Intelligent temperature and humidity control method and device in microcement construction

Abstract

The present invention discloses a method and device for intelligent temperature and humidity control during microcement construction, relating to the technical field of construction temperature and humidity control. The method comprises: dividing the coating layers according to the microcement construction plan for the building decoration construction area to obtain the microcement coating plan; performing environmental prediction to obtain the construction environment; calculating the baseline performance parameters of the microcement formula to determine the microcement performance baseline vector; predicting the microcement performance loss in the construction environment to establish the microcement performance loss vector; optimizing the temperature and humidity control equipment set to generate a microcement construction control strategy; and performing adaptive temperature and humidity control. The present invention solves the technical problem in the prior art of the inability to accurately control temperature and humidity during building decoration microcement construction, resulting in significant microcement performance loss and difficulty in ensuring construction quality. The present invention achieves the technical effect of accurately controlling temperature and humidity in the building decoration construction area to reduce microcement performance loss.

Description

Technical Field

[0001] The present invention relates to the technical field of construction temperature and humidity control, and in particular to a method and device for intelligent temperature and humidity control in micro-cement construction. Background Art

[0002] In the field of architectural decoration, microcement offers unique decorative effects and excellent durability. However, microcement application is extremely sensitive to environmental conditions, particularly temperature and humidity. Traditional construction processes often lack precise correlation analysis between microcement coating schemes and environmental factors at different construction stages. Temperature and humidity control is largely based on experience, lacking a scientific basis or systematic approach. This makes it difficult to maintain a stable construction environment within the range required for optimal microcement performance, leading to performance issues such as poor leveling, poor film formation, and reduced adhesion, seriously compromising the decorative effect of microcement.

[0003] The existing technology has a technical problem that it is impossible to accurately control the temperature and humidity during the construction of building decoration microcement, resulting in a significant loss of microcement performance and difficulty in ensuring construction quality. Summary of the Invention

[0004] The present application provides a method and device for intelligent temperature and humidity control in microcement construction, which is used to solve the technical problem in the existing technology that temperature and humidity cannot be accurately controlled in the construction of architectural decoration microcement, resulting in a large loss of microcement performance and difficulty in ensuring construction quality.

[0005] In view of the above problems, the present application provides a method and device for intelligent control of temperature and humidity in microcement construction.

[0006] The first aspect of the present application provides a method for intelligently controlling temperature and humidity in microcement construction, the method comprising:

[0007] The method comprises the following steps: dividing the coating layers according to the microcement construction scheme of the building decoration construction area to obtain M-stage microcement coating schemes, where M is a positive integer greater than 1; performing environmental prediction on the M-stage microcement coating schemes according to the current environmental perception information of the building decoration construction area to obtain M-stage construction environments; calculating benchmark performance parameters of M microcement formulations corresponding to the M-stage microcement coating schemes according to the microcement performance factor to determine M microcement performance benchmark vectors; predicting microcement performance loss of the M-stage construction environments according to a microcement performance prediction model based on the M microcement performance benchmark vectors to establish M microcement performance loss vectors; optimizing and adjusting the temperature and humidity control equipment set according to the M microcement performance loss vectors to generate a microcement construction control strategy; and controlling the temperature and humidity control equipment set according to the microcement construction control strategy to perform adaptive temperature and humidity control on the building decoration construction area.

[0008] The second aspect of the present application provides an intelligent temperature and humidity control device for microcement construction, the device comprising:

[0009] a microcement coating scheme acquisition module for dividing the coating levels according to the microcement construction scheme of the building decoration construction area to obtain M-stage microcement coating schemes, where M is a positive integer greater than 1; a construction environment acquisition module for performing environmental prediction for the M-stage microcement coating schemes based on the current environmental perception information of the building decoration construction area to obtain M-stage construction environments; a microcement performance benchmark vector determination module for calculating benchmark performance parameters for M microcement formulations corresponding to the M-stage microcement coating schemes based on microcement performance factors to determine M microcement performance benchmark vectors; a microcement performance loss vector establishment module for predicting microcement performance loss for the M-stage construction environments based on the M microcement performance benchmark vectors and a microcement performance prediction model to establish M microcement performance loss vectors; a microcement construction control strategy generation module for optimizing the temperature and humidity control equipment set based on the M microcement performance loss vectors to generate a microcement construction control strategy; and an adaptive temperature and humidity control module for controlling the temperature and humidity control equipment set to perform adaptive temperature and humidity control on the building decoration construction area according to the microcement construction control strategy.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The microcement construction plan for the building decoration construction area is divided into coating layers to obtain M-stage microcement coating plans. The environment of these M-stage microcement coating plans is predicted to obtain M-stage construction environments. Baseline performance parameters are calculated for the M microcement formulations corresponding to these M-stage microcement coating plans to determine M microcement performance baseline vectors. Microcement performance loss is predicted for these M-stage construction environments using a microcement performance prediction model to establish M microcement performance loss vectors. Based on these M microcement performance loss vectors, a set of temperature and humidity control devices is optimally adjusted to generate a microcement construction control strategy. Adaptive temperature and humidity control is then performed for the building decoration construction area. This achieves the technical effect of precisely controlling temperature and humidity in the building decoration construction area to reduce microcement performance loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A flow chart of the intelligent temperature and humidity control method for microcement construction provided in an embodiment of the present application;

[0014] Figure 2 This is a schematic diagram of the structure of the intelligent temperature and humidity control device in microcement construction provided in an embodiment of the present application.

[0015] Explanation of the accompanying symbols: microcement coating plan acquisition module 10, construction environment acquisition module 20, microcement performance baseline vector determination module 30, microcement performance loss vector establishment module 40, microcement construction regulation strategy generation module 50, adaptive temperature and humidity control module 60. DETAILED DESCRIPTION

[0016] This application provides a method and device for intelligent temperature and humidity control in microcement construction, which is used to solve the technical problems in the existing technology of building decoration microcement construction, such as the inability to accurately control temperature and humidity, resulting in a large loss of microcement performance and difficulty in ensuring construction quality.

[0017] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0018] Example 1, as Figure 1 As shown, the present application provides a method for intelligent temperature and humidity control in microcement construction, the method comprising:

[0019] Step S100: Divide the coating layers according to the microcement construction plan of the building decoration construction area to obtain M-stage microcement coating plans, where M is a positive integer greater than 1.

[0020] Specifically, we analyze specific microcement construction plans for architectural decoration construction areas. These plans detail key information such as construction techniques, material usage, and quality standards. Given the need for meticulous planning, the typical microcement construction process is typically divided into base, middle, and surface microcement application stages. The base microcement primarily serves as a foundation bond and leveling mechanism, requiring specific application thickness and uniformity to ensure secure adhesion to the base layer. The middle microcement layer focuses on enhancing overall structural strength and stability, with application density and thickness control crucial. The surface microcement layer contributes to the final decorative effect and texture, requiring extremely high standards for smoothness and color uniformity. Based on the process requirements and quality standards for each layer in the construction plan, as well as actual construction experience, construction personnel systematically divide the entire microcement construction process into layers. This division results in a clearly defined M-stage microcement application plan, where M is a positive integer greater than 1. Each stage has clear construction objectives, operating procedures, and quality acceptance criteria, laying a solid foundation for smooth and high-quality subsequent construction.

[0021] Step S200: performing environmental prediction on the M-stage microcement coating solutions according to current environmental perception information of the building decoration construction area to obtain M-stage construction environments.

[0022] Specifically, after obtaining M phases of microcement coating plans, various environmental sensing devices installed within the building decoration construction area, such as temperature and humidity sensors, anemometers, and light intensity sensors, collect real-time environmental information. These devices accurately measure environmental parameters such as temperature, humidity, wind speed, and light intensity at the construction site. By combining the construction characteristics and schedules of each M phase of microcement coating plan, the environmental conditions for each phase of construction are predicted. For example, by taking into account weather patterns across seasons and time periods, as well as factors such as the construction site's ventilation conditions and spatial layout, environmental data such as the temperature fluctuation range, humidity trends, and wind speed levels expected during each phase of construction are predicted. Through these predictions, the construction environment for each M phase is ultimately determined. These predictions provide an important basis for adjusting the microcement construction strategy accordingly, helping to ensure that microcement is applied under suitable conditions and improve construction quality and effectiveness.

