Intelligent printing method for thermal temperature adjusting wall body and thermal temperature adjusting wall body printed by adopting method

Abstract

The invention relates to an intelligent printing method for a thermal temperature-adjusting wall and the thermal temperature-adjusting wall printed by adopting the method, and solves the problems that the printing process lacks real-time dynamic adjustment capability and is difficult to cope with complex and changeable printing environments and wall performance requirements due to the fact that the prior art mostly depends on fixed preset parameters. The method comprises the steps that in the aggregate switching process, the opening degree of a valve and the pressure of a conveying pipeline are regulated and controlled through a preset regulation and control method, so that new aggregate enters a printing spray head, and in the aggregate switching process, the rotating speed of a stirring paddle and the conveying amount of the aggregate are synchronously regulated through a self-adaptive fuzzy PID algorithm; the particle size distribution of the aggregate after the new aggregate and the radiation cooling enhanced cement-based material are mixed is within a preset aggregate particle size distribution standard range. The method has the following effect that the printing quality and performance of the thermal temperature adjusting wall are improved.

Description

Technical Field

[0001] The present invention relates to the field of building construction technology, and in particular to an intelligent printing method for a thermal temperature regulating wall and a thermal temperature regulating wall printed by the method. Background Technology

[0002] In the field of construction, walls are an important part of building structures, and their performance plays a key role in the overall quality, energy consumption and living comfort of buildings. With the continuous advancement of science and technology, 3D printing technology has gradually been applied to the construction industry, bringing new ideas and methods to the construction of walls. Thermal thermostatic walls, as a kind of wall with special functions, can automatically adjust the thermal performance of the wall according to the changes in ambient temperature, thereby effectively reducing the energy consumption of buildings and improving indoor comfort. The research and development and innovation of its printing technology have attracted much attention.

[0003] Currently, there are some related technical means in wall printing technology. Some traditional wall printing methods mainly use a single nozzle to print ordinary cement-based materials layer by layer along a preset path to build the basic structure of the wall. Some technologies also try to add some ordinary aggregates to the printing materials to enhance the strength and other properties of the wall.

[0004] For the above-mentioned related technologies, the inventors found that there are the following defects: a single nozzle can only deliver a single or simply mixed material, which makes it impossible to achieve efficient and accurate mixing of radiant cooling-enhanced cement-based materials and aggregates when printing thermal temperature control walls, which have strict requirements on material diversity and precise proportions, seriously affecting the thermal temperature control performance of the wall. Moreover, in the data processing and printing parameter control links, the previous technology mostly relies on fixed preset parameters, which leads to the lack of real-time dynamic adjustment capabilities in the printing process, making it difficult to cope with complex and changing printing environments and wall performance requirements. SUMMARY OF THE INVENTION

[0005] In order to improve the printing quality and performance of thermal temperature control walls, the present application provides a thermal temperature control wall intelligent printing method and a thermal temperature control wall printed by the method.

[0006] In the first aspect, the present application provides a method for intelligent printing of thermal temperature-controlled walls, which adopts the following technical solutions: A thermal temperature control wall intelligent printing method, comprising: Start the 3D printing equipment, and extrude and print the mixed material of the radiation cooling enhanced cement-based material and the aggregate layer by layer through the special nozzle according to the preset printing path and the preliminary set printing parameters of each layer. The special nozzle is a printing nozzle with two independent inlets, one of which is connected to the radiation cooling enhanced cement-based material, and the other is connected to the aggregate. A stirring paddle is arranged inside the special nozzle; During the printing process, the sensor matrix is ​​used to collect multi-source data, including environmental data, printing progress data, and wall thermal performance related data; Collect the historical climate data of the printing area, and pre-process the historical climate data with the collected multi-source data; The multi-source data and historical climate data that have completed data preprocessing are input into the pre-built multi-objective optimization model, and the optimal parameter combination for printing each layer of the wall is output, and the initially set printing parameters for each layer are replaced according to the optimal parameter combination for printing each layer of the wall; During the printing process, based on the real-time collected and pre-processed multi-source data, determine whether the pre-set trigger conditions for aggregate switching are met; If yes, an aggregate switching instruction is issued to control the aggregate channel of the special nozzle to switch. During the aggregate switching process, the valve opening and closing degree and the conveying pipeline pressure are regulated by a preset control method to allow new aggregate to enter the printing nozzle. During the aggregate switching process, an adaptive fuzzy PID algorithm is used to synchronously adjust the stirring paddle speed and the aggregate conveying amount, so that the aggregate particle size distribution after the new aggregate and the radiation cooling enhanced cement-based material are mixed is within the preset aggregate particle size distribution standard range; If no, keep the original setting.

[0007] By adopting the above technical solution, the printing method uses multi-source data collection and processing, combined with historical climate data, and accurately adjusts the printing parameters through multi-objective optimization to improve the comprehensive performance of the wall. Real-time judgment of aggregate switching conditions and fine-tuning ensures the adaptation of new aggregates, optimizes the mixing effect, ensures the reliability of the wall's thermal temperature control function, and improves the printing quality.

[0008] Optionally, it also includes a step after preprocessing the historical climate data and the collected multi-source data, as follows: Unify the data format of multi-source data and historical climate data that have completed data preprocessing, and perform data standardization to form various data vectors; Input various data vectors into the preset fuzzy membership function, calculate the membership of various data vectors in different fuzzy sets, and obtain the comprehensive membership by weighted summation of multiple fuzzy membership functions; Analyze whether the calculated comprehensive membership exceeds the trigger threshold, where the calculation formula for the trigger threshold is as follows: Analyze whether the calculated comprehensive membership exceeds the trigger threshold, where the calculation formula for the trigger threshold is as follows: , among which, is the trigger threshold, is the adjustment coefficient, which is used to adjust the weight of the comprehensive membership mean when calculating the trigger threshold. is the mean of comprehensive membership, ​​is the adjustment coefficient, which is used to adjust the weight of the comprehensive membership standard deviation when calculating the trigger threshold. is the standard deviation of comprehensive membership; If no, continue with the next steps; If yes, extract key state features based on multi-source data that have completed data preprocessing and historical climate data, and use principal component analysis to reduce the dimension to obtain the principal component feature vector representing the environment and equipment status, where the key state features include the environment change rate, printing progress deviation, and wall thermal performance deviation; Use the fuzzy logic system to calculate the adjustment coefficient of each performance target weight based on the principal component eigenvector and preset rules, and update the weight of the multi-objective optimization model, where the performance targets include thermal regulation performance, structural stability, and material cost; Use the trained deep Q network to input different principal component feature vectors to obtain the corresponding printing parameter combination, and continue this operation until a preset number of printing parameter combinations under different states are generated; According to the updated multi-objective optimization model weights, each set of generated printing parameter combinations is evaluated to obtain the performance target scores, and the performance target scores are weighted and summed according to the adjusted weights to obtain the comprehensive score of each parameter combination; Sort the parameter combinations of all groups according to the comprehensive scores, select the parameter combination with the highest comprehensive score as the optimal parameter combination for printing each layer of the wall, and replace the initially set printing parameters of each layer according to the optimal parameter combination for printing each layer of the wall.

[0009] Optionally, the method further includes steps after obtaining the comprehensive score of each parameter combination, as follows: Analyze whether the relative deviation between the comprehensive membership and the trigger threshold is always within the preset deviation range within the preset number of continuous monitoring cycles; If yes, continue with the next steps; If not, then select the parameter combination with the highest comprehensive score ranking in the preset position from all the generated parameter combinations as the candidate parameter combination; For the preliminary candidate parameter combinations for wall printing at each layer, determine the basic parameters related to wall printing, run a random algorithm to generate random parameters within the floating range of basic parameters related to wall printing of different categories, and integrate the random parameters and input them into the multi-physics field coupling digital twin model; For each candidate parameter combination for printing each layer of the wall, run the digital twin model for a preset number of times in each set scenario. During each run, the newly generated random parameters are called, and the wall performance-related data output by each simulation is recorded; According to the wall performance-related data output by simulation, the preset performance index evaluation scoring method is used to obtain performance evaluation scores of different categories;​ Based on the obtained performance evaluation scores of different categories and according to the weight allocation schemes matched under different simulation conditions, calculate the average comprehensive scores of different candidate parameter combinations under all simulation conditions; Calculate the coefficient of variation of the comprehensive score of each candidate parameter combination under different simulation conditions; Select the parameter combinations whose comprehensive scores under all simulation conditions are ranked before the preset positions and whose coefficient of variation is less than the preset value as the optimal parameter combinations for printing each layer of the wall, and replace the initially set printing parameters for each layer according to the optimal parameter combinations for printing each layer of the wall.

[0010] Optionally, judging whether the preset triggering conditions for aggregate switching are met includes: Extract the key features of environment, printing progress, and wall thermal performance, and use them as matrix elements to construct a correlation matrix , where environmental data has m key features, printing progress has n key features, and wall thermal performance has p key features; At each monitoring moment, the state vector is composed according to the key characteristic state , among which, , is the status value of the i-th key feature. When the feature meets the trigger condition, the value is 1, and when it does not meet the trigger condition, the value is 0; The formula for multi-condition association score is as follows: ; Among them, is the multi-condition association score, is the state vector The transpose of , which converts a row vector into a column vector for matrix multiplication, is the correlation matrix The element in the i-th row and j-th column of represents the strength of association between the i-th feature and the j-th feature; According to multiple conditions association score The comparison result with the associated score threshold determines whether the pre-set trigger condition for aggregate switching is met.

[0011] Optionally, judging whether the preset triggering condition for aggregate switching is met also includes building a correlation matrix The previous steps are as follows: Analyze whether the relative deviation between the comprehensive membership and the trigger threshold is always within the preset deviation range within the preset number of continuous monitoring cycles; If yes, continue to execute the subsequent original steps; If not, then according to the multi-source data that has completed data preprocessing, a preset cluster analysis method is used to determine the parameter combination category, wherein the parameter combination category includes insulation priority type wall and structure priority type wall; According to the mapping relationship between the parameter combination category and the key features, the key features matching the determined parameter combination category are determined, and the determined parameter combination category and the key features matching it are input into the trigger condition judgment model constructed based on the decision tree algorithm, and the trigger condition is output as the pre-set trigger condition for aggregate switching; Based on the real-time collected and pre-processed multi-source data, determine whether the pre-set trigger conditions for aggregate switching are met.

[0012] Optionally, it also includes steps before determining whether a preset trigger condition for aggregate switching is met, specifically as follows: Determine the thermal performance requirements of the wall according to the different functions of the building, and determine the quantitative indicators of the thermal performance requirements of the wall based on the preset energy-saving standards; Preprocess the real-time environmental data and the determined quantitative value of the wall thermal performance requirement; Match the collected real-time environmental data and the preset quantitative value of wall thermal performance requirements with the set aggregate type selection criteria to screen out the aggregate types that meet the conditions; Determine whether to execute the subsequent steps based on the comparison results between the screened aggregate category and the currently used aggregate category.

[0013] Optionally, the analysis process of the triggering conditions for aggregate switching is as follows: According to the selected aggregate categories, determine the characteristics of the corresponding aggregate categories; According to the mapping relationship between the characteristics of aggregate categories and the trigger condition setting information of aggregate switching, the trigger condition setting information of aggregate switching is analyzed and determined.

