Low-carbon building material building energy consumption intelligent management method and system

Abstract

The application discloses a kind of low-carbon building material building energy consumption intelligent management method and system, it is related to building management technical field, method includes: obtaining building component attribute information, the building component attribute information includes unique identifier, material type, geometric dimension and location;According to the building component attribute information, select an item maintenance strategy from maintenance strategy library as candidate maintenance strategy;The economic evaluation of the candidate maintenance strategy is carried out, and economic evaluation result is obtained;According to the economic evaluation result, the maintenance strategy with highest economy in the maintenance strategy library is used as target maintenance strategy;According to the target maintenance strategy, the building component attribute information and the economic evaluation result, generate maintenance suggestion.The application can combine building component attribute information and economic evaluation to generate maintenance suggestion, to realize building energy consumption management, improve accuracy and reliability.

Description

Technical Field

[0001] This invention relates to the field of building management technology, and in particular to a method and system for intelligent management of energy consumption in low-carbon building materials. Background Technology

[0002] In modern building management, achieving low-carbon operation throughout the building's entire lifecycle, especially in buildings that widely adopt low-carbon building materials, requires refined and intelligent management of building energy consumption. Existing systems integrate building material attributes and structural information provided by Building Information Modeling (BIM) with data collected from real-time energy consumption monitoring devices distributed throughout the building to assess the actual energy-saving effects of building materials and optimize building operation strategies. However, the natural degradation of building material performance is an unavoidable problem during long-term building operation. This causes the energy consumption prediction models in existing systems, based on ideal design parameters, to gradually deviate from the actual physical state of the building, thus affecting the accuracy of system decisions and resulting in low accuracy and reliability in energy consumption management.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose a method and system for intelligent management of building energy consumption using low-carbon building materials. This method can combine building component attribute information and economic assessment to generate maintenance recommendations, thereby achieving building energy consumption management and improving accuracy and reliability.

[0005] On one hand, embodiments of the present invention provide a method for intelligent management of energy consumption in low-carbon building materials, comprising the following steps:

[0006] Obtain the attribute information of building components, which includes a unique identifier, material type, geometric dimensions, and location;

[0007] Based on the building component attribute information, select a maintenance strategy from the maintenance strategy library as a candidate maintenance strategy;

[0008] An economic evaluation is performed on the candidate maintenance strategies to obtain the economic evaluation results;

[0009] Based on the economic evaluation results, the most economical maintenance strategy in the maintenance strategy library will be selected as the target maintenance strategy.

[0010] Based on the target maintenance strategy, the building component attribute information, and the economic assessment results, maintenance recommendations are generated.

[0011] In some embodiments, the step of performing an economic evaluation on the candidate maintenance strategies to obtain an economic evaluation result includes:

[0012] Calculate the estimated cost of the candidate maintenance strategy based on the cost database;

[0013] Energy consumption simulations were performed on the candidate maintenance strategies to calculate energy-saving benefits;

[0014] Based on the estimated costs and energy-saving benefits, an economic evaluation is conducted on the candidate maintenance strategies to obtain the economic evaluation results.

[0015] In some embodiments, the step of performing energy consumption simulation on the candidate maintenance strategy and calculating energy-saving benefits includes:

[0016] Acquire current building component status data, current environmental data, and historical meteorological data;

[0017] Construct a thermal response mapping relationship;

[0018] Based on the thermal response mapping relationship, the current building component status data and the current environmental data are mapped to target thermal response parameters;

[0019] Based on the target thermal response parameters, the historical meteorological data, and the indoor environmental setpoints, energy consumption simulation is performed on the candidate maintenance strategies to obtain the building energy consumption before and after maintenance.

[0020] The energy-saving benefits are calculated based on the building energy consumption before and after maintenance.

[0021] In some embodiments, constructing the thermal response mapping relationship includes:

[0022] Acquire historical building component status data and historical environmental data. The historical building component status data includes internal component temperature data, component surface temperature data, and component heat flow data. The historical environmental data includes external temperature, external humidity, external air pressure, external wind speed, and light intensity.

[0023] Based on the historical environmental data, environmental change events are identified, including sudden drops in temperature, prolonged periods of high humidity, or intense sunlight.

[0024] Based on the environmental change events, identify the component thermal response parameters, which include the surface temperature change rate, the magnitude of heat flow change, thermal resistance, and heat transfer coefficient.

[0025] Based on the historical building component status data, the historical environmental data, and the component thermal response parameters, the thermal response mapping relationship is constructed using nonlinear regression analysis.