[0023] Step S300: Calculating benchmark performance parameters for the M microcement formulations corresponding to the M-stage microcement coating schemes according to the microcement performance factors to determine M microcement performance benchmark vectors.

[0024] Specifically, a comprehensive search for normal performance parameter samples was conducted for M microcement formulations based on microcement performance factors encompassing leveling, film quality, and adhesion. By analysing extensive experimental data, actual construction case records, and authoritative industry standards, the specific parameters for each formulation under standard construction conditions, including leveling, film quality, and adhesion, were precisely identified. M microcement performance sample sets were then compiled, each containing detailed performance data for the corresponding formulation. Centralized values ​​were then calculated for each of the M microcement performance sample sets. The average values ​​of the leveling coefficient, film quality coefficient, and adhesion coefficient within the sample sets were calculated to obtain the central values ​​of each coefficient, thereby generating M microcement performance benchmark sequences. Finally, based on these M microcement performance benchmark sequences and following established vector construction rules, the central values ​​of the leveling coefficient, film quality coefficient, and adhesion coefficient were used as the three dimensional components of the vector, and these values ​​were combined in an orderly manner to construct M microcement performance benchmark vectors. These vectors comprehensively and intuitively present the comprehensive performance benchmarks of each microcement formulation under normal construction conditions under different microcement coating schemes at different stages. They provide a key reference for subsequent evaluation of the differences between microcement performance in actual construction and ideal conditions, thereby ensuring the quality of microcement construction.

[0025] Step S400: Based on the M microcement performance benchmark vectors, the microcement performance loss prediction is performed on the M stage construction environments according to the microcement performance prediction model to establish M microcement performance loss vectors.

[0026] Specifically, a multivariate linear regression algorithm from machine learning was used to predict microcement performance loss. Data on various environmental factors, such as temperature, humidity, and wind speed, from M construction environments during each phase were standardized and converted into dimensionless data to ensure comparability. M microcement performance benchmark vectors were also converted to the appropriate format so that they could be used as model inputs along with the environmental factor data. A large amount of historical microcement construction data, including actual measurements of microcement performance under different construction environments, was used to train the multivariate linear regression model. During training, the model continuously adjusted the coefficients corresponding to each environmental factor, identifying a linear relationship between the environmental factor and microcement performance, minimizing the error between the model's predicted values ​​and the actual measured values. After training, the environmental factor data and the corresponding microcement performance benchmark vector for each construction environment phase were input into the trained multivariate linear regression model. The model then calculated the input data according to the established linear relationship and output a predicted microcement performance vector. Microcement performance loss was then quantified by calculating the Euclidean distance between the predicted microcement performance vector and the corresponding microcement performance benchmark vector. For example, for the mth stage, the sum of the squares of the differences between the mth microcement performance baseline vector and the predicted mth microcement performance vector for each dimension is calculated, and the square root is taken to obtain a value representing the degree of performance loss. The microcement performance loss values ​​for each stage are arranged in a specific order and constructed into a vector form, thereby establishing M microcement performance loss vectors that accurately reflect the performance loss of microcement at different construction stages.

[0027] Step S500: Optimizing and adjusting the temperature and humidity control equipment set according to the M microcement performance loss vectors to generate a microcement construction control strategy.

[0028] Specifically, by optimizing and adjusting the temperature and humidity control equipment set, an effective microcement construction control strategy is generated to minimize microcement performance loss and ensure construction quality. First, microcement performance loss weighting criteria are determined, including weights for leveling loss, film quality loss, and adhesion loss. These weights are determined based on key microcement construction indicators and actual requirements, and are used to measure the impact of different performance losses on overall construction quality. For each microcement performance loss vector, a weighted calculation is performed according to the aforementioned weighting criteria to obtain the corresponding microcement performance loss coefficient. A microcement performance loss threshold is set and the calculated loss coefficient is compared with it. If the microcement performance loss coefficient at a given stage is greater than or equal to the threshold, it indicates that the microcement performance loss at that stage is severe, and optimal adjustment of the temperature and humidity control equipment set is required. During this adjustment, adjustment decisions are first made for the temperature and humidity control equipment set based on the microcement performance loss vector at that stage, forming a first microcement construction adjustment space containing multiple possible equipment adjustment combinations that meet a predetermined number of decision requirements. These options are then optimized and analyzed based on the microcement performance prediction model and the performance loss weighting criteria. Taking a specific decision-making scenario as an example, the change in the construction environment at stage m is predicted based on this scenario, resulting in the first predicted change in construction environment. The mth microcement formulation and this predicted environment are then input into the microcement performance prediction model to obtain the first decision-making microcement performance prediction vector. This vector is then weighted according to the performance loss weighting condition to obtain the first decision-making microcement performance loss coefficient. If this coefficient is less than the microcement performance loss threshold, the decision-making scenario is feasible and is added to the second microcement construction adjustment space; otherwise, it is eliminated. A variation optimization search is performed to expand the second microcement construction adjustment space. By constructing a coordinate system for evaluating the variation value of adjustment decisions, the microcement performance loss coefficient corresponding to each decision in the space is input into this coordinate system to obtain the variation value coefficients of each adjustment decision. Based on these coefficients, the second space is mutated to obtain the first variation domain of microcement construction adjustment. Based on the microcement performance prediction model and the performance loss weighting condition, an optimization analysis is performed on the first variation domain to obtain the second variation domain of microcement construction adjustment. This second variation domain is then used to expand the second space to form the third microcement construction adjustment space. Finally, the performance loss of microcement is minimized in the third space to determine the best adjustment plan, that is, the microcement construction control strategy for the mth stage. The control strategies of all stages are integrated to form a complete microcement construction control strategy.

[0029] Step S600: controlling the temperature and humidity control equipment set to perform adaptive temperature and humidity control on the building decoration construction area according to the micro-cement construction control strategy.

[0030] Specifically, based on the established microcement construction control strategy, precise control instructions are issued to the temperature and humidity control equipment set, achieving adaptive temperature and humidity control within the building decoration construction area. This temperature and humidity control equipment set includes various devices such as air conditioners, humidifiers, dehumidifiers, and ventilation systems, which work together to regulate the temperature and humidity environment within the construction area. The microcement construction control strategy specifies the target temperature and humidity values ​​to be achieved in the construction area during different construction phases, as well as the operating parameters of the control equipment. For example, during the microcement basecoat application phase, based on construction process requirements and preliminary analysis, the strategy might set a temperature of 20-22°C and a relative humidity of 50%-55%. These parameters are transmitted to the temperature and humidity control equipment set, which automatically adjusts its operating state based on the received instructions. If the current temperature is above the target value, the air conditioning system activates cooling mode to reduce the temperature; if the humidity is below the target value, the humidifier activates to increase the humidity. Throughout the construction process, the temperature and humidity environment is not static. Factors such as weather changes, the operation of construction equipment, and personnel activities can affect the temperature and humidity of the construction area. At this time, various environmental monitoring sensors installed within the construction area collect real-time temperature and humidity data and feed it back to the control system. The control system compares and analyzes the actual monitoring data with the target values ​​in the control strategy. If the actual temperature and humidity deviate from the target range, it promptly adjusts the operating parameters of the temperature and humidity control equipment according to the control strategy, achieving dynamic and adaptive regulation of temperature and humidity. This ensures that the construction area maintains appropriate temperature and humidity conditions at every stage of microcement construction, providing a strong guarantee for the quality of microcement construction.

[0031] In one possible implementation, step S400 further includes:

[0032] Step S410: extracting the m-th stage construction environment and the m-th microcement performance reference vector according to the M stage construction environments and the M microcement performance reference vectors, where m is a positive integer, 1≤m≤M.

[0033] Step S420: inputting the mth microcement formula and the mth stage construction environment into the microcement performance prediction model to obtain the mth microcement performance prediction vector.

[0034] Step S430: performing deviation identification on the mth microcement performance prediction vector according to the mth microcement performance reference vector to obtain an mth microcement performance loss vector, and adding the mth microcement performance loss vector to the M microcement performance loss vectors.

