CN118673572B - A control method and control system for a low-carbon energy-saving building system - Google Patents

Abstract

The invention discloses a control method and a control system of a low-carbon energy-saving building system, in particular to the technical field of building energy efficiency management, and the thermal flow data of a building group is accurately acquired and analyzed through a thermal imaging technology and a computational fluid dynamics model, so that a thermal anomaly area is effectively identified and marked; by monitoring the light and shadow mode changes of the building surface and the surrounding environment and the contribution of the light and shadow mode changes to micro-climate regulation, and combining with the synchronicity evaluation of the building structure to the environmental heat fluctuation reaction, the method is not only beneficial to optimizing the sunshade system and other energy-saving designs of the building, but also can foresee and avoid possible heat efficiency problems at the initial stage of the design, and greatly reduces the additional cooling requirement and energy consumption caused by heat tunnel effect; the angle and the motion strategy of the sun shield are optimized through intelligent design adjustment steps and genetic algorithm, so that the whole building system can dynamically respond to external environment changes, and the internal environment is adjusted in real time to achieve the optimal energy efficiency state.

Description

A control method and control system for a low-carbon energy-saving building system

Technical Field

The present invention relates to the technical field of building energy efficiency management, and more specifically, to a control method and a control system for a low-carbon energy-saving building system.

Background Art

In a high-density building cluster environment, due to the narrow space between buildings, complex airflow and heat flow phenomena are usually formed, especially in the narrow passages between buildings, which easily leads to hot air stagnation, thus forming the so-called "heat tunnel effect". This heat tunnel effect usually occurs in an environment where buildings are closely arranged, wind speed is low and sunlight is strong, which will significantly increase the temperature of these narrow passage areas. As a result, it is difficult for hot air to be effectively dissipated, which increases the cooling demand inside the building, directly affecting the efficiency of the HVAC (heating, ventilation and air conditioning) system of each building in the building cluster, resulting in a significant increase in overall energy consumption.

Existing building control systems often optimize management for a single building, lacking comprehensive consideration of the heat tunnel effect between building groups. Without effective countermeasures, the heat tunnel effect will not only increase the cooling load inside the building, but also reduce the operating efficiency of the HVAC system, leading to energy waste and increased operating costs. In addition, due to the complex interaction of light and shadow between the building surface and the surrounding environment, traditional energy-saving measures and design adjustment strategies have limited effects in dealing with such environmental problems, and cannot fully solve the problems of uneven heat distribution and increased energy consumption in building groups.

In order to solve the above problems, a technical solution is now provided.

Summary of the invention

In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a control method and a control system of a low-carbon energy-saving building system to solve the problems raised in the above-mentioned background technology.

To achieve the above object, the present invention provides the following technical solutions:

A control method for a low-carbon energy-saving building system comprises the following steps:

S1: Obtain thermal flow data of the building complex; perform preliminary analysis using thermal imaging technology and computational fluid dynamics models to mark thermal anomaly areas;

S2: For thermal anomaly areas: By monitoring the changes in light and shadow patterns generated by the interaction between the building surface and the surrounding environment, quantitatively evaluate the contribution of light and shadow pattern changes to microclimate regulation; by analyzing the synchronization of the building structure's response to environmental thermal fluctuations, quantitatively evaluate the performance of the building structure in dynamic thermal stability;

S3: Based on the contribution of light and shadow pattern changes to microclimate regulation and the performance of the building structure in dynamic thermal stability, determine whether to enter the design adjustment step;

S4: The design adjustment step is to collect the window positions and material reflectivity of the thermal anomaly area, and evaluate the impact of the window positions and material reflectivity on the overall thermal performance by generating a thermal energy conduction model;

S5: Based on the design adjustment steps, the weight of the dynamic shading system is adjusted, and the angle and movement strategy of the shading board are optimized using genetic algorithm.

In a preferred embodiment, the thermal flow data of the building complex is obtained; a preliminary analysis is performed using thermal imaging technology and a computational fluid dynamics model to mark thermal anomaly areas, specifically including:

S101: Deploy a group of thermal imaging sensors to collect thermal imaging data of the building complex;

S102: Preliminary analysis of thermal imaging data using computational fluid dynamics models;

S103: Based on the physical consistency principle and environmental matching algorithm, multi-dimensional feature comparison is performed to automatically identify and mark thermal anomaly areas that may cause thermal tunneling effects.

In a preferred embodiment, by monitoring the changes in light and shadow patterns generated by the interaction between the building surface and the surrounding environment, the contribution of the light and shadow pattern changes to microclimate regulation is quantitatively evaluated, specifically:

Acquire light and shadow image data from buildings and their surroundings;

Extract light and shadow features, including shadow length, width, edge sharpness, light intensity and its changing rate;

Calculate the entropy value of each light and shadow feature; by calculating the entropy value of each light and shadow feature, allocate the weight in inverse proportion, and calculate the weight of the light and shadow feature;

Define fuzzy sets for each light and shadow feature, and each fuzzy set has a corresponding membership function;

Input the actually measured light and shadow feature values into the corresponding membership function to obtain the fuzzy value of each light and shadow feature;

Apply fuzzy logic reasoning to infer the fuzzy output of the building dynamic shadow factor based on fuzzy rules and input fuzzy values;

Considering the output weights and membership of each fuzzy set, the weighted average method is used to defuzzify;

The dynamic shadow factor of the building is calculated with the following calculation logic: multiply the membership value of each output by the specific numerical value represented by the output, add up all these multiplication results, divide the accumulated value by the accumulated sum of all membership values, and get the dynamic shadow factor of the building.