[0014] Optionally, the valve opening and closing degree and the pipeline pressure can be regulated by a preset regulation method, including: Control the valve that is currently conveying aggregate to close; Collect and obtain new aggregate property data; According to the new aggregate characteristic data, the valve opening and closing adjustment scheme is matched and obtained from the preset valve opening and closing adjustment scheme analysis model, and the valve opening and closing adjustment is performed; According to the new aggregate characteristic data, the predicted conveying resistance is obtained by matching and analyzing the aggregate conveying resistance analysis model; According to the predicted conveying resistance, the additional power required by the conveying pump in the conveying pipeline is determined in real time, and the power of the conveying pump in the conveying pipeline is adjusted accordingly.

[0015] In the second aspect, the present application provides a thermal temperature control wall, which adopts the following technical solution: A thermal temperature control wall, which has a layered structure, and is composed of an outer layer, a middle layer and an inner layer from the outside to the inside; the outer layer is made of a mixture of a radiation cooling enhanced cement-based material and a polystyrene particle aggregate, the middle layer is made of a mixture of a radiation cooling enhanced cement-based material and a 35℃ phase change aggregate, and the inner layer is made of a mixture of a radiation cooling enhanced cement-based material and a 25℃ phase change aggregate. Brief Description of the Figures

[0016] Figure 1 This is a flow chart of a method for intelligent printing of thermal temperature-controlled walls according to an embodiment of the present application.

[0017] Figure 2 is a schematic diagram related to the manufacture of the thermal temperature control wall in the embodiment of the present application. Specific implementation method

[0018] The following is a further detailed description of this application in conjunction with the accompanying drawings.

[0019] Reference Figure 1 , a thermal temperature control wall intelligent printing method disclosed in this application, comprising the following steps: Step S100, start the 3D printing device, and extrude and print the mixed material of the radiation cooling enhanced cement-based material and the aggregate layer by layer through a special nozzle according to the preset printing path and the preliminarily set printing parameters of each layer.

[0020] Preset printing path: According to the design shape and size of the thermal temperature control wall, the printing trajectory planned in the 3D modeling software determines the movement route of the nozzle during the printing process to ensure the accurate formation of each part of the wall.

[0021] The special nozzle is specially designed for printing thermal temperature-controlled wall materials. It has two inlets, which are connected to the radiation cooling enhanced cement-based material and aggregate respectively. There is a stirring paddle inside to mix the two evenly during printing. The specific settings of the special nozzle can refer to Figure 2 Figures a, b and c in . For information on using this nozzle to print out radiation cooling enhanced cement-based material-aggregate composite strips to form layered composite walls, please refer to Figure 2 Figure d in .

[0022] Preliminary setting of printing parameters for each layer: Parameters preliminarily determined according to the wall design requirements before printing, including printing speed, nozzle temperature, thickness of each layer, etc. These parameters will be adjusted according to the results of the subsequent multi-objective optimization model.

[0023] ​The general process is as follows: Taking the printing of residential walls in tropical areas as an example, after starting the 3D printing equipment, the equipment reads the printing path data pre-planned in the modeling software. The special nozzle absorbs materials from the connection of the radiation cooling enhanced cement-based material inlet and the aggregate inlet according to the initially set parameters. For example, when printing the outer layer, the radiation cooling enhanced cement-based material and the polystyrene particle aggregate are sucked into the nozzle in a certain proportion. After the stirring paddle inside the nozzle stirs evenly, the mixed material is extruded and printed layer by layer along the preset path to form the outer structure of the wall.

[0024] Step S200, during the printing process, using the sensor matrix to collect multi-source data.

[0025] Among them, the multi-source data includes environmental data, printing progress data, and wall thermal performance related data.

[0026] Sensor Matrix: An array of multiple different types of sensors used to collect various types of data during the printing process. These sensors work together to obtain information from different dimensions and provide a basis for subsequent analysis and decision-making.

[0027] Environmental data: refers to various physical parameters in the printing environment, including temperature, humidity, light intensity, wind speed, etc. These data will affect the performance of wall materials and printing quality. For example, temperature and humidity may affect the solidification speed of cement-based materials. The environmental data can be obtained as follows: Use professional environmental monitoring sensors, such as temperature and humidity sensors such as DHT11 and SHT11, and light intensity sensors such as BH1750. Install these sensors around the printing area, and connect them to the data acquisition device through the sensor's built-in interface to obtain environmental data.

[0028] Printing progress data: Information reflecting the progress of the printing process, such as the number of layers printed, the completion ratio of the current printing layer, printing time, etc. By monitoring the printing progress data, the printing strategy can be adjusted in time to ensure the smooth progress of the printing process. The printing progress data is directly obtained from the control system of the 3D printing device. The 3D printing device usually records the relevant information during the printing process and reads the printing progress data through the communication interface of the device (such as USB, serial port, etc.) using the corresponding software tools.

[0029] Wall thermal performance related data: data closely related to the thermal regulation function of the wall, such as temperature distribution, thermal conductivity, specific heat capacity, etc. of each layer of the wall. These data are used to evaluate the thermal performance of the wall and determine whether it meets the design requirements. Wall thermal performance related data are obtained with the help of thermal performance test sensors, such as heat flow sensors, infrared temperature sensors, etc. These sensors are installed inside the wall or at specific locations on the surface to monitor the thermal performance data of the wall in real time and transmit the data to the data acquisition equipment.

[0030] Step S300, collect historical climate data of the printing area, and perform data preprocessing on the historical climate data and the collected multi-source data.

[0031] Historical climate data of the printing area: refers to the climate information of the area where the thermal temperature control wall is printed in the past period of time, covering meteorological elements such as temperature, humidity, sunshine duration, wind speed and direction. These data reflect the long-term climate characteristics of the area and are of great significance for analyzing the performance of the wall under different climatic conditions.

[0032] Data preprocessing: Clean, convert and normalize the collected raw data to remove noise and outliers, unify the data format and dimension, improve data quality, and make it more suitable for subsequent analysis and model calculation.

[0033] Step S400, input the pre-processed multi-source data and historical climate data into the pre-built multi-objective optimization model, output the optimal parameter combination for printing each layer of the wall, and replace the initially set printing parameters for each layer according to the optimal parameter combination for printing each layer of the wall.

[0034] Multi-objective optimization model: A mathematical model that comprehensively considers multiple conflicting objectives (such as wall thermal regulation performance, structural stability, material cost, etc.) and seeks the optimal solution or non-inferior solution set under certain constraints. In this application, it is used to determine the optimal parameter combination for printing each layer of the wall based on the input data. The multi-objective optimization model is based on common multi-objective optimization algorithms, such as the non-dominated sorting genetic algorithm (NSGA-II), the multi-objective particle swarm optimization algorithm (MOPSO), etc., and is built using Python's related scientific computing libraries (such as DEAP, PyGMO, etc.). The model can also be built and solved through professional optimization software (such as MATLAB's optimization toolbox).

[0035] Optimal parameter combination: For the printing of each layer of the thermal temperature control wall, a series of parameters are set to achieve the best comprehensive performance goals (such as thermal regulation performance, structural stability, material cost, etc.), including printing speed, thickness of each layer, aggregate ratio, amount of radiation cooling material, etc.

[0036] Step S500, during the printing process, based on the multi-source data collected in real time and pre-processed, determine whether the preset triggering conditions for aggregate switching are met. If yes, execute step S600; if no, execute step S700.

[0037] Pre-set trigger conditions for aggregate switching: Based on the wall design, material properties and printing process, the criteria for determining whether to switch aggregates are determined in advance. It is the basis for deciding when to replace aggregates and can ensure that the wall performance meets the standards. ​

[0038] The general process is as follows: Existing aggregate switching: Taking the printing of residential walls in tropical areas as an example, polystyrene granule aggregate is used when printing the outer layer. During the printing process, the sensor collects data in real time. If the ambient temperature continues to be higher than 30°C and the temperature of the middle layer of the wall is close to 35°C, and the printing progress reaches 80% of the outer layer design thickness, these data are pre-processed and compared with the preset conditions. If the trigger conditions of "the ambient temperature is high and the middle layer of the wall is about to reach the 35°C phase change temperature, and the outer layer printing is nearly completed" are met, the system determines that the aggregate needs to be switched from polystyrene granule aggregate to 35°C phase change aggregate to meet the temperature regulation requirements of the middle layer of the wall.

[0039] Switching from no aggregate to with aggregate: Assuming that no aggregate is used when printing the wall base (which may be a pure radiation cooling enhanced cement-based material layer), when the base printing is 80% completed and the ambient temperature fluctuates greatly, it is expected that the subsequent wall will require stronger temperature regulation capabilities. The sensor collects this data and matches it with the preset conditions (such as a certain proportion of the base is completed and the ambient temperature fluctuation exceeds the threshold) after preprocessing. The system determines that aggregate needs to be added and switches to adding polystyrene granular aggregate to enhance the thermal insulation performance of the wall, and starts the subsequent printing process with the participation of aggregate.

[0040] Step S600, then an aggregate switching instruction is issued to control the aggregate channel of the special nozzle to switch. During the aggregate switching process, the valve opening and closing degree and the conveying pipeline pressure are regulated by a preset control method to allow new aggregate to enter the printing nozzle. During the aggregate switching process, an adaptive fuzzy PID algorithm is used to synchronously adjust the stirring paddle speed and the aggregate conveying amount, so that the aggregate particle size distribution after the new aggregate and the radiation cooling enhanced cement-based material are mixed is within the preset aggregate particle size distribution standard range.

[0041] Aggregate switching command: A command signal issued by the control system to control the switching of the aggregate channel of the printing nozzle to ensure that different types of aggregates are supplied in an orderly manner according to printing requirements.

[0042] Preset control method: A pre-set strategy for accurately controlling valve opening and closing and conveying pipeline pressure to ensure that the new aggregate enters the printing nozzle smoothly and accurately and mixes well with the radiation cooling enhanced cement-based material.

[0043] Adaptive fuzzy PID algorithm: An algorithm that combines the advantages of fuzzy control and PID control. It can automatically adjust the control parameters according to the real-time error and error change rate, realize the precise synchronous adjustment of the impeller speed and the aggregate delivery amount, and ensure that the aggregate particle size distribution after mixing meets the standard.

[0044] General process description: Existing aggregate switching situation: Assume that when printing residential walls in temperate regions, polystyrene granular aggregate is used for the outer layer. When step S500 determines that it is necessary to switch to 23°C phase change aggregate, the control system issues an aggregate switching command. After receiving the command, the special nozzle controls the valve to close the polystyrene granular aggregate channel and open the 23°C phase change aggregate channel at the same time. The preset control method is started, and the valve opening and closing degree is adjusted according to the characteristics of the new aggregate, so that the 23°C phase change aggregate can stably enter the conveying pipeline, and the pipeline pressure is adjusted to ensure smooth delivery. The adaptive fuzzy PID algorithm works synchronously, and according to the real-time monitoring of the aggregate mixing situation, the stirring paddle speed and the 23°C phase change aggregate delivery amount are adjusted to ensure that the aggregate particle size distribution after mixing meets the standard and achieve efficient and uniform mixing.