[0035] Specifically, after completing the environmental prediction for the building decoration construction area, obtaining M construction environment phases, and determining M microcement performance benchmark vectors, to accurately analyze how the environmental impact of each construction phase affects microcement performance, it is necessary to filter data from the overall data for a specific phase. Here, m serves as a loop variable, taking a positive integer value from 1 to M, representing any of the M construction phases. Using an indexing mechanism, the mth phase construction environment is precisely located and extracted from the M phase construction environment dataset. This data set contains environmental parameters such as temperature, humidity, and ventilation conditions during that phase. Simultaneously, the corresponding mth microcement performance benchmark vector is found within the M microcement performance benchmark vector set. This vector contains information on the performance standards for the microcement formulation under ideal conditions, such as leveling, film quality, and adhesion. These two extracted data sets are the key foundation for subsequent in-depth analysis of microcement performance changes under actual construction conditions, providing targeted data support for further prediction of microcement performance loss.

[0036] Data on the ratios of various ingredients and additive content in the mth microcement formula, as well as environmental factors such as temperature, humidity, light intensity, and ventilation rate during the mth construction phase, are collated and preprocessed, converting them into a format suitable for input into a multiple linear regression model. These data serve as independent variables, while microcement performance indicators such as leveling, film quality, and adhesion serve as dependent variables. A training dataset is constructed based on a large amount of existing microcement construction data, and the model is trained using a multiple linear regression algorithm. During the training process, the algorithm automatically searches for an optimal set of regression coefficients that maximizes the linear relationship between the independent and dependent variables to fit the training data, minimizing the error between the predicted and actual values. After training is complete, the preprocessed mth microcement formula and the mth construction phase data are input into the trained multiple linear regression model. Based on the determined regression coefficients and input data, the model uses linear calculations to predict the microcement performance values, such as leveling, film quality, and adhesion, under the specified construction environment. Arrange these predicted performance values ​​in a specific order to form a vector form, and finally obtain the mth microcement performance prediction vector, which provides an important basis for the subsequent evaluation of microcement performance loss.

[0037] First, a microcement performance prediction model was constructed and its structure was clarified. It consists of a microcement leveling prediction model, a microcement film quality prediction model, and a microcement adhesion prediction model. For each sub-model, the independent and dependent variables were determined. The component ratio and additive dosage in the mth microcement formula, as well as the temperature, humidity, and ventilation conditions in the mth construction environment were used as independent variables, and the corresponding leveling, film quality, and adhesion were used as dependent variables. A linear relationship was constructed, such as Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ....βn X n + . Data collection was then carried out, and a large number of actual measurement values ​​of leveling, film-forming quality and adhesion of different microcement formulas in various construction environments were widely collected to form the original data set. In order to ensure data quality, the data needs to be preprocessed, the data needs to be carefully cleaned, outliers and missing values ​​need to be removed, and the data needs to be standardized so that independent variables of different dimensions can be converted into dimensionless data to enhance the comparability of the data. Next, the preprocessed data is divided into a certain proportion, usually 70% as a training set and 30% as a test set. The training set is used to train the model. By continuously adjusting the regression coefficient, the least squares method is used to minimize the sum of squares of the error between the model prediction value and the actual value in the training set. After multiple iterative optimizations, the error of the model on the training set converges to a more ideal range, so that the model can learn the intrinsic relationship between the independent variable and the dependent variable. After model training is complete, the test set is used to evaluate the model and calculate the mean squared error (MSE) metric. First, data is extracted from the pre-defined test set. This data contains different microcement formulations, construction environment factors (such as temperature, humidity, and ventilation conditions), and corresponding actual microcement performance data (leveling, film quality, adhesion, etc.). For example, the test set contains 100 data sets, each of which records the actual performance of a specific microcement formulation under a specific construction environment. The microcement formulations and construction environment data from the test set are sequentially input into the trained microcement performance prediction model, which outputs the corresponding predicted microcement performance value. For each input data set, the model's predicted and actual values ​​are recorded. For example, for a given data set, the actual microcement leveling coefficient is 0.8, while the model predicts it to be 0.75; the actual film quality coefficient is 0.9, while the predicted value is 0.85; and the actual adhesion coefficient is 0.88, while the predicted value is 0.83. The predicted and actual values ​​for all test data are recorded in sequence. MSE is the average of the squared errors between the predicted value and the actual value. For the microcement performance prediction model, the MSE of performance indicators such as leveling, film quality, and adhesion are calculated separately. Taking leveling as an example, suppose there are n data points in the test set, and the actual leveling of the i-th data point is , the predicted leveling is , then the MSE calculation formula for leveling is The MSE value for leveling is calculated by summing the squared errors of all data points and dividing by the number of data points, n. Similarly, the MSE values ​​for film quality and adhesion are calculated. Larger MSE values ​​indicate a greater squared deviation between the model's predicted and actual values, and therefore a poorer model prediction. Unsatisfactory evaluation results indicate that the model's accuracy and generalization capabilities need improvement. In this case, adjustments to the model structure are necessary, such as adding or removing independent variables or adjusting the model's complexity. Training and re-evaluation should be repeated until the model achieves satisfactory prediction accuracy on the test set. Finally, a qualified microcement performance prediction model is trained. After training is complete, the pre-processed mth microcement formulation and the mth stage construction environment data are input into the trained multivariate linear regression model. Based on the determined regression coefficients and input data, the model uses linear calculations to predict the microcement's performance values ​​for leveling, film quality, adhesion, and other properties under the specified construction conditions. These predicted performance values ​​are arranged in a specific order to form a vector, ultimately yielding the mth microcement performance prediction vector. This vector provides an important basis for subsequent assessments of microcement performance loss.

[0038] Identify performance losses of microcement under the mth stage of construction conditions. The mth microcement performance benchmark vector represents the performance indicators of microcement under ideal conditions, covering standard values ​​for various aspects such as leveling, film quality, and adhesion. The mth microcement performance prediction vector, on the other hand, is performance data derived from the microcement performance prediction model based on the actual construction conditions at stage m. To identify deviations, these two vectors are first compared dimension by dimension. For example, in the leveling dimension, the leveling value in the mth microcement performance benchmark vector is subtracted from the corresponding value in the mth microcement performance prediction vector. A positive difference indicates that the actual leveling is below ideal, indicating a performance loss; a negative difference means that the actual performance is better than the ideal, but this difference is usually also recorded to comprehensively assess performance differences. The same comparison method is used for other dimensions such as film quality and adhesion. After comparing all dimensions, the differences in each dimension are combined in the order of the original vectors to obtain the mth microcement performance loss vector. This vector clearly shows the loss of each performance dimension of microcement compared to the ideal state under the mth stage of construction conditions. Finally, the generated mth microcement performance loss vector is added to the existing set of M microcement performance loss vectors. As the process from stage 1 to stage M is completed, this set will fully reflect the performance loss of microcement at each stage throughout the construction process, laying the foundation for the subsequent formulation of scientific and reasonable construction control strategies based on this loss data.

[0039] In one possible implementation, step S420 further includes:

[0040] Step S421: the microcement performance prediction model includes a microcement leveling prediction model, a microcement film-forming quality prediction model, and a microcement adhesion prediction model.

[0041] Step S422: inputting the m-th microcement formula and the m-th stage construction environment into the microcement leveling prediction model to obtain the m-th leveling prediction coefficient.

[0042] Step S423: inputting the m-th microcement formula and the m-th stage construction environment into the microcement film quality prediction model to obtain the m-th film quality prediction coefficient.

[0043] Step S424: inputting the m-th microcement formula and the m-th stage construction environment into the microcement adhesion prediction model to obtain the m-th adhesion prediction coefficient.

[0044] Step S425: constructing the mth microcement performance prediction vector according to the mth leveling prediction coefficient, the mth film-forming quality prediction coefficient, and the mth adhesion prediction coefficient.