In a preferred embodiment, the performance of the building structure in dynamic thermal stability is quantitatively evaluated by analyzing the synchronization of the building structure's response to environmental thermal fluctuations, specifically:

Calculate the ambient temperature change between two consecutive measurements and mark it as the ambient temperature change rate;

Calculate the change in building temperature during the same time interval and label it as the building temperature response rate;

Linear correlation was calculated using Pearson's correlation coefficient;

The dynamic thermal stability adjustment coefficient is set, and the product of the dynamic thermal stability adjustment coefficient and the linear correlation is marked as the dynamic thermal stability index.

In a preferred embodiment, based on the contribution of light and shadow pattern changes to microclimate regulation and the performance of the building structure in dynamic thermal stability, it is determined whether to enter the design adjustment step, specifically:

Set the threshold of dynamic thermal stability index and building dynamic shadow factor; compare the dynamic thermal stability index with the threshold of dynamic thermal stability index, and compare the building dynamic shadow factor with the threshold of building dynamic shadow factor:

When the dynamic thermal stability index is greater than the dynamic thermal stability index threshold, and the building dynamic shadow factor is greater than the building dynamic shadow factor threshold, it is determined to enter the design adjustment step; otherwise, it is determined not to enter the design adjustment step.

In a preferred embodiment, the window positions and material reflectances of the thermal anomaly area are collected, and the influence of the window positions and material reflectances on the overall thermal efficiency is evaluated by generating a thermal energy conduction model, specifically including:

S401: Collect window positions and material reflectivity in thermal anomaly areas: digitize the information of window positions and material reflectivity to generate a spatial thermal feature map; use data fusion technology to synthesize a unified three-dimensional thermal feature model;

S402: Generate a heat conduction model using a multivariate regression analysis algorithm to evaluate the impact of window position and material reflectivity on the overall thermal performance of the building: Use a multivariate regression analysis algorithm to generate a heat conduction model using window position and material reflectivity as input variables; use historical data and simulation data to train the model, and optimize the regression coefficients through cross-validation and error analysis;

S403: Verify the matching degree between data from different sources and the heat conduction model through consistency check algorithm: Based on the algorithm of residual analysis and chi-square test, verify the consistency between the actual measured data and the predicted value of the heat conduction model; residual analysis is used to identify systematic errors, and chi-square test is used to evaluate the goodness of fit of the model.

In a preferred embodiment, based on the design adjustment step, the weight of the dynamic shading system is adjusted, and the angle and movement strategy of the visor are optimized using a genetic algorithm, specifically:

Real-time collection of environmental parameters, including ambient light intensity, solar radiation angle, and indoor temperature;

The modified entropy weight method is used to redistribute the weights of various environmental parameters in the dynamic shading system;

Generate an initial population for visor angles and movement strategies, with each individual representing a specific angle and movement strategy combination;

Set the initial population size; calculate the fitness value of each individual;

Use the roulette wheel selection method to select individuals with higher fitness to enter the next generation of reproduction;

Perform single-point or multi-point crossover on the selected individuals to generate new individual combinations;

Mutate some of the newly generated individuals and randomly adjust the angles of some visors;

Through multiple iterations, the genetic algorithm gradually optimizes the angle and movement strategy of the visor to maximize the value of the fitness function; when the fitness change of the population tends to be stable, the iteration is stopped and the final optimized visor angle combination is output.

On the other hand, the present invention provides a control system for a low-carbon energy-saving building system, including an abnormal area marking module, a light and shadow pattern evaluation module, a dynamic stability evaluation module, a design adjustment determination module, a design adjustment implementation module, and a sunshade system optimization module;

Abnormal area marking module: obtain thermal flow data of the building complex; use thermal imaging technology and computational fluid dynamics model to conduct preliminary analysis and mark thermal abnormal areas;

Light and shadow pattern assessment module: for thermal anomaly areas, the light and shadow pattern changes generated by the interaction between the building surface and the surrounding environment are monitored, and the contribution of light and shadow pattern changes to microclimate regulation is quantitatively assessed;

Dynamic stability assessment module: For thermal anomaly areas, the module analyzes the synchronization of the building structure's response to environmental thermal fluctuations to quantitatively assess the performance of the building structure in dynamic thermal stability;

Design adjustment judgment module: based on the contribution of light and shadow pattern changes to microclimate regulation and the performance of the building structure in dynamic thermal stability, it determines whether to enter the design adjustment step;

Design adjustment implementation module: The design adjustment step is to collect the window positions and material reflectivity of the thermal anomaly area, and evaluate the impact of the window positions and material reflectivity on the overall thermal efficiency by generating a thermal energy conduction model;

Sunshade system optimization module: Based on the design adjustment steps, the weight of the dynamic sunshade system is adjusted, and the angle and movement strategy of the sunshade are optimized using genetic algorithms.