[0045] Switch from no aggregate to aggregate: For example, in the starting layer of the wall printing, the pure radiation cooling enhanced cement-based material layer may be printed first. When the switching conditions are met (such as a certain thickness is completed and the ambient temperature changes greatly), the system issues an aggregate switching command. The special nozzle opens the aggregate channel (assuming that polystyrene particle aggregate is connected), and the preset control method adjusts the valve opening and closing degree and pipeline pressure to allow the polystyrene particle aggregate to enter the nozzle smoothly. The adaptive fuzzy PID algorithm starts to work, adjusting the stirring paddle speed and the polystyrene particle aggregate delivery rate to evenly mix the polystyrene particles with the radiation cooling enhanced cement-based material to ensure the subsequent printing quality.

[0046] Step S700, maintain the original settings.

[0047] Among them, maintain the original settings: during the thermal temperature control wall printing process, when the multi-source data collected in real time and pre-processed does not meet the preset aggregate switching trigger conditions, keep the aggregate conveying state of the current print head, the stirring paddle speed, the valve opening and closing degree, the conveying pipeline pressure and the printing parameters of each layer unchanged, and continue to print according to the established printing process.

[0048] A method for intelligent printing of thermal temperature-controlled walls also includes steps after preprocessing the historical climate data and the collected multi-source data, which are as follows: Step S301, unify the data format of the multi-source data and historical climate data that have completed data preprocessing, and perform data standardization to form various data vectors.

[0049] Data format unification: The pre-processed multi-source data (environmental data, printing progress data, wall thermal performance data, etc.) and historical climate data are organized according to the same standards and specifications to have a consistent data format for subsequent processing and analysis. For example, temperature data in different units are unified into degrees Celsius, and time data in different formats are unified into a standard date and time format.

[0050] Data standardization: Scale the data in a unified format to have specific statistical characteristics, such as a mean of 0 and a standard deviation of 1. This can eliminate the dimensional differences between different data features and prevent certain features from having too large or too small an impact on subsequent analysis due to too large or too small a numerical range.

[0051] Data vector: The data that has been formatted and standardized is combined into a vector form according to certain rules. Each vector represents a group of related data information, which is convenient for input into subsequent models or algorithms for calculation.

[0052] Step S302, inputting various data vectors into preset fuzzy membership functions, calculating the membership of various data vectors in different fuzzy sets, and performing weighted summation of multiple fuzzy membership functions to obtain a comprehensive membership.

[0053] Preset fuzzy membership function: a mathematical function used to describe the degree to which a data element belongs to a fuzzy set, with a value between 0 and 1. In this step, the preset function is used to quantify the degree of association between various data vectors and different fuzzy sets. For example, for the ambient temperature data vector, a fuzzy membership function can be set to determine the degree to which it belongs to fuzzy sets such as "high temperature", "medium temperature" and "low temperature".

[0054] Fuzzy set: a set with unclear boundaries. Unlike traditional sets, the membership of elements to fuzzy sets is not simply "belong" or "do not belong", but is expressed by membership. For example, "comfortable temperature" is a fuzzy set, and different temperature values ​​have different memberships to it.

[0055] Membership: It indicates the degree to which a data element belongs to a fuzzy set. It is the result of the fuzzy membership function. The closer the value is to 1, the higher the degree to which the data element belongs to the fuzzy set; the closer the value is to 0, the lower the degree to which the data element belongs to the fuzzy set.

[0056] The general process is as follows: Assume that during the printing process, there is a data vector containing ambient temperature, printing progress and wall thermal performance data, such as [28,0.6,0.1] (representing ambient temperature 28°C, printing progress 60%, and wall thermal performance deviation 0.1). First, for the ambient temperature 28°C, according to the preset fuzzy membership function of temperature (such as triangular function), calculate its membership in the "high temperature" fuzzy set, assuming that the calculation result is 0.8; for the printing progress 60%, according to the corresponding fuzzy membership function, the membership in the "normal progress" fuzzy set is calculated to be 0.7; for the wall thermal performance deviation 0.1, the membership in the "low deviation" fuzzy set is 0.9.

[0057] If the weights of the three fuzzy membership functions of ambient temperature, printing progress, and wall thermal performance deviation are set in advance to 0.4, 0.3, and 0.3 respectively.

[0058] Then the comprehensive membership degree = 0.8×0.4+0.7×0.3+0.9×0.3=0.8. Through such calculation, the comprehensive membership degree of the data vector is obtained for subsequent analysis and judgment.

[0059] Step S303, analyzing whether the calculated comprehensive membership exceeds the trigger threshold.

[0060] The calculation formula for the trigger threshold is as follows: , among which, is the trigger threshold, is the adjustment coefficient, which is used to adjust the weight of the comprehensive membership mean when calculating the trigger threshold. is the mean of comprehensive membership, is the adjustment coefficient, which is used to adjust the weight of the comprehensive membership standard deviation when calculating the trigger threshold. is the comprehensive membership standard deviation. If no, execute step S304; if yes, execute step S305.

[0061] Step S304, continue with the subsequent steps.

[0062] Continue with the subsequent steps: When the comprehensive membership does not exceed the trigger threshold, no additional parameter adjustment or status analysis operation is performed, but the established printing process is continued. That is, the current print head working status, material delivery status, printing parameter settings, etc. are kept unchanged, and the subsequent printing control logic is executed in sequence, such as continuing data monitoring, judging whether the aggregate switching conditions are met, etc.

[0063] Step S305, based on the multi-source data and historical climate data that have completed data preprocessing, extract key state features, and use principal component analysis to reduce the dimension to obtain the principal component feature vector representing the environment and equipment status.

[0064] Among them, the key status characteristics include environmental change rate, printing progress deviation, and wall thermal performance deviation.

[0065] Environmental change rate: An indicator that measures how fast the printing environment (such as temperature, humidity, etc.) changes over time. It can be calculated by the ratio of the difference in environmental data at adjacent moments to the time interval. A large environmental change rate may affect material performance and printing quality.

[0066] Printing progress deviation: The difference between the actual printing progress and the preset printing progress. For example, if a certain proportion of wall printing should be completed within a preset time period, the difference between the actual completion proportion and the preset printing progress is the printing progress deviation, which reflects whether the printing process is normal.​​​

[0067] Wall thermal performance deviation: The degree of deviation between the actual thermal performance of the wall (such as thermal conductivity, specific heat capacity, etc.) and the thermal performance required by the design, reflecting whether the thermal performance of the wall meets expectations.

[0068] Principal component analysis dimensionality reduction: A data dimensionality reduction technique that converts multiple related raw data features into a few unrelated comprehensive indicators (i.e., principal components), while retaining most of the data information, reduces the data dimension and simplifies subsequent analysis.

[0069] Principal component feature vector: A new feature vector obtained by principal component analysis. Each vector represents a comprehensive feature and can effectively summarize the status information of the environment and equipment.

[0070] Step S306, using the fuzzy logic system, according to the principal component feature vector and preset rules, calculate the adjustment coefficient of each performance target weight, and update the multi-objective optimization model weight.

[0071] Among them, performance targets include thermal regulation performance, structural stability, and material cost.

[0072] Fuzzy logic system: A system based on fuzzy set theory and fuzzy reasoning rules, which can process imprecise and fuzzy information and convert the input fuzzy data into output results through fuzzy reasoning. In this step, it is used to calculate the adjustment coefficient of each performance target weight according to the main component eigenvector and preset rules.

[0073] Preset rules: Before printing the thermal temperature control wall, a series of conditional statements are pre-formulated based on the design requirements, material properties, printing process and other factors of the wall to guide the fuzzy logic system to perform reasoning calculations. For example, if the principal component eigenvector shows that the environmental change rate is large and the printing progress is slow, the weight of the thermal regulation performance target is appropriately increased.

[0074] Performance target weight adjustment coefficient: a value used to adjust the weight of performance targets such as thermal regulation performance, structural stability, and material cost. By changing these coefficients, the importance of different performance targets in the multi-objective optimization model can be dynamically adjusted.

[0075] Multi-objective optimization model weights: The weights corresponding to different performance objectives in the multi-objective optimization model. These weights determine the relative importance of each objective in the model. By adjusting the weights, the model can be made more inclined to optimize certain objectives in different situations.

[0076] The general process is described as follows: Assume that during the printing of the thermal temperature control wall, the principal component eigenvector shows that the environmental change rate is large, the printing progress deviation is negative (that is, the printing progress is lagging behind), and the wall thermal performance deviation is small. This information is input into the fuzzy logic system built using the Scikit-fuzzy library. The system makes inferences based on preset rules. For example, the preset rules stipulate that when the environmental change rate is large and the printing progress is lagging behind, the weight of the thermal regulation performance target should be appropriately increased and the weight of the material cost target should be reduced. After fuzzy reasoning calculation, the adjustment coefficient of the thermal regulation performance target weight is 1.2, the adjustment coefficient of the structural stability target weight is 1.0, and the adjustment coefficient of the material cost target weight is 0.8. Then, these adjustment coefficients are used to update the weights of each performance target in the multi-objective optimization model, so that the model pays more attention to the optimization of thermal regulation performance in subsequent calculations, and relatively reduces the consideration of material cost to adapt to the current printing status.

[0077] Step S307, using the trained deep Q network, input different principal component feature vectors to obtain corresponding printing parameter combinations, and continue this operation until a preset number of printing parameter combinations under different states are generated.

[0078] Deep Q Network (DQN): A neural network model based on reinforcement learning that makes decisions by learning the environment state and the corresponding optimal behavior strategy. In thermal wall printing, it is used to output the corresponding printing parameter combination according to different principal component feature vectors (representing the environment and equipment state).

[0079] Preset number of groups: The number of printing parameter combinations pre-set according to printing requirements and experimental experience, used to ensure that enough parameter combinations under different states are obtained to meet the needs of multi-objective optimization.

[0080] The general process is as follows: Assuming that a thermal temperature-controlled wall is printed in a cold area, multiple principal component eigenvectors are obtained from step S305. The deep Q network model has been built and trained using TensorFlow. The first principal component eigenvector is input into the deep Q network model, and the model outputs a set of printing parameter combinations after calculation, such as a printing speed of 50mm / s, a nozzle temperature of 200℃, a thickness of 5mm per layer, and a ratio of phase change aggregate to radiation cooling enhanced cement-based material of 3:7. Next, the next principal component eigenvector is input into the model, and another set of different printing parameter combinations is obtained. This process continues until a preset number of groups (assuming 20 groups) of printing parameter combinations under different states are generated. In this way, a variety of parameter combination schemes suitable for different printing states are obtained, laying the foundation for the subsequent selection of the optimal parameter combination.

[0081] Step S308: Based on the updated multi-objective optimization model weights, perform performance evaluation on each generated set of printing parameter combinations to obtain the scores of each performance objective, and sum up the scores of each performance objective weighted by the adjusted weights to obtain the comprehensive score of each set of parameter combinations.

[0082] Updated multi-objective optimization model weights: In step S306, the weight values obtained by modifying the original multi-objective optimization model weights according to the performance objective weight adjustment coefficients calculated by the fuzzy logic system. These weights are used to measure the relative importance of performance objectives such as thermal regulation performance, structural stability, and material cost in the comprehensive evaluation.