[0045] Specifically, in order to accurately predict the performance of microcement in the mth stage, the microcement performance prediction model needs to be refined. The model includes a microcement leveling prediction model, a microcement film-forming quality prediction model, and a microcement adhesion prediction model. These three sub-models are constructed using a multivariate linear regression algorithm. The model structure and training process are described in detail below. In terms of model structure, taking the microcement leveling prediction model as an example, it uses the ingredient ratio, additive dosage, etc. in the mth microcement formula, as well as the temperature, humidity, ventilation conditions, etc. in the mth stage construction environment as independent variables, and leveling as the dependent variable. By establishing a linear relationship between the independent variable and the dependent variable, that is, Y=β0+β1X1+β2X2+β3X3+β4X4+....β n X n + , where Y is the predicted value of leveling, X i are the independent variables, β i is the regression coefficient to be determined, is the error term. The microcement film quality prediction model and the microcement adhesion prediction model share similar structures, except that the dependent variables are film quality and adhesion, respectively. During model training, a large amount of historical data is first collected. This data contains actual measurements of leveling, film quality, and adhesion under different microcement formulations and construction environments. This data is then divided into a training set and a test set. The training set is used to determine the regression coefficients, while the test set is used to evaluate the model's accuracy. The least squares method is used to estimate the regression coefficients, minimizing the sum of squared errors between the model's predicted values ​​and the actual values. Multiple iterations of optimization are performed until the model achieves satisfactory prediction accuracy on the test set.

[0046] The mth microcement formula and the mth stage construction environment are input into the trained microcement leveling prediction model. The model calculates based on the determined regression coefficient and input data and outputs the mth leveling prediction coefficient, which reflects the predicted value of the leveling performance of the microcement under the current formula and environment.

[0047] The same mth microcement formula and mth stage construction environment are input into the microcement film quality prediction model. After calculation within the model, the mth film quality prediction coefficient is obtained. This coefficient reflects the prediction of the microcement film quality.

[0048] The data is input into the microcement adhesion prediction model to obtain the mth adhesion prediction coefficient, which represents the prediction result of the microcement adhesion performance.

[0049] The mth leveling prediction coefficient, mth film quality prediction coefficient, and mth adhesion prediction coefficient are arranged in a specific order to form a vector, the mth microcement performance prediction vector. This vector comprehensively reflects the performance predictions of the microcement under the mth construction environment, providing an important basis for subsequent analysis of microcement performance loss.

[0050] In one possible implementation, step S500 further includes:

[0051] Step S510: obtaining a microcement performance loss weight condition, wherein the microcement performance loss weight condition includes a leveling loss weight, a film-forming quality loss weight, and an adhesion loss weight.

[0052] Step S520: performing weighted calculation on the m-th microcement performance loss vector according to the microcement performance loss weight condition to obtain the m-th microcement performance loss coefficient.

[0053] Step S530: determining whether the mth microcement performance loss coefficient is greater than or equal to a microcement performance loss threshold.

[0054] Step S540: If the mth microcement performance loss coefficient is greater than or equal to the microcement performance loss threshold, the temperature and humidity control equipment set is optimally adjusted according to the mth microcement performance loss vector to obtain the mth stage microcement construction control strategy.

[0055] Step S550: adding the m-th stage microcement construction control strategy to the microcement construction control strategy.

[0056] Specifically, by collecting a large amount of historical construction data, we analyzed the extent to which the leveling, film-forming quality, and adhesion of microcement are affected by changes in environmental factors such as temperature and humidity. At the same time, combined with professional standards and actual construction experience within the industry, experts were organized to conduct evaluations and demonstrations, taking into account the application scenarios of microcement, such as whether it is used for indoor floors or walls. Different scenarios have different emphases on the various properties of microcement. If it is indoor floor construction, leveling and adhesion may be more critical; if it is wall construction, the importance of film-forming quality and adhesion is more prominent. After multiple rounds of data research, analysis, and expert discussion, the weights of leveling loss, film-forming quality loss, and adhesion loss were finally determined. These weights constitute the weight conditions for microcement performance loss, providing an important quantitative basis for the subsequent accurate evaluation of the degree of microcement performance loss.

[0057] The mth microcement performance loss vector is weighted to obtain the mth microcement performance loss coefficient. Assume that in the microcement performance loss weight condition, the leveling loss weight is W1, the film quality loss weight is W2, and the film quality loss weight is W3, and W1+W2+W3=1. The mth microcement performance loss vector =( ),in Represents the leveling loss value of microcement in stage m, Represents the mass loss value of microcement film formation in stage m, Represents the microcement adhesion loss value at stage m. Based on these data, the weighted calculation formula C m =W1× +W2× +W3× The mth microcement performance loss coefficient is calculated. This formula combines the microcement performance loss weighting criteria with the mth microcement performance loss vector, fully accounting for the significance of different performance losses in the overall performance loss. This weighted calculation accurately measures the comprehensive performance loss of microcement during the mth application phase due to losses in leveling, film quality, and adhesion, providing a key quantitative indicator for determining whether temperature and humidity control equipment needs to be adjusted.

[0058] The microcement performance loss threshold is a pre-defined, important reference standard, determined based on extensive experimental data, past construction experience, and the quality requirements of microcement products. The mth microcement performance loss coefficient is compared with this threshold for judgment.

[0059] If the mth microcement performance loss coefficient is greater than or equal to the microcement performance loss threshold, this indicates that the performance loss of the microcement in stage m is severe, and optimal adjustment of the temperature and humidity control equipment set is necessary. Based on the loss of each performance dimension in the mth microcement performance loss vector, the impact of temperature and humidity on these properties is analyzed. Different temperature and humidity control schemes are tried. Through continuous comparison and evaluation, the adjustment scheme that minimizes performance loss is found, thereby obtaining the microcement construction control strategy for stage m.

[0060] Finally, the microcement construction control strategy for stage m is added to the overall microcement construction control strategy. As these steps are repeated for each stage (from m=1 to m=M), a complete control strategy is formed for the entire microcement construction process, ensuring that the microcement construction quality meets the expected standards.

[0061] In one possible implementation, step S540 further includes:

[0062] Step S541: making an adjustment decision on the temperature and humidity control equipment set according to the mth microcement performance loss vector, and establishing a first microcement construction adjustment space that meets a predetermined number of decisions.

[0063] Step S542: performing an optimization analysis on the first microcement construction adjustment space according to the microcement performance prediction model and the microcement performance loss weight condition to obtain a second microcement construction adjustment space.

[0064] Step S543: performing variation optimization expansion based on the second micro-cement construction adjustment space to establish a third micro-cement construction adjustment space.

[0065] Step S544: performing optimization to minimize microcement performance loss according to the microcement construction adjustment of the third space, and obtaining the microcement construction control strategy of the mth stage.

[0066] Specifically, after determining that the mth microcement performance loss coefficient is greater than or equal to the microcement performance loss threshold, to determine the mth stage microcement construction control strategy, the mth microcement performance loss vector is used as the basis for adjusting the temperature and humidity control equipment set. Because the temperature and humidity control equipment set has a variety of adjustment parameters, such as the air conditioner temperature setting and the operating power of a humidifier or dehumidifier, different combinations of these parameters are set to establish a first microcement construction control space that meets the predetermined number of decision points. This space encompasses numerous possible temperature and humidity control solutions, each corresponding to a combination of equipment adjustments.

[0067] Using a microcement performance prediction model and a microcement performance loss weighting criterion, we conducted an optimization analysis for the first microcement construction adjustment space. The microcement performance prediction model predicts microcement performance under varying temperature and humidity conditions, while the microcement performance loss weighting criterion clarifies the importance of performance dimensions such as leveling, film quality, and adhesion in overall performance loss. By comprehensively considering these two factors, we evaluated various adjustment options within the first space and screened those that minimized microcement performance loss, thereby establishing the second microcement construction adjustment space.

[0068] The second space for microcement construction adjustment is expanded through a variation-based optimization approach. Based on the second space, subtle variations and adjustments are made to the parameters of each adjustment solution to explore more possible optimization directions. For example, fine-tuning the air conditioner temperature setpoint or changing the humidifier spray frequency can be done. Through this variation-based optimization approach, a third space for microcement construction adjustment is established, encompassing even more potential high-quality adjustment solutions.

