Building energy consumption optimization method and system based on Internet of Things

Abstract

The present invention provides a building energy consumption optimization method and system based on the Internet of Things, which relates to the field of energy consumption optimization technology, including: dividing the entire life cycle of the target building into stages, determining the key optimization stages, including the building material production stage and the building operation stage; building a building dynamics model; obtaining the building environment field and the building state matrix based on the building state requirements, performing field simulation and building effect decision-making, and determining the energy control strategy; the energy management system responds to the energy control strategy to perform energy distribution and equipment control management; identifying the building environment field, combining the energy control strategy of the preset time zone, and locating the building material optimization features; based on the building material optimization features and the building material production energy consumption points, guiding the building material production stage to perform low-carbon production management. The present invention solves the technical problem that the prior art often relies on static energy control strategies, fails to make dynamic adjustments according to changes in the internal and external environment of the building, and leads to low energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption optimization, and in particular to a building energy consumption optimization method and system based on the Internet of Things. Background Art

[0002] Green buildings are gradually becoming a trend in the construction industry. Through effective building energy consumption management, not only can carbon emissions be reduced, but also the environmental friendliness of buildings can be improved. However, there are still some problems with existing technologies for building energy consumption management. On the one hand, traditional building energy consumption management is usually concentrated in the building operation stage, and fails to fully consider the energy consumption in the production and transportation of building materials. This single-stage optimization method often ignores the overall energy efficiency of the building during its life cycle, resulting in insufficient optimization of the overall energy efficiency of the building and failure to achieve the goal of low-carbon buildings; on the other hand, the energy control strategies in traditional methods are often static and cannot be adjusted in real time to cope with changes in the internal and external environment of the building. This static control strategy is inefficient in practical applications and cannot adapt to the complex and changeable building environment, resulting in insufficient energy utilization. Summary of the invention

[0003] This application provides a building energy consumption optimization method and system based on the Internet of Things, aiming to solve the technical problems that the existing technology lacks in-depth analysis of different stages of construction, resulting in poor accuracy of energy consumption control, and traditional methods often rely on static energy control strategies and fail to make dynamic adjustments according to changes in the internal and external environment of the building, resulting in low energy utilization efficiency.

[0004] The first aspect disclosed in the present application provides a building energy consumption optimization method based on the Internet of Things, the method comprising: dividing the entire life cycle of the target building into stages, determining the key optimization stages, wherein the key optimization stages include the building materials production stage and the building operation stage; for the building operation stage, building a building dynamics model based on the internal and external environment of the building, wherein the building dynamics model includes a heat and mass transfer branch, a lighting branch, and an aerodynamic branch; obtaining the building environment field and the building state matrix based on the building state requirements, combining the building dynamics model, performing field simulation and building effect decision-making, and determining the energy control strategy; the energy management system deployed in the building responds to the energy control strategy to perform energy distribution and equipment control management; identifying the building environment field, combining the energy control strategy of the preset time zone, and locating the building material optimization characteristics; based on the building material optimization characteristics and the building material production energy consumption points, guiding the building material production stage to perform low-carbon production management, wherein the building material production energy consumption points include building raw materials and production processes.

[0005] The second aspect disclosed in the present application provides a building energy consumption optimization system based on the Internet of Things, and the system is used for the above-mentioned building energy consumption optimization method based on the Internet of Things. The system includes: a stage division module, the stage division module is used to divide the target building into stages throughout its life cycle and determine the key optimization stage, wherein the key optimization stage includes the building material production stage and the building operation stage; a model building module, the model building module is used to build a building dynamics model based on the internal and external environment of the building for the building operation stage, wherein the building dynamics model includes a heat and mass transfer branch, a lighting branch, and an aerodynamic branch; a decision module, the decision module is used to obtain the building environment field and the basic A building state matrix based on building state requirements is combined with the building dynamics model to perform field simulation and building effect decision-making to determine the energy control strategy; a control management module, the control management module is used for the energy management system deployed in the building to respond to the energy control strategy to perform energy distribution and equipment control management; a feature positioning module, the feature positioning module is used to identify the building environment field, and locate the building material optimization features in combination with the energy control strategy of the preset time zone; a production management module, the production management module is based on the building material optimization features and the building material production energy consumption points, and guides the low-carbon production management of the building material production stage, wherein the building material production energy consumption points include building raw materials and production processes.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] By dividing the entire life cycle of the target building into stages and determining the key optimization stages, including the building materials production stage and the building operation stage, it is possible to accurately optimize the stage with the highest energy consumption; by building a building dynamics model that includes heat and mass transfer branches, lighting branches, and aerodynamic branches, it is possible to fully simulate the interaction between the internal and external environments of the building. This comprehensive model helps analyze the energy consumption performance of the building under different environmental conditions and provide more accurate energy consumption predictions; by obtaining the building environment field and the building state matrix, and combining it with the building dynamics model, it is possible to accurately simulate the energy usage inside the building. This simulation helps to consider various environmental and state factors when making decisions and formulate more effective energy control strategies; through the energy management system deployed in the building, energy distribution and equipment control are automatically carried out according to the energy control strategy, thereby realizing intelligent building energy efficiency management; by identifying the building environment field and combining the energy control strategy with the preset time zone to locate the building material optimization characteristics, it is possible to reduce the energy consumption loss caused by building material problems during the use of the building; based on the building material optimization characteristics and production energy consumption points, it guides the implementation of low-carbon management in the building material production stage, reduces the energy consumption in building raw materials and production processes, and reduces the overall carbon footprint of building materials.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of a method for optimizing building energy consumption based on the Internet of Things provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the structure of a building energy consumption optimization system based on the Internet of Things provided in an embodiment of the present application.

