls a clustering algorithm to classify the physical coordinate nodes, merges consecutive physical coordinate nodes whose feature distance is less than a set threshold, reconstructs the merged physical coordinate nodes into superpixel nodes, and updates the logical control boundary with superpixel nodes as independent calculation units.

[0039] Step S500: Enter the driving phase. The BIM central control platform 100 calculates the temperature gradient of the superpixel node by combining the transient heat source generation rate. The BIM central control platform 100 maps the humidity decrease rate of the superpixel node by combining the real part characteristic data of the equivalent complex impedance. When the BIM central control platform 100 determines that the temperature gradient or humidity decrease rate of a certain superpixel node reaches the set threshold, it outputs a DC driving voltage to the indium tin oxide addressing electrode array 310 associated with the superpixel node.

[0040] In step S600, the microporous solid-liquid interface within the microfluidic intelligent curing membrane 300 undergoes a dielectric wetting effect under the action of a DC driving voltage. This reduces the interfacial contact angle within the micropores, leading to a change in capillary pressure within the micropores. The formula for the change in capillary pressure is:

[0041] In the formula: This represents the change in capillary pressure. The gas-liquid interfacial tension of the curing solution; The effective hydraulic radius of the micropores; The contact angle after applying a DC drive voltage; The initial contact angle when no DC drive voltage is applied;

[0042] After the capillary pressure change overcomes the physical viscous resistance in the micropores, the curing fluid penetrates from inside the microfluidic intelligent curing membrane 300 to the concrete surface covered by the superpixel node. When the BIM central control platform 100 stops outputting DC driving voltage to the superpixel node, the solid-liquid interface of the micropores returns to its initial contact angle state, and the microfluidic intelligent curing membrane 300 blocks the penetration of the curing fluid.

[0043] In step S700, the BIM central control platform 100 continuously and cyclically executes the working steps of the detection phase and the driving phase. The BIM central control platform 100 synchronously calculates the real part time derivative of the equivalent complex impedance. When the BIM central control platform 100 determines that the real part time derivative of the equivalent complex impedance approaches zero and the real part value of the equivalent complex impedance is greater than the set resistance threshold, it terminates the time division multiplexing control sequence and ends the operation.

[0044] In step S100 of the aforementioned macroscopic workflow, in order to achieve precise docking between digital space and physical space, this embodiment further discloses the specific implementation details of the spatial voxelization modeling and soft / hard coordinate mapping mechanism, the workflow of which includes:

[0045] Step S101: The BIM central control platform 100 establishes a three-dimensional geometric physical domain for the concrete structure to be poured. The BIM central control platform 100 uses a finite difference mesh generation algorithm to discretize the three-dimensional geometric physical domain and generate a three-dimensional voxel mesh set. The three-dimensional voxel mesh set contains multiple spatial voxel nodes.

[0046] Step S102: The BIM central control platform 100 assigns three-dimensional spatial logical coordinates to each spatial voxel node in the three-dimensional voxel mesh set. The three-dimensional spatial logical coordinates are represented as follows: In the formula: For spatial voxel nodes; These are the coordinate values ​​of three orthogonal directions in a three-dimensional Cartesian coordinate system;

[0047] Step S103: The BIM central control platform 100 extracts the set of surface voxel nodes that are in direct contact with the external environment from the three-dimensional voxel mesh set. The BIM central control platform 100 uses a conformal unfolding algorithm to map the set of surface voxel nodes onto a two-dimensional plane to generate a virtual pixel matrix.

[0048] Step S104: The virtual pixel matrix contains multiple discrete virtual pixel coordinates, which are represented as follows: In the formula: For virtual pixel nodes; These are the virtual row index and virtual column index in the two-dimensional plane, respectively;

[0049] Step S105: The indium tin oxide (ITO) addressing electrode array 310 inside the microfluidic intelligent curing membrane 300 has a two-dimensional physical topology. The BIM central control platform 100 acquires the physical coordinates of each pixel intersection node 313 in the ITO addressing electrode array 310. The physical coordinates are represented as follows: In the formula: For pixel intersection node 313; These are the physical row index and physical column index within the indium tin oxide addressing electrode array 310, respectively.

