Temperature and humidity intelligent control method and device in micro cement construction

Abstract

The invention discloses a temperature and humidity intelligent control method and device in micro cement construction, and relates to the technical field of construction temperature and humidity control, and the method comprises the steps: carrying out the coating layer division according to a micro cement construction scheme of a building decoration construction area, and obtaining a micro cement coating scheme; performing environment prediction to obtain a construction environment; performing reference performance parameter calculation on the micro-cement formula, and determining a micro-cement performance reference vector; performing micro-cement performance loss prediction on the construction environment, and establishing a micro-cement performance loss vector; optimizing and adjusting the temperature and humidity regulation and control equipment set to generate a micro-cement construction regulation and control strategy; and self-adaptive temperature and humidity control is carried out. The technical problems that in building decoration micro-cement construction in the prior art, due to the fact that temperature and humidity cannot be accurately regulated and controlled, the performance loss of the micro-cement is large, and the construction quality is difficult to guarantee are solved, and the technical effect that the temperature and humidity are accurately controlled in a building decoration construction area to reduce the performance loss of the micro-cement is achieved.

Description

Technical Field

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

[0002] In the field of architectural decoration, microcement has unique decorative effects and good durability. However, microcement construction is extremely sensitive to environmental conditions, especially temperature and humidity. In the traditional construction process, there is often a lack of accurate correlation analysis between microcement coating schemes and environmental factors at different construction stages. Temperature and humidity control is mostly based on experience, lacking scientific basis and systematic methods. This makes it difficult to stably maintain the construction environment within the range required for the optimal performance of microcement, which in turn causes performance loss problems such as poor leveling, poor film quality, and reduced adhesion of microcement, which seriously affects 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 large loss of microcement performance and difficulty in ensuring the construction quality. Summary of the invention

[0004] The present application provides a method and device for intelligently controlling temperature and humidity in microcement construction, which is used to solve the technical problem in the prior art that temperature and humidity cannot be accurately controlled in microcement construction for architectural decoration, 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: According to the microcement construction scheme of the building decoration construction area, coating levels are divided to obtain M-stage microcement coating schemes, where M is a positive integer greater than 1; according to the current environmental perception information of the building decoration construction area, the environment of the M-stage microcement coating schemes is predicted to obtain M-stage construction environments; according to the microcement performance factor, the benchmark performance parameters of the M microcement formulas corresponding to the M-stage microcement coating schemes are calculated to determine M microcement performance benchmark vectors; based on the M microcement performance benchmark vectors, according to the microcement performance prediction model, the microcement performance loss of the M-stage construction environment is predicted to establish M microcement performance loss vectors; according to the M microcement performance loss vectors, the temperature and humidity control equipment set is optimized and adjusted to generate a microcement construction control strategy; according to the microcement construction control strategy, the temperature and humidity control equipment set is controlled to perform adaptive temperature and humidity control on the building decoration construction area.

[0007] The second aspect of the present application provides an intelligent temperature and humidity control device for microcement construction, the device comprising: A microcement coating scheme acquisition module is used to divide the coating levels 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 is used to predict the environment of the M-stage microcement coating schemes according to the current environmental perception information of the building decoration construction area, and obtain M-stage construction environments; a microcement performance benchmark vector determination module is used to calculate the benchmark performance parameters of the M microcement formulations corresponding to the M-stage microcement coating schemes according to the microcement performance factor, and determine M microcement performance benchmark vectors; a microcement performance loss vector establishment module is used to predict the microcement performance loss of the M-stage construction environment based on the M microcement performance benchmark vectors and a microcement performance prediction model, and establish M microcement performance loss vectors; a microcement construction control strategy generation module is used to optimize the temperature and humidity control equipment set according to the M microcement performance loss vectors and generate a microcement construction control strategy; an adaptive temperature and humidity control module is used to control 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] One or more technical solutions provided in this application have at least the following technical effects or advantages: According to the microcement construction scheme of the building decoration construction area, the coating level is divided to obtain M-stage microcement coating schemes; the environment of the M-stage microcement coating schemes is predicted to obtain M-stage construction environments; the benchmark performance parameters of the M microcement formulas corresponding to the M-stage microcement coating schemes are calculated to determine M microcement performance benchmark vectors; the microcement performance loss of the M-stage construction environment is predicted according to the microcement performance prediction model to establish M microcement performance loss vectors; the temperature and humidity control equipment set is optimized according to the M microcement performance loss vectors to generate a microcement construction control strategy; the building decoration construction area is adaptively controlled in temperature and humidity. The technical effect of accurately controlling the temperature and humidity in the building decoration construction area to reduce the performance loss of microcement is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] 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.

[0010] Figure 1A schematic diagram of the process flow of the intelligent temperature and humidity control method in microcement construction provided in an embodiment of the present application; 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.

[0011] Explanation of the accompanying drawings: microcement coating scheme acquisition module 10, construction environment acquisition module 20, microcement performance benchmark 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

[0012] The present application provides a method and device for intelligently controlling temperature and humidity in microcement construction, which is used to solve the technical problem in the prior art that temperature and humidity cannot be accurately controlled in microcement construction for architectural decoration, resulting in a large loss of microcement performance and difficulty in ensuring construction quality.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a method for intelligently controlling temperature and humidity in microcement construction, the method comprising: 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.

[0015] Specifically, the specific microcement construction plan for the building decoration construction area is analyzed, and the plan specifies key information such as construction process, material use, and quality standards in detail. In view of the need for fine planning of the microcement construction process, taking the common microcement construction layering as an example, it is usually divided into the bottom layer microcement, middle layer microcement, and surface layer microcement construction stages. The bottom layer microcement mainly plays the role of foundation bonding and leveling. During construction, there are specific requirements for the coating thickness and uniformity, and it is necessary to ensure that the base layer is firmly attached; the middle layer microcement focuses on enhancing the overall structural strength and stability, and the key points of construction are the density and thickness control of the coating; the surface layer microcement is related to the final decorative effect and texture, and has extremely high requirements for the flatness and color uniformity of the coating. The construction personnel systematically divide the coating layer of the entire microcement construction process based on the process requirements, quality standards, and actual construction experience of each layer in the construction plan. Through such a division, M-stage microcement coating plans can be clearly obtained, where M is a positive integer greater than 1, and each stage has clear construction goals, operating specifications, and quality acceptance standards, laying a solid foundation for the smooth advancement and high-quality completion of subsequent construction.