[0069] Minimizing microcement performance loss is optimized within the third dimension of microcement construction. By accurately calculating and comparing the performance loss of each adjustment scheme within the third dimension, the solution that minimizes microcement performance loss is identified. The corresponding adjustment parameters for the temperature and humidity control equipment set are the microcement construction control strategy for the mth stage. This strategy minimizes performance loss during the mth stage of microcement construction and ensures construction quality.

[0070] In one possible implementation, step S542 further includes:

[0071] Step S5421: extracting a first microcement construction adjustment decision according to the microcement construction adjustment first space.

[0072] Step S5422: Predict changes in the construction environment of the mth stage according to the first microcement construction adjustment decision to obtain a first predicted changed construction environment.

[0073] Step S5423: inputting the mth microcement formula and the first predicted changed construction environment into the microcement performance prediction model to obtain a first decision microcement performance prediction vector.

[0074] Step S5424: performing weighted calculation on the first decision microcement performance prediction vector according to the microcement performance loss weight condition to obtain a first decision microcement performance loss coefficient.

[0075] Step S5425: Determine whether the first decision microcement performance loss coefficient is less than the microcement performance loss threshold.

[0076] Step S5426: If the first-decision microcement performance loss coefficient is less than the microcement performance loss threshold, the first microcement construction adjustment decision is added to the second microcement construction adjustment space.

[0077] Step S5427: If the microcement performance loss coefficient of the first decision is greater than or equal to the microcement performance loss threshold, the first decision for microcement construction adjustment is eliminated.

[0078] Specifically, the first decision for microcement construction adjustment is extracted from the constructed first space for microcement construction adjustment. This space contains a combination of adjustment schemes for a large number of temperature and humidity control equipment sets. The first decision is one of the specific schemes, which clarifies the operating parameters of equipment such as air conditioners, humidifiers, and dehumidifiers.

[0079] The first decision for microcement construction adjustments includes specific operating instructions for temperature and humidity control equipment, such as adjusting air conditioning temperature settings and arranging the power and operating hours of humidifiers or dehumidifiers. Utilizing environmental simulation technology, the construction area is treated as a complex dynamic system, comprehensively considering factors such as the spatial layout, building structural characteristics, current baseline temperature and humidity values, and ventilation conditions. The simulation, drawing on the principles of heat transfer, fluid mechanics, and humidity balance, analyzes in detail the conduction, convection, and radiation paths of heat within the space, as well as the evaporation, condensation, and diffusion processes of water. For example, if the first decision requires the air conditioner to lower the temperature by 2°C, the simulation system will calculate the rate and time of heat loss based on the space volume, wall insulation performance, and ventilation rate, thereby predicting the temperature trend. Regarding humidity, the magnitude of the increase or decrease is predicted based on the operating parameters of the humidifier or dehumidifier, the air holding capacity, and moisture exchange within the space. Through the collaborative simulation and precise calculation of these environmental factors, detailed data on the first predicted changing construction environment is ultimately generated, including specific temperature and humidity values ​​and the distribution of environmental parameters, providing a key environmental basis for subsequent evaluation of the impact of the adjustment decision on the performance of microcement.

[0080] The mth microcement formula and the first predicted construction environment are input into a pre-established microcement performance prediction model. Based on this input, the model predicts the microcement's leveling, film-forming quality, and adhesion properties under the predicted environment. The model then outputs the first decision microcement performance prediction vector, which reflects the expected performance of the microcement under this adjustment decision.

[0081] Based on the microcement performance loss weighting criteria, a weighted calculation is performed on the first-decision microcement performance prediction vector. The predicted loss values ​​for each performance dimension in the vector are multiplied by their corresponding weights and summed to obtain the first-decision microcement performance loss coefficient, which comprehensively reflects the degree of microcement performance loss under this adjustment decision.

[0082] The first decision microcement performance loss coefficient is compared with a pre-set microcement performance loss threshold, which is used to measure whether the microcement performance loss is acceptable.

[0083] If the microcement performance loss coefficient of the first decision is less than the microcement performance loss threshold, it means that the adjustment decision can effectively reduce the performance loss of microcement and meet the construction quality requirements. This first decision for microcement construction adjustment is added to the second space for microcement construction adjustment as an alternative for subsequent optimization and determination of the final construction control strategy.

[0084] Conversely, if the microcement performance loss coefficient of the first decision is greater than or equal to the microcement performance loss threshold, it indicates that the adjustment decision cannot control the microcement performance loss within an acceptable range and is eliminated. By sequentially performing the above evaluation and screening on the decisions within the first microcement construction adjustment space, a more targeted and effective second microcement construction adjustment space is gradually constructed.

[0085] In one possible implementation, step S543 further includes:

[0086] Step S5431: Construct an adjustment decision variation value evaluation coordinate system based on the adjustment decision variation value evaluation record set.

[0087] Step S5432: inputting the decision microcement performance loss coefficient corresponding to each microcement construction adjustment decision in the second microcement construction adjustment space into the adjustment decision variation value evaluation coordinate system to obtain each adjustment decision variation value coefficient.

[0088] Step S5433: mutate the second microcement construction adjustment space according to the variation value coefficients of each adjustment decision to obtain a first variation domain of microcement construction adjustment.

[0089] Step S5434: performing an optimization analysis on the first variation domain of microcement construction adjustment according to the microcement performance prediction model and the microcement performance loss weight condition to obtain a second variation domain of microcement construction adjustment.

[0090] Step S5435: Expanding the second microcement construction adjustment space according to the second variation domain of microcement construction adjustment to obtain the third microcement construction adjustment space.

[0091] Specifically, the record set for evaluating the value of adjustment decision variation gathers detailed information on various adjustment decisions and their variation attempts in past microcement construction projects. These records cover changes to various adjustment parameters of the temperature and humidity control equipment set, as well as the actual or predicted performance loss of the microcement after each change. To construct a coordinate system for evaluating the value of adjustment decision variation based on this record set, the coordinate system's axes are first determined. One axis represents the degree of variation in a key parameter within the adjustment decision, such as the air conditioner temperature adjustment amplitude or the proportional change in humidifier power. The other axis represents the change in microcement performance loss caused by this variation, measured as the change in the performance loss coefficient. Each data point in the record set—that is, each adjustment decision variation and its corresponding performance loss change—is marked as a point in the coordinate system. By analyzing the distribution and trends of these points and fitting curves or surfaces, a complete coordinate system for evaluating the value of adjustment decision variation is constructed. This coordinate system intuitively demonstrates the relationship between different adjustment decision variations and changes in microcement performance loss, providing a visual and quantitative analysis tool for subsequent evaluation of the variation value of each decision within the second space of microcement construction adjustment, helping to accurately determine the appropriate degree of variation for each adjustment decision.

