Print medium moisture regulation method and related apparatus

Abstract

The application provides a printing medium moisture regulation method and related equipment. The method comprises the following steps: obtaining initial reflection spectrum data of multiple positions in a printing medium interface during printing by a 3D printing device; preprocessing each initial reflection spectrum data to obtain target reflection spectrum data corresponding to each position in the printing medium interface; determining a spectral feature response parameter corresponding to each position based on the target reflection spectrum data of each position in the printing medium interface, and calling an interface moisture content prediction model to determine an actual interface moisture content of the printing medium interface based on all the spectral feature response parameters; calculating a target moisture regulation parameter according to the target interface moisture content and the actual interface moisture content, and regulating the moisture content of the printing medium interface based on the target moisture regulation parameter, so that the actual interface moisture content of the printing medium interface approaches the target interface moisture content. The application can solve the problem of insufficient moisture content of the printing medium of the printing device and improve the printing quality.

Description

Technical Field

[0001] This application relates to the field of spectral detection technology, and in particular to a method for controlling the moisture content of printing media and related equipment. Background Technology

[0002] With the rapid development of the construction industry towards industrialization and digitalization, intelligent construction has become a core driving force for the transformation and upgrading of the construction field. Among them, concrete 3D printing, as an emerging formwork-free digital construction technology, has been widely used in intelligent construction systems due to its automation, precision, and programmability.

[0003] However, traditional 3D printing equipment may have insufficient moisture content in the printing media, resulting in lower print quality. Summary of the Invention

[0004] The main objective of this application is to propose a method and related equipment for controlling the moisture content of printing media, aiming to solve the problem of insufficient moisture content in the printing media of 3D printing equipment and improve printing quality.

[0005] To achieve the above objectives, a first aspect of this application proposes a method for controlling the moisture content of a printing medium, applied to a 3D printing device, the method comprising: During the printing process of the 3D printing equipment, initial reflectance spectral data at multiple locations on the printing medium interface are acquired; Each of the initial reflectance spectral data is preprocessed to obtain target reflectance spectral data corresponding to each position in the printing medium interface; Based on the target reflectance spectrum data of each position in the printing medium interface, the spectral characteristic response parameters corresponding to each position are determined, and the pre-built interface moisture content prediction model is called to determine the actual interface moisture content of the printing medium interface based on all the spectral characteristic response parameters. Target moisture control parameters are calculated based on the target interface moisture content and the actual interface moisture content, and the moisture content of the printing media interface is controlled based on the target moisture control parameters so that the actual interface moisture content of the printing media interface approaches the target interface moisture content.

[0006] To achieve the above objectives, a second aspect of the present application provides a control device for implementing the method described in the first aspect.

[0007] To achieve the above objectives, a third aspect of the present application provides a 3D printing device, which includes the control device as described in the second aspect.

[0008] The printing medium moisture control method and related equipment proposed in this application acquire initial reflectance spectral data at multiple locations on the printing medium interface during the 3D printing process, enabling non-destructive, real-time monitoring of the interlayer moisture content. Then, by preprocessing the initial reflectance spectral data, noise interference from complex construction environments and interface physical morphology is effectively eliminated, ensuring the reliability of the target reflectance spectral data. Based on this, spectral feature response parameters corresponding to each location are extracted, and a pre-built interface moisture content prediction model is invoked to accurately invert the spectral features into the spatial distribution of the actual interface moisture content, achieving a high-precision quantitative assessment of interface moisture. Finally, the target moisture control parameter is obtained by calculating the difference between the target interface moisture content and the actual interface moisture content, and the moisture content of the printing medium interface is controlled based on this parameter. Thus, this embodiment can accurately replenish the moisture content of the printing medium based on real-time spectral feedback, bringing it close to the optimal target interface moisture content. This solves the problem of insufficient printing medium moisture content in 3D printing equipment under actual construction exposure conditions, effectively avoiding interlayer drying defects and improving the bonding performance and printing quality between adjacent printing layers. Attached Figure Description

[0009] Figure 1 This is a flowchart of the printing media moisture control method provided in this application; Figure 2 yes Figure 1 The flowchart of step S102 in the document; Figure 3 yes Figure 2 The flowchart of step S202 in the document; Figure 4A This is a schematic diagram of the initial reflectance spectral data provided in this application; Figure 4B A schematic diagram of the second reflectance spectral data obtained by baseline correction provided in this application; Figure 4C This is a schematic diagram of the target reflectance spectral data provided in this application; Figure 4D This is a visual quantization diagram of the extracted spectral feature response parameters provided in this application; Figure 5 This is a flowchart of the simulation analysis of the interface moisture content prediction model provided in this application; Figure 6 yes Figure 1 The flowchart of step S103 in the process; Figure 7 yes Figure 6 The flowchart of step S602 in the document; Figure 8 yes Figure 1 The flowchart for step S104 in the process. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0011] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0013] In response to the rapid industrialization and digitalization of the construction industry, intelligent construction has become a core driving force for the transformation and upgrading of the construction sector. Among them, concrete 3D printing, as an emerging formworkless digital construction technology, has great potential in solving problems such as excessive reliance on manpower, low construction efficiency, and high costs in traditional concrete construction due to its automation, precision, and programmability, and has been widely used in intelligent construction systems.

[0014] However, the layer-by-layer stacking construction process used in concrete 3D printing can easily create weak interfaces between adjacent printed layers, thereby weakening the overall structural load-bearing capacity and durability. Studies have shown that the mechanical properties of the printed interface mainly depend on its moisture content; too much or too little moisture will lead to a significant decrease in the overall structural performance. In actual printing processes, due to long single-layer printing paths, equipment cycle time limitations, or excessively long interlayer placement (waiting) times, the formed printing media interface is exposed to the construction environment for extended periods, leading to excessive moisture evaporation. This lack of interfacial moisture results in insufficient moisture between adjacent layers to maintain the secondary hydration reaction of the cement-based materials, easily causing interface drying defects between the formed layers. This reduces the interfacial adhesion between adjacent printed layers, ultimately leading to insufficient moisture content in the printing media, reduced printing quality, and decreased mechanical stability of the overall structure.

[0015] Precise control of moisture content at the printing interface has become a key issue in current 3D printed concrete research. Currently, traditional methods for quantifying and evaluating interface moisture during construction primarily rely on physical adsorption (such as desiccant adsorption) and wind tunnel drying. These testing methods not only damage the samples but also produce inaccurate moisture content readings, failing to meet the dynamic monitoring needs of the on-site printing process and resulting in a severe deficiency in the ability to control moisture in real time.

