An artificial intelligence-based green building digital management method

Abstract

The application discloses a kind of green building digital management methods based on artificial intelligence, belong to building management technical field.The method includes: using building information model data and real-time sensor network, the digital twin model of building is created;The short-term, medium-term and long-term prediction of building performance is carried out to capture the dynamic nature and long-term trend of building performance at different time scales;According to the life cycle stage of building and external environment, the weight of each target is dynamically adjusted;Using Monte Carlo simulation technology, generate multiple future scenarios, and evaluate the long-term impact of potential management strategies and quantify uncertainty;By comparing the output results of multi-scale time prediction engine with the actual performance data collected by dynamic digital twin model, continuously optimize engine parameters and adaptive multi-objective optimization algorithm;The simulation results, prediction data and optimization suggestions are converted into intuitive visual interface, and transparent, multi-dimensional decision-making suggestions are provided.

Description

Technical Field

[0001] This application relates to the field of building management technology, and more specifically, to an artificial intelligence-based digital management method for green buildings. Background Technology

[0002] With the increasing prominence of global environmental issues and the popularization of the green building concept, the demand for energy-saving, environmentally friendly, and sustainable building management is growing. Traditional building management methods often rely on static building design documents and manual adjustments afterward, which cannot effectively cope with the dynamic changes and optimization needs during building operation.

[0003] In existing technologies, BIM-based static building information models have been widely used in the architectural design and construction phases, but their application in building operation and management still faces challenges. The development of sensor technology has made real-time monitoring of internal and external environmental parameters possible, but how to combine this real-time data with the building model to achieve accurate prediction and optimization of the building's real-time performance remains a technical challenge.

[0004] Furthermore, long-term optimization of building energy efficiency requires consideration of the complex interactions of multiple factors, including energy consumption, cost control, occupant comfort, and environmental impact. Existing management methods often lack the adaptability to these multi-objective optimization processes, resulting in the inability to continuously optimize and improve building operation strategies.

[0005] Therefore, a new building management approach is needed that can combine advanced artificial intelligence technology and digital tools to achieve dynamic management and optimization of the entire building lifecycle, thereby improving the building's energy efficiency, comfort, and sustainability. Summary of the Invention

[0006] To overcome a series of shortcomings in existing technologies, the purpose of this application is to provide an artificial intelligence-based digital management method for green buildings, comprising the following steps: Step 1: Using building information modeling data and real-time sensor networks, create a digital twin model of the building to simulate the building's geometric features, material properties, electrical equipment operating status, and environmental factors in real time, forming a dynamic virtual representation of the building. Step 2: Make short-term, medium-term and long-term forecasts of building performance to capture the dynamics and long-term trends of building performance at different time scales; Step 3: Seek a balance between energy efficiency, cost, comfort, and environmental impact, and dynamically adjust the weights of each objective according to the building's life cycle stage and the external environment to achieve continuous adaptive optimization. Step 4: Using Monte Carlo simulation techniques, generate multiple future scenarios and assess the long-term impact of potential management strategies and quantify uncertainty; Step 5: By comparing the output of the multi-scale time prediction engine with the actual performance data collected by the dynamic digital twin model, the engine parameters and adaptive multi-objective optimization algorithm are continuously optimized. Step 6: Transform the simulation results, prediction data, and optimization suggestions into an intuitive visualization interface to provide transparent and multi-dimensional decision-making advice.

[0007] Furthermore, step 1 includes the following steps: Extract static data of a building from a building information model, including geometry, spatial layout, structural information, and material specifications; Using 3D modeling software, a high-precision three-dimensional model of a building is created based on static data, which intuitively displays its geometric features and integrates detailed property information of building materials to accurately reflect the physical performance of the building. Deploy multiple types of sensor networks within buildings to continuously collect real-time dynamic data, providing dynamic information input for digital twin models; Establish a real-time data processing mechanism to fuse sensor data with static models and achieve dynamic updates of the digital twin model; The digital twin model simulates the operating status of various electrical devices and integrates real-time data of indoor and outdoor environmental factors to comprehensively analyze and reflect various factors affecting building energy consumption, thereby enabling real-time monitoring and optimization of building performance and energy consumption.