[0092] The Adjustment Decision Variation Value Evaluation Coordinate System is a multidimensional spatial coordinate system constructed based on a record set of adjustment decision variation value evaluations. This record set contains data related to a large number of historical microcement construction adjustment decisions. It records changes in adjustment decision parameters (such as temperature and humidity adjustment ranges and additive ratio adjustments), as well as the impact of these changes on microcement performance loss (dimensions such as leveling, film quality, and adhesion). Each dimension of the coordinate system corresponds to a different adjustment decision parameter variation index and microcement performance loss change index. The specific evaluation process is as follows: First, the adjustment decision variation value evaluation coordinate system is divided into multiple grid regions, each representing a range of combinations of adjustment decision parameter variation and microcement performance loss. Simultaneously, a basic value weight is assigned to each grid region based on historical data and expert experience. For example, if historical data shows that a small adjustment of temperature and humidity can significantly reduce microcement adhesion loss in a given region, then that region will be assigned a higher basic value weight. Conversely, if parameter adjustments in a given region have little effect on microcement performance, then that region will be assigned a lower weight. When inputting the microcement performance loss coefficient corresponding to each microcement construction adjustment decision within the second microcement construction adjustment space into the coordinate system, the specific location of this coefficient within the coordinate system is first determined. Taking a simplified two-dimensional coordinate system as an example, assume that the horizontal axis represents the variation in the temperature and humidity adjustment amplitude, and the vertical axis represents the variation in the microcement leveling loss coefficient. If the microcement performance loss coefficient corresponding to a certain adjustment decision is a leveling loss coefficient of 0.3 and the temperature and humidity adjustment amplitude variation is 5%, then the intersection of the horizontal and vertical coordinates of 5% and 0.3, respectively, is found in the coordinate system to determine the location of this adjustment decision within the coordinate system. Then, based on the grid area where this location is located, combined with the base value weight within the area and considering the influence of surrounding grid areas (through interpolation), the adjustment decision's variation value coefficient is calculated. For example, if the base value weight of the grid area where this location is located is 0.8 and the interpolated influence coefficient of the surrounding grid areas is 0.1, then the variation value coefficient of this adjustment decision might be 0.8 + 0.1 = 0.9. For example, in a microcement construction project, the constructed adjustment decision variation value evaluation coordinate system includes three dimensions: temperature adjustment amplitude variation, humidity adjustment amplitude variation, and microcement film quality loss coefficient variation. Within the second microcement construction adjustment space, there are three adjustment decisions. Decision A corresponds to a microcement performance loss coefficient of 0.2, a temperature adjustment amplitude variation of 3°C, and a humidity adjustment amplitude variation of 8%. Decision B corresponds to a microcement performance loss coefficient of 0.4, a temperature adjustment amplitude variation of -2°C, and a humidity adjustment amplitude variation of 4%. Decision C corresponds to a microcement performance loss coefficient of 0.1, a temperature adjustment amplitude variation of 5°C, and a humidity adjustment amplitude variation of 6%.Entering the parameters of decision A into the coordinate system and determining its position within the coordinate system, the grid area in which it resides has a base value weight of 0.7, and the interpolation influence coefficient of the surrounding grids is 0.05. The calculated adjustment decision variation value coefficient for decision A is 0.75. The grid area in which decision B resides has a base value weight of 0.3, an interpolation influence coefficient of 0.03, and an adjustment decision variation value coefficient of 0.33. The grid area in which decision C resides has a base value weight of 0.8, an interpolation influence coefficient of 0.08, and an adjustment decision variation value coefficient of 0.88. Therefore, decision C has the greatest potential for further optimization and adjustment to reduce microcement performance loss. Subsequently, decision C can be prioritized for variation and screening to find a more optimal microcement construction adjustment decision.

[0093] Mutation is performed on the second microcement construction regulation space to generate the first microcement construction regulation domain. The value of each adjustment decision's variation is a quantitative indicator of its potential for variation optimization, and its value is positively correlated with the number of variations that can be applied to that decision. Based on the second microcement construction regulation space, the scale of variation for each adjustment decision is determined based on its corresponding value of variation coefficient. Decisions with larger value of variation coefficients have greater potential for further optimizing microcement performance losses, and therefore require more and wider-ranging mutations. For example, operating parameters of a set of temperature and humidity control devices, such as air conditioner temperature settings or humidifier or dehumidifier operating levels, can be adjusted in a variety of ways to generate multiple new regulation scenarios. For regulation decisions with smaller value of variation coefficients, the number and range of variations are reduced accordingly. By performing this targeted mutation on all adjustment decisions within the second space, the numerous new regulation scenarios generated are aggregated to form the first microcement construction regulation domain. This domain provides a richer selection for subsequent screening of optimal microcement construction regulation strategies.

[0094] Using a microcement performance prediction model and microcement performance loss weighting criteria, an optimization analysis was conducted on the first variation domain of microcement construction adjustment to obtain the second variation domain of microcement construction adjustment. The first variation domain of microcement construction adjustment contains numerous adjustment decisions generated through variation. The microcement performance prediction model predicts microcement performance in terms of leveling, film quality, adhesion, and other aspects based on the temperature and humidity environment corresponding to each decision. Furthermore, the microcement performance loss weighting criteria clearly define the contribution of each performance dimension to the overall performance loss. The construction environment data corresponding to each adjustment decision within the first variation domain was input into the microcement performance prediction model to obtain performance predictions for microcement under different environments. Then, based on the performance loss weighting criteria, the predicted performance was compared with the ideal performance standard to calculate the comprehensive performance loss of microcement for each adjustment decision. By evaluating and ranking these loss values ​​and integrating them according to pre-defined screening criteria, such as selecting adjustment decisions with a certain proportion of low comprehensive performance loss values, these selected decisions constitute the second variation domain of microcement construction adjustment. The adjustment decisions in this variation domain are more effective in reducing the performance loss of microcement, providing a better solution reserve for the subsequent determination of the final construction control strategy.

[0095] By integrating high-quality control decisions from the second variation domain into the second space of microcement construction regulation, the set of solutions is expanded, ultimately resulting in the third space of microcement construction regulation. Compared to the second space, this third space contains more optimized control decisions with minimal performance loss, providing a richer and higher-quality selection for determining the final microcement construction control strategy for the mth stage.

[0096] In one possible implementation, step S300 further includes:

[0097] Step S310: performing a normal performance parameter sample search on the M microcement formulations according to the microcement performance factor to obtain M microcement performance sample sets.

[0098] Step S320: performing centralized value calculation based on the M microcement performance sample sets to obtain M microcement performance benchmark sequences.

[0099] Step S330: constructing the M microcement performance benchmark vectors according to the M microcement performance benchmark sequences.

[0100] Specifically, microcement performance factors encompass key elements such as raw material type, mix ratio, and additive characteristics, which collectively determine the ultimate performance of microcement. Based on these performance factors, a comprehensive and detailed search for normal performance parameter samples was conducted for M microcement formulations. Past laboratory reports were reviewed, detailing the performance test data of different microcement formulations under standard construction conditions. Construction records from numerous actual project cases were also referenced, encompassing the application of various microcement formulations under typical field conditions. Furthermore, the performance parameter ranges specified by authoritative industry standards were strictly adhered to. For each microcement formulation, relevant parameters for key performance dimensions such as leveling, film quality, and adhesion were identified. These parameters were systematically compiled and summarized, ultimately resulting in M ​​microcement performance sample sets, each of which fully and accurately captured the specific performance data for each microcement formulation under normal construction conditions.

[0101] After obtaining M microcement performance sample sets, key performance indicators were refined through centralized value calculation to generate M microcement performance benchmark sequences. For each microcement performance sample set, centralized value calculations were performed on the leveling coefficient, film quality coefficient, and adhesion coefficient. Taking the leveling coefficient as an example, the sample set contains multiple leveling coefficient data from different experiments or construction records. Statistical methods, such as calculating the average, summing all leveling coefficients and dividing by the number of data points, were used to obtain a value representing the typical leveling level. This value was used as the centralized value of the leveling coefficient. The same method was applied to the film quality coefficient and adhesion coefficient. After the centralized value calculations for these three coefficients within each sample set were completed, the centralized values ​​of the leveling coefficient, film quality coefficient, and adhesion coefficient were combined to form a microcement performance benchmark sequence. This process was repeated sequentially for each of the M microcement performance sample sets, ultimately resulting in M ​​microcement performance benchmark sequences that accurately reflect the performance of each microcement formulation under normal application conditions.

[0102] Each microcement performance benchmark sequence includes concentrated values ​​for leveling coefficient, film quality coefficient, and adhesion coefficient, reflecting microcement performance from different dimensions. These three concentrated values ​​are then combined in an ordered manner according to established vector construction rules. These values ​​are typically presented as a three-dimensional vector, with the concentrated value for leveling coefficient as the component in the first dimension, the concentrated value for film quality coefficient as the component in the second dimension, and the concentrated value for adhesion coefficient as the component in the third dimension. This approach integrates previously dispersed performance data into a single vector space. This process is repeated for each of the M microcement performance benchmark sequences, resulting in the construction of M microcement performance benchmark vectors. These vectors intuitively and comprehensively demonstrate the comprehensive performance of different microcement formulations under normal application conditions. They provide a concise and effective quantitative tool for comparing the performance of microcement in actual applications against the ideal benchmark, helping to quickly and accurately determine the impact of the construction environment on microcement performance and formulate targeted adjustment strategies accordingly.

[0103] In one possible implementation, step S300 further includes:

[0104] Step S340: The microcement performance factors include leveling, film-forming quality and adhesion.