[0026] In some embodiments, after constructing the thermal response mapping, the method further includes:

[0027] Acquire historical component microstructure data, current component microstructure data, historical moisture accumulation data, and current moisture accumulation data;

[0028] The degree of improvement in the component's microstructure is assessed based on the historical component microstructure data and the current component microstructure data.

[0029] The degree of improvement in water accumulation is assessed based on the historical water accumulation data and the current water accumulation data.

[0030] The thermal response mapping relationship is adjusted based on the degree of improvement in the microstructure of the component and the degree of improvement in moisture accumulation.

[0031] In some embodiments, the step of conducting an economic evaluation of the candidate maintenance strategies based on the expected costs and the energy-saving benefits to obtain the economic evaluation results includes:

[0032] Obtain building materials market price data;

[0033] Based on the building materials market price data, predict the price fluctuation range and price probability distribution;

[0034] The estimated cost is updated based on the price fluctuation range and the price probability distribution;

[0035] Identify the resource competition relationships between different maintenance strategies in the maintenance strategy library;

[0036] Identify the correlation of repair effects between different maintenance strategies in the maintenance strategy library;

[0037] Based on the resource competition relationship, the correlation of repair effect, the energy saving benefit, and the expected cost after the update, the economic evaluation of the candidate maintenance strategy is carried out to obtain the economic evaluation result.

[0038] In some embodiments, identifying the correlation of repair effects between different maintenance strategies in the maintenance strategy library includes:

[0039] Obtain the physical proximity and material properties of adjacent building components;

[0040] Based on the physical proximity and the material properties, assess the performance impact of each maintenance strategy on the adjacent building components;

[0041] Based on the aforementioned performance impact, determine the intensity and scope of the repair effect;

[0042] The repair effect correlation is constructed based on the physical proximity relationship, the material properties, the repair effect intensity, and the repair effect influence range.

[0043] In some embodiments, identifying resource competition relationships between different maintenance strategies in the maintenance strategy library includes:

[0044] Obtain the resource requirement types and quantities corresponding to different maintenance strategies;

[0045] Monitor and maintain the real-time resource availability in the resource pool;

[0046] Set a fluctuation range for resource requirements;

[0047] Based on the resource requirement type, determine whether there is any overlap in resource requirements between different maintenance strategies;

[0048] If there is an overlap in resource requirements among different maintenance strategies, the resource allocation order shall be adjusted according to the quantity of resource requirements, the real-time availability of resources, the fluctuation range of resource requirements, and the priority sequence of maintenance strategies.

[0049] The resource competition relationship is constructed based on the resource demand type, the resource demand quantity, and the resource allocation order.

[0050] In some embodiments, setting the resource demand fluctuation range includes:

[0051] Retrieve historical maintenance data;

[0052] Extract the resource consumption fluctuation range of the same type of resource from the historical maintenance data;

[0053] Based on the fluctuation range of resource consumption, the fluctuation range of resource demand is set.

[0054] On the other hand, embodiments of the present invention provide a low-carbon building materials building energy consumption intelligent management system, including:

[0055] The information acquisition module is used to acquire the attribute information of building components, including unique identifiers, material types, geometric dimensions, and locations.

[0056] The candidate maintenance strategy selection module is used to select a maintenance strategy from the maintenance strategy library as a candidate maintenance strategy based on the building component attribute information.

[0057] An economic evaluation module is used to perform an economic evaluation on the candidate maintenance strategies and obtain the economic evaluation results.

[0058] The target maintenance strategy determination module is used to select the most economical maintenance strategy from the maintenance strategy library as the target maintenance strategy based on the economic evaluation results.

[0059] The maintenance suggestion generation module is used to generate maintenance suggestions based on the target maintenance strategy, the building component attribute information, and the economic assessment results.

[0060] The embodiments of this application include at least the following beneficial effects: First, the embodiment of this application obtains the attribute information of building components, selects a maintenance strategy from the maintenance strategy library as a candidate maintenance strategy, then performs an economic evaluation on the candidate maintenance strategy to obtain the economic evaluation result, and then, based on the economic evaluation result, selects the maintenance strategy with the highest economic efficiency in the maintenance strategy library as the target maintenance strategy, and finally generates maintenance suggestions based on the target maintenance strategy, the attribute information of building components, and the economic evaluation result. This enables the generation of maintenance suggestions by combining the attribute information of building components and the economic evaluation, thereby realizing building energy consumption management and improving accuracy and reliability.