The technical effects and advantages of the control method and control system of a low-carbon energy-saving building system of the present invention are as follows:

1. Accurately acquire and analyze the heat flow data of the building complex through thermal imaging technology and computational fluid dynamics models, effectively identify and mark thermal anomaly areas, so that subsequent control strategies can solve specific problem areas in a targeted manner, rather than making one-size-fits-all adjustments to the entire building complex, thereby improving the accuracy and effectiveness of control. By monitoring the changes in light and shadow patterns on the building surface and the surrounding environment and their contribution to microclimate regulation, combined with the synchronous evaluation of the building structure's response to environmental thermal fluctuations, it is possible to deeply understand and quantify the dynamic interaction between the building complex and its surrounding environment. This not only helps to optimize the building's shading system and other energy-saving designs, but also can foresee and avoid possible thermal efficiency problems in the early stages of design, greatly reducing the additional cooling demand and energy consumption caused by the heat tunnel effect.

2. Through intelligent design adjustment steps and genetic algorithms to optimize the angle and movement strategy of the sunshade, the entire building system can dynamically respond to changes in the external environment and adjust the internal environment in real time to achieve the best energy efficiency. This not only reduces the burden on the HVAC system and reduces energy consumption, but also improves the comfort of living and working spaces, creates a healthier and more livable environment for building users, and achieves efficient and sustainable management of high-density building clusters.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a control method for a low-carbon energy-saving building system according to the present invention;

FIG. 2 is a schematic diagram of the structure of a control system of a low-carbon energy-saving building system according to the present invention.

DETAILED DESCRIPTION

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

Example 1

FIG1 shows a control method of a low-carbon energy-saving building system according to the present invention, which comprises the following steps:

S1: Obtain thermal flow data of the building complex; perform preliminary analysis using thermal imaging technology and computational fluid dynamics models to mark thermal anomaly areas.

S2: For thermal anomaly areas: By monitoring the changes in light and shadow patterns generated by the interaction between the building surface and the surrounding environment, quantitatively evaluate the contribution of light and shadow pattern changes to microclimate regulation; by analyzing the synchronization of the building structure's response to environmental thermal fluctuations, quantitatively evaluate the performance of the building structure in dynamic thermal stability.

S3: Based on the contribution of changes in light and shadow patterns to microclimate regulation and the performance of the building structure in dynamic thermal stability, determine whether to enter the design adjustment step.

S4: The design adjustment step is to collect the window positions and material reflectivity of the thermal anomaly area, and evaluate the impact of the window positions and material reflectivity on the overall thermal performance by generating a thermal energy conduction model.

S5: Based on the design adjustment steps, the weight of the dynamic shading system is adjusted, and the angle and movement strategy of the shading board are optimized using genetic algorithm.

Obtain thermal flow data for the complex; perform preliminary analysis using thermal imaging technology and computational fluid dynamics models to mark thermal anomaly areas, including:

S101: Deploy a group of thermal imaging sensors to collect thermal imaging data of buildings:

Choose a thermal imaging sensor with high resolution and wide dynamic range, such as a FLIR system with infrared capabilities.

Sensors are deployed at strategic locations around the complex to cover heat flow in all directions.

Configuration includes the angle, height, and spacing of sensors to maximize coverage area and data accuracy.

Set the data collection frequency and resolution, such as collecting data once every minute, to ensure that dynamically changing thermal images are obtained.

Data preprocessing includes noise filtering and signal enhancement, using digital filtering technology to reduce environmental interference.

Test the sensor network under experimental conditions to verify coverage and data accuracy.

S102: Preliminary analysis of thermal imaging data using computational fluid dynamics models:

ANSYS Fluent computational fluid dynamics software was selected. ANSYS Fluent is widely used in dealing with complex boundary conditions and multi-physics field problems.

Configure the model's boundary conditions, such as building exterior surface temperature, surrounding air temperature, and wind speed.

Adjust the mesh density to ensure detailed simulation of thermal flow. Use adaptive mesh refinement techniques to enhance the model's ability to resolve areas of high temperature gradients.

By comparing with the actual collected thermal imaging data, the model is calibrated and the simulation parameters are adjusted until they are consistent with the actual observations.

S103: Based on the principle of physical consistency and the environment matching algorithm, multi-dimensional feature comparison is performed to automatically identify and mark thermal anomaly areas that may cause thermal tunneling effects:

Use a convolutional neural network (CNN) to identify unusual patterns in thermal images that may represent heat accumulation due to narrow spaces between buildings, known as thermal tunneling.