[0008] Furthermore, step 2 includes the following steps: Historical data on building performance, including energy consumption, comfort, and equipment operating status indicators, were collected and organized, and then cleaned, standardized, and time-aligned. The data was also divided into training and test sets according to different prediction time scales. Design and implement a long short-term memory network structure. Through the gating mechanism of LSTM, capture long-term dependencies in building performance data, effectively handle time series prediction problems, and adjust the network depth and number of neurons of the long short-term memory network according to different prediction time scales. An attention layer is introduced on the basis of long short-term memory network, which can automatically focus on the important parts of the input sequence. At the same time, an attention calculation method suitable for building performance prediction is designed and implemented. We propose to construct a multi-scale framework that includes short-term, medium-term, and long-term predictions, and train a specialized LSTM-attention model for each time scale to realize the information transfer mechanism between models. The models at each time scale are trained using the prepared dataset, and the prediction performance at each time scale is evaluated and optimized through cross-validation.

[0009] Furthermore, step 3 includes the following steps: Define multiple objectives for building performance optimization, including energy efficiency, operating costs, user comfort, and environmental impact, and establish evaluation indicators and quantification methods for each objective to ensure accurate measurement and comparison of each objective during the optimization process; We construct an optimization framework based on genetic algorithms, encode the various adjustable parameters of the building into "genes", and design appropriate crossover and mutation operations to generate new candidate solutions, thereby promoting the efficient exploration of the solution space. Develop a reinforcement learning model to learn building operation strategies, taking building state as input, outputting control decisions, and continuously improving the strategy through interaction with the environment to maximize long-term benefits; Genetic algorithms and reinforcement learning models are integrated into an adaptive multi-objective optimization algorithm, which dynamically adjusts the weights of each objective during the optimization process to adapt to different optimization stages and changes in the external environment. The weights of optimization objectives are automatically adjusted based on the building's life cycle stage and external environmental factors to ensure that the optimization process remains relevant and effective over time. Regularly collect building performance data, evaluate the optimization effect, and feed the results back to the adaptive multi-objective optimization algorithm.

[0010] Furthermore, a reinforcement learning model is developed to learn building operation strategies, including the following steps: Define the building's state space S, including the operating status of various equipment within the building, environmental conditions, and energy consumption data; Determine the possible action space A of the building, including adjusting equipment operation modes and regulating indoor environmental parameters; Define the reward function R(s,a) according to the specific goals of building management. Initialize the state-action value function Q(s,a); At each time step t: based on the current state s t Select action a t ; Perform action a t The next state s is obtained by observing environmental feedback. t+1 and instant reward R(s) t ,a t Update the Q value according to the Q-learning update rule: Q(s) t ,a t )Q(s t ,a t )+[R(s t ,a t )+max a Q(s t+1,a)?Q(s t ,a t )], where max a Q(s t+1 a) indicates the next state s t+1 The maximum action value obtained below; Q(s) t ,a t） Indicates the current state s t Next, take action a t The state-action value function; is a discount factor used to balance the importance of immediate rewards and future rewards; Q(s) t+1 a) indicates the state s in the next state. t+1 The state-action value function for taking action 'a' represents the adjustment range of the crossover rate, used to control the step size or degree of influence when updating the state-action value function Q(s,a) each time; R(s) t ,a t） Indicates that in state s t Take action a t The instant reward obtained afterward; By continuously repeating the above steps, the Q value is updated through interaction with the environment, and the building's operation strategy is gradually optimized.

[0011] Furthermore, genetic algorithms and reinforcement learning are integrated into an adaptive multi-objective optimization algorithm using the following formula: ,in, These are dynamically adjusted weighting coefficients used to adjust... The relative importance; It is the first The reward function for an optimization objective represents the immediate reward obtained after taking action a in state s; Indicates the next state The maximum action value obtained.

[0012] Furthermore, step 4 includes the following steps: Identify the key uncertainties affecting building performance and set reasonable value ranges and probability distributions for each factor to accurately reflect future uncertainties; Based on the parameters and their probability distribution defined in step 4.1, a variety of possible future scenarios are generated, each scenario representing a specific combination of parameters; Develop a range of building management strategies, including energy use optimization, maintenance plan adjustments, and equipment upgrades, to address different future scenarios; For each generated future scenario, different management strategies are applied and the long-term performance of the building is simulated, while the effectiveness of each strategy under various scenarios is evaluated. The simulation results were summarized, and the statistical distribution of the performance indicators of each management strategy under different scenarios was calculated. At the same time, sensitivity analysis was used to quantify uncertainty and identify the factors with the greatest impact.