[0011] Explanation of the accompanying drawings: stage division module 10, model building module 20, decision module 30, control management module 40, feature positioning module 50, production management module 60. DETAILED DESCRIPTION

[0012] The embodiment of the present application provides a building energy consumption optimization method based on the Internet of Things, which solves the technical problems that the existing technology lacks in-depth analysis of different stages of construction, resulting in poor accuracy of energy consumption control, and traditional methods often rely on static energy control strategies and fail to make dynamic adjustments according to changes in the internal and external environment of the building, resulting in low energy utilization efficiency.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] like Figure 1 As shown, the embodiment of the present application provides a building energy consumption optimization method based on the Internet of Things, the method comprising:

[0015] The entire life cycle of the target building is divided into stages to determine the key optimization stages, where the key optimization stages include the building materials production stage and the building operation stage.

[0016] The division of the entire life cycle stages refers to decomposing the entire life cycle of a building from design to scrapping into different stages, which usually include planning and design stage, building materials production stage, construction stage, building operation stage, building scrapping stage, etc. Among all the stages, it is most effective to find the stage with the highest energy consumption and optimize it. Therefore, the building materials production stage and the building operation stage are determined as the key optimization stages. Among them, the building materials production stage is the production and transportation stage of materials required for construction, including the collection, manufacturing, processing, transportation and storage of raw materials. The energy consumption and carbon emissions in this stage mainly come from production processes, transportation energy consumption and the characteristics of the materials themselves; the building operation stage is the operation and maintenance stage of the building during use, including daily operating activities such as heating, cooling, lighting, ventilation, etc. The energy consumption in this stage mainly comes from the use of various equipment, electricity consumption, heating and cooling, etc.

[0017] For the building operation stage, a building dynamics model is constructed based on the internal and external environment of the building, wherein the building dynamics model includes a heat and mass transfer branch, a lighting branch, and an aerodynamic branch.

[0018] During the building operation phase, the building dynamics model is built to simulate changes in the building's internal environment and optimize the building's energy use based on these changes. The building dynamics model includes heat transfer and mass transfer branches, lighting branches, and aerodynamic branches. In the heat transfer and mass transfer branch, heat transfer is the process of heat transfer from one place to another. The heat transfer sub-branch is used to simulate heat transfer inside the building, including heat conduction, convection, and radiation of the building envelope. Mass transfer is the process of moving matter from one place to another. The mass transfer sub-branch is used to simulate moisture transfer in the air, involving processes such as moisture diffusion, convection, and evaporation. The lighting branch is used to simulate the lighting conditions inside the building, including the influence of natural light and artificial light sources. The aerodynamic branch is used to simulate the behavior of air flow and ventilation systems inside the building.

[0019] Furthermore, the building dynamics model based on the internal and external environment of the building includes:

[0020] Perform three-dimensional modeling on the target building to determine the three-dimensional model of the building, wherein the three-dimensional model of the building includes an equipment topology network determined by assembly equipment that interacts with the target building; based on the three-dimensional model of the building, a spatial flow field is constructed based on an evaluation dimension, and data-driven modeling is performed to determine the heat and mass transfer branch, the illumination branch, and the aerodynamic branch; the heat and mass transfer branch, the illumination branch, and the aerodynamic branch are parallelized to generate the building dynamics model, wherein the building dynamics model is scalable.

[0021] Use modeling software, such as AutoCAD, to perform three-dimensional digital modeling of the target building. Specifically, collect the design drawings and construction drawings of the target building, or conduct on-site measurements of the target building to obtain the actual size and spatial layout of the building. Based on the collected data, construct a three-dimensional geometric model of the building in the modeling software to ensure that the model is consistent with the size and structure of the actual building.

[0022] The equipment topology network refers to the mutual relationship and connection structure between the various assembled equipment inside the building. All assembled equipment inside the building is obtained, including air-conditioning equipment, lighting equipment, water supply and power supply equipment, security monitoring equipment, etc., and the installation position of each equipment is located in the three-dimensional model to ensure that it is consistent with the actual situation. According to the function of the equipment, the connection relationship between each equipment is determined, for example, the connection between water supply equipment and water supply pipelines, and the connection between lighting equipment and power lines. The equipment topology network diagram is drawn to display the connection relationship and information flow direction between the equipment. Based on the equipment topology network diagram, the interaction relationship of the equipment is defined in the model to ensure the collaborative work between the equipment.

[0023] Spatial flow field refers to the distribution and flow of physical quantities such as air, heat, and light inside and around a building. The evaluation dimensions include physical dimension, spatial dimension, and temporal dimension. The physical dimension includes temperature, humidity, wind speed, light intensity, etc. The spatial dimension refers to the various spatial areas inside the building, such as rooms, corridors, stairwells, etc. The temporal dimension includes the changes in flow field at different time periods, such as morning, afternoon, and night.