[0016] Near-infrared spectroscopy (NIR), as a low-energy and non-destructive technique, can effectively study the physical forms of water in various types of materials, including cement-based materials, providing a new approach for precise moisture quantification. Currently, existing technologies have established quantitative correlation models between spectral and quality parameters for detection using NIR spectroscopy. However, these technologies still have significant limitations: First, existing methods are mainly geared towards static organic systems, whose spectral characteristics are relatively stable, making them unsuitable for inorganic systems like cement-based composites, which have complex compositions and significant dynamic moisture changes. Second, the detection process lacks automated design, only allowing static scanning of local samples, resulting in limited information acquisition and an inability to comprehensively reflect the distribution characteristics of moisture and components within the material. Finally, existing methods lack real-time feedback and closed-loop control mechanisms, preventing the detection results from being used to drive adaptive adjustments for quality optimization, thus failing to meet the continuous monitoring and dynamic optimization requirements of complex production processes such as 3D printing.

[0017] Therefore, how to improve the accuracy and automation of moisture quantification at the interface of the printed layer during concrete 3D printing, so as to achieve precise closed-loop control of interface moisture, has become a technical problem that urgently needs to be solved in this field.

[0018] Based on this, this application provides a method and related equipment for controlling the moisture content of printing media, aiming to solve the problem of insufficient moisture content in the printing media of 3D printing equipment, improve the accuracy of moisture quantification at the interface of the printing layer during concrete 3D printing, thereby achieving precise control of interface moisture and improving printing quality.

[0019] The printing media moisture control method and related equipment provided in this application are specifically described through the following embodiments. First, the printing media moisture control method in this application embodiment is described.

[0020] The printing media moisture control method provided in this application relates to the field of spectral detection technology. This printing media moisture control method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the printing media moisture control method, but is not limited to the above forms.

[0021] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0022] Figure 1 This is an optional flowchart of the printing media moisture control method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0023] Step S101: During the printing process of the 3D printing equipment, acquire the initial reflectance spectrum data of multiple locations on the printing medium interface; Step S102: Preprocess each initial reflectance spectral data to obtain target reflectance spectral data corresponding to each position in the printing medium interface; Step S103: Determine the spectral characteristic response parameters corresponding to each position based on the target reflectance spectral data of each position in the printing medium interface, and call the pre-built interface moisture content prediction model to determine the actual interface moisture content of the printing medium interface based on all spectral characteristic response parameters. Step S104: Calculate the target moisture control parameter based on the target interface moisture content and the actual interface moisture content, and control the moisture content of the printing medium interface based on the target moisture control parameter so that the actual interface moisture content of the printing medium interface approaches the target interface moisture content.

[0024] The printing medium moisture control method provided in this application is applied to 3D printing equipment. This application constructs a fully automated closed-loop system from in-situ non-destructive testing to intelligent adaptive water replenishment. In actual construction, after the layer-forming mechanism of the 3D printing equipment lays the printing medium, the in-situ mounted spectral acquisition component can capture the near-infrared reflectance spectrum data of the newly formed interface in real time along a preset path, and convert it into the spectral characteristic absorption peak area characterizing the moisture intensity after standardization processing. Subsequently, the control device provided in this application uses a built-in quantitative analysis model to accurately invert the spectral characteristics into the actual moisture content of the interface, and calculates the amount of water replenishment required to compensate for moisture evaporation by combining the printing medium density and interface size parameters. Finally, the control device drives the moisture compensation component mounted on the 3D printing equipment to perform precise and uniform spray water replenishment operation on the exposed printing medium interface according to the target moisture control parameters, and accurately controls the moisture content according to real-time monitoring feedback until the interface moisture content reaches the optimal target moisture content. At this time, the 3D printing equipment executes the next layer stacking printing task, thereby realizing precise closed-loop control of the interlayer moisture content during the printing process.

[0025] In step S101, during the printing process of the 3D printing equipment, the initial reflectance spectral data of multiple locations on the printing medium interface can be obtained by combining the spectral detection module and the programmable mobile platform module. Specifically, the spectral detection module provided in this embodiment includes a tungsten lamp light source with a rated power of 12V / 100W, a near-infrared fiber optic probe, and a near-infrared spectrometer with a working wavelength range covering 1100-2500nm. The printing medium may include liquid photosensitive resin, as well as cement-based materials used in intelligent building construction solutions, etc., without limitation. This application uses cement-based materials as an example for illustration.

[0026] During 3D printing, a tungsten lamp source provides continuous, broadband radiation covering the near-infrared detection range. This radiation is uniformly guided to a fiber optic probe via a ring-shaped multi-fiber coaxial optical path, illuminating the unhardened interface of the 3D-printed cement-based material being tested. Simultaneously, the near-infrared fiber optic probe emits near-infrared radiation and receives the diffuse reflectance spectral signal modulated by interface moisture, transmitting it to a near-infrared spectrometer for photoelectric conversion, thereby capturing the spectral response data at the corresponding spatial location.

[0027] To acquire initial reflectance spectral data at multiple locations on the printing media interface, this embodiment utilizes a programmable motion platform module for multi-dimensional spatial motion control of the near-infrared fiber optic probe. Based on a built-in motion controller and programmable logic control program, the programmable motion platform module automatically adjusts the displacement and orientation of the fiber optic probe in three-dimensional space according to a preset planar scanning path.

[0028] In actual testing, to ensure the highest signal-to-noise ratio and steady-state response of the spectral signal, the sampling parameters of the fiber optic probe were optimized through gradient experiments: the detection distance between the probe and the printing medium interface was maintained at 3.5 to 4.5 mm, the incident angle of near-infrared radiation was maintained at 15° to 45°, and the spectral signal was continuously acquired and stored according to an integration time of 30 ms and a sampling frequency of 5 Hz. The automated continuous uniform acquisition strategy provided in this application can comprehensively acquire initial reflectance spectral data at multiple points on the spatial distribution of the entire printing medium interface.

[0029] In some embodiments, the 3D printing device includes a spectral acquisition component to acquire initial reflectance spectral data at multiple locations on the printing medium interface, including: The detection distance between the spectral acquisition component and the printing medium interface, the incident angle of the detection beam of the spectral acquisition component, the integration time of the spectral acquisition component for a single spectral signal acquisition, and the sampling frequency of the spectral acquisition component are obtained. The spectral data sampling strategy is determined based on the detection distance, the incident angle of the detection beam, the integration time, and the sampling frequency. The spectral acquisition component is then controlled to perform spatial scanning along multiple positions on the printing medium interface according to the spectral data sampling strategy, so as to obtain the initial reflection spectral data of multiple positions on the printing medium interface.

[0030] In this embodiment, the 3D printing equipment includes a spectral acquisition component, which is used to perform real-time, online, automated monitoring of the interlayer morphology of the printing medium during the construction process. As described above, the spectral acquisition component includes a tungsten lamp light source capable of outputting continuous broadband radiation, a near-infrared fiber optic probe with a ring-shaped multi-fiber coaxial optical path configuration, and a near-infrared spectrometer whose detection range covers the characteristic absorption peaks of water molecules.