[0050] Step S106: The BIM central control platform 100 establishes virtual pixel coordinates. With physical coordinates The initial mapping relationship is obtained by spatial registration using an affine transformation matrix model. The formula for calculating the affine transformation matrix model is as follows:

[0051] In the formula: To characterize the mapping coefficient of the microfluidic smart curing membrane 300 on the application surface for scaling and rotational deformation; To characterize the translation coefficient of the physical layup position offset of the microfluidic smart curing membrane 300;

[0052] Step S107: After the microfluidic intelligent curing membrane 300 is laid, the BIM central control platform 100 obtains the characteristic impedance abrupt change values ​​of at least three non-collinear pixel intersection nodes 313 at the edge position of the indium tin oxide addressing electrode array 310 through the edge sensing gateway 200 as a reference alignment electrical signal. The BIM central control platform 100 confirms the actual physical coordinates of the three non-collinear pixel intersection nodes 313 based on the reference alignment electrical signal. The BIM central control platform 100 simultaneously extracts three reference virtual pixel coordinates corresponding to the actual physical coordinates from the virtual pixel matrix. ,in The values ​​are 1, 2, and 3;

[0053] Step S108: The BIM central control platform 100 substitutes the three sets of corresponding actual physical coordinates and reference virtual pixel coordinates into the affine transformation matrix model and solves them simultaneously. The BIM central control platform 100 calculates the mapping coefficients and translation coefficients in the affine transformation matrix model. The BIM central control platform 100 then substitutes the calculated mapping coefficients and translation coefficients into the affine transformation matrix model to complete the hard mapping and binding of virtual pixel coordinates and physical coordinates.

[0054] After completing the coordinate mapping, the system proceeds to the detection phase in step S300. To accurately obtain the true physical state of the concrete micro-interface, this embodiment further discloses the high-frequency AC complex impedance measurement mechanism under a time-division multiplexing architecture, as follows:

[0055] Step S301: The BIM central control platform 100 calls the time division multiplexing control module 120. The time division multiplexing control module 120 allocates the time window of the detection phase and the time window of the driving phase according to the set time cycle ratio.

[0056] Step S302: Within the time window of the detection phase, the edge sensing gateway 200 activates its built-in AC excitation signal generation circuit 230. The AC excitation signal generation circuit 230 applies a high-frequency AC excitation voltage to a specific pixel intersection node 313 in the indium tin oxide addressing electrode array 310. The formula for the high-frequency AC excitation voltage is:

[0057]

[0058] In the formula: It is a transient high-frequency AC excitation voltage; This refers to the amplitude of the high-frequency AC excitation voltage. The angular frequency of the excitation signal; It is a time variable;

[0059] Step S303: The edge sensing gateway 200 uses the signal conditioning circuit 220 to acquire the initial response current signal fed back from the solid-liquid interface within the microporous channel 341. The initial response current signal contains superimposed high-frequency electrical noise. The edge sensing gateway 200 calls the mathematical morphology filtering module 240 to perform morphological opening and closing combination operations on the initial response current signal. The mathematical morphology filtering module 240 filters out the high-frequency electrical noise and outputs the target response current signal. The formula for the target response current signal is:

[0060]

[0061] In the formula: The target response current signal; To respond to the current amplitude; The phase difference between the target response current signal and the high-frequency AC excitation voltage;

[0062] Step S304: The edge sensing gateway 200 uses the analog-to-digital converter 210 to convert the waveform data of the target response current signal and the high-frequency AC excitation voltage into digital quantities. The edge sensing gateway 200 transmits the digital quantities to the BIM central control platform 100. The BIM central control platform 100 uses the amplitude of the high-frequency AC excitation voltage, the amplitude of the response current, and the phase difference to calculate the real and imaginary parts of the equivalent complex impedance. The formula for calculating the real part is:

[0063]

[0064] The formula for calculating the imaginary part is:

[0065]

[0066] In the formula: This represents the real part of the equivalent complex impedance; This represents the imaginary part of the equivalent complex impedance;

[0067] Step S305: The BIM central control platform 100 establishes a mapping relationship between the real part of the equivalent complex impedance and the physical state of the microenvironment. The real part represents the equivalent ohmic resistance of the secreted water solution inside the microporous channel 341. The BIM central control platform 100 evaluates the water content parameters of the area corresponding to the pixel cross node 313 based on the real part.

[0068] Step S306: The BIM central control platform 100 establishes a mapping relationship between the imaginary part value of the equivalent complex impedance and the material phase distribution of the surface layer of the concrete to be cured. The imaginary part value characterizes the equivalent capacitance of the solid-liquid interface. There is a physical difference in the dielectric constant between the cement paste-rich area and the coarse aggregate area. The BIM central control platform 100 determines the material phase composition of the corresponding area below the pixel intersection node 313 based on the imaginary part value.

[0069] After obtaining the real and imaginary part feature data of the equivalent complex impedance, this embodiment provides a boundary dynamic reconstruction algorithm based on the impedance feature matrix to specifically implement the merging of superpixel nodes and the updating of the logic control boundary in step S400. The specific steps include:

[0070] Step S401: The BIM central control platform 100 obtains the real part and imaginary part of the equivalent complex impedance of each physical coordinate node and constructs a global equivalent complex impedance feature matrix. This matrix contains Euclidean geometric coordinate data in the spatial position dimension and complex impedance component data in the electrical characteristic dimension.