[0024] The spatial flow field is constructed based on the physical dimension, spatial dimension, and time dimension, including simulating the heat conduction and mass transfer process inside the building to generate the thermodynamic flow field, simulating the propagation and distribution of natural light and artificial light inside the building to generate the illumination flow field, and simulating the air flow inside and around the building based on fluid dynamics to generate the aerodynamic flow field.

[0025] Substitute the spatial flow field into the three-dimensional model of the building to determine the distribution and change law of each physical quantity inside the building, and determine the heat transfer and mass transfer branch, illumination branch and aerodynamic branch of the building. In the heat transfer and mass transfer branch, heat transfer is the process of heat transfer from one place to another. The heat transfer sub-branch is used to simulate the heat transfer inside the building, including the heat conduction, convection and radiation of the building envelope structure. Mass transfer is the process of matter moving from one place to another. The proton transfer sub-branch is used to simulate the moisture transfer in the air, involving the diffusion, convection and evaporation of moisture. The illumination branch is used to simulate the lighting conditions inside the building, including the influence of natural light and artificial light sources. The aerodynamic branch is used to simulate the behavior of the air flow and ventilation system inside the building.

[0026] Multi-threading technology is used to run different branches simultaneously to ensure that the calculation tasks of each branch are completed independently within the same time period, so as to integrate the branches and generate a comprehensive building dynamics model. The building dynamics model is scalable. For example, a plug-in mechanism is designed for the model, allowing users to add new functional modules or expand the functions of existing modules as needed.

[0027] The building environment field and the building state matrix based on the building state requirements are obtained, and combined with the building dynamics model, field simulation and building effect decision-making are carried out to determine the energy control strategy.

[0028] The building environment field refers to the state of the interior and surrounding environment of a building, including temperature, humidity, light intensity, air flow, etc. By installing environmental sensors inside and outside the building, such as temperature sensors, humidity sensors, light sensors, airflow sensors, etc., real-time environmental data is collected to obtain the building environment field.

[0029] The building status matrix is ​​a multidimensional data matrix that describes the current operating status of the building. It contains the energy demand, equipment status, environmental conditions, etc. of each area inside the building. It obtains status information about the equipment inside the building from the building management system or related monitoring system, such as heating and water supply, internal equipment, air conditioning, lighting, ventilation equipment, etc., and organizes these data into a matrix form to reflect the operating status and energy demand of different equipment under different environmental conditions.

[0030] Field simulation is the simulation of the building environment field, simulating the dynamic processes such as heat transfer, light changes and air flow in different areas of the building under different conditions. The building environment field and building state matrix are input into the building dynamics model, and the building dynamics model is used to simulate the changes in the internal environment of the building under different conditions, simulating heat, humidity, light and air flow, etc. The simulation results are analyzed to evaluate the energy demand and environmental effects of each area in the building.

[0031] Building effect decisions are optimization decisions made based on simulation results to adjust the operating mode of equipment in the building to achieve energy saving and comfort. The overall performance of the building is evaluated based on the simulation results, and areas that need improvement are identified. Specific energy control strategies are formulated based on the evaluation results. Energy control strategies are operation control plans for different equipment inside the building, such as heating and water supply, air conditioning, lighting, ventilation equipment, etc., to achieve optimal use of energy.

[0032] Furthermore, combining the building dynamics model, field simulation and building effect decision making are carried out, including:

[0033] The building environment field is decomposed to determine the decomposed field; based on the decomposed field, the building dynamics model is branch matched and initialized to determine the initialization branch; the initialization branch is traversed to determine the three-dimensional control field in combination with the building state matrix, and the three-dimensional control field is the building state amplitude modulation; the three-dimensional control field is traversed to determine the real-time control strategy based on the device topology network.

[0034] The purpose of field decomposition is to divide the complex building environment field into multiple smaller and relatively independent sub-areas so as to conduct detailed analysis of each sub-area. Specifically, according to the structural characteristics of the building, the building is divided into different functional areas, such as office areas, residential areas, public areas, etc. According to physical characteristics such as temperature distribution, humidity distribution, air flow, etc., the sub-areas within each functional area are further divided to determine the final decomposition field.

[0035] After determining the decomposition field, each decomposition field is matched with each branch of the building dynamics model. Specifically, the corresponding heat transfer sub-branch is matched according to the temperature distribution in the decomposition field, the corresponding mass transfer sub-branch is matched according to the humidity distribution in the decomposition field, the corresponding illumination branch is matched according to the illumination condition in the decomposition field, and the corresponding aerodynamic branch is matched according to the air flow condition in the decomposition field. After the branch matching is completed, each branch is initialized so that the model can correctly reflect the actual situation of the decomposition field.

[0036] Combine the data in the state matrix with the initialization branch, analyze the interaction of different control variables to find the optimal control range and determine the three-dimensional control field. The three-dimensional control field is the spatial distribution range of various control variables in the building, such as temperature, humidity, light, etc. It describes the range of changes in various state variables in the building and their mutual relationship. Building state modulation refers to optimizing the range of changes in the building state by adjusting the control variables to ensure that the building environment is within a comfortable range. For example, a comfortable indoor environment can be maintained by adjusting the air conditioning and lighting systems.