[0061] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0063] Figure 1 This is a flowchart illustrating an intelligent energy consumption management method for low-carbon building materials according to an embodiment of the present invention;

[0064] Figure 2 This is a schematic diagram of the structure of a low-carbon building materials building energy consumption intelligent management system according to an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0066] In related technologies, in modern building management, to achieve the goal of low-carbon operation throughout the entire building lifecycle, especially in buildings that widely adopt low-carbon building materials, refined and intelligent management of building energy consumption is particularly important. Existing systems integrate building material attributes and structural information provided by Building Information Modeling (BIM) with data collected by real-time energy consumption monitoring devices distributed throughout the building to evaluate the actual energy-saving effect of building materials and optimize building operation strategies. However, during the long-term operation of a building, the natural degradation of building material performance is an unavoidable problem. This causes the energy consumption prediction models in existing systems, based on ideal design parameters, to gradually deviate from the actual physical state of the building, thereby affecting the accuracy of system decisions and resulting in low accuracy and reliability of energy consumption management.

[0067] For example, in the early stages of operation of a newly constructed large public building, such as a city exhibition hall, a low-carbon building materials and intelligent building energy consumption management system was fully adopted in its design and construction process. During the project planning phase, a detailed building information model was established. This model not only included conventional information such as the building's structure and pipelines, but also specifically recorded detailed performance parameters for each low-carbon building material used, such as the thermal conductivity of the external wall insulation board, the power generation efficiency of the roof photovoltaic tiles, and the relationship between the light transmittance and heat insulation rate of the smart dimming glass. These parameters were all derived from design values ​​provided by the material suppliers. After the building was completed, numerous sensors were deployed inside and outside the building to collect real-time environmental information, including temperature, humidity, and light intensity in various areas, as well as the actual power consumption of various electrical systems, such as air conditioning, lighting, and elevator systems. The core working principle of this intelligent management system is to use the real-time environmental information collected by the sensors as input conditions, substitute it into the building information model for thermal and energy consumption calculations, and derive a theoretical energy consumption prediction value. The system then compares this predicted value with the actual energy consumption values ​​collected by sensors during the same time period. This system operates normally during the initial phase of a building's use. Through comparative analysis, the system can accurately identify irrational energy consumption behaviors caused by human activity, improper equipment use, etc., and guide the operations team to make optimizations and adjustments, such as adjusting the air conditioning's air delivery strategy and optimizing the lighting system's on / off times. This ensures that the building's actual energy consumption closely matches the theoretical design value, fully leveraging the energy-saving benefits of low-carbon building materials.

[0068] However, as the building operated for some time, problems began to emerge. Despite technicians strictly following the system's optimization recommendations and eliminating short-term disturbances such as personnel and equipment issues, the system's monitoring reports consistently showed that the building's actual total energy consumption gradually and systematically exceeded the model's predictions, and this gap widened year by year. Initially, technicians attributed this to the efficiency degradation of certain large energy-consuming equipment, such as the central air conditioning chiller units. However, after inspecting and testing all major equipment, they found that the equipment performance was within the normal degradation range, and its impact was insufficient to explain such a large energy consumption deviation. This shifted the focus to the building envelope itself, namely the low-carbon building materials. The building material performance parameters in the Building Information Model (BIM) are a set of static, fixed values ​​from the moment the building is completed. But in the real world, the performance of these materials changes over time. For example, external wall insulation materials may experience changes in their internal structure due to minor water seepage or repeated freeze-thaw cycles, leading to a gradual decline in insulation performance. The sealing strips used for window sealing will age and crack under prolonged exposure to wind and sun, leading to poorer airtightness of the building and increased penetration of hot and cold air. The waterproof and heat-insulating coatings on the roof may also degrade in performance due to ultraviolet radiation. This gradual degradation of material performance throughout the building is the root cause of the ever-widening energy consumption imbalance.

[0069] The problem is that existing systems cannot identify the performance degradation of low-carbon building materials. Sensor networks can only measure macroscopic environmental changes and total energy consumption, such as an increase in room temperature and increased air conditioning power consumption, but the system cannot determine whether this is due to a decline in the insulation performance of west-facing walls or a problem with window sealing. For technicians, they only know that there is a problem with the building's performance, but cannot make a precise diagnosis. If the root cause cannot be found, technicians can only take general, costly remedial measures, such as completely renovating the entire south facade or replacing all windows that have been in use for more than five years. This approach is not only expensive, but also likely involves a large portion of the replaced materials still performing well, resulting in huge waste and violating the original intention of low-carbon building life cycle management.