[0071] Step S402: Call the spatial constraint clustering module 130 to select unassigned physical coordinate nodes from the global equivalent complex impedance feature matrix as core seed nodes, extract their neighboring physical coordinate nodes based on the 8-connected topology of the array, and calculate the normalized feature distance between them; the formula for calculating the normalized feature distance is:

[0072] In the formula: Normalized feature distance; For the set weight coefficient and ; This represents the Euclidean spatial distance between the two nodes; This represents the maximum spatial span of the array; This represents the difference in the real parts of the equivalent complex impedances of the two nodes. This represents the maximum extreme value of the global impedance.

[0073] Step S403: Perform boundary expansion operation on the neighboring physical coordinate nodes according to the set feature distance threshold. The mathematical condition formula for the boundary expansion operation is:

[0074] In the formula: For the first The cluster set after the next iteration; For the first The cluster set after the next iteration; This is the set union operator; For neighborhood physical coordinate nodes; As the core seed node; The feature distance between the core seed node and its neighboring physical coordinate nodes; The set feature distance threshold;

[0075] Step S404: When the feature distance is less than or equal to the feature distance threshold, determine that the neighborhood physical coordinate node and the core seed node have the same physical state, and classify them into the cluster set to which the core seed node belongs.

[0076] Step S405: Set the newly assigned neighboring physical coordinate nodes as new core seed nodes, iteratively execute boundary expansion operations to continuously expand the spatial coverage, and stop the operation when there are no neighboring physical coordinate nodes that meet the conditions around the edge nodes.

[0077] Step S406: Traverse all physical coordinate nodes in the matrix to generate multiple independent final cluster sets, and merge them into superpixel nodes to characterize heterogeneous regions with similar surface hydration properties and spatial continuity.

[0078] Step S407: Call the boundary mapping module 140 and the convex hull algorithm to calculate the minimum convex polygon set of the physical coordinates of the edge of each superpixel node, extract the outer contour coordinates and map them to the virtual pixel matrix, thereby updating the logical control boundary of the microfluidic intelligent curing membrane 300 in the driving phase and eliminating the calculation fragmentation of the continuous hydration feature region by the fixed mesh.

[0079] When the system switches to the driving phase of step S500 according to the updated logic boundary, in order to achieve crosstalk-free and accurate DC voltage output to a specific superpixel node, the indium tin oxide addressing electrode array 310 includes: row scan electrode line 311, column data electrode line 312 and pixel cross node 313.

[0080] The indium tin oxide addressing electrode array 310 is configured as an orthogonal matrix topology. The row scanning electrode line 311 extends horizontally along the lower base layer 340, and the column data electrode line 312 extends vertically along the lower base layer 340. The row scanning electrode line 311 and the column data electrode line 312 are orthogonally arranged in physical space.

[0081] An interlayer insulating film 314 is provided between the row scanning electrode line 311 and the column data electrode line 312. The interlayer insulating film 314 is configured to prevent physical contact between the row scanning electrode line 311 and the column data electrode line 312, and the interlayer insulating film 314 blocks the short-circuit current between the row scanning electrode line 311 and the column data electrode line 312.

[0082] The orthogonal overlapping area of ​​the row scanning electrode line 311 and the column data electrode line 312 forms a pixel cross node 313. The pixel cross node 313 is arrayed in the two-dimensional plane of the lower layer 340. The two-dimensional physical coordinates of the pixel cross node 313 are mapped to the discrete coordinates in the virtual pixel matrix. The pixel cross node 313 corresponds to an independent microfluidic execution unit in the microfluidic intelligent curing membrane 300.

[0083] The addressing control process of the indium tin oxide addressing electrode array 310 in the driving phase is as follows:

[0084] Step S501: The edge sensing gateway 200 establishes an electrical connection with the row scanning electrode line 311 and the column data electrode line 312, and the BIM central control platform 100 controls the system to enter the drive phase.

[0085] Step S502: The BIM central control platform 100 applies a gating voltage to the target row scanning electrode line 311 through the edge sensing gateway 200, and the BIM central control platform 100 simultaneously inputs a data signal voltage to the target column data electrode line 312.

[0086] Considering the resistance voltage drop (IRDrop) phenomenon that exists in the large-area indium tin oxide addressing electrode array 310 when transmitting electrical signals, the BIM central control platform 100 introduces adaptive voltage amplitude compensation based on the physical coordinate distance of the target pixel intersection node 313 before outputting the voltage, so as to ensure that the driving voltage formed at each node of the global array is consistent.