[0013] Furthermore, sensitivity analysis is used to quantify uncertainty and identify the most influential factors, including the following steps: Select factors that may significantly affect building performance, and define a reasonable range of variation for each parameter based on historical data and expert opinions; A global sensitivity analysis method is used to systematically change the input values ​​within a defined parameter range, run the building performance model, and record the changes in the output results. The sensitivity coefficient metric is used to quantify the impact of each input parameter on the output, and the parameters are ranked by importance accordingly. Heat maps visually represent the relative importance of each parameter, making it easier for decision-makers to understand them quickly. For highly sensitive parameters, develop more robust management strategies to cope with potential changes; Based on new data and environmental changes, sensitivity analysis is updated regularly to ensure continuous optimization of building management strategies.

[0014] Furthermore, step 5 includes the following steps: Step 5.1: Collect actual performance data of the building in real time from the dynamic digital twin model and ensure that it is consistent with the output of the multi-scale time prediction engine in terms of time scale and format; Step 5.2: Periodically compare the output of the prediction engine with the actual performance data. Calculate the prediction error for each indicator and evaluate the prediction accuracy at different time scales; Step 5.3: Based on the comparative analysis results, the gradient descent method is used to automatically adjust the parameters of the multi-scale time prediction engine to balance the accuracy of short-term predictions and the ability to grasp long-term trends. Step 5.4: Dynamically adjust the weights and constraints in the adaptive multi-objective optimization algorithm based on actual performance data and prediction errors; Step 5.5: Identify significant deviations between the prediction results and the actual data. When an anomaly is detected, trigger an in-depth analysis process to determine whether the prediction model needs adjustment or whether the building system itself has an abnormal condition, and take corresponding optimization or alarm measures. Step 5.6: Regularly generate optimization effect reports to show the improvement in prediction accuracy, the effect of algorithm optimization, and the challenges that still exist.

[0015] Furthermore, step 5.3 includes the following steps: Evaluation metrics are obtained by comparing prediction results with actual data at different time scales. Setting optimization objectives may involve minimizing the overall error, balancing prediction errors at different time scales, or setting trade-off parameters based on specific application requirements. The parameters of the prediction engine are represented as vectors, including the weights and learning rates of various models, and gradient descent is used to optimize the parameters with the goal of minimizing the prediction error. Define an optimization objective function J(), including error weighting or other adjustment factors at various time scales, to reflect the ability to grasp long-term trends; Update parameters using gradient descent: , where is the learning rate; It is the objective function The gradient relative to the parameters.

[0016] Compared with the prior art, this application has the following beneficial effects: This application achieves multi-scale prediction and continuous optimization of building performance by establishing a dynamically represented digital twin model and combining it with real-time sensor data. First, a high-precision 3D model is created using Building Information Modeling (BIM) data and a sensor network to simulate the building's geometric features, material properties, and operational status. Second, a Long Short-Term Memory (LSTM) network and attention mechanism are employed to predict building performance in the short, medium, and long term, capturing dynamic changes and long-term trends. Furthermore, a balance is achieved between energy efficiency, cost, comfort, and environmental impact, and strategies are dynamically adjusted through an adaptive multi-objective optimization algorithm. Finally, the simulation results and optimization recommendations are presented through an intuitive visualization interface, providing decision-makers with multi-dimensional and transparent decision support, thereby promoting the intelligent and sustainable development of green building management. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an artificial intelligence-based digital management method for green buildings disclosed in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of this invention.

[0019] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0021] like Figure 1 As shown, an artificial intelligence-based digital management method for green buildings includes the following steps: Step 1: Using building information modeling data and real-time sensor networks, create a digital twin model of the building to simulate the building's geometric features, material properties, electrical equipment operating status, and environmental factors in real time, forming a dynamic virtual representation of the building. Step 2: Make short-term, medium-term and long-term forecasts of building performance to capture the dynamics and long-term trends of building performance at different time scales; Step 3: Seek a balance between energy efficiency, cost, comfort, and environmental impact, and dynamically adjust the weights of each objective according to the building's life cycle stage and the external environment to achieve continuous adaptive optimization. Step 4: Using Monte Carlo simulation techniques, generate multiple future scenarios and assess the long-term impact of potential management strategies and quantify uncertainty; Step 5: By comparing the output of the multi-scale time prediction engine with the actual performance data collected by the dynamic digital twin model, the engine parameters and adaptive multi-objective optimization algorithm are continuously optimized. Step 6: Transform the simulation results, prediction data, and optimization suggestions into an intuitive visualization interface to provide transparent and multi-dimensional decision-making advice.