[0016] Step S200: 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 the M-stage construction environments.

[0017] Specifically, after obtaining the M-stage microcement coating scheme, the current environmental perception information is collected in real time with the help of various environmental sensing devices installed in the building decoration construction area, such as temperature and humidity sensors, anemometers, and illuminance sensors. These devices can accurately measure environmental parameters such as temperature, humidity, wind speed, and light intensity at the construction site. Combined with the construction characteristics and time arrangements of the M-stage microcement coating schemes, the environmental conditions during the construction of each stage are predicted. For example, considering the weather change laws in different seasons and time periods, as well as the ventilation conditions and spatial layout of the construction site, the environmental data such as the temperature fluctuation range, humidity change trend, and wind speed size that may occur during the construction of each stage are predicted. Through such predictions, the M-stage construction environment is finally obtained. These prediction results provide an important basis for the subsequent adjustment of the microcement construction strategy according to the environment, which helps to ensure that microcement is constructed in a suitable environment and improve the construction quality and effect.

[0018] Step S300: Calculating the benchmark performance parameters of the M microcement formulations corresponding to the M-stage microcement coating schemes according to the microcement performance factors, and determining M microcement performance benchmark vectors.

[0019] Specifically, based on the microcement performance factors covering leveling, film quality and adhesion, a comprehensive normal performance parameter sample search was conducted for M microcement formulas. By studying the rich experimental data, actual construction case records and authoritative industry standards in the past, the specific parameters of each formula in terms of leveling, film quality and adhesion under the standard construction environment were accurately found, and then M microcement performance sample sets were summarized. Each sample set recorded the various performance data of the corresponding formula in detail. For the M microcement performance sample sets, the centralized value calculation work was carried out. The average values ​​of the leveling coefficient, film quality coefficient and adhesion coefficient in the sample set were calculated respectively, and the centralized values ​​of each coefficient were obtained, thereby generating M microcement performance benchmark sequences. Finally, based on the M microcement performance benchmark sequences, according to the established vector construction rules, the centralized values ​​of the leveling coefficient, the film quality coefficient and the adhesion coefficient were used as the three dimensional components of the vector, and the M microcement performance benchmark vectors were constructed in an orderly combination. These vectors comprehensively and intuitively present the comprehensive performance benchmarks of each microcement formulation under normal construction conditions under different stages of microcement coating schemes, providing a key reference for subsequent evaluation of the difference between microcement performance in actual construction and the ideal state, thereby ensuring the quality of microcement construction.

[0020] Step S400: Based on the M microcement performance reference vectors, the microcement performance loss is predicted for the M stage construction environments according to the microcement performance prediction model, and M microcement performance loss vectors are established.

[0021] Specifically, the multivariate linear regression algorithm in machine learning is used to predict the performance loss of microcement. The data of various environmental factors in the M-stage construction environment, such as temperature, humidity, wind speed, etc., are standardized and converted into dimensionless data to ensure the comparability of data of different factors. At the same time, the M microcement performance benchmark vectors are also converted into corresponding formats so that they can be used as model input together with the environmental factor data. The multivariate linear regression model is trained using a large amount of historical microcement construction data, including the actual measured values ​​of microcement performance under different construction environments. During the training process, the model will continuously adjust the coefficients corresponding to each environmental factor, find the linear relationship between the environmental factor and the microcement performance, and minimize the error between the model prediction value and the actual measured value. After the training is completed, for each stage of the construction environment, the environmental factor data of that stage and the corresponding microcement performance benchmark vector are input into the trained multivariate linear regression model. The model will calculate according to the input data and the determined linear relationship, and output the predicted microcement performance vector. After that, the microcement performance loss is 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 benchmark vector and the predicted mth microcement performance vector in each dimension is calculated, and then the square root is taken to obtain a value representing the degree of performance loss. The microcement performance loss values ​​of each stage are arranged in a specific order and constructed into a vector form, thereby establishing M microcement performance loss vectors, which accurately reflect the performance loss of microcement at different construction stages.

[0022] Step S500: optimizing the temperature and humidity control equipment set according to the M micro-cement performance loss vectors to generate a micro-cement construction control strategy.

[0023] Specifically, by optimizing and adjusting the temperature and humidity control equipment set, an effective microcement construction control strategy is generated to reduce the performance loss of microcement and ensure the construction quality. First, the weight conditions of microcement performance loss are determined, including the weight of leveling loss, the weight of film quality loss and the weight of adhesion loss. These weights are determined according to the key indicators and actual needs of microcement construction, and are used to measure the degree of influence of different performance losses on the overall construction quality. For each microcement performance loss vector, a weighted calculation is performed according to the above weight conditions to obtain the corresponding microcement performance loss coefficient. By setting a microcement performance loss threshold, the calculated loss coefficient is compared with it. If the microcement performance loss coefficient of a certain stage is greater than or equal to the threshold, it means that the performance loss of microcement at this stage is more serious, and the temperature and humidity control equipment set needs to be optimized. When adjusting, the temperature and humidity control equipment set is first adjusted according to the microcement performance loss vector of this stage to form the first space of microcement construction adjustment that meets the predetermined number of decisions. This space contains a variety of possible equipment adjustment combination schemes. Then, these schemes are optimized and analyzed based on the microcement performance prediction model and performance loss weight conditions. Taking a decision plan as an example, according to the plan, the change of the construction environment in the mth stage is predicted to obtain the first predicted change construction environment, the mth microcement formula and this predicted environment are input into the microcement performance prediction model to obtain the first decision microcement performance prediction vector, and then the performance loss coefficient of the first decision microcement is obtained by weighted calculation according to the performance loss weight condition. If the coefficient is less than the microcement performance loss threshold, it means that the decision plan is feasible and it is added to the second space of microcement construction adjustment; otherwise, it is eliminated. The second space of microcement construction adjustment is expanded by mutation optimization. By constructing the adjustment decision variation value evaluation coordinate system, the decision microcement performance loss coefficient corresponding to each decision in the space is input into the coordinate system to obtain the variation value coefficients of each adjustment decision. According to these coefficients, the second space is mutated to obtain the first variation domain of microcement construction adjustment. Then, according to the microcement performance prediction model and the performance loss weight condition, the first variation domain is optimized and analyzed to obtain the second variation domain of microcement construction adjustment. The second variation domain is used to expand the second space to form the third space of microcement construction adjustment. 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, and the control strategies of all stages are integrated to form a complete microcement construction control strategy.