[0083] Performance evaluation: According to certain standards and methods, quantitatively evaluate the performance of each set of printing parameter combinations in terms of thermal regulation performance, structural stability, material cost, etc., to obtain the scores of each performance objective, and thereby judge the influence degree of the parameter combination on the wall performance.

[0084] Performance objective scores: The quantitative evaluation results for each set of printing parameter combinations on performance objectives such as thermal regulation performance, structural stability, and material cost. The higher the score, the better the performance on that performance objective.

[0085] Comprehensive score: The value obtained by summing up the scores of each performance objective of each set of printing parameter combinations weighted by the updated multi-objective optimization model weights, which comprehensively reflects the overall quality of each set of printing parameter combinations and is used for subsequent ranking and selection of different parameter combinations.

[0086] Step S309: Sort all sets of parameter combinations according to the comprehensive scores, select the parameter combination with the highest comprehensive score as the optimal parameter combination for printing each layer of the wall, and replace the initially set printing parameters for each layer according to the optimal parameter combination for printing each layer of the wall.

[0087] Comprehensive score: In step S308, the value obtained by summing up the scores of each performance objective of each set of printing parameter combinations weighted by the updated multi-objective optimization model weights, which is used to comprehensively measure the quality of the printing parameter combinations.

[0088] Parameter combination ranking: Arrange all generated printing parameter combinations in order according to the high and low comprehensive scores. The combinations with high scores are ranked in the front, and the combinations with low scores are ranked in the back, which is convenient for screening out the optimal combination.

[0089] Optimal parameter combination: Among all printing parameter combinations, the set of parameters with the highest comprehensive score, which achieves the best balance on multiple performance objectives such as thermal regulation performance, structural stability, and material cost, and is most suitable for printing each layer of the wall.

[0090] A method for intelligent printing of thermal temperature-controlled walls further includes steps after obtaining a comprehensive score for each parameter combination, specifically as follows: Step S30A, analyzing whether the relative deviation between the comprehensive membership and the trigger threshold is always within the preset deviation range within the preset number of continuous monitoring cycles. If yes, execute step S30B; if no, execute step S30C.

[0091] Preset number of continuous monitoring cycles: The number of time periods for continuous monitoring of the comprehensive membership degree is pre-set. In each monitoring cycle, the comprehensive membership degree is calculated and compared with the trigger threshold to determine the stability of the printing process.

[0092] Preset deviation range: According to the printing process requirements and wall quality standards, the preset allowable range of relative deviation between the comprehensive membership and the trigger threshold. If the relative deviation is within this range, it means that the printing process is relatively stable, otherwise the printing parameters may need to be adjusted.

[0093] Step S30B, continue with the subsequent steps.

[0094] Continue with the next steps: When the relative deviation between the comprehensive membership and the trigger threshold is always within the preset deviation range within the preset number of continuous monitoring cycles, no additional parameter adjustment or screening operation is performed, but the established printing process is continued. This means that the current print head working status, material delivery status, printing parameter settings, etc. are kept unchanged, and the subsequent printing control logic is continued, such as continuing data monitoring, judging whether other conditions are met, etc.

[0095] Step S30C, from all generated parameter combinations, select the parameter combination with the highest comprehensive score ranking as the candidate parameter combination.

[0096] All generated parameter combinations: In step S307, multiple sets of printing parameter combinations are obtained by inputting different principal component feature vectors using the trained deep Q network, covering different value combinations of parameters such as printing speed, nozzle temperature, thickness of each layer, and aggregate ratio.

[0097] Comprehensive score ranking: After all generated parameter combinations are sorted from high to low according to the comprehensive score (calculated in step S308), the order of each parameter combination.

[0098] Preset ranking: A ranking limit value is preset according to printing requirements and actual conditions. Parameter combinations ranked before this ranking are considered to have relatively good comprehensive performance and have the potential to become the object of further screening.

[0099] ​Candidate parameter combinations: From all generated parameter combinations, select those parameter combinations with comprehensive scores ranked in the top preset positions. These combinations are initially screened and have relatively outstanding performance in thermal regulation performance, structural stability, material cost, etc., and are used for subsequent more detailed evaluation and selection.

[0100] Step S30D, for the preliminary candidate parameter combinations for printing each layer of the wall, determine the basic parameters related to wall printing, and run a random algorithm to generate random parameters within the floating range of basic parameters related to wall printing of different categories, and integrate the random parameters and input them into the multi-physics field coupling digital twin model.

[0101] Basic parameters related to wall printing: basic parameters directly related to the wall printing process, such as printing speed, nozzle temperature, thickness of each layer, proportion of different aggregates, etc. These parameters directly affect the printing quality and performance of the wall.

[0102] Random algorithm: An algorithm that can generate random numbers. In this step, it is used to generate random parameters that fluctuate within a specific range to simulate various uncertain factors that may occur in the actual printing process.

[0103] Floating range: For each wall printing related basic parameter, the pre-set fluctuation range, the random parameters generated by the random algorithm will take values ​​within this range to take into account the uncertainty in the printing process.

[0104] Multi-physics field coupling digital twin model: A model based on digital twin technology that couples multiple physical fields (such as thermal field, mechanical field, etc.) during the wall printing process. By inputting different parameter combinations, the performance of the wall during the printing process and subsequent use can be simulated.

[0105] Step S30E, for each candidate parameter combination for printing each layer of the wall, run the digital twin model for a preset number of times in each set scenario, call the newly generated random parameters each time, and record the wall performance-related data output by each simulation.

[0106] Preset number of times: Based on the model accuracy requirements and the comprehensiveness requirements of the simulation, the number of times each candidate parameter combination is pre-set to run the digital twin model in each scenario. Multiple runs can take into account the uncertainty caused by random parameters and improve the reliability of the simulation results.

[0107] Random parameters: In step S30D, the parameter values ​​within the floating range of the basic parameters related to wall printing are generated by the random algorithm to simulate the variable factors that may appear in the actual printing process, such as slight differences in material properties, small fluctuations in environmental factors, etc.

[0108] Wall performance related data: Various data reflecting wall performance output by the digital twin model during operation, such as temperature distribution, structural stress, thermal conductivity, compressive strength, etc. of the wall. These data are used to evaluate the impact of candidate parameter combinations on wall performance.

[0109] Step S30F, based on the wall performance related data output by simulation, use the preset performance index evaluation scoring method to obtain performance evaluation scores of different categories.

[0110] Simulated output of wall performance related data: In step S30E, the digital twin model outputs data after running each candidate parameter combination in different scenarios, including thermal performance, structural performance, durability and other information of the wall, such as temperature distribution, stress strain, material aging degree and other data.

[0111] Preset performance index evaluation scoring method: a set of rules and algorithms pre-established for quantitative evaluation of wall performance based on the design objectives and quality requirements of the wall. This method converts the simulated output of various wall performance-related data into specific scores to measure the performance level of the wall in different performance aspects.

[0112] Take thermal regulation performance as an example, temperature fluctuation: within 24 hours, if the indoor surface temperature fluctuates ≤2℃, 10 points will be awarded, 2-4℃ will be awarded 8 points, 4-6℃ will be awarded 6 points, and >6℃ will be awarded 4 points. If the fluctuation is 3℃, this item will be awarded 8 points. Temperature difference adjustment: 10 points for the average temperature difference between indoor and outdoor ≥8℃, 8 points for 6-8℃, 6 points for 4-6℃, 4 points for <4℃. If the temperature difference is 7℃, 8 points will be awarded. Take structural stability as an example, compressive strength: 10 points for actual compressive strength reaching 100% or more of the design value, 8 points for 90%-100%, 6 points for 80%-90%, and 4 points for <80%. Design strength is 20MPa, actual 18MPa (90% compliance rate), 8 points. Deformation degree: 10 points for maximum deformation <0.1% of wall height, 8 points for 0.1%-0.3%, 6 points for 0.3%-0.5%, and 4 points for >0.5%. If the deformation is 0.2% of the wall height, 8 points. Performance evaluation scores of different categories: Based on the preset performance index evaluation scoring method, specific scores are calculated for different performance categories of the wall (such as thermal regulation performance, structural stability, waterproof performance, etc.), which are used to analyze in detail the impact of candidate parameter combinations on various aspects of wall performance.

[0113] Step S30G, based on the obtained performance evaluation scores of different categories and according to the weight allocation schemes matched under different simulation conditions, calculate the average comprehensive scores of different candidate parameter combinations under all simulation conditions.

[0114] Different simulation conditions: different scenarios set in step S30E, such as different climate conditions (high temperature, low temperature, humidity, etc.) and different building usage scenarios (residential, commercial, industrial, etc.).

[0115] Weight distribution scheme: For different simulation conditions, the pre-set weights for different categories of performance evaluation scores reflect the importance of each performance under different conditions.

[0116] Comprehensive score: The score obtained by weighted summing the performance evaluation scores of different categories according to the corresponding weights is used to comprehensively measure the advantages and disadvantages of candidate parameter combinations under specific simulation conditions.

[0117] Average value of comprehensive score: The arithmetic mean of the comprehensive score of each candidate parameter combination under all simulated working conditions can more comprehensively reflect the overall performance of the parameter combination.

[0118] Assume that there are 3 candidate parameter combinations when printing a thermal temperature-controlled wall, and set two simulation conditions: "high temperature and dry residential scene" and "low temperature and humid commercial scene". Under each condition, there are three types of performance evaluation scores: thermal regulation performance, structural stability, and waterproof performance.

[0119] For the "high temperature and dry residential scene", the weight distribution scheme is 0.6 for thermal regulation performance, 0.3 for structural stability, and 0.1 for waterproof performance; for the "low temperature and humid commercial scene", the weight distribution scheme is 0.4 for thermal regulation performance, 0.3 for structural stability, and 0.3 for waterproof performance.

[0120] Take the first candidate parameter combination as an example. In the "high temperature and dry residential scenario", the thermal regulation performance score is 8 points, the structural stability score is 7 points, and the waterproof performance score is 6 points. The comprehensive score under this condition is 8×0.6+7×0.3+6×0.1=7.5 points. In the "low temperature and humid commercial scenario", the thermal regulation performance score is 7 points, the structural stability score is 8 points, and the waterproof performance score is 7 points. The comprehensive score under this condition is 7×0.4+8×0.3+7×0.3=7.3 points. The average comprehensive score of this candidate parameter combination under all simulated conditions is (7.5+7.3)÷2=7.4 points.

[0121] The same calculation process is performed on the other two candidate parameter combinations to obtain the average value of the comprehensive score of each candidate parameter combination under all simulation conditions, providing a basis for subsequent screening of the optimal parameter combination.

[0122] Step S30H, calculating the coefficient of variation of the comprehensive score of each candidate parameter combination under different simulation conditions.

[0123] Coefficient of variation: A statistic used to measure the degree of data dispersion. In this step, the coefficient of variation of the comprehensive score of the candidate parameter combination under different simulated working conditions is calculated to reflect the stability of the comprehensive performance of the parameter combination under different usage environments. The smaller the coefficient of variation, the more stable the comprehensive score, that is, the less the parameter combination is affected by different working conditions. The formula for calculating the coefficient of variation is: Coefficient of variation = (standard deviation of the comprehensive score ÷ average value of the comprehensive score) × 100%.