[0087] Step S503: The voltage difference between the target row scanning electrode line 311 and the target column data electrode line 312 forms a local DC driving voltage at the target pixel intersection node 313. The local DC driving voltage acts on the dielectric insulating layer 342 at the target pixel intersection node 313.

[0088] Step S504: The physical condition for the local DC driving voltage to trigger the dielectric wetting effect is that the absolute value of the local DC driving voltage is greater than or equal to the dielectric wetting threshold voltage. The calculation formula for the driving voltage of the target pixel intersection node 313 and the threshold determination condition are as follows:

[0089] In the formula: This is the local DC driving voltage applied to the target pixel intersection node 313; The selection voltage for the target row scan electrode line 311; The data signal voltage for the target column data electrode line 312; The dielectric wetting threshold voltage is configured as the minimum equivalent potential difference required to break the capillary resistance within the microporous channel 341.

[0090] Step S505: The absolute value of the half-select bias voltage generated at the non-target pixel intersection node corresponding to the non-target row scan electrode line 311 and the non-target column data electrode line 312 is less than the dielectric wetting threshold voltage. The dielectric insulating layer 342 at the non-target pixel intersection node position remains in the initial hydrophobic state, blocking the leakage of the curing liquid at the non-target pixel intersection node position.

[0091] The indium tin oxide addressing electrode array 310 has a pin terminal 315 at its end. The pin terminal 315 is physically connected to the signal conditioning circuit 220 in the edge sensing gateway 200. The signal conditioning circuit 220 detects the response current signal fed back by the pixel cross node 313.

[0092] After completing electrode addressing and outputting DC drive voltage, to explain in detail how the curing solution overcomes capillary resistance to achieve targeted release in step S600, this embodiment discloses in detail the microfluidic hydrodynamic execution mechanism of the dielectric wetting effect of the microfluidic smart curing membrane, including the following steps:

[0093] In step S601, when the BIM central control platform 100 is in the detection phase or not outputting DC drive voltage, the surface of the dielectric insulating layer 342 at the target pixel cross node 313 maintains its intrinsic hydrophobic physical state. The wall of the microporous channel 341 inside the microfluidic intelligent curing membrane 300 forms an initial contact angle with the curing liquid. The initial contact angle is obtuse. The curing liquid in the middle reservoir 330 forms a convex meniscus interface at the physical port of the microporous channel 341. The convex meniscus interface generates capillary resistance opposite to the leakage direction of the curing liquid. The capillary resistance maintains the static balance of the curing liquid in the middle reservoir 330.

[0094] Step S602: After the system timing is switched to the driving phase, the BIM central control platform 100 outputs a DC driving voltage to the target pixel intersection node 313. The DC driving voltage penetrates the dielectric insulating layer 342 and induces charge space rearrangement at the solid-liquid-gas three-phase interface. Charge accumulates on the surface of the dielectric insulating layer 342 and constructs a microscopic double-layer capacitor physical structure.

[0095] Step S603: The physical structure of the microscopic double-layer capacitor disrupts the original solid-liquid interfacial tension balance, reducing the initial contact angle value within the microporous channel 341. The solid-liquid interface reverses from a hydrophobic state to a hydrophilic state. The physical law governing the contact angle evolution satisfies the Lippmann-Young electrocapillary equation, and the contact angle evolution formula is:

[0096] In the formula: The transient contact angle after applying a DC drive voltage; The initial contact angle is the angle without a DC drive voltage applied, for hydrophobic surfaces. , If the transient contact angle is after applying voltage Reduce to less than ,but ,lead to A positive value indicates that the capillary pressure direction has reversed. The relative permittivity of dielectric insulating layer 342; Vacuum permittivity; The gas-liquid interfacial tension of the curing solution; The physical thickness of dielectric insulating layer 342; This is the DC driving voltage applied to the target pixel intersection node 313;

[0097] Step S604: The decrease in contact angle causes the gas-liquid interface in the microporous channel 341 to change from a convex meniscus to a concave meniscus. The concave meniscus causes the direction of the additional capillary pressure inside the microporous channel 341 to reverse, and the additional capillary pressure is transformed into an active driving force pointing towards the surface of the concrete to be cured.

[0098] Step S605: The active driving force provides the net driving pressure required for the curing fluid to flow within the microporous channel 341. This net driving pressure is the amount of capillary pressure change generated within the micropores in step S600. ;

[0099] Step S606: After the capillary pressure change overcomes the Darcy resistance and fluid viscosity loss inside the porous medium, it is converted into a net driving force to promote the directional seepage of the maintenance fluid from the middle reservoir 330 to the external environment. Under the action of the active driving force, the maintenance fluid seeps directionally from the middle reservoir 330 to the external environment. The local fluid release rate of the microfluidic intelligent maintenance membrane 300 is constrained by the geometric characteristics of the micropores and the fluid dynamics parameters.