[0024] 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.

[0025] Specifically, according to the formulated microcement construction control strategy, precise control instructions are issued to the temperature and humidity control equipment set to achieve adaptive temperature and humidity control of the building decoration construction area. The temperature and humidity control equipment set covers a variety of devices such as air conditioners, humidifiers, dehumidifiers, ventilation equipment, etc., which work together to adjust the temperature and humidity environment of the construction area. The microcement construction control strategy specifies in detail the temperature and humidity target values ​​that the construction area needs to achieve at different construction stages and the operating parameters of the control equipment. For example, in the microcement base coating stage, according to the construction process requirements and preliminary analysis, the strategy may set the temperature to be maintained at 20-22℃ and the relative humidity to be controlled at 50%-55%. These parameters are transmitted to the temperature and humidity control equipment set, and the equipment set automatically adjusts the operating status according to the received instructions. If the current temperature is higher than the target value, the air conditioning system will start the cooling mode to reduce the temperature; if the humidity is lower than the target value, the humidifier will start working to increase the humidity. During the entire construction process, the temperature and humidity environment is not static. Factors such as external weather changes, the operation of construction equipment, and personnel activities may affect the temperature and humidity of the construction area. At this time, various environmental monitoring sensors installed in the construction area will collect temperature and humidity data in real time and feed it back to the control system. The control system will compare and analyze the actual monitoring data with the target values ​​in the control strategy. Once it is found that the actual temperature and humidity deviate from the target range, it will adjust the operating parameters of the temperature and humidity control equipment set in time according to the control strategy to achieve dynamic and adaptive adjustment of temperature and humidity. In this way, it is ensured that at every stage of microcement construction, the construction area can always maintain appropriate temperature and humidity conditions, providing a strong guarantee for the quality of microcement construction.

[0026] In a possible implementation, step S400 further includes: 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.

[0027] Step S420: inputting the m-th microcement formula and the m-th stage construction environment into the microcement performance prediction model to obtain the m-th microcement performance prediction vector.

[0028] Step S430: performing deviation identification on the mth microcement performance prediction vector according to the mth microcement performance reference vector to obtain the mth microcement performance loss vector, and adding the mth microcement performance loss vector to the M microcement performance loss vectors.

[0029] Specifically, after completing the prediction of the construction environment of the building decoration construction area, obtaining M stages of construction environment, and determining M microcement performance benchmark vectors, in order to accurately analyze the impact of the environment on the performance of microcement in each construction stage, it is necessary to filter out the data of a specific stage from the overall data. Here, m is a loop variable with a value range of 1 to M, representing any one of the M construction stages. Through the indexing mechanism, the construction environment of the mth stage is accurately located and extracted from the M-stage construction environment data set. This set of data contains environmental parameters such as temperature, humidity, and ventilation conditions during the construction of this stage; at the same time, the corresponding mth microcement performance benchmark vector is found in the M microcement performance benchmark vector set, which contains the performance standard information such as leveling, film quality, and adhesion of the microcement formula in this stage under an ideal environment. The two sets of data extracted are the key basis for the subsequent in-depth analysis of the performance changes of microcement under the actual construction environment, and provide targeted data support for further prediction of microcement performance loss.

[0030] The data of the proportion of each component and the content of additives in the mth microcement formula, as well as the data of environmental factors such as temperature, humidity, light intensity, ventilation rate, etc. in the mth stage construction environment, are sorted and preprocessed, and converted into a format suitable for the input of the multivariate linear regression model. These data are used as independent variables, and the performance indicators of microcement, such as leveling, film quality, adhesion, etc., are used as dependent variables. Based on a large amount of existing microcement construction data, a training data set is constructed, and the model is trained using the multivariate linear regression algorithm. During the training process, the algorithm will automatically find a set of optimal regression coefficients so that the linear relationship between the independent variable and the dependent variable can fit the training data to the greatest extent, that is, the error between the predicted value and the actual value is minimized. After the training is completed, the preprocessed mth microcement formula 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 predicts the performance values ​​of microcement such as leveling, film quality, adhesion, etc. in the construction environment through linear calculation. 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.

[0031] First, a microcement performance prediction model was constructed and the model 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 variables and dependent variables were determined respectively. The proportion of ingredients in the mth microcement formula, the amount of additives, and the temperature, humidity, and ventilation conditions in the mth stage construction environment were taken as independent variables, and the corresponding leveling, film quality, and adhesion were taken as dependent variables to construct a linear relationship, such as Y=β 0 +β 1 X1 +β 2 X 2 +β 3 X 3 +β 4 X 4 +....β n X n + . Then, data collection was carried out, and a large number of actual measured values ​​of leveling, film quality and adhesion of different microcement formulas in various construction environments were widely collected to form the original data set. In order to ensure the 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. Then, 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 iterations of optimization, 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 the model training is completed, the test set is used to evaluate it and the mean square error (MSE) error index is calculated. First, data is extracted from the divided test set. These data contain different microcement formulas, construction environment factors (such as temperature, humidity, ventilation conditions, etc.) and corresponding actual microcement performance data (leveling, film quality, adhesion, etc.). For example, there are 100 sets of data in the test set, and each set of data records the actual performance of a specific microcement formula under the corresponding construction environment. The microcement formula and construction environment data in the test set are input into the trained microcement performance prediction model in turn, and the model will output the corresponding microcement performance prediction value. For each set of input data, record the model prediction value and the actual value. Assume that in a set of data, the actual microcement leveling coefficient is 0.8, and the model prediction value is 0.75; the actual value of the film quality coefficient is 0.9, and the predicted value is 0.85; the actual value of the adhesion coefficient is 0.88, and the predicted value is 0.83. Record the predicted values ​​and actual values ​​of all test data in turn. MSE is the average of the squared error 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 respectively. 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 square error sum of all data points is divided by the number of data points n to obtain the MSE value of leveling. Similarly, the MSE values ​​of film quality and adhesion are calculated. The larger the MSE value, the larger the square deviation between the model prediction value and the actual value, and the worse the model prediction effect. If the evaluation result is not ideal, it means that the accuracy and generalization ability of the model need to be improved. At this time, it is necessary to adjust the model structure, such as adding or reducing independent variables, changing the complexity of the model, and then retraining and evaluating, and repeating this process until the model achieves satisfactory prediction accuracy on the test set. Finally, a qualified microcement performance prediction model is trained. After the training is completed, the pre-processed m-th microcement formula and the m-th stage construction environment data are input into the trained multivariate linear regression model. Based on the determined regression coefficients and input data, the model predicts the performance values ​​of microcement such as leveling, film quality, and adhesion in the construction environment through linear calculation. These predicted performance values ​​are arranged in a specific order to form a vector form, and finally the m-th microcement performance prediction vector is obtained, which provides an important basis for the subsequent evaluation of microcement performance loss.