[0037] Analyze each area in the three-dimensional control field and evaluate its performance under different control conditions. For example, analyze the temperature, humidity and light changes in different areas. Based on the analysis results of the equipment topology network and the three-dimensional control field, determine the real-time control strategy applicable to each control field area, including adjusting the settings of air conditioning, lighting, ventilation and other systems to achieve optimal building environment control.

[0038] Furthermore, traversing the three-dimensional control field and determining a real-time control strategy based on the device topology network includes:

[0039] Traversing the three-dimensional control field, identifying the trajectory flow field branch by branch, and determining the characteristic elements of the flow field cycle, wherein the characteristic elements include characteristic vectors and characteristic spatial trends; making control decisions based on the characteristic elements in combination with the device topology network; wherein making control decisions also includes device control collisions, including: reading control collision devices, determining avoidance principles based on control priority and energy consumption priority, wherein the control priority is determined based on the building status requirements, and the energy consumption priority is determined based on low energy consumption; and performing strategy control constraints based on the avoidance principles.

[0040] Traverse the three-dimensional control field, analyze each branch, and identify their respective flow field characteristics. The flow field is an area that describes the movement of fluids, such as air, heat, etc. in space. Trajectory flow field recognition refers to analyzing and identifying the paths and patterns of these fluid flows. For example, the thermal buoyancy flow field refers to the air flow caused by uneven heat distribution. Other examples include wind speed flow field, humidity flow field, etc. The characteristic elements of the flow field cycle are determined based on the recognition results. Among them, the characteristic vector represents the main direction and intensity of the fluid movement in the flow field, and the characteristic spatial trend is the spatial distribution trend of the fluid movement in the flow field. For example, some areas may have strong convection, while other areas are relatively stable.

[0041] Apply flow field characteristic elements to the device topology network and formulate control strategies. For example, if the thermal buoyancy flow field in a certain area affects the indoor temperature, the air conditioning settings in that area can be adjusted to optimize the temperature distribution. According to the control strategy, adjust the settings of related equipment, such as adjusting the fan speed, changing the operation mode of the air conditioner, or adjusting the opening and closing degree of the curtains to achieve the desired environmental effect.

[0042] Equipment control collision refers to the situation in which multiple devices in a building may affect the overall performance of the system due to operational conflicts or resource contention. The collision problem may cause energy waste or reduced equipment efficiency. Through system monitoring, devices with control collisions, such as air conditioners, lighting systems, fans, etc., can be identified.

[0043] Control priority refers to which devices should be given priority when conflicts occur between devices. Priority is usually based on building status requirements, such as temperature, humidity or other environmental conditions. For example, if a constant temperature is required in a building, air conditioning has a higher control priority than lighting systems. Energy consumption priority refers to the priority sorting of multiple device controls with the lowest energy consumption as the goal, that is, giving priority to low-energy operations without affecting the building status, for example, giving priority to more energy-efficient devices or running devices in low-energy mode. Combine control priority and energy consumption priority to develop specific avoidance principles.

[0044] According to the avoidance principle, that is, resources are allocated according to energy consumption priority and control priority, and strategic control constraints are performed according to the resource allocation results to improve energy utilization efficiency.

[0045] Furthermore, the determining of the energy control strategy includes:

[0046] Read environmental sample data, and train the environmental prediction unit with time phase and seasonal phase as constraints; take the building environmental field as the initial node, combine with the environmental prediction unit, perform environmental trend prediction for a preset time zone, and determine the predicted environmental field; determine the time zone trend strategy based on the predicted environmental field; integrate the real-time control strategy with the time zone trend strategy to determine the energy control strategy.

[0047] Retrieve historical environmental data inside and outside the building and extract environmental sample data, which includes environmental parameters such as temperature, humidity, and light intensity inside and outside the building. Temporal phase refers to the daily variation of environmental parameters, such as the change of temperature and light intensity within a day, and seasonal phase refers to the seasonal variation of environmental parameters, such as the temperature difference between summer and winter.

[0048] Select a suitable prediction model, such as a neural network model, extract features related to temporal and seasonal characteristics from environmental sample data, such as timestamps, seasonal indicators, etc., input the prediction model, train the model with historical data, learn the temporal and seasonal patterns of environmental parameters, and establish an environmental prediction unit based on the model training results to predict future environmental conditions.

[0049] Taking the current state of the building environment field as the starting point of the prediction model, the initial environmental state is input into the prediction model, and the temporal and seasonal characteristics are combined to predict the environmental trend of the future preset time zone, such as the next 24 hours or a week. The predicted values ​​of the environmental parameters in the preset time zone are generated, including the future temperature, humidity, light, etc., to determine the predicted environmental field.

[0050] The time zone trend strategy is a control strategy developed based on predicted environmental parameters to optimize the energy use and environmental comfort of buildings. Specifically, it analyzes and predicts the environmental field, evaluates the environmental change trends within the future preset time zone, including temperature fluctuations, humidity changes, etc., and adjusts temperature and light according to the analysis results to generate a time zone trend strategy to ensure that the time zone trend strategy not only meets comfort requirements but also minimizes energy consumption.