[0124] Step S30I, filter out the parameter combinations whose comprehensive scores under all simulation conditions are ranked before the preset positions and whose coefficient of variation is less than the preset value, as the optimal parameter combinations for printing each layer of the wall, and replace the initially set printing parameters for each layer according to the optimal parameter combinations for printing each layer of the wall.

[0125] Default value: determined by technicians based on actual needs and experience of wall printing, stored in the configuration file of the 3D printing equipment control system, and called when executing step S30I.

[0126] Determining whether the preset triggering conditions for aggregate switching are met include: Step S510, extracting key features of environment, printing progress, and wall thermal performance, and using the key features of environment, printing progress, and wall thermal performance as matrix elements to construct a correlation matrix , where environmental data has m key features, printing progress has n key features, and wall thermal performance has p key features.

[0127] Step S520, at each monitoring moment, form a state vector according to the key feature state , among which, , is the status value of the i-th key feature. When the feature meets the trigger condition, the value is 1, and when it does not meet the trigger condition, the value is 0.

[0128] The formula for multi-condition association score is as follows: ; Among them, is the multi-condition association score, is the state vector The transpose of , which converts a row vector into a column vector for matrix multiplication, is the correlation matrix The element in the i-th row and j-th column in represents the strength of association between the i-th feature and the j-th feature.

[0129] Monitoring time: During the wall printing process, the time points set at certain time intervals are used to check and analyze the status of various key features, so as to obtain data in real time and determine whether the trigger conditions are met.

[0130] Key feature status: refers to the status of key features such as environment, printing progress, wall thermal performance, etc. at the time of monitoring, which is expressed as whether the pre-set trigger conditions are met.

[0131] State vector: A vector composed of the state values ​​of each key feature. The number of elements in the vector is equal to the total number of key features, where the value of each element is 0 or 1, indicating that the corresponding feature does not meet or meets the trigger condition.

[0132] The state value of the i-th key feature: In the state vector, it represents the state value of the i-th key feature (one of the key features of the environment, printing progress or wall thermal performance) at the monitoring time. 1 means that the feature meets the trigger condition, and 0 means it does not meet the condition.

[0133] Step S530, scoring based on multiple condition associations The comparison result with the associated score threshold determines whether the pre-set trigger condition for aggregate switching is met.

[0134] Multi-condition correlation score: The value obtained by performing matrix operation on the state vector in step S520 and the correlation matrix in step S510. This score comprehensively reflects the correlation between multiple key features such as environment, printing progress, wall thermal performance, etc. and their satisfaction of the trigger conditions.

[0135] Association score threshold: A pre-set standard value used to compare with the multi-condition association score. If the multi-condition association score reaches or exceeds the threshold, it is considered that the trigger condition for aggregate switching is met; otherwise, it is not met. This threshold is determined based on the actual needs, experience and a large amount of experimental data of wall printing. It balances various factors in the printing process to ensure that aggregate switching is performed at the right time. Determining whether the preset triggering conditions for aggregate switching are met also includes building a correlation matrix The previous steps are as follows: Step S5A0, analyzing whether the relative deviation between the comprehensive membership and the trigger threshold value is always within the preset deviation range within the preset number of continuous monitoring cycles. If yes, execute step S5B0; if no, execute step S5C0.

[0136] Step S5B0, continue to execute the subsequent original steps.

[0137] Continue to execute the original subsequent steps: When the relative deviation between the comprehensive membership and the trigger threshold is always within the preset deviation range within the preset number of continuous monitoring cycles, the printing process is judged to be relatively stable and no additional special processing or adjustment is required. At this time, the 3D printing device will continue to execute the subsequent operation steps according to the established printing procedures and processes. These steps include but are not limited to continuous monitoring of the printing environment, advancing the printing progress, and real-time detection of the wall's thermal performance to ensure the smooth progress of the printing work until the wall printing task is completed.

[0138] Step S5C0, based on the multi-source data that has completed data preprocessing, a preset cluster analysis method is used to determine the parameter combination category, wherein the parameter combination category includes insulation priority type wall and structure priority type wall.

[0139] Preset clustering analysis method: A data mining technology that groups data objects into clusters of similar objects so that data objects within the same cluster have high similarity, while data objects between different clusters have large differences. In this step, it is used to analyze multi-source data that has completed data preprocessing and classify similar parameter combinations into the same category. Common clustering analysis methods include K-Means clustering, hierarchical clustering, etc. Parameter combination categories: Different categories obtained by processing multi-source data through cluster analysis methods. These categories reflect the inherent structure and characteristics of the data. In the wall printing scenario, it mainly includes insulation-first walls and structure-first walls. The parameter combination of insulation-first walls focuses on improving the insulation performance of the wall, such as using more insulation materials, adjusting the printing process to optimize the insulation structure of the wall, etc.; the parameter combination of structure-first walls focuses more on the structural strength and stability of the wall, such as adjusting the aggregate ratio, adding support structures, etc.

[0140] Step S5D0, according to the mapping relationship between the parameter combination category and the key feature, determine the key feature that matches the determined parameter combination category, and input the determined parameter combination category and the key feature that matches it into the trigger condition judgment model constructed based on the decision tree algorithm, and output the trigger condition as the pre-set trigger condition for aggregate switching.

[0141] Key features: important features closely related to the wall printing process and performance, covering multiple dimensions such as environmental aspects (such as temperature, humidity), printing progress (such as the number of printed layers, printing speed), and wall thermal performance (such as wall surface temperature, thermal conductivity).

[0142] Mapping relationship: A corresponding relationship between parameter combination categories and key features, which indicates which key features play an important role under different parameter combination categories and how these key features affect the performance of the wall.

[0143] Trigger condition judgment model: It is constructed by training with a large amount of historical data based on the decision tree algorithm using tools such as the Scikit-learn library in Python. The trained model is stored in the control system of the 3D printing device in the form of a file and is loaded and used when needed.

[0144] The general process is described as follows: Suppose in step S5C0, two parameter combination categories, namely heat preservation priority type wall and structure priority type wall, are obtained through cluster analysis. By analyzing historical data, technicians determine that the key features corresponding to the heat preservation priority type wall are ambient temperature, wall surface temperature, and heat preservation material ratio; the key features corresponding to the structure priority type wall are the number of printed layers, printing speed, and aggregate strength.

[0145] In a certain printing project, according to the current printing situation, it is determined that the current parameter combination category is the heat preservation priority type wall. Key feature data such as the current ambient temperature, wall surface temperature, and heat preservation material ratio are collected from sensors. The parameter combination category of the heat preservation priority type wall and the corresponding key feature data are input into the trigger condition judgment model constructed based on the decision tree algorithm.

[0146] The model makes a judgment according to the rules learned during training. For example, if the ambient temperature is lower than a certain threshold, the wall surface temperature is higher than another threshold, and the heat preservation material ratio is lower than a certain value, the model outputs the trigger condition for aggregate switching. The control system stores these trigger conditions in the configuration file for subsequent judgment of whether to perform the aggregate switching operation.

[0147] Step S5E0, based on the multi-source data collected in real time and preprocessed, determines whether the trigger conditions for aggregate switching set in advance are met.

[0148] The general process is described as follows: Suppose when printing a thermally regulated wall, the trigger condition for aggregate switching set in advance is: when the ambient temperature is lower than 10°C, the number of printed layers reaches 30 layers, and the wall surface temperature is lower than 25°C, aggregate switching is required. During the printing process, the ambient temperature data is collected in real time by the ambient sensor, the number of printed layers data is recorded by the monitoring system of the printing device, and the wall surface temperature data is obtained by the wall thermal performance detection device. These data are transmitted to the data processing module in real time, and after preprocessing operations such as cleaning, denoising, and normalization, they are stored in the data storage area of the control system. At a certain moment, the data collected and pre-processed by the sensor showed that the ambient temperature was 8°C, the number of printed layers was 32, and the wall surface temperature was 23°C. The control system reads the pre-set trigger conditions from the configuration file and compares the real-time data with them. It is found that the current ambient temperature of 8°C is lower than 10°C, the number of printed layers has reached 30 layers from 32, and the wall surface temperature of 23°C is lower than 25°C, which meets all the pre-set trigger conditions. At this time, the control system determines that an aggregate switching operation is required, and then issues a corresponding instruction to start the aggregate switching process. If at a certain moment, for example, the ambient temperature is 12°C, even if the number of printed layers and the wall surface temperature meet the conditions, but because the ambient temperature does not meet the condition of being lower than 10°C, the control system determines that the trigger conditions are not met, and the printing process continues according to the current aggregate usage.

[0149] A thermal temperature control wall intelligent printing method also includes steps before determining whether a preset trigger condition for aggregate switching is met, which are as follows: Step SA00, determine the thermal performance requirements of the wall according to the different functions of the building, and determine the quantitative indicators of the thermal performance requirements of the wall based on the preset energy-saving standards.

[0150] Different functions of buildings: buildings have various functions due to different purposes. For example, residential buildings are mainly used for living, and require good thermal insulation to ensure indoor comfort; commercial buildings such as shopping malls have a large flow of people and have high requirements for ventilation and rapid adjustment of indoor temperature; industrial buildings may have specific requirements for fire resistance, moisture resistance, thermal insulation and other properties of the wall due to different production processes.

[0151] Thermal performance requirements of walls: Performance requirements that walls should meet in terms of heat transfer, heat preservation, and thermal insulation, determined according to the building function. Buildings with different functions have different thermal performance requirements for walls. For example, residential walls need to effectively prevent indoor and outdoor heat exchange to maintain a stable indoor temperature; cold storage building walls require extremely strong thermal insulation to prevent cold loss.

[0152] Preset energy-saving standards: A series of specifications and indicators on building energy conservation formulated by the state or industry to reduce building energy consumption. These standards stipulate the minimum requirements that different types of buildings should meet in terms of energy consumption, thermal performance, etc., aiming to promote the construction industry towards energy conservation and emission reduction.

[0153] Quantitative indicators of wall thermal performance requirements: The wall thermal performance requirements are expressed with specific values ​​to more accurately guide the parameter selection and material configuration during the wall printing process. Common quantitative indicators include heat transfer coefficient, thermal resistance, heat storage coefficient, etc. The lower the heat transfer coefficient, the better the thermal insulation performance of the wall; the greater the thermal resistance, the stronger the wall's ability to prevent heat transfer.

[0154] The general process is as follows: Suppose you want to print the thermal temperature control wall of a residential building. First, from the architectural design drawings, we know that the building is an ordinary residence, and its function is to provide a comfortable living space. Then, query the local energy-saving standards for residential buildings. For example, the local requirement is that the heat transfer coefficient of the exterior wall of the residential building does not exceed 1.5W / (m²・K). Next, use the Hongye load calculation software to enter the relevant parameters of the residence, including information such as the building faces south and the exterior wall is made of ordinary brick walls. The software calculates according to the energy-saving standards and input parameters to obtain the quantitative indicators of the thermal performance requirements of the residential wall, such as the heat transfer coefficient must be controlled within 1.2W / (m²・K), and the thermal resistance must reach 0.83m²・K / W or above. Through this process, the quantitative indicators of the thermal performance requirements of the residential building wall are clarified, which provides a basis for material selection and process control in the subsequent wall printing process.