[0032] Identify the performance loss of microcement under the mth stage construction environment. The mth microcement performance benchmark vector represents the performance index of microcement under ideal conditions, covering standard values ​​of leveling, film quality, adhesion and other aspects; while the mth microcement performance prediction vector is the performance data obtained by the microcement performance prediction model based on the actual construction environment of the mth stage. To complete the deviation identification, the 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. If the difference is positive, it means that the actual leveling is lower than the ideal state and there is a performance loss; if the difference is negative, it means that the actual performance is better than the ideal situation, but the difference is usually recorded to comprehensively evaluate the performance difference. The same comparison method is used for other dimensions such as film quality and adhesion. After comparing all dimensions, the differences of each dimension are combined in the order of the original vector to obtain the mth microcement performance loss vector. This vector clearly shows the loss of each performance dimension of microcement compared with the ideal state under the mth stage construction environment. Finally, the generated mth microcement performance loss vector is added to the existing M microcement performance loss vector set. As the processes from stage 1 to stage M are completed in sequence, this set can fully reflect the performance loss status of microcement at each stage during the entire construction process, laying the foundation for the subsequent formulation of scientific and reasonable construction control strategies based on these loss data.

[0033] In a possible implementation, step S420 further includes: 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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 quality prediction model, and a microcement adhesion prediction model. These three sub-models are all 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=β 0 +β 1 X 1 +β 2 X 2 +β 3 X 3 +β 4 X 4 +....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 structures of the microcement film quality prediction model and the microcement adhesion prediction model are similar, except that the dependent variables are film quality and adhesion, respectively. During the model training process, a large amount of historical data is first collected. These data contain the actual measured values ​​of leveling, film quality and adhesion under different microcement formulas and different construction environments. Then these data are divided into a training set and a test set. The training set is used to determine the regression coefficient, and the test set is used to evaluate the accuracy of the model. The least squares method is used to estimate the regression coefficient so that the sum of squares of the error between the model prediction value and the actual value is minimized. After multiple iterations of optimization, the model reaches a satisfactory prediction accuracy on the test set.

[0039] The mth microcement formula and the mth stage construction environment are input into the trained microcement leveling prediction model. The model calculates according to 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.

[0040] The same m-th microcement formula and m-th stage construction environment are input into the microcement film quality prediction model. After calculation within the model, the m-th film quality prediction coefficient is obtained. This coefficient reflects the prediction of the microcement film quality.

[0041] 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.

[0042] The obtained m-th leveling prediction coefficient, m-th film quality prediction coefficient and m-th adhesion prediction coefficient are arranged in a specific order to construct a vector, namely the m-th microcement performance prediction vector. This vector comprehensively reflects the performance prediction of microcement under the construction environment of the m-th stage, and provides an important basis for the subsequent analysis of microcement performance loss.

[0043] In a possible implementation, step S500 further includes: Step S510: obtaining microcement performance loss weight conditions, wherein the microcement performance loss weight conditions include leveling loss weight, film-forming quality loss weight and adhesion loss weight.

[0044] 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.

[0045] Step S530: determining whether the mth microcement performance loss coefficient is greater than or equal to a microcement performance loss threshold.

[0046] Step S540: If the m-th 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 m-th microcement performance loss vector to obtain the m-th stage microcement construction control strategy.

[0047] Step S550: adding the m-th stage microcement construction regulation strategy to the microcement construction regulation strategy.

[0048] Specifically, by collecting a large amount of historical construction data, we analyzed the extent to which the leveling, film quality, and adhesion of microcement are affected when environmental factors such as temperature and humidity change. At the same time, combined with professional standards and actual construction experience in the industry, experts were organized to conduct evaluations and demonstrations, and the application scenarios of microcement were comprehensively considered. For example, 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 quality and adhesion is more prominent. After multiple rounds of data research, analysis, and expert discussion, the weights of leveling loss, film 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.

[0049] The mth microcement performance loss vector is weighted to obtain the mth microcement performance loss coefficient. Assume that the leveling loss weight in the microcement performance loss weight condition is W 1 , the film mass loss weight is W 2 , the film mass loss weight is W 3 , and satisfy W 1 +W 2 +W 3 = 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 adhesion loss value of microcement at stage m. Based on these data, the weighted calculation formula C m =W 1 × +W 2 × +W 3 × To calculate the mth microcement performance loss coefficient. This formula combines the microcement performance loss weight condition with the mth microcement performance loss vector, fully considering the importance of different performance losses in the overall performance loss. Through such weighted calculation, the comprehensive performance loss of microcement caused by the loss of leveling, film quality and adhesion during the mth stage of construction can be accurately measured, providing key quantitative indicators for subsequent judgment on whether the temperature and humidity control equipment needs to be adjusted.

[0050] The microcement performance loss threshold is an important pre-set reference standard, which is determined based on a large amount of experimental data, past construction experience and quality requirements of microcement products. The mth microcement performance loss coefficient is compared with the threshold.

[0051] If the mth microcement performance loss coefficient is greater than or equal to the microcement performance loss threshold, it indicates that the performance loss of microcement in the mth stage is relatively serious, and the temperature and humidity control equipment set needs to be optimized. According to the loss of each performance dimension in the mth microcement performance loss vector, the impact of temperature and humidity on these properties is analyzed, and different temperature and humidity adjustment schemes are tried. Through continuous comparison and evaluation, the adjustment scheme that can minimize the performance loss is found, thereby obtaining the mth stage microcement construction control strategy.

[0052] Finally, the m-th stage microcement construction control strategy is added to the overall microcement construction control strategy. As the above operations are performed in each stage (from m=1 to m=M) in turn, a complete set of control strategies applicable to the entire microcement construction process is finally formed to ensure that the microcement construction quality meets the expected standards.

[0053] In a possible implementation, step S540 further includes: Step S541: making adjustment decisions on the temperature and humidity control equipment set according to the mth micro-cement performance loss vector, and establishing a first micro-cement construction adjustment space that meets a predetermined number of decisions.

[0054] Step S542: performing 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.