[0070] In intelligent management systems based on Building Information Modeling (BIM) and real-time energy consumption monitoring, the performance parameters of building materials in the BIM are typically set based on theoretical or initial values ​​from the design phase, and these parameters remain fixed within the model. However, during the long-term use of a building, the actual performance of various low-carbon building materials inevitably degrades due to environmental erosion, material aging, and other factors, causing the BIM to fail to accurately reflect the building's true physical state. This discrepancy between the model and reality means that while the system can detect an abnormal increase in total building energy consumption, it cannot effectively distinguish whether the increase is caused by the degradation of building material performance or by other operational problems (such as equipment failure), let alone pinpoint which building material or specific location experienced performance degradation. Therefore, a correction mechanism is needed to utilize existing sensor network data (such as indoor and outdoor temperatures, regional energy consumption, etc.) that do not typically directly measure the intrinsic properties of materials to infer and quantify the degree of degradation of the performance of low-carbon building materials on specific building components over time. These corrected performance parameters can then be updated into the building information model, enabling the model to approximate the actual state of the building, achieve root cause diagnosis of energy consumption anomalies, and provide a basis for accurate and low-cost maintenance decisions.

[0071] The embodiments of this application will be explained in detail below with reference to the accompanying drawings:

[0072] Figure 1 This is an optional flowchart of a method for intelligent management of building energy consumption using low-carbon building materials, provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0073] Step S101: Obtain the attribute information of building components, which includes unique identifiers, material types, geometric dimensions, and locations.

[0074] Step S102: Select a maintenance strategy from the maintenance strategy library as a candidate maintenance strategy based on the building component attribute information;

[0075] Step S103: Conduct an economic evaluation of the candidate maintenance strategies and obtain the economic evaluation results;

[0076] Step S104: Based on the economic evaluation results, select the most economical maintenance strategy from the maintenance strategy library as the target maintenance strategy.

[0077] Step S105: Generate maintenance recommendations based on the target maintenance strategy, building component attribute information, and economic assessment results.

[0078] Steps S101 to S105 as shown in the embodiments of this application can combine building component attribute information and economic assessment to generate maintenance recommendations, thereby realizing building energy consumption management and improving accuracy and reliability.

[0079] In some embodiments, steps S101-S105 can first obtain the attribute information of building components, which includes a unique identifier, material type, geometric dimensions, and location. This information can be manually entered into the system one by one from architectural design drawings and construction documents. For example, for an office building, its exterior wall's unique identifier can be manually entered as "exterior wall-A01," material type as "aerated concrete," geometric dimensions as "10m long, 3m high," and location as "first floor, south facade." While this method requires some manual effort, it ensures the accuracy of the initial data. Alternatively, the information can be obtained from an attribute information database, which pre-stores the attribute information of building components.

[0080] Then, based on the building component attribute information, a maintenance strategy is selected from the maintenance strategy library as a candidate maintenance strategy. For example, when the system obtains that the material type of a building component is "insulation board," it can filter all maintenance strategies related to "insulation board" from the maintenance strategy library according to preset rules, such as "insulation board repair," "insulation board replacement," or "insulation layer reinforcement," and use them as candidate maintenance strategies. Alternatively, based on the component's geometric dimensions and location, combined with historical maintenance data, the most likely applicable maintenance strategy can be manually selected as a candidate. For example, for an insulation layer located on a roof, strategies such as "waterproofing layer repair" or "roof insulation layer repaving" might be given priority. It is understood that the maintenance strategy library is a preset collection containing various maintenance solutions for different building components and materials, with each strategy detailing the maintenance measures, required resources, and expected results.

[0081] Next, an economic evaluation is conducted on the candidate maintenance strategies to obtain the economic evaluation results. For example, for the candidate maintenance strategy of "insulation board repair," the required material costs, labor costs, and construction time can be manually estimated and used as inputs for the economic evaluation. For the "insulation board replacement" strategy, the costs of removing the old insulation board, purchasing new insulation board, and installing the new insulation board need to be estimated. These estimates will be part of the economic evaluation results.

[0082] Based on the economic evaluation results, the maintenance strategy with the highest economic efficiency in the maintenance strategy library will be selected as the target maintenance strategy. For example, if an economic evaluation of the two candidate maintenance strategies, "insulation board repair" and "insulation board replacement," reveals that the expected cost of "insulation board repair" is significantly lower than that of "insulation board replacement," and the expected results meet the requirements, then "insulation board repair" will be selected as the target maintenance strategy.