The algorithm input includes image data obtained from the thermal imaging sensor, which has undergone preprocessing such as normalization, edge detection, and feature extraction.

Select key features in thermal images, such as the shape, size, boundary definition, and temperature difference of hot spots relative to the surrounding environment.

The algorithm is trained and validated using a labeled training dataset that contains thermal images with known thermal tunneling effects and normal thermal images.

The algorithm compares the newly captured thermal images with the images in the training set and identifies thermal anomaly areas with similar characteristics.

Once thermal anomalies matching the thermal tunneling effect are identified, these thermal anomaly areas will be automatically marked on the software interface and corresponding reports and warnings will be generated.

By monitoring the changes in light and shadow patterns generated by the interaction between the building surface and the surrounding environment, the contribution of light and shadow pattern changes to microclimate regulation is quantitatively evaluated, specifically:

Light and shadow image data are obtained from buildings and their surroundings through infrared thermal imaging sensors and visible light sensors.

Among them, infrared thermal imaging sensors and visible light sensors are evenly deployed around the top and sides of the building to ensure all-round capture of sunlight and shadow changes.

The light and shadow image data can be collected every 15 minutes to capture the dynamic changes of light and shadow. After the data is collected, a high-pass filter is applied to remove environmental noise, and the data is normalized to adjust the data range to between 0 and 1 for subsequent processing.

Extract light and shadow features, which include shadow length, width, edge sharpness, light intensity and its changing rate.

The extraction formula is: ;in, Indicates a point in time The next The average value of the light and shadow characteristics, Indicates The measurement of The value of the light and shadow characteristics, is the number of measurements, is the number corresponding to each measurement.

Calculate the entropy value of each light and shadow feature, the expression is: ;in, For the The entropy value of the light and shadow features, Among all samples, Sample pair The normalized value of a light and shadow feature (obtained by dividing the light and shadow feature value by the sum of all sample values of the light and shadow feature), , is the total number of samples.

By calculating the entropy value of each light and shadow feature, that is, the uncertainty of its distribution, the weight can be allocated in inverse proportion, so that light and shadow features with greater information content and higher variability receive higher weights.

Calculate the The weight of the light and shadow features is expressed as: ;in, For the The weight of each light and shadow feature.

Define fuzzy sets for each lighting characteristic, such as "low", "medium", and "high".

Each fuzzy set has a corresponding membership function, such as triangular or trapezoidal functions, which are adjusted according to the actual environment and building characteristics.

Based on the input from architectural and environmental experts, fuzzy rules are constructed. For example, “if the shadow coverage is high and the rate of change is fast, the building dynamic shadow factor is high”.

These rules combine features such as shadow coverage, length, and rate of change, and are adjusted using pre-computed weights of light and shadow features.

The actually measured light and shadow feature values are input into the corresponding membership function to obtain the fuzzy value of each light and shadow feature.

Fuzzy logic reasoning is applied to infer the fuzzy output of the building dynamic shadow factor based on fuzzy rules and input fuzzy values.

Considering the output weights and membership of each fuzzy set, the weighted average method is used for defuzzification.

The dynamic shadow factor of the building is calculated with the following calculation logic: multiply the membership value of each output by the specific numerical value represented by the output, add up all these multiplication results, divide the accumulated value by the accumulated sum of all membership values, and get the dynamic shadow factor of the building.

The larger the dynamic shadow factor of the building, the more significant the contribution of changes in light and shadow patterns to microclimate regulation, which indicates that the shadow effect of the building plays a more effective role in regulating the surrounding temperature, reducing the heat island effect and improving environmental comfort.

By analyzing the synchronization of the building structure's response to environmental thermal fluctuations, the performance of the building structure in dynamic thermal stability is quantitatively evaluated, specifically:

Deploy multiple point temperature sensors and humidity sensors at key locations of the building structure (such as exterior walls, roofs, infrastructure, etc.) to continuously monitor the ambient temperature and the temperature response inside the building.

The change in ambient temperature between two consecutive measurements is calculated and marked as the ambient temperature change rate.

The change in building temperature during the same time interval is calculated and labeled as the building temperature response rate.

Statistical methods are used to identify and handle outlier data points, such as using the IQR (interquartile range) method. A sliding window technique is used to calculate the average ambient temperature change and building response within each window.

The linear correlation is calculated using the Pearson correlation coefficient, which is expressed as: ;in, is the linear correlation, It is in The rate of change of ambient temperature during the measurement, yes The average value of It is The building temperature response rate at the time of measurement, yes The average value of is the number corresponding to the measurement.

Using the Pearson correlation coefficient to analyze the synchronization of the building structure's response to environmental thermal fluctuations can intuitively quantify the linear relationship between the two. By calculating the linear correlation, we can determine whether the building's response to external temperature fluctuations is timely and effective, which is crucial for evaluating the dynamic thermal stability of the building. If the linear correlation is close to 1, it means that the building responds quickly to external temperature changes and exhibits good thermal stability; conversely, if the linear correlation is close to 0 or a negative value, it indicates that the building has deficiencies in thermal management and may need further design optimization.