[0031] Specifically, to establish the optimal optical path and sampling strategy that conforms to the characteristics of cement-based interfaces in 3D printing, the above four parameters, as parameters in the detection process, need to be systematically extracted and evaluated in advance. Specifically, the light source provided by the tungsten lamp is uniformly introduced into the near-infrared fiber optic probe through an optical coupling system, serving as the incident light source to illuminate the interface of the printed medium under test. The detection distance refers to the vertical distance between the end of the near-infrared fiber optic probe and the interface of the 3D printed medium under test. The incident angle of the detection beam refers to the angle formed by the near-infrared radiation beam and the vertical axis when it is projected onto the printed medium interface. The integration time is the duration of a single exposure for the near-infrared spectrometer to collect spectral energy. The sampling frequency represents the number of consecutive samples taken by the near-infrared spectrometer per unit time.

[0032] The above parameters were obtained through gradient design experiments. Under the premise of keeping the ambient light and sample condition constant, multiple sets of variable gradients were set for spectral detection, including detection distances of 0 to 9 mm (e.g., 0 mm, 1 mm, 2 mm), incident angles of 0° to 80° (e.g., 0°, 5°, 10°, 15°), integration times of 0 to 35 ms, and sampling frequencies of 1 to 9 Hz. Multifactor variance analysis was used to evaluate the influence of the coupling effect between these parameters on the spectral signal-to-noise ratio and steady-state response, thereby determining the optimal combination of sampling parameters under the coupled state of the highest signal-to-noise ratio and steady-state spectral response. The experimental results of this application show that the optimal combination of sampling parameters for the interface characteristics of 3D printed cement-based materials is a detection distance of 3.5 to 4.5 mm, an incident angle of 15° to 45°, an integration time of 30 ms, and a sampling frequency of 5 Hz.

[0033] After obtaining the optimal combination of sampling parameters with the highest signal-to-noise ratio in the aforementioned steps, the spectral data sampling strategy is determined based on this optimal combination. When performing interlayer moisture detection on the printing medium, a programmable mobile platform module is used to drive the spectral acquisition component to achieve multi-dimensional spatial motion control within the detection area. Specifically, the control unit of the programmable mobile platform module is built on a programmable logic controller (PLC), using standard G-code language as the motion command input format. The built-in command parsing system performs semantic parsing and trajectory interpolation calculations on the self-compiled G-code file, converting the motion layer commands into real-time control pulses and pose parameters for the servo drive axes.

[0034] Under the high-precision (comprehensive control accuracy not lower than 0.1mm) coordinated drive of the servo motor, linear sliding table and micro-stepping transmission unit, the near-infrared optical fiber probe performs automated planar scanning and attitude adjustment above the interface of the tested printing medium according to the preset scanning path, the detection distance and incident angle determined in the foregoing steps. During the continuous moving scanning process, the data acquisition module matched with the near-infrared spectrometer (the data acquisition module is connected to the near-infrared spectrometer through a high-speed data acquisition interface, which can issue parameter instructions such as integration time and sampling frequency to the near-infrared spectrometer, and perform automatic recording, continuous storage, classified management and real-time visual display on the original spectral signal converted by the near-infrared spectrometer) continuously and stably records and stores the diffuse reflection spectral signal captured by the near-infrared spectrometer through the high-speed data acquisition interface according to the set integration time and sampling frequency. This collaborative process realizes continuous and uniform acquisition and real-time position registration of multi-point spectral signals on the interface, so as to obtain initial reflection spectral data that comprehensively reflects the overall water content distribution on the interface of the printing medium, including the initial reflection spectral data of multiple measurement positions on the interface of the printing medium.

[0035] In step S102, each piece of initial reflection spectral data is preprocessed. The preprocessing refers to a spectral standardization processing algorithm comprising multiple stages, so as to eliminate the interference of noise in the complex construction environment and the physical morphology of the printing medium interface on the collected reflection spectral data, and enhance the actual moisture spectral signal.

[0036] In some embodiments, please refer to Figure 2 , preprocessing each piece of initial reflection spectral data to obtain target reflection spectral data corresponding to each position in the interface of the printing medium includes: Step S201: acquiring an environmental background spectrum and a reference spectrum, and sequentially removing the environmental background spectrum and the reference spectrum from each piece of initial reflection spectral data to obtain first reflection spectral data; Step S202: performing smoothing filtering on the first reflection spectral data to obtain target reflection spectral data corresponding to each position in the interface of the printing medium.

[0037] In step S201, an environmental background spectrum and a reference spectrum are first acquired. The environmental background spectrum is collected under the condition that the tungsten light source of the spectrum acquisition assembly is turned off, and is used to characterize the spectral information of the external light source and environmental noise at the test site. The reference spectrum is collected from a fixed measurement point on the surface of the concrete sample in a dry state (i.e., a state free of free water), and is used to characterize the inherent reflection property of the building material itself.

[0038] After acquiring the two reference spectra mentioned above, the environmental background spectrum and reference spectrum are sequentially removed from each initial reflectance spectral data to obtain the corresponding first reflectance spectral data. For example, in actual 3D printing construction sites, workshop lighting, natural diffused light, and dark current from the spectrometer detector itself may contaminate the initial reflectance spectral signal; simultaneously, the mineral composition of the cement medium itself will also produce basic spectral reflectance. Subtracting the environmental background spectrum under the light-off state and the reference spectrum under the completely dry state from the initial reflectance spectral data can eliminate the interference of environmental noise and enhance the true spectral characteristic signals related to interface moisture absorption.

[0039] In step S202, after subtracting the environmental background spectrum and reference spectrum, the first reflectance spectral data still retains some high-frequency random signal fluctuations caused by hardware response or uneven diffuse reflection at the interface. Therefore, further spectral preprocessing is required. Specifically, the Savitzky-Golay smoothing filter algorithm can be used to process the first reflectance spectral data after background subtraction. This algorithm performs local polynomial fitting and sliding weighted smoothing calculations on the spectral sequence data points within the analysis window by moving the analysis window. After smoothing filtering, this embodiment can effectively reduce high-frequency noise interference and finally output target reflectance spectral data with smooth waveform and high signal-to-noise ratio. The target reflectance spectral data is a standardized spectral dataset used for subsequent model analysis.

[0040] In actual 3D printing smart construction sites, non-target factors such as fluctuations in external lighting, inherent instrument noise, and the roughness of cement-based material interfaces can easily interfere with weak near-infrared moisture characteristic signals. This embodiment precisely isolates environmental noise and printing medium background noise by subtracting the environmental background spectrum and reference spectrum. Then, Savitzky-Golay smoothing filtering is used to reduce high-frequency random spikes, protecting the effective moisture absorption peaks in the reflectance spectral data. This transforms the original spectral signal containing various interferences into a pure characteristic spectrum, effectively suppressing environmental interference, compensating for system errors, and enhancing the moisture spectral characteristic signal. This lays a high-quality data foundation for subsequently establishing a high-precision, high-generalization-capability interface moisture content quantitative prediction model.