[0022] The key to Step 1 lies in creating a digital twin model of the building using Building Information Modeling (BIM) data and a real-time sensor network. This model can not only simulate the building's geometric features and material properties in real time, but also track the operating status of electrical equipment and external environmental factors such as temperature and humidity. Through the digital twin model, building managers can understand the building's operating status in real time, predict potential problems, and make optimizations and adjustments, thereby improving the building's operational efficiency and comfort.

[0023] Step 2 focuses on short-, medium-, and long-term forecasting of building performance, capturing the dynamics and long-term trends of buildings across different time scales. These forecasts cover aspects such as energy usage, maintenance needs, comfort assessments, and environmental impact, providing building managers with data-driven decision support to help them develop timely and effective management strategies.

[0024] Step 3 focuses on striking a balance between energy efficiency, cost, comfort, and environmental impact. This is achieved through dynamic adjustments to the weighting of these objectives, enabling continuous adaptive optimization based on the building's lifecycle stage and changes in the external environment. It considers not only the building's operational efficiency and economy but also the comfort of residents or users and its environmental impact, thereby ensuring the building achieves optimal overall performance in all aspects.

[0025] In step 4, Monte Carlo simulation technology is used to generate multiple future scenarios to assess the long-term impact of potential management strategies and quantify uncertainty. This helps building managers better understand the possible outcomes of different decision-making paths and predict trends in building performance under various environmental conditions. By comprehensively considering uncertainties, more robust and reliable management strategies can be developed to address various future possibilities.

[0026] Step 5 focuses on continuously optimizing engine parameters and adaptive multi-objective optimization algorithms by comparing the output of the multi-scale time prediction engine with the actual performance data collected by the dynamic digital twin model. This real-time feedback mechanism enables continuous learning and adjustment to more accurately reflect the actual situation and specifically improve prediction accuracy and optimization results. Through this continuous optimization process, building managers can more effectively manage the operation and maintenance of buildings, improving overall management efficiency.

[0027] Step 6 aims to transform simulation results, forecast data, and optimization suggestions into an intuitive visual interface, providing building managers with transparent and multi-dimensional decision-making advice. Through this intuitive interface, managers can quickly understand the building's operational status and forecast results, make data-driven decisions, and adjust management strategies in a timely manner to achieve expected management goals. This not only improves management efficiency but also enhances the scientific rigor and credibility of decision-making.

[0028] The AI-based digital management method for green buildings described above achieves intelligent monitoring and management of the entire building operation process through key steps such as establishing digital twin models, predicting building performance, adaptive optimization, and multi-scale prediction engines. This method not only improves building energy efficiency and reduces costs but also optimizes comfort and reduces environmental impact, providing strong technical support and implementation pathways for future sustainable building management. Through continuous optimization and innovation, this method will play an increasingly important role in promoting intelligent management of green buildings.

[0029] Furthermore, step 1 includes the following steps: Extract static data of a building from a building information model, including geometry, spatial layout, structural information, and material specifications; Using 3D modeling software, a high-precision three-dimensional model of a building is created based on static data, which intuitively displays its geometric features and integrates detailed property information of building materials to accurately reflect the physical performance of the building. Deploy multiple types of sensor networks within buildings to continuously collect real-time dynamic data, providing dynamic information input for digital twin models; Establish a real-time data processing mechanism to fuse sensor data with static models and achieve dynamic updates of the digital twin model; The digital twin model simulates the operating status of various electrical devices and integrates real-time data of indoor and outdoor environmental factors to comprehensively analyze and reflect various factors affecting building energy consumption, thereby enabling real-time monitoring and optimization of building performance and energy consumption.

[0030] The above steps achieve a comprehensive workflow from building information model (BIM) data extraction to real-time data processing and simulation. This not only enables building managers to accurately grasp the static and dynamic characteristics of buildings but also allows for real-time monitoring and optimization of building energy efficiency. As an intelligent tool for building management, digital twin models provide strong support for achieving sustainable building management, promoting improved building operational efficiency and environmental friendliness.