[0100] Step S607: The curing liquid is released through the microporous channel 341 to the local concrete surface corresponding to the target pixel cross node 313, participating in the physical dissipation of surface hydration heat and humidity compensation. When the BIM central control platform 100 stops outputting DC drive voltage, the accumulated charge on the surface of the dielectric insulating layer 342 is dissipated through the internal circuit of the indium tin oxide addressing electrode array 310.

[0101] Step S608: The physical structure of the micro double-layer capacitor disintegrates with charge dissipation, the transient contact angle of the solid-liquid interface spontaneously regresses to the initial contact angle, the concave meniscus in the microporous channel 341 is restored to a convex meniscus, the active driving force is eliminated, the reverse capillary resistance is re-established, the reverse capillary resistance cuts off the downward flow path of the curing liquid, and the physical flow path of the microporous channel 341 is closed.

[0102] It should be noted that the alternation of the probe phase and the driving phase described above is not only a static cycle, but also requires macroscopic dynamic adjustment in conjunction with the stage of hydration. Therefore, this embodiment provides a high-frequency timing configuration and adaptive adjustment method for the probe-driving phase for the global timing of the macroscopic process, including the following steps:

[0103] Step S201: The BIM central control platform 100 calls the time division multiplexing control module 120. The time division multiplexing control module 120 establishes the global reference cycle of the system operation. The global reference cycle consists of the detection phase time window and the driving phase time window.

[0104] Step S202: The BIM central control platform 100 acquires the time derivative data of the transient internal heat source generation rate output by the finite difference calculation module 110. The time derivative data of the transient internal heat source generation rate characterizes the physical change trend of the heat release rate of the hydration reaction inside the concrete to be cured.

[0105] Step S203: The BIM central control platform 100 divides the physical stage of the hydration reaction based on the time derivative data of the transient internal heat source generation rate. When the time derivative data is greater than zero, the BIM central control platform 100 determines that the concrete to be cured is in the hydration heating period. When the time derivative data is less than or equal to zero, the BIM central control platform 100 determines that the concrete to be cured is in the hydration cooling period.

[0106] Step S204: During the hydration heating period, the temperature gradient in the region corresponding to the superpixel node shows an increasing trend. The BIM central control platform 100 shortens the total physical duration of the global reference cycle and increases the switching frequency of the time-division multiplexing control module 120 between the detection phase and the driving phase. The formula for calculating the total physical duration of the global reference cycle is:

[0107]

[0108] In the formula, The total physical duration of the global baseline period; The maximum reference period duration is set. It is the periodic adjustment sensitivity coefficient with time dimension; The relative rate of change of the transient internal heat source generation rate; increasing the alternation switching frequency increases the sampling density of the system for the physical state of the micro-environment.

[0109] Step S205: The BIM central control platform 100 dynamically adjusts the duty cycle of the drive phase time window within the global reference cycle. The adjustment formula for the drive phase duty cycle is:

[0110]

[0111] In the formula: To drive the phase duty cycle; The dimensionless duty cycle adjustment ratio coefficient is set. This is the difference between the actual temperature gradient of the superpixel node and the set safe temperature gradient. The nominal temperature gradient constant is set. The basic duty cycle constant;

[0112] Step S206: The BIM central control platform 100 extends the driving phase time window based on the increased driving phase duty cycle. The BIM central control platform 100 synchronously adjusts the amplitude of the DC driving voltage output to the indium tin oxide addressing electrode array 310. Considering the contact angle saturation characteristics of the dielectric wetting effect, the formula for calculating the DC driving voltage amplitude is:

[0113]

[0114] In the formula, This represents the amplitude of the DC drive voltage. This is the dielectric wetting threshold voltage; The upper limit of the dielectric wetting saturation voltage is set. For nonlinear gain coefficients; This is the difference between the actual temperature gradient of the superpixel node and the set safe temperature gradient.

[0115] Step S207: During the hydration heating period, the BIM central control platform 100 increases the amplitude of the DC drive voltage. The increase in the amplitude of the DC drive voltage increases the net drive pressure in the microporous channel 341. The increase in net drive pressure increases the amount of curing liquid released by the microfluidic intelligent curing membrane 300 per unit time.

[0116] Step S208: During the hydration cooling period, the hydration heat release rate slows down. The BIM central control platform 100 extends the total physical duration of the global reference cycle and reduces the alternation switching frequency. Based on the reduced temperature gradient difference, the BIM central control platform 100 reduces the duty cycle of the driving phase.