[0041] In some embodiments, please refer to Figure 3 The first reflectance spectral data is smoothed and filtered to obtain the target reflectance spectral data corresponding to each position on the printing medium interface, including: Step S301: Baseline correction is performed on the first reflectance spectral data by constructing a baseline correction model through linear interpolation to obtain the second reflectance spectral data; Step S302: Normalize the second reflectance spectral data to obtain the target reflectance spectral data corresponding to each position on the printing medium interface.

[0042] In step S301, after the initial smoothing and denoising, systematic baseline deviations may still exist in the spectral data. A baseline correction model can be constructed using linear interpolation to correct the baseline of the first reflectance spectral data, resulting in the second reflectance spectral data. Specifically, during the actual monitoring of 3D printing construction, the luminous intensity of the tungsten lamp light source is unstable over long periods of operation, and the performance of the detector inside the near-infrared spectrometer fluctuates with temperature or operating time. These hardware factors cause baseline tilting in the acquired spectral signals. To eliminate errors introduced by equipment, this embodiment uses a linear interpolation algorithm to construct a baseline correction model. Specifically, by selecting the two ends of the spectral curve or flat regions with no obvious absorption as anchor points, a baseline reference line is fitted between these anchor points using linear interpolation. Then, the first reflectance spectral data is subtracted from this interpolated baseline, thereby flattening the tilted spectrum. Thus, this embodiment, through linear interpolation baseline correction, can effectively eliminate the influence of spectral signal baseline drift on the calculation of the absorption intensity of water molecules in a specific near-infrared band.

[0043] In step S302, after correcting the baseline tilt, it is also necessary to address the signal intensity differences caused by the physical morphology of the object's surface. In concrete 3D printing, the interface of cement-based materials has high roughness. Small differences in the roughness of the material interface can cause varying degrees of light scattering at different detection points, resulting in a vertical drift phenomenon of overall spectral shift. To overcome this physical interference, this embodiment introduces a vector normalization processing algorithm. Specifically, the second reflectance spectral data is normalized, treating each reflectance spectral data as a multi-dimensional vector. By calculating the modulus of this vector and dividing the absorbance at each wavelength point by the modulus, all spectra are mapped to the same scale space, thereby achieving scale uniformity of spectral energy distribution. For example, two points on a printing interface with identical actual moisture content may have slightly lower spectral absorption curves on the Y-axis (absorbance) because one of the points is located in a small depression on a rough interface (with weaker reflection). Through vector normalization, these two spectra are stretched or compressed to the same energy reference, thereby eliminating amplitude differences caused by different sampling conditions or roughness. Thus, this embodiment improves the comparability of spectral data in multi-sample analysis and subsequent feature extraction, resulting in highly pure and standardized target reflectance spectral data.

[0044] This embodiment employs a combined strategy of first flattening and then unifying the baseline. It specifically utilizes a linear interpolation baseline correction strategy to eliminate instrument-induced baseline tilt, and then uses vector normalization to smooth out vertical drift and amplitude fluctuations caused by the roughness of the printing medium interface. In this way, this embodiment can isolate physical scattering and instrument drift noise unrelated to moisture content, ensuring that the extracted spectral characteristic response parameters can accurately and uniquely map the moisture content of the 3D printed interlayer interface, providing reliable data input for subsequent construction of a high-precision moisture quantification analysis model.

[0045] In some embodiments, please refer to Figure 4A , Figure 4A This is a schematic diagram of the initial reflectance spectrum data. The horizontal axis represents wavelength (in nm), and the vertical axis represents absorbance. Figure 4A The data includes multiple reflectance spectra that differ due to varying water content states. These reflectance spectra exhibit distinct absorption peaks characteristic of water molecules at approximately 1450 nm and 1950 nm (not shown in the figure). Due to instability in the light source intensity or fluctuations in detector performance during the testing process, the initial reflectance spectra (original spectra) show a noticeably non-horizontal tilt. To eliminate this systematic error introduced by the hardware, this embodiment selects anchor points in the flat substrate regions at both ends of the two characteristic absorption peaks and fits them using linear interpolation. Figure 4A The blue baseline 1 and red baseline 2 in the image can be used to flatten the tilted spectrum by subtracting the locally fitted reference baseline from the original spectral data, thereby effectively avoiding the impact of baseline drift on the accuracy of subsequent quantitative calculation of the area of ​​the characteristic absorption peaks of moisture.

[0046] In some embodiments, please refer to Figure 4B , Figure 4B The results show the state of the initial reflectance spectral data after linear interpolation baseline correction and demonstrate the vertical drift phenomenon present in the initial reflectance spectral data. Figure 4B The horizontal axis represents wavelength, and the vertical axis represents absorbance. It can be observed that the previously tilted baseline has been successfully corrected, represented by a horizontal blue and red baseline connected by three black anchor points. However, despite the flattened system baseline, multiple absorption spectral curves still exhibit significant overall parallel separation and vertical shift in the longitudinal direction. This phenomenon is mainly due to the slight differences in surface roughness at the interface of the 3D-printed cement-based material, resulting in vertical drift caused by varying degrees of diffuse reflection and physical scattering of the light beam at different sampling locations. Figure 4B This study demonstrates the influence of the morphology of the printing media interface on the spectral absorption intensity. It also explains why vector normalization is necessary after baseline correction to eliminate amplitude fluctuation errors caused by interface roughness, thus providing standardized data with uniform scale for subsequent quantitative analysis.

[0047] In some embodiments, please refer to Figure 4C , Figure 4C This is a schematic diagram of the target reflectance spectrum obtained by normalizing the second reflectance spectrum data. The horizontal axis represents wavelength, and the vertical axis represents absorbance. The vertical drift caused by the surface roughness differences of the 3D-printed cement-based material has been eliminated. The non-absorbing substrate of all reflectance spectrum curves has been precisely unified to the same energy reference plane (absorbance starting at almost zero). Within this pure scale space, the pure chemical characteristic absorption peaks of the water molecule overtone absorption band (approximately 1450 nm) and the combined absorption band of stretching and bending vibrations (approximately 1950 nm) are prominent, with complete and smooth peak shapes. This processing result resolves the amplitude fluctuation interference caused by physical scattering, improves the lateral comparability of spectral data between samples with different water contents, and lays a reliable high signal-to-noise ratio data foundation for subsequent accurate determination of the integration upper and lower limits and calculation of the area of ​​spectral characteristic absorption peaks.

[0048] In step S103, the spectral characteristic response parameters corresponding to each location are first determined based on the target reflectance spectral data at each location on the printing medium interface. These spectral characteristic response parameters include the total spectral absorption area of ​​the band corresponding to the moisture characteristic absorption peak extracted from the target reflectance spectral data at each detection location. By extracting the total spectral absorption area at multiple detection points, this embodiment can quantitatively characterize the moisture signal intensity at the corresponding location.