Set the dynamic thermal stability adjustment coefficient. The dynamic thermal stability adjustment coefficient is a coefficient adjusted according to building materials and design. It is used to adjust the relationship between synchronization and actual thermal stability and is usually determined through experiments or historical data.

The dynamic thermal stability index is calculated as follows: ;in, is the dynamic thermal stability index; is the dynamic thermal stability adjustment factor.

The larger the dynamic thermal stability index, the better the building structure performs in dynamic thermal stability, which means that the building can respond more effectively and quickly to fluctuations in ambient temperature and maintain a relatively stable indoor temperature, thereby improving overall thermal comfort and energy efficiency. A high dynamic thermal stability index indicates that the building structure has a higher thermal inertia and responsiveness, and can better maintain the stability of the internal environment when the external temperature changes.

Based on the contribution of light and shadow pattern changes to microclimate regulation and the performance of the building structure in dynamic thermal stability, it is determined whether to enter the design adjustment step, specifically:

Set the threshold for the dynamic thermal stability index and the building dynamic shadow factor.

The threshold of the dynamic thermal stability index is set based on historical data and experimental test results, and the minimum dynamic thermal stability index that can ensure that the building can maintain stable indoor temperature when the ambient temperature fluctuates greatly is selected. Through statistical analysis of the thermal response data of multiple building structures, it is determined that the threshold should be able to effectively distinguish between good and poor building thermal stability. Usually, the minimum dynamic thermal stability index that keeps the fluctuation range of the internal temperature of the building within the human comfort range is selected.

The threshold of building dynamic shadow factor is set based on actual measurement and simulation data of the microclimate impact around the building. Through quantitative analysis of the building thermal effect under different light and shadow modes, it is determined that the threshold should be able to distinguish the shadow mode that contributes significantly to microclimate regulation. Usually, the minimum building dynamic shadow factor that achieves the expected effect of reducing local temperature and energy consumption is selected as the threshold.

Compare the Dynamic Thermal Stability Index to the Dynamic Thermal Stability Index Threshold and the Building Dynamic Shadow Factor to the Building Dynamic Shadow Factor Threshold:

When the dynamic thermal stability index is greater than the dynamic thermal stability index threshold, and the building dynamic shadow factor is greater than the building dynamic shadow factor threshold, it is determined to enter the design adjustment step; otherwise, it is determined not to enter the design adjustment step.

The decision on whether to enter the design adjustment step is made through the joint judgment of two key indicators, the dynamic thermal stability index and the dynamic shadow factor of the building, which reflects the comprehensive performance of the building in thermal management and microclimate regulation. The rationality of this judgment logic is reflected in its comprehensive consideration of the responsiveness of the internal and external thermal environment of the building, avoiding the one-sidedness that may be caused by a single indicator. The dynamic thermal stability index ensures that the building can effectively cope with ambient temperature fluctuations, while the dynamic shadow factor of the building evaluates its contribution to microclimate regulation. The combination of the two ensures that design adjustments are only made when there is a real need for improvement, avoiding unnecessary design iterations and saving resources and time. In addition, integrating the two different performance indicators of thermal stability and light and shadow regulation into a comprehensive design evaluation standard makes the architectural design more adaptable and accurate, thereby improving the scientificity and operability of the overall design.

Thermal tunneling is usually caused by heat flow between buildings being restricted by local topography or building layout. These thermal anomaly areas often become hotspots for heat accumulation, directly affecting the thermal stability and local microclimate of the building. Therefore, focusing on these thermal anomaly areas for analysis can more accurately assess the thermal management performance of the building in the most critical areas. By analyzing only the thermal anomaly areas that have been automatically marked, unnecessary extensive analysis of the entire building structure can be avoided. This can make more efficient use of computing resources and time, focusing on solving the most serious problem areas, thereby improving the pertinence and efficiency of the analysis.

The window positions and material reflectivity of the thermal anomaly area are collected, and the influence of the window positions and material reflectivity on the overall thermal efficiency is evaluated by generating a thermal energy conduction model, including:

S401: Collect window positions and material reflectivity in thermal anomaly areas:

Deploy high-resolution optical sensors and infrared sensors at key locations within thermal anomaly areas, such as high heat accumulation areas, strong reflection areas, etc. Optical sensors are used to capture the specific location and shape information of windows, while infrared sensors are used to detect the reflectivity of building materials.

The system automatically collects and processes sensor data, digitizes information on window locations and material reflectivity, and generates a spatial thermal signature map.

Data fusion technology is used to integrate multi-source data into a unified three-dimensional thermal feature model, so that a complete view of the thermal conductivity characteristics of each thermal anomaly area can be obtained. This method can improve the accuracy of the data and reduce the uncertainty caused by a single source of data.

S402: Generate a heat transfer model using a multivariate regression analysis algorithm to evaluate the impact of window position and material reflectivity on the overall thermal performance of the building:

Using a multivariate regression analysis algorithm, the window position (such as height, orientation, area) and material reflectivity are used as input variables to generate a thermal energy conduction model. The output of the regression model is the overall thermal performance of the building (such as internal temperature change rate, energy consumption index, etc.).