[0031] Furthermore, step 2 includes the following steps: Historical data on building performance, including energy consumption, comfort, and equipment operating status indicators, were collected and organized, and then cleaned, standardized, and time-aligned. The data was also divided into training and test sets according to different prediction time scales. Design and implement a long short-term memory network structure. Through the gating mechanism of LSTM, capture long-term dependencies in building performance data, effectively handle time series prediction problems, and adjust the network depth and number of neurons of the long short-term memory network according to different prediction time scales. An attention layer is introduced on the basis of long short-term memory network, which can automatically focus on the important parts of the input sequence. At the same time, an attention calculation method suitable for building performance prediction is designed and implemented. We propose to construct a multi-scale framework that includes short-term, medium-term, and long-term predictions, and train a specialized LSTM-attention model for each time scale to realize the information transfer mechanism between models. The models at each time scale are trained using the prepared dataset, and the prediction performance at each time scale is evaluated and optimized through cross-validation.

[0032] A multi-scale framework based on Long Short-Term Memory (LSTM) networks and attention mechanisms provides robust technical support for building performance prediction. From data collection and cleaning to model structure design and optimization, and then to model training and evaluation, each step contributes to achieving accurate predictions of building performance across different time scales. This not only improves the efficiency of building energy management but also effectively reduces energy costs, promoting the development of intelligent green building management.

[0033] Furthermore, step 3 includes the following steps: Define multiple objectives for building performance optimization, including energy efficiency, operating costs, user comfort, and environmental impact, and establish evaluation indicators and quantification methods for each objective to ensure accurate measurement and comparison of each objective during the optimization process; We construct an optimization framework based on genetic algorithms, encode the various adjustable parameters of the building into "genes", and design appropriate crossover and mutation operations to generate new candidate solutions, thereby promoting the efficient exploration of the solution space. Develop a reinforcement learning model to learn building operation strategies, taking building state as input, outputting control decisions, and continuously improving the strategy through interaction with the environment to maximize long-term benefits; Genetic algorithms and reinforcement learning models are integrated into an adaptive multi-objective optimization algorithm, which dynamically adjusts the weights of each objective during the optimization process to adapt to different optimization stages and changes in the external environment. The weights of optimization objectives are automatically adjusted based on the building's life cycle stage and external environmental factors to ensure that the optimization process remains relevant and effective over time. Regularly collect building performance data, evaluate the optimization effect, and feed the results back to the adaptive multi-objective optimization algorithm.

[0034] The technical analysis in step 3 demonstrates powerful technological effects and application potential under a multi-objective framework. From defining multiple optimization objectives and establishing evaluation indicators to introducing genetic algorithms and reinforcement learning models and integrating them into an adaptive multi-objective optimization algorithm, each step aims to improve building energy efficiency, reduce operating costs, enhance user comfort, and minimize environmental impact. This comprehensive optimization framework not only optimizes building performance under static conditions but also dynamically adjusts with time and environmental changes, providing crucial support for green building management and sustainable development.

[0035] Furthermore, a reinforcement learning model is developed to learn building operation strategies, including the following steps: Define the building's state space S, including the operating status of various equipment within the building, environmental conditions, and energy consumption data; Determine the possible action space A of the building, including adjusting equipment operation modes and regulating indoor environmental parameters; Define the reward function R(s,a) according to the specific goals of building management. Initialize the state-action value function Q(s,a); At each time step t: based on the current state s t Select action a t ; Perform action a t The next state s is obtained by observing environmental feedback. t+1 and instant reward R(s) t ,a t Update the Q value according to the Q-learning update rule: Q(s) t ,a t )Q(s t ,a t )+[R(s t ,a t )+max a Q(s t+1 ,a)?Q(s t ,a t )], where max a Q(s t+1 a) indicates the state s in the next state. t+1 The maximum action value obtained below; Q(s) t ,a t） Indicates the current state s t Next, take action a t The state-action value function; is a discount factor used to balance the importance of immediate rewards and future rewards; Q(s) t+1 a) indicates the state s in the next state. t+1 The state-action value function for taking action 'a' represents the adjustment range of the crossover rate, used to control the step size or degree of influence when updating the state-action value function Q(s,a) each time; R(s) t ,a t） Indicates that in state s t Take action a t The instant reward obtained afterward; By continuously repeating the above steps, the Q value is updated through interaction with the environment, and the building's operation strategy is gradually optimized.