[0051] According to the actual needs and goals of the building, set the priority of real-time control and time zone trend strategies. For example, real-time data may have a higher priority in the short term, while long-term trends are used to adjust the overall energy use strategy. Combine the real-time control strategy with the time zone trend strategy according to the priority to form a comprehensive energy control strategy. For example, when the predicted environmental field shows that the temperature will rise in the next few hours, the real-time control strategy should adjust the air conditioning operation to achieve the optimization of energy control and environmental comfort.

[0052] The energy management system deployed in the building responds to the energy control strategy to perform energy distribution and equipment control management.

[0053] The energy control strategy is input into the energy management system deployed in the building. The system automatically controls the operating status of the equipment to ensure that energy is distributed according to the predetermined strategy. For example, the working intensity and operating time of the equipment are adjusted using the automation control module to ensure that they operate normally according to the strategy, thereby achieving the optimal use of building energy and energy saving goals.

[0054] The building environment field is identified, and the building material optimization features are located in combination with the energy control strategy of the preset time zone.

[0055] Define energy control strategies for different time periods, such as energy demand in the morning, daytime, evening, and different seasons, determine preset time zones, and extract energy control strategies for preset time zones, including equipment operating parameters and energy allocation rules for each time period. Match the energy control strategies for preset time zones with the data of the current building environment field, identify energy demand for different time periods, and adjust the time zone strategy based on environmental data to optimize energy use, for example, reduce artificial lighting during periods of strong daylight, or increase air conditioning operation during high temperature periods.

[0056] The optimization characteristics of building materials are identified by analyzing the building environment field and energy control strategies to improve the energy efficiency of the building by identifying the characteristics of building materials that can be improved. Specifically, according to the energy control strategy, the energy consumption target that needs to be optimized is determined, and data related to building performance is obtained, such as thermal conductivity, light transmittance, ventilation effect, etc. The performance of building materials used in the building under different environmental conditions, such as thermal insulation, heat preservation, light transmittance and air tightness, is evaluated, and the deficiencies of building materials are identified, such as excessively high thermal conductivity, insufficient light transmittance, poor sealing, etc. According to the identified defects and target requirements, the characteristics of building materials that need to be improved are determined. Building material characteristics are points that can be optimized, which can be combined with the current construction production market analysis. For example, if some points can be optimized but the cost is too high, they will be removed.

[0057] Furthermore, the positioning building material optimization features include:

[0058] Identify the energy control strategy and read the energy consumption control target, wherein the energy consumption control target is determined based on a preset control frequency and a preset energy consumption threshold; traverse the energy consumption control target and trace back to determine the defect characteristics of building materials, wherein the building material defects are defects of the building materials themselves excluding building assembly; screen and optimize the defect characteristics of the building materials to determine the optimized characteristics of the building materials.

[0059] The energy consumption control target is determined based on the preset control frequency and the preset energy consumption threshold. The control frequency refers to the number of times the system controls and adjusts the equipment and energy within a certain period of time. The frequency is set according to the timeliness and stability of the system response. The energy consumption threshold refers to the upper limit of the building's energy consumption under specific conditions. The threshold is set according to the building's actual energy consumption needs and energy-saving targets.

[0060] Break down the energy consumption control target into specific time periods and equipment, clarify the energy consumption requirements and upper limits of each device in different time periods, collect the actual energy consumption data of each device in different time periods, including the energy consumption of heating, water supply, air conditioning, lighting, ventilation and other equipment, compare the actual energy consumption data with the energy consumption control target, and identify the time periods and equipment where energy consumption exceeds the standard.

[0061] Analyze the causes of time periods and equipment where energy consumption exceeds the standard, trace the source of energy consumption data through data tracing, identify problematic building materials and equipment, correlate energy consumption data with building materials usage data, and identify the characteristics of defective building materials, including the type of building materials, production batch, and location of use.

[0062] Analyze the building assembly data to exclude energy consumption problems caused by improper assembly, that is, exclude problems existing in the assembly process and ensure that the identified defects are problems with the building materials themselves. Based on the data analysis results, extract the defect characteristics of the building materials themselves, such as substandard material quality, poor durability, poor thermal insulation performance, etc.

[0063] Evaluate the defect characteristics of building materials to determine their specific impact on the quality and performance of building materials. Prioritize the defect characteristics according to the degree of impact on building energy consumption, and give priority to defects with greater impact.

[0064] Identify defect features that can be optimized, including material improvement, production process optimization, assembly technology improvement, etc., conduct technical feasibility analysis on each optimization point, and determine the optimization solution that can be achieved under existing technical conditions. Combined with the current construction production market, evaluate the cost of each optimization solution and determine the economic feasibility of the optimization. For example, analyze the current market demand for building material quality and performance, and determine the market acceptance and potential benefits of the optimization solution.

[0065] Comprehensively consider the technical feasibility and economic feasibility of the optimization plan, evaluate the benefits of each optimization point, and determine the optimal optimization direction. Remove the optimization points with too high costs and unclear benefits to ensure that the final optimization direction has practical feasibility and economic benefits. According to the results of the optimization analysis, select the best optimization plan and extract specific building material optimization features.

[0066] Based on the optimization characteristics of building materials and the energy consumption points of building materials production, low-carbon production management is guided in the building materials production stage, wherein the energy consumption points of building materials production include building raw materials and production processes.