The heat conduction model can be expressed as: ;in, To express the overall thermal performance of a building, Indicates window position related parameters, represents the material reflectivity, represents interaction terms (such as the interaction between window position and material reflectivity), is the regression coefficient, is the error term.

The model is trained using historical data and simulation data, and the regression coefficient is optimized through cross-validation and error analysis to improve the prediction accuracy of the model. During the training process, an adaptive weight adjustment mechanism is applied to dynamically adjust the regression coefficient according to different regional characteristics to ensure the adaptability and versatility of the model to different types of buildings.

S403: Verify the matching degree between data from different sources and the heat conduction model through consistency check algorithm:

The consistency between the actual measured data and the predicted values of the heat transfer model was verified based on the residual analysis and chi-square test algorithms. Residual analysis was used to identify systematic errors, and chi-square test was used to evaluate the goodness of fit of the model.

The formula for the consistency check algorithm is: ;in, is the chi-square statistic, which measures the difference between the actual observed value and the model predicted value; is the actual observed value, which is The observed value of data points; is the model prediction value, in The predicted value of the data point; is the total number of data points, indicating the number of data samples used in the model validation process; is the number of the data point.

By combining residual analysis and chi-square test, we can not only verify the overall fit of the model, but also identify and locate possible deviation areas of the model. This multi-level verification method can ensure the high reliability and accuracy of the model and avoid design errors caused by inaccurate models.

Based on the results of the consistency check, the thermal conductivity model is dynamically adjusted and the feedback is used to further optimize the decision-making process of window location and material selection. A feedback loop is introduced to improve the prediction accuracy of the model through iterative adjustments, and the model parameters are updated instantly after each adjustment, so that it can adapt to different building characteristics and environmental conditions.

By collecting and fusing multi-source data of thermal anomaly areas, applying multivariate regression analysis to generate a thermal energy conduction model, and verifying the accuracy and consistency of the model through a consistency check algorithm, the entire process forms a closed-loop system to ensure the scientificity and effectiveness of design adjustments. This approach not only improves the accuracy of the design, but also identifies and resolves potential problems at an early stage.

Based on the design adjustment steps, the weight of the dynamic shading system is adjusted, and the angle and movement strategy of the visor are optimized using a genetic algorithm, specifically:

Environmental parameters, including ambient light intensity, solar radiation angle, and indoor temperature, are collected in real time through multiple types of sensors installed on building exterior walls, windows, and sun visors.

The collected environmental parameters are noise filtered and standardized to ensure data consistency and eliminate the impact of abnormal data.

The modified entropy weight method is used to redistribute the weights of various environmental parameters in the dynamic shading system. The specific formula is: ; Indicates The weight of the environmental parameters, Indicates The entropy value of the environmental parameters, The number of environmental parameters collected, The number of the collected environmental parameters.

The weight adjustment mechanism ensures that the shading system can automatically adapt to different time periods and environmental conditions through dynamic adjustment based on real-time data, thereby optimizing the thermal management efficiency of the building.

Generate an initial population for visor angles and motion strategies, with each individual representing a specific angle and motion strategy combination. Individuals are represented as vectors ;in Indicates The angle of the sun visor, is the number of the sun visor, is the number of visors, .

Set the initial population size to , ensuring population diversity and providing a wide range of exploration space.

Calculate the fitness value of each individual, and the fitness function is defined as: ;in, is the fitness function, Indicates the reduction value of indoor temperature. Indicates energy consumption, and are weight coefficients (used to balance the effects of temperature control and energy consumption, respectively).

Using the roulette wheel selection method, individuals with higher fitness are selected to enter the next generation of reproduction.

Perform single-point or multi-point crossover on the selected individuals to generate new individual combinations. Assume that the two parent individuals are and , the sub-individuals generated by crossover are .

Mutate some of the newly generated individuals and randomly adjust the angles of some visors To introduce new features and diversity.

The genetic algorithm gradually optimizes the angle and movement strategy of the visor through multiple iterations to maximize the value of the fitness function.

When the population fitness changes tend to be stable, stop the iteration and output the final optimized sunshade angle combination .

In a virtual environment, simulation tests were conducted based on the optimized sunshade angle and movement strategy to evaluate the energy efficiency and comfort performance of the system. Computational fluid dynamics (CFD) and heat conduction simulation tools were used to comprehensively evaluate the temperature changes and energy consumption performance inside the building.

Apply the optimized shading system in the actual building environment, monitor its impact on indoor temperature control and energy consumption in real time, and compare and analyze the actual data with the simulation results. Based on the field operation results, further parameter adjustments and optimizations are made to ensure that the shading system can maintain the best effect under various environmental conditions and optimize the thermal management of the building.