[0049] Then, the spectral characteristic response parameters are input into the pre-constructed interface moisture content prediction model to obtain the actual interface moisture content of the printing medium. The interface moisture content prediction model is a quantitative relationship model established in advance through numerous physical experiments and fitting using a nonlinear regression algorithm. In the model construction stage, this embodiment selects a 0.35 water-cement ratio cement paste material consistent with the actual printing, and prepares a 1mm thick test sample. The 1mm thickness is chosen because the effective penetration depth of near-infrared spectroscopy in cement-based materials is approximately 1mm, and its spectral response is sufficient to effectively characterize the overall moisture content of the sample. Specifically, the actual moisture content of samples with different moisture contents is determined in advance using a constant-temperature drying and weighing method. Let the wet mass of the sample be... The mass after complete drying (drying in a 105℃ constant temperature oven until the difference between two consecutive mass measurements is less than 0.1%) is: This allows us to calculate the wet mass of the sample. relative to sample drying quality The difference is then compared with the sample drying quality. The sample moisture content is obtained by calculating the ratio. Then, a nonlinear regression algorithm is used, with the spectral absorption area of ​​the corresponding band of the spectral characteristic absorption peak in the large amount of standardized reflectance spectrum data of each target as the independent variable and the sample moisture content as the dependent variable, to perform fitting training and obtain a high prediction accuracy (correlation coefficient between predicted and measured values). Interfacial moisture content prediction model.

[0050] In actual 3D printing construction, the interface moisture content prediction model can be invoked to quickly invert and calculate the precise spatial distribution of the actual interface moisture content at various locations based on the real-time captured spectral absorption area. This embodiment, trained using a constant-temperature drying and weighing benchmark and with a penetration depth height matching the actual value (1mm), effectively reduces random errors caused by local surface inhomogeneities in the medium, significantly improving the model's prediction accuracy (test set correlation coefficient). It has the ability to generalize and thus achieve high-precision, spatially continuous automated quantitative analysis of the water content state between printing media layers in complex intelligent construction environments.

[0051] In some embodiments, please refer to Figure 5 , Figure 5 This is a flowchart illustrating the simulation analysis of the interface moisture content prediction model provided in this application. Specifically, in the training phase of the interface moisture content prediction model, this embodiment first retrieves pre-divided training set data, using the actual sample moisture content obtained by physical drying and weighing as the dependent variable, and the average area of ​​the total spectral characteristic absorption peaks at all positions extracted from the spectral preprocessing stage as the independent variable. These two sets of corresponding training data are input into a nonlinear regression algorithm for fitting training, constructing an interface moisture content quantitative analysis model (i.e., the interface moisture content prediction model) that can accurately map the mathematical relationship between spectral characteristics and moisture content. After the model is constructed, the process enters the verification phase, applying the model to independent test set data, using the model to calculate independent moisture content prediction values, and comparing them with the actual measured moisture content values ​​of the test set samples. This dataset division and verification mechanism intuitively demonstrates whether the quantitative analysis model possesses sufficiently high prediction accuracy and generalization ability when facing unknown spectral data.

[0052] In some embodiments, please refer to Figure 6 Based on the target reflectance spectral data at each location on the printing media interface, the spectral characteristic response parameters corresponding to each location are determined, including: Step S601: Determine the first characteristic wavelength band corresponding to the overtone absorption band of water molecules and the second characteristic wavelength band corresponding to the combined absorption band of stretching and bending vibrations in the target reflectance spectral data at each position in the printing medium interface. Step S602: Determine the area of ​​the first spectral characteristic absorption peak based on the first characteristic wavelength band, and determine the area of ​​the second spectral characteristic absorption peak based on the second characteristic wavelength band; Step S603: Sum the areas of the first and second spectral characteristic absorption peaks at each position on the printing medium interface to obtain the spectral characteristic response parameters corresponding to each position.

[0053] In step S601, the first characteristic wavelength band corresponding to the overtone absorption band of water molecules and the second characteristic wavelength band corresponding to the combined absorption band of stretching and bending vibrations are determined from the target reflectance spectral data at each position on the printing medium interface. The near-infrared spectrometer used in this application has a detection wavelength range of 1100-2500 nm, which precisely covers the two core physical characteristic absorption bands of water molecules. The first characteristic wavelength band represents the characteristic wavelength band corresponding to the overtone absorption band of water molecules, referring to the specific spectral range exhibited by the absorption of higher-order overtone vibrations of the hydroxyl (OH) bonds in water molecules; the second characteristic wavelength band represents the characteristic wavelength band corresponding to the combined absorption band of stretching and bending vibrations, referring to another specific spectral range corresponding to the combination of stretching and bending vibrations of water molecules and the occurrence of fundamental energy transitions. In actual testing, this embodiment performs full-band scanning and identification of the pre-processed target reflectance spectrum data at each position in the printing medium interface, automatically locates and extracts the two molecular-level specific wavelength bands that are most sensitive to the free water and interlayer water content of the printing interface and have the most significant absorbance characteristics, providing targeted targets for subsequent quantitative calculation of integrated area.

[0054] In step S602, the area of ​​the first spectral characteristic absorption peak can be determined based on the first characteristic wavelength band, and the area of ​​the second spectral characteristic absorption peak can be determined based on the second characteristic wavelength band. The area of ​​the first spectral characteristic absorption peak and the area of ​​the second spectral characteristic absorption peak refer to the area enclosed by the spectral absorbance curve and the baseline within a specific wavelength band.

[0055] The area of ​​the characteristic absorption peaks in the spectrum can be calculated using numerical integration methods (such as trapezoidal integration or Simpson integration) or by peak fitting estimation. Specifically, the characteristic absorption peaks can be fitted nonlinearly using the Gaussian function, the Lorentzian function, or a combination of both (Voigt function). The peak area can then be indirectly calculated using the fitted half-width and peak height parameters. Alternatively, the derivative spectral integration method can be used to first differentiate the spectral curve to eliminate linear baseline drift, and then calculate the area of ​​the differentiated characteristic peaks and valleys. No restrictions are placed on this method.

[0056] This embodiment employs a numerical integration method for area calculation. Specifically, firstly, for a determined first characteristic wavelength band, the absorbance values ​​of all discrete wavelength points within that band are integrated and accumulated to determine the area of ​​the corresponding first spectral characteristic absorption peak. Simultaneously, this embodiment uses the same integral mathematical method to perform band integration calculations on the absorbance characteristics within a determined second characteristic wavelength band, thereby determining the area of ​​the corresponding second spectral characteristic absorption peak. Since the diffuse reflectance amplitude of the near-infrared spectrum is affected by the physical morphology and molecular density of water molecules at the interface of the printed medium being tested, the size of the integrated area within these two characteristic wavelength bands can accurately and quantitatively reflect the characteristic absorption intensity of the water molecules at the corresponding detection point.

[0057] In step S603, to comprehensively characterize the overall absorption signal of moisture across the entire 1100-2500nm wavelength range, this embodiment does not use a single absorption peak. Instead, it sums the areas of the first and second spectral characteristic absorption peaks at each location on the printing medium interface to obtain the spectral characteristic response parameters corresponding to each location. Then, this embodiment calls a pre-built interface moisture content prediction model, using the average value of the total spectral absorption area corresponding to all locations on the printing medium interface as the independent variable input to the model to predict the actual moisture content of the interface. During dynamic construction monitoring of the 3D printing equipment, the interface moisture content prediction model can perform nonlinear inversion based on the received spectral characteristic response parameters from all locations, thereby accurately calculating the spatial mapping distribution of the actual moisture content of the printing medium interface.