[0067] The energy consumption points in building materials production are the main links and factors that consume energy in the process of building materials production, including building raw materials and production processes. Among them, the energy consumption of building raw materials includes the energy consumption in the selection and acquisition process of raw materials, for example, the types of raw materials required for the production of high-efficiency thermal insulation materials, their difficulty in obtaining and energy consumption; the energy consumption of production processes includes the energy consumption of each process link in the production of building materials, for example, the energy consumption of process steps such as material processing, heat treatment, and coating application.

[0068] According to the optimization characteristics of building materials and production energy consumption points, formulate low-carbon production management strategies, for example, select raw materials with higher energy efficiency and better environmental protection, such as using renewable materials or low-energy consumption materials to replace traditional materials; improve various process links in the production process of building materials to reduce energy consumption, such as using advanced processing technology and heat treatment technology to improve production efficiency and reduce energy consumption; analyze the energy consumption data of each production link, identify high energy consumption points, and take targeted measures to optimize, such as optimizing equipment operating parameters, reducing energy waste, etc. According to the low-carbon production management strategy, carry out low-carbon production management to reduce energy consumption in the production process of building materials and achieve environmental protection and energy saving goals.

[0069] Furthermore, the low-carbon production management in the building materials production stage is guided, including:

[0070] The building materials optimization features and the building materials production energy consumption points are traversed to determine the production optimization targets through fitting; the production optimization targets are traversed to perform multi-objective optimization iterations based on cost reduction and resource saving to determine the optimized production strategy, wherein the optimization method includes target variation and cross-replacement, and the optimal optimization method at the iteration layer is taboo locked, and the taboo is unlocked based on a preset number of iterations; based on the optimized production strategy, low-carbon production management is guided in the building materials production stage.

[0071] List the optimized features of building materials, such as improved material properties, optimized production processes, improved assembly technology, etc., and collect data related to these optimized features, including performance indicators, production costs, market demand, etc. List the energy consumption points of building material production, such as the energy consumption of raw materials, the energy consumption of production processes, the energy consumption of production equipment, etc., and collect data related to these energy consumption points, including energy consumption, cost, production efficiency, etc.

[0072] Identify the relationship between the building material optimization features and the energy consumption points of building material production, and determine their common goals or influencing factors, that is, analyze how the optimization features affect the energy consumption points, and identify the potential improvement effects of the optimization features on the energy consumption points. Use data fitting methods, such as regression analysis, to match the optimization features with the data of the energy consumption points to determine the production optimization goals.

[0073] The cost reduction and resource saving benchmarks, such as the percentage of cost reduction and the amount of energy consumption reduction, are used as evaluation criteria for multi-objective optimization. Multi-objective optimization iterations are performed. Specifically, an initial set of optimization solutions is generated, including different production strategy combinations, and iterative solutions are performed. In each round of iteration, target mutation and cross-replacement methods are applied to generate new optimization solutions and evaluate their performance. Target mutation is to randomly mutate the target in the current solution to generate new solutions and explore potential optimization directions. Cross-replacement is to cross-combine the optimization targets of two or more solutions to generate new optimization solutions. Combining the advantages of different targets, the fitness of each solution is evaluated, and its performance is measured based on the cost reduction and resource saving benchmark.

[0074] The best performing optimization solution in the iteration process is taboo locked to avoid repeated exploration of the same optimization solution. After the preset number of iterations, the taboo solution is unlocked to allow new solutions to enter the optimization process, ensuring the breadth and diversity of the search space. The optimization process is terminated based on the preset number of iterations, and the best production strategy is selected as the final optimized production strategy in all iterations.

[0075] Integrate optimized production strategies into the building materials production stage, improve production processes, select low-carbon raw materials, optimize production processes, reduce carbon emissions and energy consumption, and achieve low-carbon production management.

[0076] In summary, the building energy consumption optimization method based on the Internet of Things provided in the embodiments of the present application has the following technical effects:

[0077] By dividing the entire life cycle of the target building into stages and determining the key optimization stages, including the building materials production stage and the building operation stage, it is possible to accurately optimize the stage with the highest energy consumption; by building a building dynamics model that includes heat and mass transfer branches, lighting branches, and aerodynamic branches, it is possible to fully simulate the interaction between the internal and external environments of the building. This comprehensive model helps analyze the energy consumption performance of the building under different environmental conditions and provide more accurate energy consumption predictions; by obtaining the building environment field and the building state matrix, and combining it with the building dynamics model, it is possible to accurately simulate the energy usage inside the building. This simulation helps to consider various environmental and state factors when making decisions and formulate more effective energy control strategies; through the energy management system deployed in the building, energy distribution and equipment control are automatically carried out according to the energy control strategy, thereby realizing intelligent building energy efficiency management; by identifying the building environment field and combining the energy control strategy with the preset time zone to locate the building material optimization characteristics, it is possible to reduce the energy consumption loss caused by building material problems during the use of the building; based on the building material optimization characteristics and production energy consumption points, it guides the implementation of low-carbon management in the building material production stage, reduces the energy consumption in building raw materials and production processes, and reduces the overall carbon footprint of building materials.