[0036] Furthermore, genetic algorithms and reinforcement learning are integrated into an adaptive multi-objective optimization algorithm using the following formula: ,in, These are dynamically adjusted weighting coefficients used to adjust... The relative importance; It is the first The reward function for an optimization objective represents the immediate reward obtained after taking action a in state s; Indicates the next state The maximum action value obtained.

[0037] Furthermore, step 4 includes the following steps: Identify the key uncertainties affecting building performance and set reasonable value ranges and probability distributions for each factor to accurately reflect future uncertainties; Based on the parameters and their probability distribution defined in step 4.1, a variety of possible future scenarios are generated, each scenario representing a specific combination of parameters; Develop a range of building management strategies, including energy use optimization, maintenance plan adjustments, and equipment upgrades, to address different future scenarios; For each generated future scenario, different management strategies are applied and the long-term performance of the building is simulated, while the effectiveness of each strategy under various scenarios is evaluated. The simulation results were summarized, and the statistical distribution of the performance indicators of each management strategy under different scenarios was calculated. At the same time, sensitivity analysis was used to quantify uncertainty and identify the factors with the greatest impact.

[0038] Through the technical process in step 4, the building performance optimization method demonstrates significant technical effectiveness in managing uncertainty and formulating strategies. From identifying key uncertainties to generating multiple future scenarios, developing diverse management strategies, and simulating and evaluating the performance of various strategies under different scenarios, each step provides data support and scientific basis for building management decisions. This comprehensive optimization approach not only improves the overall performance and efficiency of buildings but also effectively addresses complex external environmental changes and uncertainties, providing crucial technical support for sustainable building management and development.

[0039] Furthermore, sensitivity analysis is used to quantify uncertainty and identify the most influential factors, including the following steps: Select factors that may significantly affect building performance, and define a reasonable range of variation for each parameter based on historical data and expert opinions; A global sensitivity analysis method is used to systematically change the input values ​​within a defined parameter range, run the building performance model, and record the changes in the output results. The sensitivity coefficient metric is used to quantify the impact of each input parameter on the output, and the parameters are ranked by importance accordingly. Heat maps visually represent the relative importance of each parameter, making it easier for decision-makers to understand them quickly. For highly sensitive parameters, develop more robust management strategies to cope with potential changes; Based on new data and environmental changes, sensitivity analysis is updated regularly to ensure continuous optimization of building management strategies.

[0040] Furthermore, step 5 includes the following steps: Step 5.1: Collect actual performance data of the building in real time from the dynamic digital twin model and ensure that it is consistent with the output of the multi-scale time prediction engine in terms of time scale and format; Step 5.2: Periodically compare the output of the prediction engine with the actual performance data. Calculate the prediction error for each indicator and evaluate the prediction accuracy at different time scales; Step 5.3: Based on the comparative analysis results, the gradient descent method is used to automatically adjust the parameters of the multi-scale time prediction engine to balance the accuracy of short-term predictions and the ability to grasp long-term trends. Step 5.4: Dynamically adjust the weights and constraints in the adaptive multi-objective optimization algorithm based on actual performance data and prediction errors; Step 5.5: Identify significant deviations between the prediction results and the actual data. When an anomaly is detected, trigger an in-depth analysis process to determine whether the prediction model needs adjustment or whether the building system itself has an abnormal condition, and take corresponding optimization or alarm measures. Step 5.6: Regularly generate optimization effect reports to show the improvement in prediction accuracy, the effect of algorithm optimization, and the challenges that still exist.

[0041] The detailed technical process in step 5 enables comprehensive optimization, from real-time performance monitoring to multi-scale time-based forecasting. The technical effects of each step complement each other, collectively providing building managers with accurate performance predictions and optimization strategies. Through real-time data acquisition, prediction error analysis, parameter optimization, and anomaly handling, the system effectively improves building energy efficiency, optimizes operating costs, and enhances user comfort, thereby achieving intelligent and sustainable building management.