[0117] In step S209, the BIM central control platform 100 synchronously reduces the amplitude of the DC drive voltage output to the indium tin oxide addressing electrode array 310, and alternately switches the reduction of frequency and the reduction of drive phase duty cycle to reduce the total amount of curing liquid released by the microfluidic intelligent curing membrane 300. The adaptive adjustment of fluid release parameters matches the actual thermodynamic requirements of the hydration cooling period, and prevents the formation of supercooled water accumulation boundary on the surface of the concrete to be cured.

[0118] Finally, regarding the shutdown logic in step S700 of the above macro process, to ensure the system safely and orderly exits after the concrete has fully hardened, this embodiment provides a workflow for monitoring the hydration state evolution and adaptive termination judgment conditions for curing, including the following steps:

[0119] In step S701, the BIM central control platform 100 continuously acquires the real part of the equivalent complex impedance of each physical coordinate node in the indium tin oxide addressing electrode array 310 within the detection phase of the global reference cycle. The trajectory of the change of the real part of the equivalent complex impedance over time characterizes the formation progress of the hydrated calcium silicate gel on the surface of the concrete to be cured and the physical evolution of the fluid ion concentration at the solid-liquid interface.

[0120] Step S702: The BIM central control platform 100 establishes time series data of the real part of the equivalent complex impedance. The BIM central control platform 100 performs discrete difference operations on the time series data to calculate the real part time derivative of the equivalent complex impedance. The formula for calculating the real part time derivative is:

[0121] In the formula: The real-time derivative; This represents the real part of the equivalent complex impedance for the current sampling period; This represents the real part of the equivalent complex impedance from the previous sampling period. The sampling time interval for the probe phase;

[0122] Step S703: The free water inside the concrete to be cured is consumed and transformed into solid hydration products during the hydration reaction. The conductive ions at the solid-liquid interface gradually solidify. The real part of the equivalent complex impedance shows a nonlinear upward trend over time. The real part time derivative is positive during the period of intense hydration reaction.

[0123] Step S704: When the concrete to be cured enters the later stage of hydration reaction, the microenvironment tends to be in a thermodynamic and hydrochemical stable state, the real part of the equivalent complex impedance approaches the stable extreme value, and the real part time derivative gradually decreases and approaches zero as the hydration reaction progresses.

[0124] Step S705: The BIM central control platform 100 calls the built-in termination judgment logic module 150. The termination judgment logic module 150 obtains the real part value of the equivalent complex impedance and the real part time derivative. The termination judgment logic module 150 compares the real part value of the equivalent complex impedance with the set equivalent resistance threshold. The termination judgment logic module 150 simultaneously compares the absolute value of the real part time derivative with the set derivative convergence threshold.

[0125] Step S706: The termination judgment logic module 150 executes the maintenance adaptive termination composite judgment algorithm for a single physical coordinate node. The composite judgment condition formula is as follows:

[0126]

[0127] In the formula: The equivalent resistance threshold is set in advance and needs to be calibrated in conjunction with standard experiments to map the physical boundary of the concrete surface to be cured to reach the target hardening strength. The set threshold for derivative convergence; The set temperature gradient convergence threshold is used to prevent misjudgments caused by increased impedance due to abnormal surface dryness.

[0128] Step S707: The termination decision logic module 150 counts the number of physical coordinate nodes that meet the composite decision conditions, and calculates the global hardening convergence rate. The formula for calculating the global hardening convergence rate is:

[0129] In the formula: The global hardening convergence rate; The number of physical coordinate nodes required to satisfy the composite judgment conditions; The total number of physical coordinate nodes in the microfluidic intelligent curing membrane 300;

[0130] Step S708: When the global hardening convergence rate is greater than or equal to the set convergence rate threshold, the termination judgment logic module 150 determines that the overall surface of the concrete to be cured has been hardened, and the BIM central control platform 100 sends a system shutdown command to the edge sensing gateway 200.

[0131] In step S709, the edge sensing gateway 200 cuts off the power supply circuit of the microfluidic intelligent curing membrane 300, the time-division multiplexing control module 120 stops the alternating switching control logic of the detection phase and the driving phase, the indium tin oxide addressing electrode array 310 stops performing voltage output operation, the micropore channels 341 inside the microfluidic intelligent curing membrane 300 are restored and maintained in a physically hydrophobic and closed state, and the system completes the dynamic curing process of the entire life cycle.