[0055] Step S543: performing variation optimization expansion according to the second micro-cement construction adjustment space to establish a third micro-cement construction adjustment space.

[0056] Step S544: adjusting the third space according to the microcement construction to minimize the microcement performance loss and obtain the microcement construction control strategy for the mth stage.

[0057] Specifically, after determining that the mth microcement performance loss coefficient is greater than or equal to the microcement performance loss threshold, in order to obtain the mth stage microcement construction control strategy, the temperature and humidity control equipment set is adjusted based on the mth microcement performance loss vector. Since the adjustment parameters of the temperature and humidity control equipment set are diverse, such as the temperature setting of the air conditioner, the operating power of the humidifier or dehumidifier, etc., by setting these parameters in different combinations, the first microcement construction adjustment space that meets the predetermined number of decisions is established. This space contains many possible temperature and humidity adjustment schemes, each of which corresponds to a combination of equipment adjustment.

[0058] The microcement performance prediction model and microcement performance loss weight condition are used to optimize the first space of microcement construction adjustment. The microcement performance prediction model can predict the performance of microcement according to different temperature and humidity conditions, while the microcement performance loss weight condition clarifies the importance of performance dimensions such as leveling, film quality and adhesion in the overall performance loss. By comprehensively considering these two factors, each adjustment scheme in the first space is evaluated, and those schemes that can make the microcement performance loss relatively small are screened out, thereby obtaining the second space of microcement construction adjustment.

[0059] The second space of micro-cement construction adjustment is expanded by variation optimization. On the basis of the second space, the parameters of each adjustment scheme are slightly varied and adjusted to explore more possible optimization directions. For example, the air conditioner temperature setting value is fine-tuned, or the spray frequency of the humidifier is changed. Through this variation optimization method, the third space of micro-cement construction adjustment is established, which contains more potential high-quality adjustment schemes.

[0060] Minimize the performance loss of microcement in the third space of microcement construction adjustment. By accurately calculating and comparing the performance loss of microcement for each adjustment scheme in the third space, find a scheme that can minimize the performance loss of microcement. The adjustment parameters of the temperature and humidity control equipment set corresponding to this scheme are the microcement construction control strategy for the mth stage, which can minimize the performance loss of microcement during the mth stage of construction and ensure the construction quality.

[0061] In a possible implementation, step S542 further includes: Step S5421: extracting a first decision for microcement construction adjustment according to the first space for microcement construction adjustment.

[0062] Step S5422: predicting the change of the construction environment in the mth stage according to the first decision of microcement construction adjustment to obtain a first predicted changed construction environment.

[0063] Step S5423: input 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.

[0064] 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.

[0065] Step S5425: Determine whether the first decision microcement performance loss coefficient is less than the microcement performance loss threshold.

[0066] 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 microcement construction adjustment second space.

[0067] 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 of microcement construction adjustment is eliminated.

[0068] Specifically, the first decision of microcement construction adjustment is extracted from the constructed first space of microcement construction adjustment. The 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.

[0069] The first decision of microcement construction regulation covers the specific operating instructions for the temperature and humidity control equipment set, such as the temperature setting adjustment of the air conditioner, the power and operation time arrangement of the humidifier or dehumidifier, etc. Using environmental simulation technology, the construction area is regarded as a complex dynamic system, and the spatial layout of the area, the characteristics of the building structure, the current basic values ​​of temperature and humidity, and the ventilation conditions are comprehensively considered. During the simulation process, based on the principles of heat transfer, fluid mechanics, and humidity balance, the conduction, convection, and radiation paths of heat in the space, as well as the evaporation, condensation, and diffusion processes of water are analyzed in detail. For example, if the first decision requires the air conditioner to reduce the temperature by 2°C, the simulation system will combine the space volume, wall insulation performance, and ventilation rate to calculate the rate and time of heat loss, and then predict the temperature change trend. For humidity, the increase or decrease of humidity is predicted based on the working parameters of the humidifier or dehumidifier, combined with the air holding capacity and moisture exchange in the space. Through the coordinated simulation and precise calculation of these environmental factors, the detailed data of the first predicted change in the construction environment is finally generated, including the specific temperature, humidity values ​​and the distribution of environmental parameters, which provides a key environmental basis for the subsequent evaluation of the impact of the adjustment decision on the performance of microcement.

[0070] The mth microcement formula and the first predicted change construction environment are input into the pre-established microcement performance prediction model. Based on the input information, the model predicts the leveling, film quality and adhesion of microcement under the predicted environment, and outputs the first decision microcement performance prediction vector, which reflects the expected performance of various properties of microcement under the adjustment decision.

[0071] According to the microcement performance loss weight condition, the first decision microcement performance prediction vector is weighted and calculated. The predicted loss value of each performance dimension in the vector is multiplied by the corresponding weight and summed to obtain the first decision microcement performance loss coefficient, which comprehensively reflects the performance loss degree of microcement under this adjustment decision.

[0072] The first decision microcement performance loss coefficient is compared with a preset microcement performance loss threshold, which is a standard for measuring whether the microcement performance loss is acceptable.

[0073] 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. The first decision of microcement construction adjustment is added to the second space of microcement construction adjustment as an alternative for subsequent optimization and determination of the final construction control strategy.

[0074] On the contrary, if the microcement performance loss coefficient of the first decision is greater than or equal to the microcement performance loss threshold, it means that the adjustment decision cannot control the microcement performance loss within an acceptable range and it will be eliminated. By sequentially performing the above evaluation and screening on the decisions in the first microcement construction adjustment space, a more targeted and effective microcement construction adjustment second space is gradually constructed.

[0075] In a possible implementation, step S543 further includes: Step S5431: construct an adjustment decision variation value evaluation coordinate system according to the adjustment decision variation value evaluation record set.

[0076] Step S5432: input 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.

[0077] Step S5433: mutate the second space of microcement construction adjustment according to the variation value coefficients of each adjustment decision to obtain the first variation domain of microcement construction adjustment.

[0078] Step S5434: 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.

[0079] Step S5435: expanding the second space for micro-cement construction adjustment according to the second variation domain for micro-cement construction adjustment to obtain the third space for micro-cement construction adjustment.