[0155] Step SB00, preprocessing the real-time environmental data and the determined quantitative value of the wall thermal performance requirement.

[0156] Data preprocessing: Perform a series of processing operations on the collected raw data to improve the data quality and make it more suitable for subsequent analysis and use. Common data preprocessing operations include data cleaning (removing noise data, outliers, and duplicate data), data normalization (converting data to a specific numerical range to facilitate comparison and calculation between different data), data encoding (converting non-numeric data to numerical data), etc.

[0157] Step SC00, matching the collected real-time environmental data, the preset wall thermal performance demand quantified value, and the set aggregate type selection criteria to screen out the aggregate types that meet the conditions.

[0158] Set aggregate type selection criteria: A set of pre-defined rules for selecting the appropriate aggregate type based on real-time environmental data and the quantified value of the wall thermal performance requirements. These criteria take into account the impact of different aggregates on the thermal performance of the wall under different environmental conditions.

[0159] Qualified aggregate categories: After matching with the set aggregate type selection criteria, the aggregate types that can meet the current real-time environmental data and the quantitative value requirements of the wall thermal performance requirements are selected.

[0160] Step SD00, based on the comparison result between the screened aggregate category and the currently used aggregate category, determine whether to execute the subsequent steps.

[0161] Aggregate categories selected: In step SC00, based on the real-time environmental data, the preset wall thermal performance demand quantified value and the set aggregate type selection criteria, the aggregate categories that meet the conditions are determined, such as lightweight thermal insulation aggregate, composite phase change aggregate, ordinary aggregate, etc. Currently used aggregate category: During the wall printing process, the type of aggregate currently being used for printing. As the printing process progresses, the aggregate may need to be replaced due to various factors, and the category will change accordingly. Comparison results: By comparing the selected aggregate category with the currently used aggregate category, determine whether the two are consistent. If they are consistent, it means that the currently used aggregate meets the current environment and wall thermal performance requirements; if they are inconsistent, it means that the aggregate may need to be replaced to better meet the requirements. Determine whether to execute subsequent steps: Determine whether to continue with subsequent steps related to aggregate switching based on the comparison results. If the comparison results are consistent, there is usually no need to execute subsequent complex aggregate switching judgment and operation steps, and the printing process can continue in the current state; if the comparison results are inconsistent, it may be necessary to further evaluate whether the trigger conditions for aggregate switching are met and other subsequent steps.

[0162] The analysis process of the triggering conditions for aggregate switching is as follows: Step S5a0, determining the characteristics of the corresponding aggregate category according to the screened aggregate category.

[0163] Selected aggregate types: In the previous step (such as SC00), based on the real-time environmental data, the preset wall thermal performance demand quantification value and the set aggregate type selection criteria, the qualified aggregate types are determined, such as lightweight thermal insulation aggregate, composite phase change aggregate, ordinary aggregate, etc. Characteristics of the corresponding aggregate categories: Each type of aggregate has unique physical and chemical properties, which will affect the performance of the aggregate during the printing process and the final performance of the wall. For example, lightweight thermal insulation aggregates usually have the characteristics of low density and good thermal insulation performance; composite phase change aggregates can absorb or release heat according to changes in ambient temperature and have good thermal regulation capabilities; ordinary aggregates have their own characteristics in terms of strength and cost. Characteristics include but are not limited to aggregate density, particle size distribution, thermal conductivity, specific heat capacity, water absorption, etc.

[0164] The general process is as follows: Assume that when printing a thermal temperature-controlled wall, the aggregate category that meets the conditions is screened out in step SC00 as lightweight thermal insulation aggregate. When executing step S5a0, the 3D printing device control system reads from the data storage area that the screening result is lightweight thermal insulation aggregate. Then, the control system queries the database for records about the characteristics of lightweight thermal insulation aggregate, which may come from previous laboratory test data and information provided by aggregate manufacturers. The query shows that the density of the lightweight thermal insulation aggregate is 300kg / m³, the particle size is mainly distributed between 1-3mm, the thermal conductivity is 0.05W / (m・K), the specific heat capacity is 1000J / (kg・K), and the water absorption is low. By determining these characteristics, basic data is provided for the subsequent analysis of the trigger condition setting information for aggregate switching (step S5b0), because different aggregate characteristics correspond to different trigger conditions for switching aggregates under what circumstances.

[0165] Step S5b0, analyzing and determining the trigger condition setting information for aggregate switching according to the mapping relationship between the characteristics of the aggregate category and the trigger condition setting information for aggregate switching.

[0166] Aggregate switching trigger condition setting information: a series of pre-set rules and conditions used to determine when aggregate switching is required during wall printing. This information is closely related to the characteristics of the aggregate category. Aggregates with different characteristics have different trigger switching conditions under different printing environments and wall performance requirements. For example, when the thermal performance requirements of the wall change, and the thermal conductivity and other characteristics of the current aggregate cannot meet the new requirements, aggregate switching may be triggered. Mapping relationship: The corresponding relationship between the characteristics of the aggregate category and the trigger condition setting information of the aggregate switching. Through this mapping relationship, the corresponding reasonable aggregate switching trigger condition can be quickly determined according to the characteristics of the specific aggregate. For example, for aggregates with high thermal conductivity, when the wall's insulation performance requirements are increased to a certain level, the switching is triggered; and for aggregates with strong water absorption, when the ambient humidity reaches a certain threshold and affects the quality of the wall, the switching is triggered.

[0167] Control valve opening and closing degree and pipeline pressure through preset control methods including: Step S601, control the valve currently conveying aggregate to close.

[0168] Valve currently conveying aggregate: During the wall printing process, it is responsible for controlling the valve that conveys the aggregate currently in use from the storage container to the nozzle of the printing equipment. This valve plays the role of regulating the aggregate flow and on-off to ensure that the aggregate is conveyed to the printing position according to the set requirements.

[0169] Control valve closing: Send a closing instruction from the control system of the 3D printing device to the driving device of the valve, causing the valve flap or valve core of the valve to move to the closed position, thereby blocking the aggregate conveying channel and stopping the current aggregate conveyance.

[0170] Step S602, Collect and obtain new aggregate characteristic data.

[0171] The general process is described as follows: Suppose during the wall printing process, it is necessary to switch from ordinary aggregate to lightweight thermal insulation aggregate. After the valve for the currently conveyed ordinary aggregate is closed in step S601, step S602 is started.

[0172] First, the operator or the equipment control system determines that the new aggregate is lightweight thermal insulation aggregate and locates the corresponding storage container. Then, obtain some basic characteristic data of the lightweight thermal insulation aggregate from the product manual attached to the storage container, such as the density is 300 kg / m³, and the particle size is mainly distributed between 1 - 3 mm.

[0173] Next, in order to ensure the accuracy and timeliness of the data, some laboratory tests may be carried out on the new aggregate. For example, use a moisture meter to measure its water content and find that the water content is 2%. Organize and summarize the data obtained through different methods to obtain the complete new aggregate characteristic data. These data will be input into the subsequent analysis model to determine the valve opening degree adjustment plan and predict the conveying resistance.

[0174] Step S603, According to the new aggregate characteristic data, match and obtain the valve opening degree adjustment plan from the preset valve opening degree adjustment plan analysis model, and execute the valve opening degree regulation.

[0175] Preset valve opening degree adjustment plan analysis model: A mathematical model constructed based on a large amount of experimental data, theoretical analysis, and actual engineering experience. This model inputs the characteristic data of the new aggregate and outputs the corresponding valve opening degree adjustment plan. Its purpose is to ensure that the new aggregate can be conveyed to the printing nozzle at an appropriate flow rate and flow velocity to meet the printing process requirements.

[0176] Valve opening degree adjustment plan: Specifically stipulates the opening degree to which the conveying valve needs to be adjusted to adapt to the characteristics of the new aggregate to ensure the smooth conveyance of the aggregate and a stable flow rate. For example, it may be stipulated that the valve needs to be opened to 70% of the opening degree, or the current opening degree needs to be reduced / increased by a certain proportion, etc.

[0177] Execute valve opening degree regulation: According to the obtained valve opening degree adjustment plan, send an instruction from the control system of the 3D printing device to the valve driving device to actually adjust the opening degree of the valve so that the valve reaches the opening degree set by the plan.

[0178] Step S604: According to the new aggregate characteristic data, the predicted conveying resistance is obtained by matching and analyzing the aggregate conveying resistance analysis model.

[0179] Aggregate conveying resistance analysis model: Developed by professionals in mechanical engineering, materials science and other fields using professional software and algorithms. The model is stored in the control system of the 3D printing equipment in the form of a computer program. Its development process is based on a large amount of experimental data, theoretical research and actual engineering cases. For example, the model is constructed using computational fluid dynamics (CFD) software combined with specific aggregate characteristic parameters and pipeline geometric parameters, and has been verified and optimized many times. Predicted conveying resistance: The result output by inputting the new aggregate characteristic data into the aggregate conveying resistance analysis model and running the model. This result is also stored in the data storage area of ​​the 3D printing equipment control system for use in the subsequent step S605.

[0180] Step S605, according to the predicted delivery resistance, real-time adjustment is made to determine the additional power that the delivery pump in the delivery pipeline needs to provide, and the delivery pump in the delivery pipeline is adjusted accordingly.

[0181] Predicted conveying resistance: In step S604, the resistance value that the new aggregate is expected to face when flowing in the conveying pipeline is calculated by the aggregate conveying resistance analysis model based on the new aggregate characteristic data. This value reflects the difficulty of conveying the new aggregate. Real-time adjustment: According to the predicted conveying resistance, the conveying pump power is adaptively changed immediately to ensure that the new aggregate can be smoothly and stably conveyed to the printing nozzle position in the conveying pipeline. Extra power required by the conveying pump: Due to the change in conveying resistance caused by the characteristics of the new aggregate, in order to overcome this part of the increased resistance, the conveying pump needs to output an additional power value to maintain the normal conveying flow rate and flow of the aggregate. The additional power required by the conveying pump is obtained as follows: It is calculated by combining the pre-established calculation formula of the relationship between the conveying pump power and the conveying resistance value with the predicted conveying resistance value. This calculation formula is determined based on the principles of fluid mechanics and the performance parameters of the conveying pump, and is stored in the program algorithm of the 3D printing equipment control system.

[0182] Conveying pump: A device installed in the conveying pipeline that is responsible for providing power for the flow of aggregates in the pipeline. Its power can be adjusted to meet different aggregate conveying requirements. Power adjustment: Change the output power of the conveying pump to match the additional power demand caused by the change in the conveying resistance of the new aggregate, ensuring efficient and stable aggregate conveying.