[0105] Specifically, microcement performance factors include leveling, film quality, and adhesion. Leveling determines whether the microcement can spread evenly during application, creating a smooth surface. Excellent leveling ensures smooth flow within the application area, automatically filling minor indentations and irregularities. This significantly improves application efficiency, reduces manual leveling, and ensures the smoothness and aesthetics of the finished surface. Film quality is reflected in the properties of the film formed after drying and curing, including its strength, density, and durability. High-quality film formation means that the microcement film is less susceptible to cracking and flaking, effectively resisting wear, stains, and environmental factors from daily use, extending the lifespan of the microcement decorative surface. Adhesion refers to the ability of the microcement to firmly adhere to the base material. Strong adhesion prevents hollowing and detachment during subsequent use, ensuring a tight bond between the microcement and the base material, providing a stable support for the entire decorative structure. These three performance factors are interrelated and mutually influential, collectively determining the overall performance of microcement in practical applications.

[0106] Example 2, based on the same inventive concept as the method for intelligent temperature and humidity control in microcement construction in the above embodiment, Figure 2 As shown, this application provides an intelligent temperature and humidity control device for micro-cement construction. The device and method embodiments in the embodiments of this application are based on the same inventive concept. The device includes:

[0107] The micro-cement coating scheme acquisition module 10 is used to divide the coating layers according to the micro-cement construction scheme of the building decoration construction area, and obtain M-stage micro-cement coating schemes, where M is a positive integer greater than 1.

[0108] The construction environment acquisition module 20 is used to perform environmental prediction on the M-stage micro-cement coating schemes according to the current environmental perception information of the building decoration construction area, and obtain the M-stage construction environments.

[0109] The microcement performance benchmark vector determination module 30 is configured to calculate benchmark performance parameters for the M microcement formulations corresponding to the M stage microcement coating schemes according to the microcement performance factors, and determine M microcement performance benchmark vectors.

[0110] The microcement performance loss vector establishing module 40 is configured to predict the microcement performance loss of the M stage construction environments based on the M microcement performance reference vectors and according to a microcement performance prediction model, and establish M microcement performance loss vectors.

[0111] The micro-cement construction control strategy generating module 50 is used to optimize the temperature and humidity control equipment set according to the M micro-cement performance loss vectors to generate a micro-cement construction control strategy.

[0112] The adaptive temperature and humidity control module 60 is used to control the temperature and humidity control equipment set to perform adaptive temperature and humidity control on the building decoration construction area according to the micro-cement construction control strategy.

[0113] Furthermore, the device is also used to implement the following functions:

[0114] According to the M stage construction environments and the M microcement performance benchmark vectors, the m-th stage construction environment and the m-th microcement performance benchmark vector are extracted, where m is a positive integer and 1≤m≤M; the m-th microcement formula and the m-th stage construction environment are input into the microcement performance prediction model to obtain the m-th microcement performance prediction vector; deviation identification is performed on the m-th microcement performance prediction vector according to the m-th microcement performance benchmark vector to obtain the m-th microcement performance loss vector, and the m-th microcement performance loss vector is added to the M microcement performance loss vectors.

[0115] Furthermore, the device is also used to implement the following functions:

[0116] The microcement performance prediction model includes a microcement leveling prediction model, a microcement film-forming quality prediction model, and a microcement adhesion prediction model; the mth microcement formula and the mth stage construction environment are input into the microcement leveling prediction model to obtain an mth leveling prediction coefficient; the mth microcement formula and the mth stage construction environment are input into the microcement film-forming quality prediction model to obtain an mth film-forming quality prediction coefficient; the mth microcement formula and the mth stage construction environment are input into the microcement adhesion prediction model to obtain an mth adhesion prediction coefficient; and the mth microcement performance prediction vector is constructed based on the mth leveling prediction coefficient, the mth film-forming quality prediction coefficient, and the mth adhesion prediction coefficient.

[0117] Furthermore, the device is also used to implement the following functions:

[0118] Obtaining a microcement performance loss weight condition, wherein the microcement performance loss weight condition includes a leveling loss weight, a film-forming quality loss weight, and an adhesion loss weight; performing a weighted calculation on an mth microcement performance loss vector according to the microcement performance loss weight condition to obtain an mth microcement performance loss coefficient; determining whether the mth microcement performance loss coefficient is greater than or equal to a microcement performance loss threshold; if the mth microcement performance loss coefficient is greater than or equal to the microcement performance loss threshold, optimizing the temperature and humidity control equipment set according to the mth microcement performance loss vector to obtain an mth-stage microcement construction control strategy; and adding the mth-stage microcement construction control strategy to the microcement construction control strategy.

[0119] Furthermore, the device is also used to implement the following functions:

[0120] According to the m-th microcement performance loss vector, an adjustment decision is made for the temperature and humidity control equipment set to establish a first microcement construction adjustment space that meets a predetermined number of decisions; according to the microcement performance prediction model and the microcement performance loss weight condition, an optimization analysis is performed on the first microcement construction adjustment space to obtain a second microcement construction adjustment space; according to the second microcement construction adjustment space, a variation optimization and expansion is performed to establish a third microcement construction adjustment space; according to the third microcement construction adjustment space, a microcement performance loss minimization optimization is performed to obtain the m-th stage microcement construction control strategy.

[0121] Furthermore, the device is also used to implement the following functions:

[0122] According to the first microcement construction adjustment space, a first microcement construction adjustment decision is extracted; according to the first microcement construction adjustment decision, a change prediction is performed on the m-th stage construction environment to obtain a first predicted changed construction environment; the m-th microcement formula and the first predicted changed construction environment are input into the microcement performance prediction model to obtain a first decision microcement performance prediction vector; according to the microcement performance loss weight condition, a weighted calculation is performed on the first decision microcement performance prediction vector to obtain a first decision microcement performance loss coefficient; it is determined whether the first decision microcement performance loss coefficient is less than the microcement performance loss threshold; if the first decision microcement performance loss coefficient is less than the microcement performance loss threshold, the first microcement construction adjustment decision is added to the second microcement construction adjustment space; if the first decision microcement performance loss coefficient is greater than or equal to the microcement performance loss threshold, the first microcement construction adjustment decision is eliminated.

[0123] Furthermore, the device is also used to implement the following functions:

[0124] According to the adjustment decision variation value evaluation record set, an adjustment decision variation value evaluation coordinate system is constructed; the decision microcement performance loss coefficient corresponding to each microcement construction adjustment decision in the second microcement construction adjustment space is input into the adjustment decision variation value evaluation coordinate system to obtain each adjustment decision variation value coefficient; the second microcement construction adjustment space is mutated according to each adjustment decision variation value coefficient to obtain the first variation domain of microcement construction adjustment; according to the microcement performance prediction model and the microcement performance loss weight condition, the first variation domain of microcement construction adjustment is optimized and analyzed to obtain the second variation domain of microcement construction adjustment; the second microcement construction adjustment space is expanded according to the second variation domain of microcement construction adjustment to obtain the third microcement construction adjustment space.

[0125] Furthermore, the device is also used to implement the following functions:

[0126] Based on the microcement performance factors, a normal performance parameter sample search is performed on the M microcement formulations to obtain M microcement performance sample sets; a centralized value calculation is performed on the M microcement performance sample sets to obtain M microcement performance benchmark sequences; and based on the M microcement performance benchmark sequences, the M microcement performance benchmark vectors are constructed.

[0127] Furthermore, the device is also used to implement the following functions:

[0128] The microcement performance factors include leveling, film-forming quality and adhesion.