[0080] Specifically, the record set of adjustment decision variation value evaluation gathers detailed information on various adjustment decisions and their variation attempts in past microcement construction. These records cover the 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. When constructing the adjustment decision variation value evaluation coordinate system based on the record set, the coordinate axis of the coordinate system is first determined. One coordinate axis can represent the degree of variation of a key parameter in the adjustment decision, such as the air conditioner temperature adjustment amplitude, the humidifier power change ratio, etc.; the other coordinate axis can represent the change in microcement performance loss caused by this variation, measured by the change in the performance loss coefficient. Each piece of data 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 trend of these points, curves or surfaces are fitted to construct a complete adjustment decision variation value evaluation coordinate system. This coordinate system can intuitively show the relationship between different adjustment decision variations and microcement performance loss changes, and provides a visual and quantitative analysis tool for the subsequent evaluation of the variation value of each decision in the second space of microcement construction adjustment, which helps to accurately determine the degree of variation operation that should be performed on each adjustment decision.

[0081] 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. The record set contains a large number of relevant data on microcement construction adjustment decisions in the past, and records the changes in adjustment decision parameters (such as temperature and humidity adjustment range, additive ratio adjustment amount, etc.), as well as the impact of these changes on microcement performance loss (leveling, film quality, adhesion, etc.). Each dimension of the coordinate system corresponds to different adjustment decision parameter variation indicators and microcement performance loss change indicators. The specific evaluation process is as follows: First, the adjustment decision variation value evaluation coordinate system is divided into multiple grid areas, each grid area represents a certain range of adjustment decision parameter variation and microcement performance loss change combination. At the same time, according to historical data and expert experience, a basic value weight is assigned to each grid area. For example, in a certain area, if past data shows that a small adjustment of temperature and humidity can significantly reduce the adhesion loss of microcement, then the area will be assigned a higher basic value weight; conversely, if the adjustment parameters in a certain area have little effect on improving the performance of microcement, a lower weight will be assigned. When the decision microcement performance loss coefficient corresponding to each microcement construction adjustment decision in the second space of microcement construction adjustment is input into the coordinate system, the specific position of the coefficient in the coordinate system is first determined. Taking the two-dimensional simplified coordinate system as an example, it is assumed that the horizontal axis represents the variation of the temperature and humidity adjustment amplitude, and the vertical axis represents the change of the microcement leveling loss coefficient. If the decision 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 find the intersection of the horizontal and vertical coordinates of 5% and 0.3 in the coordinate system to determine the position of the adjustment decision in the coordinate system. Then, according to the grid area where the position is located, combined with the basic value weight in the area, and considering the influence of the surrounding grid areas (through interpolation), the adjustment decision variation value coefficient of the adjustment decision is calculated. For example, the basic value weight of the grid area where the position is located is 0.8, and the influence coefficient of the surrounding grid area through interpolation is 0.1, then the adjustment decision variation value coefficient may be 0.8+0.1=0.9. For example, in a certain microcement construction project, the constructed adjustment decision variation value evaluation coordinate system contains three dimensions, namely, temperature adjustment amplitude variation, humidity adjustment amplitude variation, and microcement film quality loss coefficient change. There are three adjustment decisions in the second space of microcement construction adjustment. Decision A corresponds to a decision microcement performance loss coefficient of 0.2 film quality loss, a temperature adjustment amplitude variation of 3°C, and a humidity adjustment amplitude variation of 8%; Decision B corresponds to a decision microcement performance loss coefficient of 0.4 film quality loss, a temperature adjustment amplitude variation of -2°C, and a humidity adjustment amplitude variation of 4%; Decision C corresponds to a decision microcement performance loss coefficient of 0.1 film quality loss, a temperature adjustment amplitude variation of 5°C, and a humidity adjustment amplitude variation of 6%.The parameters of decision A are input into the coordinate system to determine its position in the coordinate system. The basic value weight of the grid area is 0.7, and the interpolation influence coefficient of the surrounding grid is 0.05. The adjustment decision variation value coefficient of decision A is calculated to be 0.75; the basic value weight of the grid area where decision B is located is 0.3, the interpolation influence coefficient is 0.03, and the adjustment decision variation value coefficient is 0.33; the basic value weight of the grid area where decision C is located is 0.8, the interpolation influence coefficient is 0.08, and the adjustment decision variation value coefficient is 0.88. It can be seen that decision C has the greatest potential in further optimizing and adjusting to reduce the performance loss of microcement. In the future, decision C can be mutated and screened first to find a better microcement construction adjustment decision.

[0082] The second space of microcement construction regulation is subjected to mutation operation to obtain the first variation domain of microcement construction regulation. The variation value coefficient of each adjustment decision is a quantitative indicator to measure the potential of each microcement construction adjustment decision in variation optimization, and its value is positively correlated with the number of variations that can be made for the decision. Based on the second space of microcement construction regulation, for each adjustment decision therein, the scale of variation is determined according to its corresponding variation value coefficient of the adjustment decision. For adjustment decisions with larger variation value coefficients, it means that they have greater potential in further optimizing the performance loss of microcement, and more and larger-scale variation operations are performed on them. For example, the operating parameters of the temperature and humidity control equipment set, such as the temperature setting of the air conditioner, the working intensity of the humidifier or dehumidifier, are adjusted in a diversified manner to generate multiple different new adjustment schemes. For adjustment decisions with smaller variation value coefficients, the number and range of variations are reduced accordingly. By performing such targeted variation processing on all adjustment decisions in the second space and summarizing the many new adjustment schemes generated, the first variation domain of microcement construction regulation is formed. This variation domain provides more abundant choices for the subsequent screening of better microcement construction regulation strategies.

[0083] Using the microcement performance prediction model and the microcement performance loss weight condition, the optimization analysis is carried out 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 many adjustment decisions generated by variation. The microcement performance prediction model can predict the performance of microcement in terms of leveling, film quality, adhesion, etc. according to the temperature and humidity environment corresponding to each decision. At the same time, the microcement performance loss weight condition clarifies the proportion of each performance dimension in the overall performance loss. The construction environment data corresponding to each adjustment decision in the first variation domain are input into the microcement performance prediction model to obtain the performance prediction results of microcement under different environments. Afterwards, according to the performance loss weight condition, the predicted performance is compared with the ideal performance standard to calculate the comprehensive performance loss value of microcement under each adjustment decision. By evaluating and sorting these loss values, according to the pre-set screening criteria, such as selecting a certain proportion of adjustment decisions with a lower comprehensive performance loss value, these selected decisions are integrated to form 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.