Claims

1. A BIM-based dynamic concrete curing system, characterized in that, include: BIM central control platform (100), edge sensing gateway (200), and microfluidic intelligent curing membrane (300). The BIM central control platform (100) establishes a two-way communication connection with the edge sensing gateway (200) to issue addressing coordinates, timing control commands and electrical signals; the microfluidic intelligent curing membrane (300) covers the outer surface of the concrete to be cured and formed, including an upper surface layer (320), a middle reservoir layer (330) and a lower base layer (340); the middle reservoir layer (330) contains a capillary network structure (331) and is configured as a reservoir space to contain curing liquid; the lower base layer (340) has microporous channels (341) distributed inside, and the lower base layer (340) is provided with an indium tin oxide addressing electrode array (310), and the indium tin oxide addressing electrode array (310) is wrapped with a dielectric insulating layer (342). The edge sensing gateway (200) is used to receive the analog electrical response signal returned by the microfluidic smart curing membrane (300) and convert it into a digital signal to be uploaded to the BIM central control platform (100); the microfluidic smart curing membrane (300) is used to receive the voltage control signal output by the BIM central control platform (100) and trigger and execute the dielectric wetting fluid release action.

2. A BIM-based dynamic curing method for concrete, characterized in that, The method for implementing the BIM-based dynamic concrete curing system according to claim 1 includes the following steps: Step S100: Obtain the three-dimensional geometric model of the concrete structure to be poured and perform spatial discretization processing to generate a three-dimensional voxel mesh set. Extract the surface voxels and unfold them in two dimensions to form a virtual pixel matrix. Establish the initial addressing mapping relationship between the virtual pixel matrix and the physical coordinates of the indium tin oxide addressing electrode array in the microfluidic intelligent curing membrane. Step S200: Divide the system operation cycle into alternating detection and driving phases; Step S300: Enter the detection phase, control the edge sensing gateway to inject high-frequency AC excitation voltage into each physical coordinate node in the indium tin oxide addressing electrode array, collect the response current signal to calculate the equivalent complex impedance of each physical coordinate node, and extract the real part feature data and imaginary part feature data of the equivalent complex impedance. Step S400: Calculate the feature distance between adjacent physical coordinate nodes, call the clustering algorithm to merge continuous physical coordinate nodes with feature distance less than a set threshold, reconstruct them into superpixel nodes, and update the logical control boundary with the superpixel nodes as independent computing units. Step S500: Enter the driving phase, combine the transient heat source generation rate with the real part feature data to calculate and determine when the temperature gradient or humidity decrease rate of a certain superpixel node reaches a set threshold, and output a DC driving voltage to the indium tin oxide addressing electrode array associated with the superpixel node. Step S600: The microporous solid-liquid interface in the microfluidic intelligent curing membrane undergoes dielectric wetting under the action of the DC driving voltage. The interface contact angle decreases, causing the capillary pressure in the micropores to change, which in turn causes the curing liquid to overcome physical viscous resistance and be released to the local concrete surface. Step S700: Continuously cycle through the detection phase and the driving phase, synchronously calculate the real part time derivative of the equivalent complex impedance, and determine when the real part time derivative approaches zero and the real part value of the equivalent complex impedance is greater than the set resistance threshold, terminate the time division multiplexing control timing and end the operation.

3. The BIM-based dynamic curing method for concrete according to claim 2, characterized in that, The process of establishing the initial addressing mapping relationship in step S100 specifically includes: Obtain the physical coordinates of each pixel intersection node in the indium tin oxide addressing electrode array; An affine transformation matrix model is used to establish the initial mapping relationship between virtual pixel coordinates and physical coordinates; Obtain the reference alignment electrical signals of at least three non-collinear pixel intersection nodes at the edge position of the indium tin oxide addressing electrode array, confirm the actual physical coordinates, and simultaneously extract the corresponding reference virtual pixel coordinates; Substitute the corresponding actual physical coordinates and the reference virtual pixel coordinates into the affine transformation matrix model to solve the problem simultaneously, obtain the mapping coefficients and translation coefficients, and substitute them into the affine transformation matrix model to complete the hard mapping binding between the virtual pixel coordinates and the physical coordinates.

4. The BIM-based dynamic curing method for concrete according to claim 2, characterized in that, The specific process of obtaining the equivalent complex impedance in step S300 includes: The initial response current signal, which is fed back from the solid-liquid interface in the microporous channel and superimposed with high-frequency electrical noise, is collected. The mathematical morphology filtering module is called to perform morphological opening and closing combination operations to filter out the high-frequency electrical noise and output the target response current signal. The real and imaginary parts of the equivalent complex impedance are calculated using the amplitude of the high-frequency AC excitation voltage, the amplitude of the response current, and the phase difference between the target response current signal and the high-frequency AC excitation voltage. The water content parameter of the region corresponding to the pixel intersection node is evaluated based on the real part value, and the material phase composition of the corresponding region is determined based on the imaginary part value.