By dynamically adjusting the weight of the shading system, using genetic algorithms to optimize the angle and movement strategy of the shading panels, and verifying and optimizing them in actual environments, the entire process forms a complete optimization closed loop to ensure the scientificity and effectiveness of the design adjustments. This method not only improves the intelligence level of the shading system, but also can adapt to complex environmental changes.

Example 2

The difference between Example 2 of the present invention and Example 1 is that this example introduces a control system of a low-carbon energy-saving building system.

Figure 2 shows a structural schematic diagram of a control system of a low-carbon energy-saving building system of the present invention, a control system of a low-carbon energy-saving building system, including an abnormal area marking module, a light and shadow pattern evaluation module, a dynamic stability evaluation module, a design adjustment judgment module, a design adjustment implementation module and a shading system optimization module.

Abnormal area marking module: obtain thermal flow data of the building complex; use thermal imaging technology and computational fluid dynamics model to conduct preliminary analysis and mark thermal anomaly areas.

Light and shadow pattern assessment module: For thermal anomaly areas, the light and shadow pattern changes caused by the interaction between the building surface and the surrounding environment are monitored to quantitatively assess the contribution of light and shadow pattern changes to microclimate regulation.

Dynamic stability assessment module: For thermal anomaly areas, the module analyzes the synchronization of the building structure’s response to environmental thermal fluctuations to quantitatively assess the performance of the building structure in dynamic thermal stability.

Design adjustment judgment module: Based on the contribution of changes in light and shadow patterns to microclimate regulation and the performance of the building structure in dynamic thermal stability, determine whether to enter the design adjustment step.

Design adjustment implementation module: The design adjustment step is to collect the window position and material reflectivity of the thermal anomaly area, and evaluate the impact of the window position and material reflectivity on the overall thermal efficiency by generating a thermal energy conduction model.

Sunshade system optimization module: Based on the design adjustment steps, the weight of the dynamic sunshade system is adjusted, and the angle and movement strategy of the sunshade are optimized using genetic algorithms.

The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

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

Claims (4)