[0058] The characteristic signal of a single band is easily affected by scattering of cement materials or environmental drift. In this embodiment, based on the two characteristic absorption peaks of water molecules in the near-infrared band (overtone absorption band and combined absorption band), the spectral characteristic absorption peak areas of the two are extracted and summed and fused to construct a multidimensional spectral characteristic response parameter that can comprehensively and reliably characterize the physical form of water.

[0059] In some embodiments, please refer to Figure 7 The method involves determining the area of ​​a first spectral characteristic absorption peak based on a first characteristic wavelength band, and determining the area of ​​a second spectral characteristic absorption peak based on a second characteristic wavelength band, including: Step S701: Determine the first integration upper limit and the first integration lower limit corresponding to the first characteristic wavelength band, and perform integration calculation on the target reflection spectrum data according to the first integration upper limit and the first integration lower limit to obtain the area of ​​the corresponding first spectral characteristic absorption peak. Step S702: Determine the upper limit and lower limit of the second integration corresponding to the second characteristic wavelength band, and perform integration calculation on the target reflection spectrum data according to the upper limit and lower limit of the second integration to obtain the area of ​​the corresponding second spectral characteristic absorption peak.

[0060] In step S701, the first upper limit and lower limit of integration corresponding to the first characteristic wavelength band are first determined. Specifically, for the first characteristic wavelength band corresponding to the overtone absorption band of water molecules, it is necessary to determine the start and end points of the absorption peak on the spectral curve, and obtain the first lower limit of integration (set as wavelength parameter). ) and the first integration upper limit (set as wavelength parameter) In this embodiment, the integration range of the first characteristic wavelength band is 1350-1667nm, that is, the upper limit of the first integration is 1667nm and the lower limit of the first integration is 1350nm. Then, the target reflectance spectrum data (the function of its absorbance as a function of wavelength is denoted as...) is used. Based on this, within the closed interval formed by the first lower limit of integration and the first upper limit of integration. Perform definite integral calculations within the range to obtain the area of ​​the first spectral characteristic absorption peak (denoted as ). ), area of ​​the first spectral characteristic absorption peak The calculation process can be represented as:

[0061] Through integral calculation, this embodiment can accumulate the absorbance characteristics at all discrete wavelength points within the specific band and convert them into a macroscopic geometric area value that can intuitively and quantitatively characterize the overtone absorption intensity of water molecules.

[0062] In step S702, the upper limit and lower limit of the second integration corresponding to the second characteristic wavelength band are determined, corresponding to the logic of step S701. Since the detection range of the near-infrared spectrometer is 1100-2500 nm, this range also covers the combined absorption band of the stretching and bending vibrations of water molecules. Therefore, it is necessary to redetermine the unique lower limit of the second integration (set as wavelength parameter) for the second characteristic wavelength band. ) and the second integration upper limit (set as wavelength parameter) In this embodiment, the integration range of the second characteristic wavelength band is 1850-2173nm, that is, the upper limit of the second integration is 2173nm and the lower limit of the second integration is 1850nm.

[0063] Then in the new integration interval Within, for the same target reflectance spectral data By performing integral and summation calculations, the area of ​​the second spectral characteristic absorption peak (denoted as ) is obtained. The formula for calculating the area of ​​the second spectral characteristic absorption peak can be expressed as:

[0064] This step ensures that when dealing with inorganic systems with complex compositions and significant dynamic changes in moisture in 3D-printed cement-based materials, the energy transition information of water molecules in the near-infrared band is not missed, thereby accurately capturing the spectral characteristic signals of moisture within the combined absorption band.

[0065] In some embodiments, please refer to Figure 4D , Figure 4D This diagram demonstrates the visualized quantification process of extracting spectral characteristic response parameters by calculating the integral area after acquiring standardized preprocessed target reflectance spectral data. The horizontal axis represents wavelength, and the vertical axis represents absorbance. Two different colored shading marks visually indicate the calculated quantitative characteristic areas. The dark blue shading on the left represents the area of ​​the first spectral characteristic absorption peak extracted by calculus calculation of the target spectral curve within the first characteristic wavelength band (approximately 1450 nm, corresponding to the water molecule overtone absorption band) based on a set first lower and upper limit of integration. The dark red shading on the right represents the area of ​​the second spectral characteristic absorption peak calculated within the second characteristic wavelength band (approximately 1950 nm, corresponding to the combined absorption band of water molecule stretching and bending vibrations). This method of truncating a continuous nonlinear spectral curve and converting it into a specific geometric area clearly illustrates how the algorithm eliminates out-of-band interference and accurately quantifies the absorption intensity of water molecule physical characteristics. This provides an intuitive and reliable quantitative input basis for subsequently fusing these two area parameters into a quantitative prediction model to accurately invert the actual water content of the printing medium interface.

[0066] In this way, the area of ​​the first spectral characteristic absorption peak and the area of ​​the second spectral characteristic absorption peak can be calculated for each position on the printing medium interface based on the corresponding target reflectance spectrum data. By adding the areas of the first spectral characteristic absorption peak and the areas of the second spectral characteristic absorption peak, the total area of ​​the spectral characteristic absorption peak corresponding to each position can be obtained, that is, the spectral characteristic response parameter.

[0067] Taking cement as the printing medium as an example, the expression of the interface moisture content prediction model constructed by the above method in this application is as follows:

[0068] in, This indicates the actual moisture content of the interface. This represents the average value of the spectral characteristic response parameters (total spectral absorption area) at all locations. It should be noted that the expressions for the interface moisture content prediction models constructed using the above method may differ for different printing media.

[0069] In actual testing, even after multi-stage preprocessing, spectral data may still have background fluctuations in non-characteristic bands. This embodiment uses targeted determination of integral boundary parameters and definite integral calculation to mathematically eliminate out-of-band noise interference. It also accurately converts complex nonlinear spectral curves into characteristic absorption peak areas that represent true water content, thereby providing accurate and reliable quantitative input for subsequent multi-dimensional feature fusion and precise inversion of the interface water content quantitative analysis model.

[0070] In step S104, during the 3D printing of concrete, the long single-layer printing path and prolonged interlayer exposure time make it susceptible to excessive evaporation of interfacial moisture due to ambient temperature or wind speed. Therefore, closed-loop moisture control is required. After determining the actual interfacial moisture content of each region of the current printing medium in step S103, the difference between this value and the pre-calibrated target interfacial moisture content (i.e., the optimal interfacial moisture content parameter) obtained through material testing can be calculated to obtain the target moisture control parameter. The target moisture control parameter is the amount of water required to replenish the interface, converted from the above difference.