[0129] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0131] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. The intelligent temperature and humidity control method in microcement construction is characterized by: include: Divide the coating layers according to the microcement construction plan of the building decoration construction area, and obtain M-stage microcement coating plans, where M is a positive integer greater than 1; Performing environmental prediction on the M-stage microcement coating schemes based on current environmental perception information of the building decoration construction area to obtain the M-stage construction environment; Calculating benchmark performance parameters for the M microcement formulations corresponding to the M-stage microcement coating schemes according to the microcement performance factors to determine M microcement performance benchmark vectors; Based on the M microcement performance benchmark vectors, predicting the microcement performance loss for the M stage construction environments according to the microcement performance prediction model, and establishing M microcement performance loss vectors; Optimizing the temperature and humidity control equipment set according to the M microcement performance loss vectors to generate a microcement construction control strategy; Controlling the temperature and humidity control equipment set to perform adaptive temperature and humidity control on the building decoration construction area according to the micro-cement construction control strategy; The temperature and humidity control equipment set is optimized and adjusted according to the M microcement performance loss vectors to generate a microcement construction control strategy, including: Obtaining a microcement performance loss weight condition, wherein the microcement performance loss weight condition includes a leveling loss weight, a film-forming quality loss weight, and an adhesion loss weight; Performing weighted calculation on the mth microcement performance loss vector according to the microcement performance loss weight condition to obtain the mth microcement performance loss coefficient; determining whether the mth microcement performance loss coefficient is greater than or equal to a microcement performance loss threshold; If the m-th microcement performance loss coefficient is greater than or equal to the microcement performance loss threshold, optimally adjusting the temperature and humidity control equipment set according to the m-th microcement performance loss vector to obtain the m-th stage microcement construction control strategy; Adding the m-th stage microcement construction control strategy to the microcement construction control strategy; The temperature and humidity control equipment set is optimally adjusted according to the m-th microcement performance loss vector to obtain the m-th stage microcement construction control strategy, including: Making adjustment decisions on the temperature and humidity control equipment set according to the mth microcement performance loss vector, and establishing a first microcement construction adjustment space that meets a predetermined number of decisions; performing an optimization analysis on the first microcement construction adjustment space according to the microcement performance prediction model and the microcement performance loss weight condition to obtain a second microcement construction adjustment space; Perform variation optimization and expansion based on the second micro-cement construction adjustment space to establish a third micro-cement construction adjustment space; According to the micro-cement construction, the third space is adjusted to minimize the micro-cement performance loss and optimize the micro-cement construction control strategy of the mth stage.

2. The intelligent temperature and humidity control method in microcement construction according to claim 1, characterized in that: Based on the M microcement performance benchmark vectors, microcement performance loss prediction is performed for the M stage construction environments according to a microcement performance prediction model, and M microcement performance loss vectors are established, including: Extracting the m-th stage construction environment and the m-th microcement performance benchmark vector according to the M stage construction environments and the M microcement performance benchmark vectors, where m is a positive integer, 1≤m≤M; Inputting the mth microcement formula and the mth stage construction environment into the microcement performance prediction model to obtain the mth microcement performance prediction vector; Deviation identification is performed on the mth microcement performance prediction vector according to the mth microcement performance reference vector to obtain an mth microcement performance loss vector, and the mth microcement performance loss vector is added to the M microcement performance loss vectors.

3. The intelligent temperature and humidity control method in micro-cement construction according to claim 2, characterized in that: Inputting the mth microcement formula and the mth stage construction environment into the microcement performance prediction model to obtain the mth microcement performance prediction vector includes: The microcement performance prediction model includes a microcement leveling prediction model, a microcement film-forming quality prediction model and a microcement adhesion prediction model; Inputting the mth microcement formula and the mth stage construction environment into the microcement leveling prediction model to obtain an mth leveling prediction coefficient; Inputting the mth microcement formula and the mth stage construction environment into the microcement film quality prediction model to obtain the mth film quality prediction coefficient; Inputting the mth microcement formula and the mth stage construction environment into the microcement adhesion prediction model to obtain the mth adhesion prediction coefficient; The mth microcement performance prediction vector is constructed according to the mth leveling prediction coefficient, the mth film-forming quality prediction coefficient, and the mth adhesion prediction coefficient.

4. The intelligent temperature and humidity control method in micro-cement construction according to claim 1, characterized in that: According to the microcement performance prediction model and the microcement performance loss weight condition, an optimization analysis is performed on the first microcement construction adjustment space to obtain a second microcement construction adjustment space, including: extracting a first microcement construction adjustment decision according to the first microcement construction adjustment space; Predicting changes in the construction environment at the mth stage according to the first microcement construction adjustment decision to obtain a first predicted changed construction environment; Inputting the mth microcement formula and the first predicted changed construction environment into the microcement performance prediction model to obtain a first decision microcement performance prediction vector; performing weighted calculation on the first decision microcement performance prediction vector according to the microcement performance loss weight condition to obtain a first decision microcement performance loss coefficient; determining whether the first decision microcement performance loss coefficient is less than the microcement performance loss threshold; If the first decision microcement performance loss coefficient is less than the microcement performance loss threshold, adding the first microcement construction adjustment decision to the second microcement construction adjustment space; If the microcement performance loss coefficient of the first decision is greater than or equal to the microcement performance loss threshold, the first decision of the microcement construction adjustment is eliminated.

5. The intelligent temperature and humidity control method in micro-cement construction according to claim 1, characterized in that: The third microcement construction adjustment space is established by performing variation optimization and expansion based on the second microcement construction adjustment space, including: Based on the adjustment decision variation value evaluation record set, an adjustment decision variation value evaluation coordinate system is constructed. The adjustment decision variation value evaluation record set aggregates detailed information on adjustment decisions and their variation attempts in past microcement construction. These records include changes in different adjustment parameters of the temperature and humidity control equipment set, as well as the actual or predicted evaluation results of microcement performance loss after each change. The adjustment decision variation value evaluation coordinate system is a multidimensional space coordinate system constructed based on the adjustment decision variation value evaluation record set. This record set contains data on microcement construction adjustment decisions, including changes in adjustment decision parameters and the impact of these changes on microcement performance loss. Each dimension of the coordinate system corresponds to a different adjustment decision parameter variation index and microcement performance loss change index. Inputting the decision microcement performance loss coefficient corresponding to each microcement construction adjustment decision in the second microcement construction adjustment space into the adjustment decision variation value evaluation coordinate system to obtain the adjustment decision variation value coefficient; mutating the second microcement construction adjustment space according to the variation value coefficients of each adjustment decision to obtain a first variation domain of microcement construction adjustment; performing an optimization analysis on the first variation domain of microcement construction adjustment according to the microcement performance prediction model and the microcement performance loss weight condition to obtain a second variation domain of microcement construction adjustment; The second microcement construction adjustment space is expanded according to the second variation domain of microcement construction adjustment to obtain the third microcement construction adjustment space.

6. The intelligent temperature and humidity control method in micro-cement construction according to claim 1, characterized in that: The benchmark performance parameters of the M microcement formulations corresponding to the M-stage microcement coating schemes are calculated according to the microcement performance factors to determine M microcement performance benchmark vectors, including: performing a normal performance parameter sample search on the M microcement formulations according to the microcement performance factor to obtain M microcement performance sample sets; Performing centralized value calculation based on the M microcement performance sample sets to obtain M microcement performance benchmark sequences; The M microcement performance benchmark vectors are constructed according to the M microcement performance benchmark sequences.

7. The intelligent temperature and humidity control method in micro-cement construction according to claim 1, characterized in that: The microcement performance factors include leveling, film-forming quality and adhesion.

8. The intelligent temperature and humidity control device in micro-cement construction is characterized by: The device is used to implement the temperature and humidity intelligent control method in microcement construction according to any one of claims 1 to 7, and the device comprises: A microcement coating scheme acquisition module is used to divide the coating layers according to the microcement construction scheme of the building decoration construction area, and obtain M-stage microcement coating schemes, where M is a positive integer greater than 1; A construction environment acquisition module, configured to perform environmental prediction on the M-stage microcement coating schemes based on current environmental perception information of the building decoration construction area, and obtain the M-stage construction environments; a microcement performance benchmark vector determination module, configured to calculate benchmark performance parameters for the M microcement formulations corresponding to the M-stage microcement coating schemes based on the microcement performance factors, and determine M microcement performance benchmark vectors; a microcement performance loss vector establishment module, configured to predict the microcement performance loss for the M stage construction environments based on the M microcement performance reference vectors and according to a microcement performance prediction model, and establish M microcement performance loss vectors; a microcement construction control strategy generation module, configured to optimize and adjust the temperature and humidity control equipment set according to the M microcement performance loss vectors to generate a microcement construction control strategy; The adaptive temperature and humidity control module is used to control the temperature and humidity control equipment set to perform adaptive temperature and humidity control on the building decoration construction area according to the micro-cement construction control strategy.

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