1. A control method for a low-carbon energy-saving building system, characterized in that it comprises the following steps: S1: Obtain thermal flow data of the building complex; perform preliminary analysis using thermal imaging technology and computational fluid dynamics models to mark thermal anomaly areas; S2: For thermal anomaly areas: By monitoring the changes in light and shadow patterns generated by the interaction between the building surface and the surrounding environment, quantitatively evaluate the contribution of light and shadow pattern changes to microclimate regulation; by analyzing the synchronization of the building structure's response to environmental thermal fluctuations, quantitatively evaluate the performance of the building structure in dynamic thermal stability; S3: Based on the contribution of light and shadow pattern changes to microclimate regulation and the performance of the building structure in dynamic thermal stability, determine whether to enter the design adjustment step; S4: The design adjustment step is to collect the window positions and material reflectivity of the thermal anomaly area, and evaluate the impact of the window positions and material reflectivity on the overall thermal performance by generating a thermal energy conduction model. The overall thermal performance includes the internal temperature change rate and energy consumption index; S5: Based on the design adjustment steps, the weight of the dynamic shading system is adjusted, and the angle and movement strategy of the shading board are optimized using genetic algorithm; By monitoring the changes in light and shadow patterns generated by the interaction between the building surface and the surrounding environment, the contribution of light and shadow pattern changes to microclimate regulation is quantitatively evaluated, specifically: Acquire light and shadow image data from buildings and their surroundings; Extract light and shadow features, including shadow length, width, edge sharpness, light intensity and its changing rate; Calculate the entropy value of each light and shadow feature; by calculating the entropy value of each light and shadow feature, allocate the weight in inverse proportion, and calculate the weight of the light and shadow feature; Define fuzzy sets for each light and shadow feature, and each fuzzy set has a corresponding membership function; Input the actually measured light and shadow feature values into the corresponding membership function to obtain the fuzzy value of each light and shadow feature; Apply fuzzy logic reasoning to infer the fuzzy output of the building dynamic shadow factor based on fuzzy rules and input fuzzy values; Considering the output weights and membership of each fuzzy set, the weighted average method is used to defuzzify; Calculate the dynamic shadow factor of the building. The calculation logic is as follows: perform a product operation on the membership value of each output and the specific value represented by the output, add up all these product results, divide the accumulated value by the accumulated sum of all membership values, and obtain the dynamic shadow factor of the building; By analyzing the synchronization of the building structure's response to environmental thermal fluctuations, the performance of the building structure in dynamic thermal stability is quantitatively evaluated, specifically: Calculate the ambient temperature change between two consecutive measurements and mark it as the ambient temperature change rate; Calculate the change in building temperature during the same time interval and label it as the building temperature response rate; Linear correlation was calculated using Pearson's correlation coefficient; A dynamic thermal stability adjustment coefficient is set, and the product of the dynamic thermal stability adjustment coefficient and the linear correlation is marked as a dynamic thermal stability index; Among them, the dynamic thermal stability adjustment coefficient is a coefficient adjusted according to building materials and design, which is used to adjust the relationship between synchronization and actual thermal stability; Based on the contribution of light and shadow pattern changes to microclimate regulation and the performance of the building structure in dynamic thermal stability, it is determined whether to enter the design adjustment step, specifically: Set the threshold of dynamic thermal stability index and building dynamic shadow factor; compare the dynamic thermal stability index with the threshold of dynamic thermal stability index, and compare the building dynamic shadow factor with the threshold of building dynamic shadow factor: When the dynamic thermal stability index is greater than the dynamic thermal stability index threshold, and the building dynamic shadow factor is greater than the building dynamic shadow factor threshold, it is determined to enter the design adjustment step; otherwise, it is determined not to enter the design adjustment step; Based on the design adjustment steps, the weight of the dynamic shading system is adjusted, and the angle and movement strategy of the visor are optimized using a genetic algorithm, specifically: Real-time collection of environmental parameters, including ambient light intensity, solar radiation angle, and indoor temperature; The modified entropy weight method is used to redistribute the weights of various environmental parameters in the dynamic shading system; Generate an initial population for visor angles and movement strategies, with each individual representing a combination of angles and movement strategies; Set the initial population size; calculate the fitness value of each individual; Use the roulette wheel selection method to select individuals with higher fitness to enter the next generation of reproduction; Perform single-point or multi-point crossover on the selected individuals to generate new individual combinations; Mutate some of the newly generated individuals and randomly adjust the angles of some visors; The genetic algorithm gradually optimizes the angle and movement strategy of the visor through multiple iterations to maximize the value of the fitness function. When the fitness of the population tends to be stable, the iteration is stopped and the final optimized visor angle combination is output. Among them, the fitness function is defined as: ;in, is the fitness function, Indicates the reduction value of indoor temperature. Indicates energy consumption, and is the weight coefficient. 2. A control method for a low-carbon energy-saving building system according to claim 1, characterized in that the thermal flow data of the building complex is obtained; a preliminary analysis is performed using thermal imaging technology and a computational fluid dynamics model to mark thermal anomaly areas, specifically including: S101: Deploy a group of thermal imaging sensors to collect thermal imaging data of the building complex; S102: Preliminary analysis of thermal imaging data using computational fluid dynamics models; S103: Based on the physical consistency principle and environmental matching algorithm, multi-dimensional feature comparison is performed to automatically identify and mark thermal anomaly areas that may cause thermal tunneling effects. 3. The control method of a low-carbon energy-saving building system according to claim 2 is characterized in that the window position and material reflectivity of the thermal anomaly area are collected, and the influence of the window position and material reflectivity on the overall thermal efficiency is evaluated by generating a thermal energy conduction model, specifically including: S401: Collect window positions and material reflectivity in thermal anomaly areas: digitize the information of window positions and material reflectivity to generate a spatial thermal feature map; use data fusion technology to synthesize a unified three-dimensional thermal feature model; S402: Generate a heat conduction model using a multivariate regression analysis algorithm to evaluate the impact of window position and material reflectivity on the overall thermal performance of the building: Use a multivariate regression analysis algorithm to generate a heat conduction model using window position and material reflectivity as input variables; use historical data and simulation data to train the model, and optimize the regression coefficients through cross-validation and error analysis; S403: Verify the matching degree between data from different sources and the heat conduction model through consistency check algorithm: Based on the algorithm of residual analysis and chi-square test, verify the consistency between the actual measured data and the predicted value of the heat conduction model; residual analysis is used to identify systematic errors, and chi-square test is used to evaluate the goodness of fit of the model. 4. A control system for a low-carbon energy-saving building system, used to implement a control method for a low-carbon energy-saving building system according to any one of claims 1 to 3, characterized in that it comprises an abnormal area marking module, a light and shadow mode evaluation module, a dynamic stability evaluation module, a design adjustment determination module, a design adjustment implementation module and a sunshade system optimization module; Abnormal area marking module: obtain thermal flow data of the building complex; use thermal imaging technology and computational fluid dynamics model to conduct preliminary analysis and mark thermal abnormal areas; Light and shadow pattern assessment module: for thermal anomaly areas, the module monitors the changes in light and shadow patterns generated by the interaction between the building surface and the surrounding environment, and quantitatively assesses the contribution of light and shadow pattern changes to microclimate regulation; Dynamic stability assessment module: For thermal anomaly areas, the module analyzes the synchronization of the building structure's response to environmental thermal fluctuations to quantitatively assess the performance of the building structure in dynamic thermal stability; Design adjustment judgment module: based on the contribution of light and shadow pattern changes to microclimate regulation and the performance of the building structure in dynamic thermal stability, it determines whether to enter the design adjustment step; Design adjustment implementation module: The design adjustment step is to collect the window positions and material reflectivity of the thermal anomaly area, and evaluate the impact of the window positions and material reflectivity on the overall thermal efficiency by generating a thermal energy conduction model; Sunshade system optimization module: Based on the design adjustment steps, the weight of the dynamic sunshade system is adjusted, and the angle and movement strategy of the sunshade are optimized using genetic algorithms.