[0071] Furthermore, an automated control module can activate the spray device mounted on the 3D printing equipment to uniformly spray the calculated amount of water onto areas of the printing media interface that are in a dry, defective state. This water control is not a one-time, indiscriminate spraying, but a dynamic, closed-loop process. During the spray water replenishment process, the NIR spectral detection system continuously monitors and provides feedback on the moisture content of the printing media interface in real time, updating the actual moisture content data in real time. Only when the monitoring system confirms that the actual moisture content of the printing media interface has stabilized within the preset target interface moisture content range will the water control action stop, and only then can the 3D printing equipment proceed with the next layer of printing media stacking and printing command.

[0072] In some embodiments, please refer to Figure 8 The target moisture control parameters are calculated based on the target interface moisture content and the actual interface moisture content, including: Step S801: Obtain the printing media density and the interface size parameters of the printing media interface; Step S802: Calculate the target water replenishment amount based on the target interface moisture content, the actual interface moisture content, the printing medium density, and the areas of the first and second spectral characteristic absorption peaks corresponding to all positions. Step S803: Calculate the target moisture control parameters based on the interface size parameters and the target water replenishment amount.

[0073] In step S801, in order to move from spectral characteristics to the actual water replenishment operation, it is necessary to obtain the basic physical and geometric properties of the target being measured. Specifically, this involves obtaining the printing medium density and the interface size parameters of the printing medium interface. The printing medium density (denoted as...) The density of 3D printing refers to the physical density of the building materials used in current 3D printing, such as the density of cement paste with a water-cement ratio of 0.35. Interface size parameters refer to the geometric dimensions of the printed media interface that has been formed and is exposed, including its length, width, and surface area. For example, when a 3D printing machine's robotic arm is fitted with a 40mm × 20mm nozzle and stacks single layers at a predetermined speed, the top surface of the extruded layer exposed to air is the detection interface. By reading the printing path and nozzle size through an automated control module, the surface area of ​​the printed media interface requiring moisture detection and control can be accurately calculated.

[0074] In step S802, the target water replenishment amount can be calculated based on the target interface moisture content, the actual interface moisture content, the printing medium density, and the areas of the first and second spectral characteristic absorption peaks corresponding to all locations. First, a summation calculation is performed based on the areas of the first and second spectral characteristic absorption peaks. ), and the calculated total spectral characteristic absorption peak area As an input variable, it is substituted into a pre-built nonlinear prediction model to accurately inversely determine the actual moisture content of the current printing medium interface (denoted as ). Simultaneously, the target interface moisture content (denoted as ) corresponding to the optimal interlayer bond performance verified through previous physical tests (such as splitting tensile tests) is obtained. ), target water replenishment volume (denoted as The mathematical formula for ) is as follows:

[0075] in, Indicates the density of the printing media ( ), Indicates the density of water ( ), Indicates the moisture content of the target interface. This indicates the actual moisture content of the interface. This represents the sum of spectral characteristic response parameters corresponding to all locations. Each measurement location has a reflectance spectrum curve. For each reflectance spectrum curve, the areas of the first and second spectral characteristic absorption peaks are calculated. These two areas are then summed to obtain the total spectral characteristic absorption peak area for each measurement location. The average of the spectral characteristic absorption peak areas for all measurement locations is then calculated and used as the spectral characterization of moisture content. It should be noted that the formula for calculating the target water replenishment amount can be adaptively adjusted for different printing media.

[0076] In step S803, target moisture control parameters are calculated based on interface size parameters and the target water replenishment volume. After calculating the target water replenishment volume, it needs to be converted into action commands that can be executed by the mechanical equipment, i.e., target moisture control parameters. Since the 3D printing interface is a continuous spatial distribution area, water cannot be concentrated in a single location but needs to be evenly covered. Therefore, this embodiment spatially allocates the target water replenishment volume based on interface size parameters, calculates the optimal spraying volume per unit area, and then, combined with the rated spraying flow rate of the spraying device, converts it into specific physical execution parameters such as nozzle moving speed, spray working pressure, or spray residence time. The required water replenishment mass of the interface is then determined. The formulas for converting this into nozzle flow rate, spraying time, or moving speed are:

[0077] in, Indicates spray flow rate ( ), Indicates the length of the printing media interface. Indicates the nozzle movement speed. This indicates the target water replenishment volume.

[0078] For example, if outdoor construction is carried out in summer (such as simulating a dry condition with a high wind speed and 40°C), resulting in severe moisture loss at the interface, this embodiment will not only calculate a large target water replenishment amount, but also instruct the spray device, driven by the robotic arm, to uniformly spray a small amount of water onto the interface at a specific rate along the previous scanning path using target moisture control parameters. After the moisture control is completed and the interface moisture content reaches the preset optimal target range, the 3D printing equipment will then issue the printing command for the next layer of media.

[0079] In this embodiment, the microscopic molecular-level absorption area captured by near-infrared spectroscopy is converted into the target water replenishment amount at the macroscopic level, and further broken down into specific control instructions for the spray device to perform water replenishment operations on the printing medium interface. This achieves precise control of the moisture content at the printing medium interface, effectively overcoming the hidden dangers caused by severe interlayer water loss in large structures under complex working conditions (such as long-term exposure caused by high temperature and strong wind), avoiding interface drying defects, improving the secondary hydration reaction conditions and interlayer adhesion between adjacent printing layers, and ensuring the stability of the overall mechanical properties of the 3D printed building structure.

[0080] In steps S101 to S104 of this embodiment, during the 3D printing process, initial reflectance spectral data at multiple locations on the printing medium interface are acquired to achieve non-destructive, real-time monitoring of the interlayer moisture content. Then, by preprocessing the initial reflectance spectral data, noise interference from complex construction environments and interface physical morphology is effectively eliminated, ensuring the reliability of the target reflectance spectral data. Based on this, spectral feature response parameters corresponding to each location are extracted, and a pre-built interface moisture content prediction model is invoked to accurately invert the spectral features into the spatial distribution of the actual interface moisture content, achieving a high-precision quantitative assessment of interface moisture. Finally, the target moisture control parameter is obtained by calculating the difference between the target interface moisture content and the actual interface moisture content, and the moisture content of the printing medium interface is controlled based on this parameter. Thus, this embodiment can accurately replenish the moisture content of the printing medium based on real-time spectral feedback, bringing it close to the optimal target interface moisture content. This solves the problem of insufficient moisture content in the printing medium under actual construction exposure conditions, effectively avoiding interlayer drying defects and improving the bonding performance and printing quality between adjacent printing layers.

[0081] This application also provides a control device that can implement the above-described printing media moisture control method. The specific implementation of the control device is basically the same as the specific implementation of the above-described printing media moisture control method, and will not be described again here.

[0082] This application also provides a 3D printing device, which includes the aforementioned control device.

[0083] In some embodiments, the 3D printing apparatus further includes: The main body of the device has a storage section for accommodating printing media; Moisture compensation component, the moisture compensation component is located in the main body of the equipment; The control device is used to control the moisture compensation component to adjust the water replenishment at the printing media interface based on the target moisture control parameters.