[0183] Description of the general process: Assume that in step S604, the predicted conveying resistance of the composite phase change aggregate is 1000Pa. After the 3D printing equipment control system reads this conveying resistance value, it calculates the additional power that the conveying pump needs to provide according to the pre-set calculation formula. Assume that according to the formula, in order to overcome the resistance of 1000Pa, the conveying pump needs to provide an additional 500W of power. The control system generates a power adjustment instruction and sends the instruction to the drive device of the conveying pump. After receiving the instruction, the drive device of the conveying pump adjusts the power of the conveying pump so that the output power of the conveying pump increases by 500W on the basis of the original power. After such power adjustment, the conveying pump can provide sufficient power to overcome the resistance of the composite phase change aggregate in the conveying pipeline, ensure that the aggregate is conveyed to the printing nozzle at a stable flow rate and flow rate, and ensure the normal printing process of the thermal temperature control wall.

[0184] Furthermore, the thermal temperature control wall intelligent printing method also includes real-time particle size monitoring and feedback and collaborative optimization steps based on machine vision, as follows: Step S70a, during printing, collect the output image of the new aggregate mixed with the radiation cooling enhanced cement-based material in real time, use the convolutional neural network to extract the multi-dimensional features of the aggregate texture, shape and spatial distribution, and combine the particle size prediction model integrating multiple factors to analyze the particle size distribution and generate a thermal map.

[0185] Specifically, the real-time acquisition of the discharge image involves the following: During the intelligent printing process of the thermal temperature-controlled wall, a high-speed high-definition camera is used to collect real-time images of the discharge of the new aggregate and the radiation-cooled enhanced cement-based material at the outlet of the special nozzle.

[0186] Multi-dimensional feature extraction refers to the use of convolutional neural networks to extract multi-dimensional features from the collected output images. CNN is a deep learning model specifically used to process grid-structured data (such as images). It automatically learns meaningful feature representations from images through components such as convolutional layers, pooling layers, and fully connected layers.

[0187] Texture features: Texture is the spatial distribution pattern of pixel grayscale values ​​in an image, reflecting information such as the roughness and fineness of the aggregate surface. In CNN, convolution kernels of different scales and directions are used to perform convolution operations with the image to extract the texture features of the aggregate. For example, using convolution kernels with different receptive fields can capture texture information of different coarseness and fineness.

[0188] Shape features: Shape features describe the geometric shape of aggregates, such as circularity, aspect ratio, etc. CNN can learn the shape features of aggregates by detecting and analyzing the contours of aggregates in images. Specifically, edge detection algorithms (such as Canny edge detection) are used to first extract the edge contours of aggregates, and then the contour information is input into CNN for further feature extraction and analysis.

[0189] Spatial distribution relationship: The spatial distribution relationship reflects the relative position and arrangement of aggregates in the mixed material. CNN can extract the spatial distribution characteristics of aggregates by learning the distance, angle and other information between aggregates in the image. In order to better capture the spatial distribution relationship, position encoding information is introduced in the input layer of CNN to integrate the position information of each pixel into the feature extraction process.

[0190] The multi-factor particle size prediction model is a regression model based on deep learning. It integrates the multi-dimensional characteristics of aggregates, printing parameters (such as agitator speed, aggregate delivery, etc.), environmental factors (such as temperature, humidity) and other factors to predict the particle size distribution of aggregates.

[0191] Model construction: The model uses a multi-layer perceptron as the basic architecture, takes multi-dimensional features, printing parameters and environmental factors as input, and outputs the prediction results of aggregate particle size distribution after nonlinear transformation of multiple fully connected layers.

[0192] Data training: In order to train the model, a large amount of actual printing data and laboratory simulation data are collected, including output images, corresponding multi-dimensional features, printing parameters, environmental factors, and actual measured aggregate particle size distribution. These data are divided into training sets, validation sets, and test sets according to a certain ratio, and the model is trained and optimized using optimization algorithms such as stochastic gradient descent until the model achieves satisfactory performance on the validation set.

[0193] Particle size distribution analysis and heat map generation involve the following: Input the extracted multi-dimensional features into the particle size prediction model that integrates multiple factors to obtain the prediction results of the aggregate particle size distribution. In order to intuitively display the particle size distribution, a particle size distribution heat map is generated based on the prediction results. The heat map uses the depth of color to represent the distribution density of aggregates of different particle sizes. The darker the color, the denser the distribution of aggregates of that particle size. Through the particle size distribution heat map, you can quickly and intuitively understand the particle size distribution of aggregates in the mixed output, providing an important basis for the formulation of subsequent adjustment strategies.

[0194] Step S70b, compare the particle size distribution obtained by analysis with the preset standard in real time. If it exceeds the range, combine the multi-source data such as printing progress, environment and wall thermal performance, and use fuzzy decision tree and reinforcement learning algorithm to determine the optimal adjustment strategy.

[0195] Among them, the preset standards are determined according to the design requirements, material properties and relevant industry standards of the thermal temperature control wall, covering key indicators such as the proportion of aggregates in different particle size ranges and average particle size.

[0196] The steps to comprehensively determine the optimal strategy include the following: Step S70b1: The fuzzy decision tree gives a recommendation value of 0-1 for each adjustment action. For example, the recommendation for reducing the impeller speed is 0.8, and the recommendation for increasing the aggregate delivery volume is 0.6. When the reinforcement learning is initialized, the fusion coefficient β is set (usually 0.6). The fuzzy decision tree recommends the adjustment of the impeller speed as R n , then the initial selection probability of the corresponding action in reinforcement learning is P n =β×R n , for example R n =0.8, when β=0.6, P n =0.48. Similarly, the initial selection probability of actions such as aggregate delivery is determined. In reinforcement learning, the agent updates the action value according to the Q-learning algorithm based on reward feedback (learning rate 0.1, discount factor 0.9). Positive rewards (such as reducing the speed of the agitator to make the particle size more standard to get 5 points) or negative rewards (such as increasing the aggregate delivery volume to delay the progress to get -3 points) will prompt the update of action value, optimize the probability of action selection, and refine the initial strategy. Step S70b2 ​​is as follows: Regarding aggregate particle size, calculate the sum of the absolute values ​​of the difference between the actual and preset particle size distribution intervals. The smaller the sum, the closer the particle size distribution is to the standard. Printing progress: measured by the ratio of actual to expected printing progress. The closer the ratio is to 1, the more in line with expectations. Environmental conditions: experimentally fit the functional relationship between temperature and humidity (T, H) and material performance change value M. The smaller M is, the less impact the environment has on material performance.

[0197] Regarding the thermal performance of the wall, the absolute value of the difference between the actual and designed thermal conductivity is used for quantification. The smaller the difference, the closer the thermal performance is to the design requirements. The weights of each factor are determined according to the printing requirements. For example, if the thermal performance of the wall is emphasized, the weight is set to 0.4; if the printing progress is emphasized, the weight is set to 0.3; the weight of the aggregate particle size adjustment is 0.2; the weight of the environmental conditions is 0.1, and the total weight is 1. For each strategy combination (different combinations of parameters such as the stirring paddle speed and the aggregate delivery volume), the weights of each factor are calculated and added to the corresponding quantitative evaluation values ​​according to the rules to obtain a comprehensive impact index, and the index values ​​are compared to weigh the adjustment effects.

[0198] ​​​​Step S70b3: Select the strategy combination with the largest comprehensive impact index value as a candidate. Verify the feasibility of the candidate strategy, ensure that the adjustment value of the impeller speed is within the rated speed range of the equipment, the adjustment value of the aggregate delivery volume is within the capacity range of the delivery equipment, the adjustment value of the valve opening and closing degree is within the adjustable angle range of the valve, and the adjustment value of the delivery pipeline pressure is within the pressure range of the pipeline and the delivery pump. If the candidate strategy is feasible, it is determined as the optimal strategy, and the parameter adjustment values ​​or ranges such as the impeller speed (rpm), aggregate delivery volume (kg / min), valve opening and closing degree (degrees), and delivery pipeline pressure (Pascal) are clearly given to guide the printing work. Step S70c, based on the optimal adjustment strategy, combined with the multi-parameter coupling model, coordinately adjust the impeller speed, aggregate delivery volume, valve opening and closing degree and pipeline pressure.

[0199] The disclosure content of step S70c is as follows: Step S70c1, using deep learning to build a multi-parameter coupling dynamic model, collect printing parameters such as agitator speed, aggregate delivery, valve opening and closing, pipeline pressure, as well as wall thermal performance, structural strength, material mixing effect and other data for training. The model can accurately grasp the complex nonlinear coupling relationship between various parameters. Compared with traditional models, it can capture high-order coupling effects and achieve more accurate multi-parameter coordinated adjustment prediction.

[0200] Step S70c2, after receiving the optimal adjustment strategy instruction, input the target parameter value (such as the expected impeller speed, aggregate delivery, etc.) into the multi-parameter coupling model. The model accurately calculates the specific values ​​of the coordinated adjustment of each parameter based on the learned coupling relationship. For example, in order to improve the thermal performance of the wall, the model may give a solution to reduce the impeller speed, increase the aggregate delivery, and synchronously adjust the valve opening and closing degree and pipeline pressure, and the adjustment amount is calculated based on the coupling relationship, not based on experience.

[0201] Step S70c3: During the adjustment process, high-precision sensors are used to monitor key parameters such as the impeller speed, aggregate delivery volume, valve opening, pipeline pressure, wall performance, and material mixing status in real time. The sensor data is transmitted to the data processing system in real time and compared with the model prediction results. Once the actual parameters deviate from the predicted values, the system will feed back the deviation information to the model. The model uses an online learning algorithm to fine-tune its own parameters and structure based on the new data, recalculate a more accurate collaborative adjustment plan, and achieve dynamic optimization. Step S70c4, the multi-parameter coupling model combines real-time and historical monitoring data, and uses machine learning prediction algorithms to predict future printing conditions. For example, based on the current changes in ambient temperature and humidity and the printing progress, the material performance changes and parameter adjustment requirements are predicted. If it is predicted that the increase in ambient temperature will increase the cohesion of aggregates, affecting the mixing effect and particle size distribution, the model will calculate in advance the values ​​of increasing the speed of the stirring paddle, adjusting the valve opening and closing, and the pipeline pressure, to take precautions in advance and ensure stable printing and reliable wall quality. The embodiment of the present application also provides a thermal temperature regulating wall, which is a layered structure, including an outer layer, a middle layer and an inner layer from the outside to the inside; each layer performs different functions to achieve good thermal regulation performance.

[0202] Outer layer: It is a mixture of radiation cooling enhanced cement-based materials and polystyrene particle aggregates. Among them, the radiation cooling enhanced cement-based material plays a key role. It is composed of white cement and radiation cooling materials. The radiation cooling materials can be selected from one or more of barium sulfate, titanium dioxide, and aluminum oxide. White cement itself has a high solar radiation reflectivity, usually between 0.7-0.8, which can reflect a large amount of solar radiation and reduce the external heat entering the room. The addition of radiation cooling materials such as barium sulfate, titanium dioxide, and aluminum oxide significantly improves the emissivity of the material, enabling it to dissipate the heat absorbed by the wall to the external environment through infrared radiation when there is no solar radiation, thereby enhancing the radiation cooling effect. Polystyrene particle aggregates, with their low thermal conductivity, effectively reduce heat conduction, prevent indoor temperature loss and external temperature from entering, act as an insulation layer in the wall, and keep the indoor temperature stable. For example, in tropical areas, the thickness of the outer layer is 10 cm. This structure can effectively block high temperatures from the outside and reduce the need for air conditioning.