[0042] Furthermore, step 5.3 includes the following steps: Evaluation metrics are obtained by comparing prediction results with actual data at different time scales. Setting optimization objectives may involve minimizing the overall error, balancing prediction errors at different time scales, or setting trade-off parameters based on specific application requirements. The parameters of the prediction engine are represented as vectors, including the weights and learning rates of various models, and gradient descent is used to optimize the parameters with the goal of minimizing the prediction error. Define an optimization objective function J(), including error weighting or other adjustment factors at various time scales, to reflect the ability to grasp long-term trends; Update parameters using gradient descent: , where is the learning rate; It is the objective function The gradient relative to the parameters.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital management method for green buildings based on artificial intelligence, characterized in that, Includes the following steps: Step 1: Using building information modeling data and real-time sensor networks, create a digital twin model of the building to simulate the building's geometric features, material properties, electrical equipment operating status, and environmental factors in real time, forming a dynamic virtual representation of the building. Step 2: Make short-term, medium-term and long-term forecasts of building performance to capture the dynamics and long-term trends of building performance at different time scales; Step 3: Seek a balance between energy efficiency, cost, comfort, and environmental impact, and dynamically adjust the weights of each objective according to the building's life cycle stage and the external environment to achieve continuous adaptive optimization. Step 4: Using Monte Carlo simulation techniques, generate multiple future scenarios and assess the long-term impact of potential management strategies and quantify uncertainty; Step 5: By comparing the output of the multi-scale time prediction engine with the actual performance data collected by the dynamic digital twin model, the engine parameters and adaptive multi-objective optimization algorithm are continuously optimized. Step 6: Transform the simulation results, prediction data, and optimization suggestions into an intuitive visualization interface to provide transparent and multi-dimensional decision-making advice.

2. The digital management method for green buildings based on artificial intelligence according to claim 1, characterized in that, Step 1 includes the following steps: Extract static data of a building from a building information model, including geometry, spatial layout, structural information, and material specifications; Using 3D modeling software, a high-precision three-dimensional model of a building is created based on static data, which intuitively displays its geometric features and integrates detailed property information of building materials to accurately reflect the physical performance of the building. Deploy multiple types of sensor networks within buildings to continuously collect real-time dynamic data, providing dynamic information input for digital twin models; Establish a real-time data processing mechanism to fuse sensor data with static models and achieve dynamic updates of the digital twin model; The digital twin model simulates the operating status of various electrical devices and integrates real-time data of indoor and outdoor environmental factors to comprehensively analyze and reflect various factors affecting building energy consumption, thereby enabling real-time monitoring and optimization of building performance and energy consumption.

3. The digital management method for green buildings based on artificial intelligence according to claim 1, characterized in that, Step 2 includes the following steps: Historical data on building performance, including energy consumption, comfort, and equipment operating status indicators, were collected and organized, and then cleaned, standardized, and time-aligned. The data was also divided into training and test sets according to different prediction time scales. Design and implement a long short-term memory network structure. Through the gating mechanism of LSTM, capture long-term dependencies in building performance data, effectively handle time series prediction problems, and adjust the network depth and number of neurons of the long short-term memory network according to different prediction time scales. An attention layer is introduced on the basis of long short-term memory network, which can automatically focus on the important parts of the input sequence. At the same time, an attention calculation method suitable for building performance prediction is designed and implemented. We propose to construct a multi-scale framework that includes short-term, medium-term, and long-term predictions, and train a specialized LSTM-attention model for each time scale to realize the information transfer mechanism between models. The models at each time scale are trained using the prepared dataset, and the prediction performance at each time scale is evaluated and optimized through cross-validation.

4. The digital management method for green buildings based on artificial intelligence according to claim 1, characterized in that, Step 3 includes the following steps: Define multiple objectives for building performance optimization, including energy efficiency, operating costs, user comfort, and environmental impact, and establish evaluation indicators and quantification methods for each objective to ensure accurate measurement and comparison of each objective during the optimization process; We construct an optimization framework based on genetic algorithms, encode the various adjustable parameters of the building into "genes", and design appropriate crossover and mutation operations to generate new candidate solutions, thereby promoting the efficient exploration of the solution space. Develop a reinforcement learning model to learn building operation strategies, taking building state as input, outputting control decisions, and continuously improving the strategy through interaction with the environment to maximize long-term benefits; Genetic algorithms and reinforcement learning models are integrated into an adaptive multi-objective optimization algorithm, which dynamically adjusts the weights of each objective during the optimization process to adapt to different optimization stages and changes in the external environment. The weights of optimization objectives are automatically adjusted based on the building's life cycle stage and external environmental factors to ensure that the optimization process remains relevant and effective over time. Regularly collect building performance data, evaluate the optimization effect, and feed the results back to the adaptive multi-objective optimization algorithm.