5. The BIM-based dynamic curing method for concrete according to claim 2, characterized in that, The process of reconstructing superpixel nodes in step S400 specifically includes: Construct a global equivalent complex impedance characteristic matrix that includes Euclidean geometric coordinate data and complex impedance component data; Unassigned physical coordinate nodes are selected as core seed nodes, and neighborhood physical coordinate nodes are extracted and feature distances are calculated based on the 8-connected topology of the array. Boundary expansion operations are performed based on the set feature distance threshold. The mathematical formula for the boundary expansion operation is as follows: In the formula: For the first The cluster set after the next iteration; For the first The cluster set after the next iteration; This is the set union operator; For neighborhood physical coordinate nodes; As the core seed node; The feature distance; The set feature distance threshold; The newly added neighboring physical coordinate nodes are set as new core seed nodes and iteratively expanded until there are no neighboring physical coordinate nodes that meet the conditions around the edge nodes. The resulting independent final cluster set is then merged into a superpixel node. The convex hull algorithm is called to extract the bounding contour coordinates and map them to the virtual pixel matrix, and the logical control boundary is updated.

6. The BIM-based dynamic curing method for concrete according to claim 2, characterized in that, The process of outputting the DC drive voltage in step S500 specifically includes: A gating voltage is applied to the target row scanning electrode line, and a data signal voltage is simultaneously input to the target column data electrode line; The voltage difference between the gate voltage and the data signal voltage forms a local DC driving voltage at the intersection node of the target pixel. The condition for triggering the dielectric wetting effect is that the absolute value of the local DC driving voltage is greater than or equal to the dielectric wetting threshold voltage. The absolute value of the half-select bias voltage generated at the non-target pixel intersection node is less than the dielectric wetting threshold voltage, so that the dielectric insulating layer at the corresponding position maintains its intrinsic hydrophobic physical state and blocks the leakage of curing liquid.

7. The BIM-based dynamic curing method for concrete according to claim 2, characterized in that, The specific micro-hydrodynamic execution mechanism of the microporous solid-liquid interface in step S600 is as follows: The DC driving voltage induces charge space rearrangement and constructs a microscopic double-layer capacitor physical structure at the solid-liquid-gas three-phase interface. The solid-liquid interface changes from a hydrophobic state to a hydrophilic state, and the gas-liquid interface changes from a convex meniscus to a concave meniscus. This causes the capillary pressure direction within the microporous channels to reverse and become an active driving force. The formula for the change in capillary pressure is: In the formula: This represents the change in capillary pressure. The gas-liquid interfacial tension of the curing solution; The effective hydraulic radius of the micropores; The transient contact angle after applying a DC drive voltage; The initial contact angle when no DC drive voltage is applied; The curing solution flows directionally into the external environment under the action of the capillary pressure change as the active driving force; when the DC driving voltage is stopped, the charge is dissipated and the transient contact angle of the solid-liquid interface spontaneously reverts to the initial contact angle, the reverse capillary resistance is re-established, and the micropore channel is locked.

8. The BIM-based dynamic curing method for concrete according to claim 2, characterized in that, The method also includes a timing configuration step for adaptively adjusting the alternation switching frequency: The time derivative data of the transient internal heat source generation rate is obtained. When the time derivative data is greater than zero, it is determined that the hydration heating period is in progress. The total physical duration of the global reference cycle is shortened to increase the alternation frequency between the detection phase and the driving phase. When the time derivative data is less than or equal to zero, it is determined that the hydration cooling period is in progress, and the total physical duration of the global reference cycle is extended to reduce the alternation switching frequency.

9. The BIM-based dynamic curing method for concrete according to claim 8, characterized in that, The timing configuration step also includes adjustment configuration for the drive phase: Based on the difference between the actual temperature gradient of the superpixel node and the set safe temperature gradient, the duty cycle of the driving phase time window within the global reference period is dynamically adjusted. During the hydration heating period, the driving phase time window is extended according to the increased driving phase duty cycle, and the DC driving voltage amplitude is increased to increase the amount of curing solution released per unit time. During the hydration cooling period, the duty cycle of the driving phase is reduced based on the decreased temperature gradient difference, and the amplitude of the DC driving voltage is reduced simultaneously.

10. The BIM-based dynamic curing method for concrete according to claim 2, characterized in that, The logic for determining the end of the process in step S700 specifically includes: Establish time series data of the real part of the equivalent complex impedance and perform discrete difference operation to calculate the real part time derivative; The maintenance adaptive termination composite judgment algorithm is executed for a single physical coordinate node, and the real part of the equivalent complex impedance is determined to be greater than or equal to the set equivalent resistance threshold, and the absolute value of the real part time derivative is less than or equal to the set derivative convergence threshold. The number of physical coordinate nodes that meet the composite judgment conditions is counted and the global hardening convergence rate is calculated. When the global hardening convergence rate is greater than or equal to the set convergence rate threshold, it is determined that the overall surface of the concrete to be cured has been hardened. A system shutdown command is sent to the edge sensing gateway and the power supply circuit of the microfluidic intelligent curing membrane is cut off.

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