[0203] Middle layer: It is composed of a mixture of radiation cooling enhanced cement-based materials and 35℃ phase change aggregates. Radiant cooling enhanced cement-based materials provide structural strength and a certain radiation cooling capacity for the wall. 35℃ phase change aggregates have special heat regulation functions. When the external temperature rises and approaches or exceeds 35℃, the phase change material will absorb heat and undergo a phase change from solid to liquid, storing heat, slowing down the transfer of heat to the inner layer or indoors, and avoiding rapid penetration of external high temperatures. In hot summer weather, the middle layer can effectively alleviate heat accumulation, reduce the direct conduction of high-temperature air to the inner layer, and help the wall maintain a relatively stable temperature during the day.

[0204] Inner layer: It is a mixture of radiation cooling enhanced cement-based materials and 25℃ phase change aggregates. Radiant cooling enhanced cement-based materials protect the wall structure while participating in certain thermal regulation. The 25℃ phase change aggregate is related to the most comfortable temperature for the human body. When the wall temperature drops and approaches or is lower than 25℃, the phase change material changes from liquid to solid, releasing the stored heat, helping the wall to maintain warmth and slowing down the impact of cold air on the room. In winter, the inner layer can effectively prevent the indoor temperature from being too low, especially at night or in cold seasons, ensuring that the room remains within a comfortable temperature range.

[0205] The embodiments of this specific implementation method are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A thermal temperature control wall intelligent printing method, characterized in that: include: The 3D printing device is started, and according to the preset printing path, the mixed material of the radiation cooling enhanced cement-based material and the aggregate is extruded and printed layer by layer through a special nozzle according to the preliminarily set printing parameters of each layer, wherein the special nozzle is a printing nozzle with two independent inlets, one of which is connected to the radiation cooling enhanced cement-based material, and the other is connected to the aggregate, and a stirring paddle is arranged inside the special nozzle; During the printing process, a sensor matrix is ​​used to collect multi-source data, including environmental data, printing progress data, and wall thermal performance related data; Collect historical climate data of the printing area, and perform data preprocessing on the historical climate data and the collected multi-source data; Input the multi-source data and historical climate data that have completed data preprocessing into the pre-built multi-objective optimization model, and output the optimal parameter combination for printing each layer of the wall, and replace the initially set printing parameters of each layer according to the optimal parameter combination for printing each layer of the wall; During the printing process, based on the multi-source data collected in real time and pre-processed, it is determined whether the pre-set trigger conditions for aggregate switching are met; If yes, an aggregate switching instruction is issued to control the aggregate channel of the special nozzle to switch. During the aggregate switching process, the valve opening and closing degree and the conveying pipeline pressure are regulated by a preset control method to allow new aggregate to enter the printing nozzle. During the aggregate switching process, an adaptive fuzzy PID algorithm is used to synchronously adjust the stirring blade speed and the aggregate conveying amount, so that the aggregate particle size distribution after the new aggregate and the radiation cooling enhanced cement-based material are mixed is within the preset aggregate particle size distribution standard range; If not, keep the original setting.

2. The intelligent printing method for thermal temperature control wall according to claim 1, characterized in that: It also includes the following steps after preprocessing the historical climate data and the collected multi-source data: The multi-source data and historical climate data that have completed data preprocessing are unified in data format and standardized to form various data vectors; Input various data vectors into the preset fuzzy membership function, calculate the membership of various data vectors in different fuzzy sets, and perform weighted summation of multiple fuzzy membership functions to obtain the comprehensive membership; Analyze and calculate whether the comprehensive membership degree calculated exceeds the trigger threshold, where the calculation formula of the trigger threshold is as follows: ,in, is the trigger threshold, is the adjustment coefficient, which is used to adjust the weight of the comprehensive membership mean when calculating the trigger threshold. is the mean of the comprehensive membership, is the adjustment coefficient, which is used to adjust the weight of the comprehensive membership standard deviation when calculating the trigger threshold. is the standard deviation of comprehensive membership; If no, continue with the next steps; If yes, then based on the multi-source data that has completed data preprocessing and historical climate data, key state features are extracted, and principal component analysis is used to reduce the dimension to obtain the principal component feature vector representing the environment and equipment status, where the key state features include the environment change rate, printing progress deviation, and wall thermal performance deviation; Using a fuzzy logic system, based on the principal component eigenvector and preset rules, the adjustment coefficient of each performance target weight is calculated to update the weight of the multi-objective optimization model, where the performance targets include thermal regulation performance, structural stability, and material cost; Using the trained deep Q network, different principal component feature vectors are input to obtain corresponding printing parameter combinations, and this operation is continued until a preset number of printing parameter combinations under different states are generated; According to the updated multi-objective optimization model weights, the performance of each printing parameter combination generated is evaluated to obtain the scores of each performance target, and the scores of each performance target are weighted and summed according to the adjusted weights to obtain the comprehensive score of each parameter combination; The parameter combinations of all groups are sorted according to the comprehensive scores, and the parameter combination with the highest comprehensive score is selected as the optimal parameter combination for printing each layer of the wall. The initially set printing parameters of each layer are replaced according to the optimal parameter combination for printing each layer of the wall.

3. The intelligent printing method for thermal temperature control wall according to claim 2, characterized in that: The steps after obtaining the comprehensive score of each parameter combination are also included, which are as follows: Analyze whether the relative deviation between the comprehensive membership and the trigger threshold is always within the preset deviation range within a preset number of continuous monitoring cycles; If yes, continue with the next steps; If not, then select the parameter combination with the highest comprehensive score ranking as the candidate parameter combination from all the generated parameter combinations; Based on the preliminary candidate parameter combinations for printing each layer of the wall, determine the basic parameters related to wall printing, run a random algorithm to generate random parameters within the floating range of basic parameters related to different types of wall printing, and integrate the random parameters and input them into the multi-physics field coupling digital twin model; For each candidate parameter combination for printing each layer of the wall, the digital twin model is run a preset number of times in each set scenario. During each run, the newly generated random parameters are called, and the wall performance-related data output by each simulation is recorded; According to the wall performance related data output by simulation, the preset performance index evaluation scoring method is adopted to obtain the performance evaluation scores of different categories; Based on the obtained performance evaluation scores of different categories and the weight allocation schemes matched under different simulation conditions, the average comprehensive scores of different candidate parameter combinations under all simulation conditions are calculated; Calculate the coefficient of variation of the comprehensive score of each candidate parameter combination under different simulation conditions; The parameter combinations whose comprehensive scores under all simulation conditions are ranked before the preset positions and whose coefficient of variation is less than the preset value are selected as the optimal parameter combinations for printing each layer of the wall, and the initially set printing parameters for each layer are replaced according to the optimal parameter combinations for printing each layer of the wall.

4. The intelligent printing method for thermal temperature control wall according to claim 3, characterized in that: Determining whether the preset triggering conditions for aggregate switching are met includes: Extract the key features of environment, printing progress, and wall thermal performance, and use them as matrix elements to construct a correlation matrix , where environmental data has m key features, printing progress has n key features, and wall thermal performance has p key features; At each monitoring moment, the state vector is composed according to the key characteristic states ,in, , is the status value of the i-th key feature. When the feature meets the trigger condition, the value is 1, and when it does not meet the trigger condition, the value is 0; The formula for multi-condition association score is as follows: ； in, is the multi-condition association score, is the state vector The transpose of , which converts a row vector into a column vector for matrix multiplication, is the incidence matrix The element in the i-th row and j-th column in represents the strength of association between the i-th feature and the j-th feature; Based on multiple condition correlation scores The comparison result with the associated score threshold determines whether the preset trigger condition for aggregate switching is met.

5. The intelligent printing method for thermal temperature control wall according to claim 4, characterized in that: Determining whether the preset triggering conditions for aggregate switching are met also includes building a correlation matrix The previous steps are as follows: Analyze whether the relative deviation between the comprehensive membership and the trigger threshold is always within the preset deviation range within a preset number of continuous monitoring cycles; If yes, continue to execute the original steps; If not, then according to the multi-source data that has completed data preprocessing, a preset cluster analysis method is used to determine the parameter combination category, wherein the parameter combination category includes a thermal insulation priority type wall and a structure priority type wall; According to the mapping relationship between the parameter combination category and the key feature, the key feature matching the determined parameter combination category is determined, and the determined parameter combination category and the key feature matching therewith are input into the trigger condition judgment model constructed based on the decision tree algorithm, and the trigger condition is output as the pre-set trigger condition for aggregate switching; Based on the multi-source data collected in real time and pre-processed, it is determined whether the pre-set triggering conditions for aggregate switching are met.

6. The intelligent printing method for thermal temperature control wall according to claim 1, characterized in that: The method also includes steps before determining whether a preset trigger condition for aggregate switching is met, which are as follows: Determine the thermal performance requirements of the wall according to the different functions of the building, and determine the quantitative indicators of the thermal performance requirements of the wall based on the preset energy-saving standards; Preprocess the real-time environmental data and the determined quantitative value of the wall thermal performance requirement; Match the collected real-time environmental data, the preset quantitative value of the wall thermal performance requirements, and the set aggregate type selection criteria to screen out the aggregate types that meet the conditions; Whether to execute the subsequent steps is determined based on the comparison results between the screened aggregate category and the currently used aggregate category.

7. The intelligent printing method for thermal temperature control wall according to claim 6, characterized in that: The analysis process of the triggering conditions for aggregate switching is as follows: According to the selected aggregate categories, determine the characteristics of the corresponding aggregate categories; According to the mapping relationship between the characteristics of the aggregate category and the trigger condition setting information of the aggregate switching, the trigger condition setting information of the aggregate switching is analyzed and determined.

8. The method for intelligent printing of thermal temperature-controlled walls according to claim 1, characterized in that: The valve opening and closing degree and pipeline pressure are regulated by the preset control methods, including: Control the valve that is currently conveying aggregate to close; Collect and obtain new aggregate property data; According to the new aggregate characteristic data, the valve opening and closing adjustment scheme is matched and obtained from the preset valve opening and closing adjustment scheme analysis model, and the valve opening and closing adjustment is performed; According to the new aggregate characteristic data, the predicted conveying resistance is obtained by matching and analyzing the aggregate conveying resistance analysis model; According to the predicted conveying resistance, real-time adjustment is made to determine the additional power that the conveying pump located in the conveying pipeline needs to provide, and the conveying pump located in the conveying pipeline is adjusted accordingly.

9. A thermal temperature control wall, characterized in that: The method of any one of claims 1 to 8 is used for printing, specifically as follows: The thermal temperature control wall is a layered structure, which consists of an outer layer, a middle layer and an inner layer from the outside to the inside; the outer layer is a mixture of radiation cooling enhanced cement-based materials and polystyrene particle aggregates, the middle layer is a mixture of radiation cooling enhanced cement-based materials and 35°C phase change aggregates, and the inner layer is a mixture of radiation cooling enhanced cement-based materials and 25°C phase change aggregates.

10. The thermal temperature control wall according to claim 9, characterized in that: The radiation cooling enhanced cement-based material consists of white cement and radiation cooling material, and the radiation cooling material is one or more of barium sulfate, titanium dioxide and aluminum oxide.

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