[0084] In this embodiment, the 3D printing equipment includes a main body and a moisture compensation component, and is controlled intelligently in a closed loop by a control device. Specifically, the main body has a storage unit for safely storing and stably supplying building materials such as 0.35 water-cement ratio cement paste as printing media; the moisture compensation component is mounted on the main body, for example, as a spray device mounted on a robotic arm mechanism, for water spraying operations. In actual intelligent construction, after accurately reversing the current interlayer drying defects through the aforementioned near-infrared spectroscopy technology and quantitative prediction model, the control device can issue specific execution commands to the moisture compensation component in real time based on the calculated target moisture control parameters (such as the nozzle movement speed, spray flow rate, or working pressure required for the corresponding water replenishment), driving and controlling the moisture compensation component to accurately and uniformly replenish water to the printing media interface exposed to the construction environment. This achieves adaptive approximation of the actual moisture content of the printing media interface towards the target optimal moisture content at both the hardware and control levels, thereby eliminating weak interlayer interfaces.

[0085] Furthermore, this application also provides a physical printing and mechanical performance testing scheme in the engineering feasibility verification stage: the experiment simulates diverse exposure conditions by setting different ambient temperature gradients (20℃, 40℃, 60℃) and wind speed gradients (0m / s, 3.8m / s, 11m / s) during the 3D printing of cement-based materials, and conducts comparative tests between a control group without moisture control and an experimental group using the printing medium moisture control method provided in this application for automated moisture control; subsequently, splitting tensile tests are performed on the hardened specimens after molding, and the data results intuitively demonstrate that the interlayer bond strength and mechanical properties of the hardened specimens after high-precision closed-loop water replenishment control of this application are significantly improved.

[0086] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0087] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0088] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0090] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0091] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0093] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0094] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0095] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for controlling the moisture content of printing media, characterized in that, Applied to 3D printing equipment, the method includes: During the printing process of the 3D printing equipment, initial reflectance spectral data at multiple locations on the printing medium interface are acquired; Each of the initial reflectance spectral data is preprocessed to obtain target reflectance spectral data corresponding to each position in the printing medium interface; Based on the target reflectance spectral data at each location in the printing medium interface, the spectral characteristic response parameters corresponding to each location are determined, and a pre-built interface moisture content prediction model is invoked to determine the actual interface moisture content of the printing medium interface based on all the spectral characteristic response parameters. Target moisture control parameters are calculated based on the target interface moisture content and the actual interface moisture content, and the moisture content of the printing media interface is controlled based on the target moisture control parameters so that the actual interface moisture content of the printing media interface approaches the target interface moisture content.

2. The method for controlling the moisture content of printing media according to claim 1, characterized in that, The step of preprocessing each of the initial reflectance spectral data to obtain target reflectance spectral data corresponding to each position in the printing medium interface includes: Obtain the ambient background spectrum and the reference spectrum, and sequentially remove the ambient background spectrum and the reference spectrum from each of the initial reflectance spectral data to obtain the first reflectance spectral data; The first reflectance spectral data is smoothed and filtered to obtain the target reflectance spectral data corresponding to each position in the printing medium interface.

3. The method for controlling the moisture content of printing media according to claim 2, characterized in that, The step of smoothing and filtering the first reflectance spectral data to obtain target reflectance spectral data corresponding to each position in the printing medium interface includes: The first reflectance spectral data is baseline corrected by constructing a baseline correction model through linear interpolation to obtain the second reflectance spectral data; The second reflectance spectral data is normalized to obtain the target reflectance spectral data corresponding to each position in the printing medium interface.

4. The method for controlling the moisture content of printing media according to claim 1, characterized in that, The determination of the spectral characteristic response parameters corresponding to each position based on the target reflectance spectral data at each position in the printing media interface includes: In the target reflectance spectral data at each location on the printing medium interface, determine the first characteristic wavelength band corresponding to the overtone absorption band of water molecules and the second characteristic wavelength band corresponding to the combined absorption band of stretching and bending vibrations. The area of ​​the first spectral characteristic absorption peak is determined based on the first characteristic wavelength band, and the area of ​​the second spectral characteristic absorption peak is determined based on the second characteristic wavelength band. The areas of the first and second spectral characteristic absorption peaks at each location on the printing medium interface are summed to obtain the spectral characteristic response parameters corresponding to each location.

5. The method for controlling the moisture content of printing media according to claim 4, characterized in that, The step of determining the area of ​​the first spectral characteristic absorption peak based on the first characteristic wavelength band and determining the area of ​​the second spectral characteristic absorption peak based on the second characteristic wavelength band includes: Determine the first upper limit and the first lower limit of integration corresponding to the first characteristic wavelength band, and perform integration calculation on the target reflectance spectrum data based on the first upper limit and the first lower limit of integration to obtain the area of ​​the corresponding first spectral characteristic absorption peak; Determine the second upper and lower limits of integration corresponding to the second characteristic wavelength band, and perform integration calculation on the target reflectance spectrum data based on the second upper and lower limits of integration to obtain the area of ​​the corresponding second spectral characteristic absorption peak.

6. The method for controlling the moisture content of printing media according to claim 5, characterized in that, The calculation of the target moisture control parameters based on the target interface moisture content and the actual interface moisture content includes: Obtain the printing media density and the interface size parameters of the printing media interface; The target water replenishment amount is calculated based on the target interface moisture content, the actual interface moisture content, the printing medium density, and the areas of the first and second spectral characteristic absorption peaks corresponding to all positions. The target moisture regulation parameters are calculated based on the interface size parameters and the target water replenishment amount.

7. The method for controlling the moisture content of printing media according to claim 1, characterized in that, The 3D printing equipment includes a spectral acquisition component, wherein acquiring initial reflectance spectral data at multiple locations on the printing medium interface includes: The detection distance between the spectral acquisition component and the printing medium interface, the incident angle of the detection beam of the spectral acquisition component, the integration time of the spectral acquisition component for a single spectral signal acquisition, and the sampling frequency of the spectral acquisition component are obtained. Based on the detection distance, the incident angle of the detection beam, the integration time, and the sampling frequency, a spectral data sampling strategy is determined, and the spectral acquisition component is controlled to perform spatial scanning along multiple positions in the printing medium interface according to the spectral data sampling strategy to obtain initial reflectance spectral data at multiple positions in the printing medium interface.

8. A control device, characterized in that, The control device is used to implement the method as described in any one of claims 1 to 7.

9. A 3D printing device, characterized in that, The 3D printing equipment includes the control device as described in claim 8.

10. The 3D printing equipment according to claim 9, characterized in that, The 3D printing equipment also includes: The device body has a storage section for accommodating printing media; A moisture compensation component is disposed on the main body of the device; The control device is used to control the moisture compensation component to adjust the water replenishment at the printing media interface based on the target moisture control parameters.

Images