[0078] Based on the same inventive concept as the method for optimizing building energy consumption based on the Internet of Things in the aforementioned embodiment, Figure 2 As shown, the embodiment of the present application provides a building energy consumption optimization system based on the Internet of Things, and the system includes:

[0079] The stage division module 10 is used to divide the target building into stages throughout its life cycle and determine the key optimization stage, wherein the key optimization stage includes the building material production stage and the building operation stage; the model building module 20 is used to build a building dynamics model based on the building internal and external environment for the building operation stage, wherein the building dynamics model includes a heat and mass transfer branch, a lighting branch, and an aerodynamic branch; the decision module 30 is used to obtain the building environment field and the building state matrix based on the building state requirements, and combine the building dynamics model to perform field optimization. Domain simulation and building effect decision-making to determine the energy control strategy; a control management module 40, the control management module 40 is used for the energy management system deployed in the building to respond to the energy control strategy to perform energy distribution and equipment control management; a feature positioning module 50, the feature positioning module 50 is used to identify the building environment field, and locate the building material optimization features in combination with the energy control strategy of the preset time zone; a production management module 60, the production management module 60 is based on the building material optimization features and the building material production energy consumption points, to guide the low-carbon production management of the building material production stage, wherein the building material production energy consumption points include building raw materials and production processes.

[0080] Furthermore, the system also includes a building dynamics model construction module to perform the following operation steps:

[0081] Perform three-dimensional modeling on the target building to determine the three-dimensional model of the building, wherein the three-dimensional model of the building includes an equipment topology network determined by assembly equipment that interacts with the target building; based on the three-dimensional model of the building, a spatial flow field is constructed based on an evaluation dimension, and data-driven modeling is performed to determine the heat and mass transfer branch, the illumination branch, and the aerodynamic branch; the heat and mass transfer branch, the illumination branch, and the aerodynamic branch are parallelized to generate the building dynamics model, wherein the building dynamics model is scalable.

[0082] Furthermore, the system further includes a real-time control strategy determination module to perform the following operation steps:

[0083] The building environment field is decomposed to determine the decomposed field; based on the decomposed field, the building dynamics model is branch matched and initialized to determine the initialization branch; the initialization branch is traversed to determine the three-dimensional control field in combination with the building state matrix, and the three-dimensional control field is the building state amplitude modulation; the three-dimensional control field is traversed to determine the real-time control strategy based on the device topology network.

[0084] Furthermore, the system further includes a policy control constraint module to perform the following operation steps:

[0085] Traversing the three-dimensional control field, identifying the trajectory flow field branch by branch, and determining the characteristic elements of the flow field cycle, wherein the characteristic elements include characteristic vectors and characteristic spatial trends; making control decisions based on the characteristic elements in combination with the device topology network; wherein making control decisions also includes device control collisions, including: reading control collision devices, determining avoidance principles based on control priority and energy consumption priority, wherein the control priority is determined based on the building status requirements, and the energy consumption priority is determined based on low energy consumption; and performing strategy control constraints based on the avoidance principles.

[0086] Furthermore, the system further includes an energy control strategy determination module to perform the following operation steps:

[0087] Read environmental sample data, and train the environmental prediction unit with time phase and seasonal phase as constraints; take the building environmental field as the initial node, combine with the environmental prediction unit, perform environmental trend prediction for a preset time zone, and determine the predicted environmental field; determine the time zone trend strategy based on the predicted environmental field; integrate the real-time control strategy with the time zone trend strategy to determine the energy control strategy.

[0088] Furthermore, the system also includes a building material optimization feature determination module to perform the following operation steps:

[0089] Identify the energy control strategy and read the energy consumption control target, wherein the energy consumption control target is determined based on a preset control frequency and a preset energy consumption threshold; traverse the energy consumption control target and trace back to determine the defect characteristics of building materials, wherein the building material defects are defects of the building materials themselves excluding building assembly; screen and optimize the defect characteristics of the building materials to determine the optimized characteristics of the building materials.

[0090] Furthermore, the system also includes a low-carbon production management module to perform the following operation steps:

[0091] The building materials optimization features and the building materials production energy consumption points are traversed to determine the production optimization targets through fitting; the production optimization targets are traversed to perform multi-objective optimization iterations based on cost reduction and resource saving to determine the optimized production strategy, wherein the optimization method includes target variation and cross-replacement, and the optimal optimization method at the iteration layer is taboo locked, and the taboo is unlocked based on a preset number of iterations; based on the optimized production strategy, low-carbon production management is guided in the building materials production stage.