[0084] The high-quality adjustment decisions in the second variation domain of microcement construction regulation are integrated into the second space of microcement construction regulation, and its solution set is expanded to finally obtain the third space of microcement construction regulation. Compared with the second space, this third space contains more adjustment decisions that have been optimized through variation and have less performance loss, providing richer and better choices for determining the final m-th stage microcement construction regulation strategy.

[0085] In a possible implementation, step S300 further includes: 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.

[0086] Step S320: performing concentrated value calculation based on the M microcement performance sample sets to obtain M microcement performance benchmark sequences.

[0087] Step S330: constructing the M microcement performance benchmark vectors according to the M microcement performance benchmark sequences.

[0088] Specifically, microcement performance factors include key elements such as raw material types, proportions, and additive characteristics, which together determine the final performance of microcement. Based on these performance factors, a comprehensive and detailed normal performance parameter sample search was conducted for M microcement formulations. Past experimental reports were consulted, which detailed the performance test data of different microcement formulations under standard construction environments. At the same time, reference was made to construction records in a large number of actual engineering cases, which included the application of various microcement formulations under conventional field conditions. In addition, the performance parameter ranges specified by industry authoritative standards were strictly followed. For each microcement formulation, relevant parameters in key performance dimensions such as leveling, film quality, and adhesion were found, and these parameters were systematically sorted and summarized, and finally M microcement performance sample sets were obtained. Each sample set fully and accurately recorded the specific data of various performances of the corresponding microcement formulation under normal construction conditions.

[0089] After obtaining M microcement performance sample sets, the key performance indicators are refined by centralized value calculation to generate M microcement performance benchmark sequences. For each microcement performance sample set, the centralized value calculation is 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 are used, such as calculating the average value, adding all the leveling coefficients and dividing them by the number of data to obtain a value representing the typical level of leveling, which is used as the centralized value of the leveling coefficient. The same method is applied to the film quality coefficient and the adhesion coefficient. After completing the centralized value calculation of the three coefficients in each sample set, the centralized value of the leveling coefficient, the centralized value of the film quality coefficient and the centralized value of the adhesion coefficient are combined to form a microcement performance benchmark sequence. For the M microcement performance sample sets, the above operations are completed in sequence, and finally M microcement performance benchmark sequences are obtained. These sequences can accurately reflect the performance of each microcement formula under normal construction conditions.

[0090] Each microcement performance benchmark sequence contains the concentrated value of leveling coefficient, the concentrated value of film quality coefficient and the concentrated value of adhesion coefficient, which reflect the performance of microcement from different dimensions. According to the established vector construction rules, these three concentrated values ​​are combined in an orderly manner. Usually presented in the form of a three-dimensional vector, the concentrated value of leveling coefficient is used as the component of the vector in the first dimension, the concentrated value of film quality coefficient is used as the component of the second dimension, and the concentrated value of adhesion coefficient is used as the component of the third dimension. In this way, the originally scattered performance data are integrated into a vector space. This operation is performed one by one for M microcement performance benchmark sequences, thereby constructing M microcement performance benchmark vectors. These vectors can intuitively and comprehensively display the comprehensive performance level of different microcement formulations under normal construction conditions, providing a concise and effective quantitative tool for subsequent comparison of the performance of microcement in actual construction with the ideal benchmark, which helps to quickly and accurately determine the impact of the construction environment on the performance of microcement, and formulate targeted adjustment strategies accordingly.

[0091] In a possible implementation, step S300 further includes: Step S340: The microcement performance factors include leveling, film-forming quality and adhesion.

[0092] Specifically, the performance factors of microcement include leveling, film quality and adhesion. Leveling is related to whether microcement can be spread evenly during the application process to form a flat and smooth surface. High-quality leveling performance ensures that microcement flows smoothly in the construction area, automatically fills small depressions and uneven places, greatly improves construction efficiency, reduces the workload of manual repeated leveling, and ensures the flatness and aesthetics of the final finished surface. Film quality is reflected in the characteristics of the film layer formed after the microcement is dried and solidified, including the strength, density and durability of the film layer. High-quality film formation means that the microcement film layer is not prone to cracks, peeling and other problems, and can effectively resist the wear, stain erosion and environmental factors in daily use, extending the service life of the microcement decorative surface. Adhesion is the ability of microcement to firmly adhere to the base material. Strong adhesion can prevent the microcement from hollowing and falling off in subsequent use, ensure that the microcement is closely combined with the base, and provide a solid support for the entire decorative structure. These three performance factors are interrelated and influence each other, and jointly determine the overall performance of microcement in practical applications.

[0093] Embodiment 2 is based on the same inventive concept as the method for intelligently controlling temperature and humidity in microcement construction in the above embodiment. Figure 2 As shown, the present application provides an intelligent temperature and humidity control device for micro-cement construction, and the device and method embodiments in the present application are based on the same inventive concept. The device includes: The micro-cement coating scheme acquisition module 10 is used to divide the coating levels 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.

[0094] The construction environment acquisition module 20 is used to perform environmental prediction on the M-stage microcement coating schemes according to the current environmental perception information of the building decoration construction area to obtain the M-stage construction environment.

[0095] The microcement performance benchmark vector determination module 30 is used to calculate benchmark performance parameters of 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.

[0096] The microcement performance loss vector establishing module 40 is used to predict the microcement performance loss of the M stage construction environments based on the M microcement performance reference vectors and according to the microcement performance prediction model, and establish M microcement performance loss vectors.

[0097] 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.

[0098] 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.

[0099] Furthermore, the device is also used to achieve the following functions: 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, 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; according to the m-th microcement performance benchmark vector, deviation identification is performed on the m-th microcement performance prediction 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.

[0100] Furthermore, the device is also used to achieve the following functions: 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 m-th microcement formula and the m-th stage construction environment are input into the microcement leveling prediction model to obtain the m-th leveling prediction coefficient; the m-th microcement formula and the m-th stage construction environment are input into the microcement film-forming quality prediction model to obtain the m-th film-forming quality prediction coefficient; the m-th microcement formula and the m-th stage construction environment are input into the microcement adhesion prediction model to obtain the m-th adhesion prediction coefficient; the m-th microcement formula and the m-th stage construction environment are input into the microcement adhesion prediction model to obtain the m-th adhesion prediction coefficient; and the m-th microcement performance prediction vector is constructed according to the m-th leveling prediction coefficient, the m-th film-forming quality prediction coefficient and the m-th adhesion prediction coefficient.