5. The digital management method for green buildings based on artificial intelligence according to claim 4, characterized in that, Developing a reinforcement learning model to learn building operation strategies includes the following steps: Define the building's state space S, including the operating status of various equipment within the building, environmental conditions, and energy consumption data; Determine the possible action space A of the building, including adjusting equipment operation modes and regulating indoor environmental parameters; Define the reward function R(s,a) according to the specific goals of building management. Initialize the state-action value function Q(s,a); At each time step t: based on the current state s t Select action a t ; Perform action a t The next state s is obtained by observing environmental feedback. t+1 and instant reward R(s) t ,a t Update the Q value according to the Q-learning update rules; By continuously repeating the above steps, the Q value is updated through interaction with the environment, and the building's operation strategy is gradually optimized.

6. The digital management method for green buildings based on artificial intelligence according to claim 5, characterized in that, The following formula integrates genetic algorithms and reinforcement learning into an adaptive multi-objective optimization algorithm.

7. The digital management method for green buildings based on artificial intelligence according to claim 1, characterized in that, Step 4 includes the following steps: Identify the key uncertainties affecting building performance and set reasonable value ranges and probability distributions for each factor to accurately reflect future uncertainties; Based on the parameters and their probability distribution defined in step 4.1, a variety of possible future scenarios are generated, each scenario representing a specific combination of parameters; Develop a range of building management strategies, including energy use optimization, maintenance plan adjustments, and equipment upgrades, to address different future scenarios; For each generated future scenario, different management strategies are applied and the long-term performance of the building is simulated, while the effectiveness of each strategy under various scenarios is evaluated. The simulation results were summarized, and the statistical distribution of the performance indicators of each management strategy under different scenarios was calculated. At the same time, sensitivity analysis was used to quantify uncertainty and identify the factors with the greatest impact.

8. The digital management method for green buildings based on artificial intelligence according to claim 7, characterized in that, Sensitivity analysis is used to quantify uncertainty and identify the most influential factors, including the following steps: Select factors that may significantly affect building performance, and define a reasonable range of variation for each parameter based on historical data and expert opinions; A global sensitivity analysis method is used to systematically change the input values ​​within a defined parameter range, run the building performance model, and record the changes in the output results. The sensitivity coefficient metric is used to quantify the impact of each input parameter on the output, and the parameters are ranked by importance accordingly. Heat maps visually represent the relative importance of each parameter, making it easier for decision-makers to understand them quickly. For highly sensitive parameters, develop more robust management strategies to cope with potential changes; Based on new data and environmental changes, sensitivity analysis is updated regularly to ensure continuous optimization of building management strategies.

9. The digital management method for green buildings based on artificial intelligence according to claim 1, characterized in that, Step 5 includes the following steps: Step 5.1: Collect actual performance data of the building in real time from the dynamic digital twin model and ensure that it is consistent with the output of the multi-scale time prediction engine in terms of time scale and format; Step 5.2: Periodically compare the output of the prediction engine with the actual performance data. Calculate the prediction error for each indicator and evaluate the prediction accuracy at different time scales; Step 5.3: Based on the comparative analysis results, the gradient descent method is used to automatically adjust the parameters of the multi-scale time prediction engine to balance the accuracy of short-term predictions and the ability to grasp long-term trends. Step 5.4: Dynamically adjust the weights and constraints in the adaptive multi-objective optimization algorithm based on actual performance data and prediction errors; Step 5.5: Identify significant deviations between the prediction results and the actual data. When an anomaly is detected, trigger an in-depth analysis process to determine whether the prediction model needs adjustment or whether the building system itself has an abnormal condition, and take corresponding optimization or alarm measures. Step 5.6: Regularly generate optimization effect reports to show the improvement in prediction accuracy, the effect of algorithm optimization, and the challenges that still exist.

10. The digital management method for green buildings based on artificial intelligence according to claim 9, characterized in that, Step 5.3 includes the following steps: Evaluation metrics are obtained by comparing prediction results with actual data at different time scales. Setting optimization objectives may involve minimizing the overall error, balancing prediction errors at different time scales, or setting trade-off parameters based on specific application requirements. The parameters of the prediction engine are represented as vectors, including the weights and learning rates of various models, and gradient descent is used to optimize the parameters with the goal of minimizing the prediction error. Define an optimization objective function J(), including error weighting or other adjustment factors at various time scales, to reflect the ability to grasp long-term trends; Update the parameters using gradient descent.

Images