[0092] Through the above detailed description of the building energy consumption optimization method based on the Internet of Things, those skilled in the art can clearly understand the building energy consumption optimization system based on the Internet of Things in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0093] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A building energy consumption optimization method based on the Internet of Things, characterized in that: The method comprises: Divide the target building's entire life cycle into stages and determine the key optimization stages, where the key optimization stages include the building materials production stage and the building operation stage; For the building operation stage, a building dynamics model is constructed based on the internal and external environment of the building, wherein the building dynamics model includes a heat and mass transfer branch, a lighting branch, and an aerodynamic branch; Acquire the building environment field and the building state matrix based on the building state requirements, combine the building dynamics model, perform field simulation and building effect decision, and determine the energy control strategy. The building state matrix is ​​a multidimensional data matrix that describes the current operating state of the building. The building effect decision is an optimization decision made based on the simulation results to adjust the operating mode of the equipment in the building. The energy management system deployed in the building performs energy distribution and equipment control management in response to the energy control strategy; Identify the building environment field, and locate the building material optimization features in combination with the energy control strategy of the preset time zone, wherein the building environment field is the state of the interior and surrounding environment of the building; Based on the building materials optimization characteristics and building materials production energy consumption points, guide the building materials production stage to carry out low-carbon production management, wherein the building materials production energy consumption points are the main links and factors that consume energy in the building materials production process, including building raw materials and production processes; The building dynamics model is constructed based on the internal and external environment of the building, including: Performing three-dimensional modeling on the target building to determine a three-dimensional building model, wherein the three-dimensional building model includes a device topology network determined by assembly devices interacting with the target building; Based on the three-dimensional building model, a spatial flow field is constructed based on the evaluation dimension, and data-driven modeling is performed to determine the heat and mass transfer branch, the illumination branch, and the aerodynamic branch; The heat and mass transfer branch, the illumination branch and the aerodynamic branch are operated in parallel to generate the building dynamics model, wherein the building dynamics model is scalable; Combined with the building dynamics model, field simulation and building effect decision-making are carried out, including: Performing field decomposition on the building environment field to determine the decomposition field; Based on the decomposition field, performing branch matching and initialization on the building dynamics model to determine an initialization branch; Traversing the initialization branch, combining the building state matrix, and determining a three-dimensional control field, wherein the three-dimensional control field is a building state amplitude modulation; Traversing the three-dimensional control field to determine a real-time control strategy based on the device topology network; The determining of the energy control strategy comprises: Read environmental sample data, and train the environmental prediction unit based on temporal and seasonal constraints; Taking the building environment field as the initial node, combined with the environment prediction unit, the environment trend prediction of the preset time zone is performed to determine the predicted environment field; Determining a time zone trend strategy based on the predicted environmental field; Integrate the real-time control strategy with the time zone trend strategy to determine the energy control strategy; The positioning building material optimization features include: Identify the energy control strategy and read the energy consumption control target, wherein the energy consumption control target is determined based on a preset control frequency and a preset energy consumption threshold; Traversing the energy consumption control target, tracing back to determine the characteristics of building material defects, wherein the building material defects are building material defects excluding building assembly defects; The defect characteristics of the building materials are screened and optimized to analyze and determine the optimized characteristics of the building materials.

2. The method for optimizing building energy consumption based on the Internet of Things according to claim 1, characterized in that: Traversing the three-dimensional control field and determining a real-time control strategy based on the device topology network, including: Traversing the three-dimensional control field, performing trajectory flow field recognition branch by branch, and determining characteristic elements of the flow field cycle, wherein the characteristic elements include characteristic vectors and characteristic space trends; Based on the characteristic elements, a control decision is made in combination with the device topology network; Among them, making control decisions also includes equipment control collision, including: Reading a control collision device, and determining an avoidance principle based on a control priority and an energy consumption priority, wherein the control priority is determined based on the building status requirement, and the energy consumption priority is determined based on low energy consumption; Based on the avoidance principle, strategic control constraints are performed.

3. The building energy consumption optimization method based on the Internet of Things according to claim 1, characterized in that: Guide the low-carbon production management in the building materials production stage, including: Traversing the building material optimization features and the building material production energy consumption points, and fitting to determine the production optimization target; Traversing the production optimization objectives, taking cost reduction and resource saving as the benchmark, performing multi-objective optimization iteration, and determining the optimized production strategy, wherein the optimization method includes target variation and cross-replacement, and locking the optimal optimization method of the iteration layer by taboo, and unlocking the taboo based on the preset number of iterations; Based on the optimized production strategy, the building materials production stage is guided to carry out low-carbon production management.

4. Building energy consumption optimization system based on the Internet of Things, characterized by: For implementing the method for optimizing building energy consumption based on the Internet of Things according to any one of claims 1 to 3, the system comprises: A stage division module, which is used to divide the target building into stages during its entire life cycle and determine key optimization stages, wherein the key optimization stages include a building material production stage and a building operation stage; A model building module, wherein the model building module is used to build a building dynamics model based on the internal and external environment of the building during the operation phase of the building, wherein the building dynamics model includes a heat and mass transfer branch, a lighting branch, and an aerodynamic branch; A decision module, the decision module is used to obtain the building environment field and the building state matrix based on the building state requirements, combine the building dynamics model, perform field simulation and building effect decision, and determine the energy control strategy. The building state matrix is ​​a multidimensional data matrix that describes the current operating state of the building. The building effect decision is an optimization decision made based on the simulation results to adjust the operating mode of the equipment in the building; A control management module, wherein the energy management system deployed in the building responds to the energy control strategy to perform energy distribution and equipment control management; A feature positioning module, the feature positioning module is used to identify the building environment field, and locate the building material optimization features in combination with the energy control strategy of the preset time zone, the building environment field is the state of the building interior and its surrounding environment; A production management module, which guides the low-carbon production management in the building materials production stage based on the building materials optimization characteristics and building materials production energy consumption points, wherein the building materials production energy consumption points are the main links and factors that consume energy in the building materials production process, including building raw materials and production processes.

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