[0101] Furthermore, the device is also used to achieve the following functions: Obtaining microcement performance loss weight conditions, wherein the microcement performance loss weight conditions include leveling loss weight, film-forming quality loss weight and adhesion loss weight; performing weighted calculation on the m-th microcement performance loss vector according to the microcement performance loss weight conditions to obtain the m-th microcement performance loss coefficient; determining whether the m-th 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, optimizing 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; and adding the m-th stage microcement construction control strategy to the microcement construction control strategy.

[0102] Furthermore, the device is also used to achieve the following functions: According to the m-th microcement performance loss vector, adjustment decisions are made on 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, variation optimization and expansion are performed to establish a third microcement construction adjustment space; according to the third microcement construction adjustment space, microcement performance loss minimization optimization is performed to obtain the m-th stage microcement construction control strategy.

[0103] Furthermore, the device is also used to achieve the following functions: According to the first microcement construction adjustment space, extract the first decision for microcement construction adjustment; predict the change of the m-th stage construction environment according to the first microcement construction adjustment decision to obtain the first predicted changed construction environment; input the m-th microcement formula and the first predicted changed construction environment into the microcement performance prediction model to obtain the first decision microcement performance prediction vector; perform weighted calculation on the first decision microcement performance prediction vector according to the microcement performance loss weight condition to obtain the first decision microcement performance loss coefficient; determine 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, add the first decision for microcement construction adjustment 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, eliminate the first decision for microcement construction adjustment.

[0104] Furthermore, the device is also used to achieve the following functions: 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.

[0105] Furthermore, the device is also used to achieve the following functions: According to the microcement performance factor, a normal performance parameter sample retrieval is performed on the M microcement formulas to obtain M microcement performance sample sets; based on the M microcement performance sample sets, a centralized value calculation is performed to obtain M microcement performance benchmark sequences; based on the M microcement performance benchmark sequences, the M microcement performance benchmark vectors are constructed.

[0106] Furthermore, the device is also used to achieve the following functions: The microcement performance factors include leveling, film-forming quality and adhesion.

[0107] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0109] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The intelligent temperature and humidity control method in microcement construction is characterized by: include: According to the micro-cement construction plan of the building decoration construction area, the coating level is divided to obtain M-stage micro-cement coating plans, 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 the M-stage construction environment; Calculate the benchmark performance parameters of the M microcement formulations corresponding to the M-stage microcement coating schemes according to the microcement performance factor to determine M microcement performance benchmark vectors; Based on the M microcement performance benchmark vectors, predict the microcement performance loss of the M stage construction environments according to the microcement performance prediction model, and establish M microcement performance loss vectors; According to the M micro-cement performance loss vectors, the temperature and humidity control equipment set is optimally adjusted to generate a micro-cement construction control strategy; The temperature and humidity control equipment set is controlled according to the micro-cement construction control strategy to perform adaptive temperature and humidity control on the building decoration construction area.

2. The temperature and humidity intelligent control method in microcement construction according to claim 1, characterized in that: Based on the M microcement performance benchmark vectors, the microcement performance loss is predicted for the M stage construction environments according to the microcement performance prediction model, and M microcement performance loss vectors are established, including: According to the M stage construction environments and the M microcement performance reference vectors, extracting the m-th stage construction environment and the m-th microcement performance reference vector, 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 temperature and humidity intelligent control method in microcement 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, including: 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 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; 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; 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; 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 temperature and humidity intelligent control method in microcement construction according to claim 1, characterized in that: The temperature and humidity control equipment set is optimally adjusted according to the M micro-cement performance loss vectors to generate a micro-cement 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 m-th microcement performance loss vector according to the microcement performance loss weight condition to obtain the m-th 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, 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; The m-th stage microcement construction regulation strategy is added to the microcement construction regulation strategy.

5. The temperature and humidity intelligent control method in microcement construction according to claim 4, characterized in that: 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; According to the microcement performance prediction model and the microcement performance loss weight condition, the first microcement construction adjustment space is optimized and analyzed to obtain the second microcement construction adjustment space; Perform variation optimization and expansion according to 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 to obtain the micro-cement construction control strategy of the mth stage.

6. The temperature and humidity intelligent control method in micro-cement construction according to claim 5, characterized in that: According to the microcement performance prediction model and the microcement performance loss weight condition, the first microcement construction adjustment space is optimized and analyzed to obtain the second microcement construction adjustment space, including: Extracting a first microcement construction adjustment decision according to the first microcement construction adjustment space; Predicting the change of the construction environment in the mth stage according to the first decision of microcement construction adjustment to obtain a first predicted changed construction environment; Inputting the mth microcement formula and the first predicted change 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 first decision microcement performance loss coefficient is greater than or equal to the microcement performance loss threshold, the first decision of microcement construction adjustment is eliminated.

7. The intelligent temperature and humidity control method in micro-cement construction according to claim 5, characterized in that: The third space for micro-cement construction adjustment is established by performing variation optimization and expansion according to the second space for micro-cement construction adjustment, including: According to the adjustment decision variation value evaluation record set, a adjustment decision variation value evaluation coordinate system is constructed; 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; The second space of micro-cement construction adjustment is varied according to the variation value coefficients of each adjustment decision to obtain a first variation domain of micro-cement 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 space for micro-cement construction adjustment is expanded according to the second variation domain for micro-cement construction adjustment to obtain the third space for micro-cement construction adjustment.

8. The temperature and humidity intelligent control method in microcement construction according to claim 1, characterized in that: According to the microcement performance factor, the benchmark performance parameters of the M microcement formulations corresponding to the M-stage microcement coating schemes are calculated to determine the M microcement performance benchmark vectors, including: According to the microcement performance factor, performing normal performance parameter sample retrieval on the M microcement formulations to obtain M microcement performance sample sets; Performing concentrated 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.

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

10. The temperature and humidity intelligent 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 9, and the device comprises: A microcement coating scheme acquisition module is used to divide the coating levels 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, used to perform environmental prediction on the M-stage microcement coating schemes according to the current environmental perception information of the building decoration construction area, and obtain the M-stage construction environment; A microcement performance benchmark vector determination module is used to calculate benchmark performance parameters of M microcement formulations corresponding to the M-stage microcement coating schemes according to the microcement performance factors, and determine M microcement performance benchmark vectors; A microcement performance loss vector establishment module is used to predict the microcement performance loss of the M stage construction environments based on the M microcement performance benchmark vectors and according to the microcement performance prediction model, and establish M microcement performance loss vectors; A micro-cement construction control strategy generation module 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; 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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