[0083] Finally, based on the target maintenance strategy, building component attribute information, and economic assessment results, maintenance recommendations are generated. For example, if the target maintenance strategy is "insulation board repair," the building component attribute information shows that the component is "exterior wall-A01," and the economic assessment result indicates that the repair cost is 5,000 yuan, then the system can generate a maintenance recommendation that includes: "It is recommended to repair the insulation board of exterior wall-A01, with an estimated cost of 5,000 yuan, a recommended construction period of 3 days, and the required materials being insulation mortar and repair tools." This recommendation can be presented to the building manager in the form of a report.

[0084] This embodiment introduces an economic evaluation of maintenance strategies and dynamically selects the optimal maintenance strategy based on the evaluation results, enabling more accurate identification and resolution of energy consumption problems caused by the degradation of building material performance. For example, in existing systems, when an abnormal increase in building energy consumption is detected, it may only be possible to vaguely suggest checking equipment or adjusting operating parameters, without pinpointing which insulation board's performance degradation is causing the increased heat loss. This embodiment can recommend the most economical and effective maintenance solution by evaluating the economics of different maintenance strategies, such as comparing the cost-effectiveness of "repairing a partial insulation layer" versus "replacing the entire wall insulation layer." This decision-making mechanism based on actual data and economic considerations demonstrates a significant advancement over existing technologies in terms of the accuracy of energy consumption diagnosis, the economy of maintenance decisions, and management efficiency.

[0085] Through the above technical solution, this embodiment can solve the problem in traditional building energy consumption management where the model does not match reality due to the degradation of building material performance, thus making it impossible to accurately diagnose the root cause of abnormal energy consumption. This embodiment obtains the attribute information of building components, providing basic data for subsequent maintenance decisions. Subsequently, candidate maintenance strategies are selected from the maintenance strategy library based on this attribute information, ensuring the targeting of maintenance solutions. The key lies in the economic evaluation of candidate maintenance strategies, which enables the system to quantify the cost-effectiveness of different maintenance solutions, thereby avoiding blind decision-making. By comparing the economic evaluation results, the system can intelligently determine the target maintenance strategy with the highest economic efficiency, ensuring the optimization of maintenance investment. Finally, by combining the target maintenance strategy, building component attribute information, and economic evaluation results, specific maintenance recommendations are generated, providing actionable guidance for building managers.

[0086] In some embodiments, step S103 involves an economic evaluation of the candidate maintenance strategies to obtain the economic evaluation results, which may include, but is not limited to, the following steps:

[0087] Step S201: Calculate the estimated cost of candidate maintenance strategies based on the cost database;

[0088] Step S202: Perform energy consumption simulation on the candidate maintenance strategies and calculate the energy-saving benefits;

[0089] Step S203: Based on the expected costs and energy-saving benefits, conduct an economic evaluation of the candidate maintenance strategies to obtain the economic evaluation results.

[0090] In some embodiments, the estimated cost of candidate maintenance strategies can be calculated first based on a cost database. The total cost required to implement a candidate maintenance strategy can be predicted using a pre-defined calculation model (e.g., based on historical data regression analysis, expert experience estimation, or market price inquiries) based on the various inputs involved in the candidate maintenance strategy, such as materials, labor, and equipment. The purpose is to quantify the direct economic input of the maintenance strategy. The cost database refers to a collection that stores historical cost data and real-time market price information related to maintenance activities, such as various building materials, construction labor, equipment rental, and transportation. Its purpose is to provide accurate and reliable data support for calculating the estimated cost of candidate maintenance strategies.

[0091] Then, energy consumption simulations are performed on candidate maintenance strategies to calculate energy-saving benefits. Professional building energy consumption simulation software or models (such as DesignBuilder, EnergyPlus, etc.) can be used to predict changes in building energy consumption before and after the implementation of maintenance strategies, considering factors such as the current state of building components, environmental conditions, and the expected effects after the implementation of the maintenance strategy. The purpose is to quantify the potential energy-saving benefits of maintenance strategies. Energy-saving benefits refer to the economic value converted from the amount of energy saved after the implementation of the maintenance strategy, as obtained through energy consumption simulation. This can be calculated by comparing building energy consumption data before and after maintenance, combined with local energy prices. The purpose is to evaluate the contribution of maintenance strategies to reducing operating costs.

[0092] Then, based on the projected costs and energy-saving benefits, an economic evaluation is conducted on the candidate maintenance strategies to obtain the economic evaluation results. The inputs (projected costs) and outputs (energy-saving benefits) of the maintenance strategies can be comprehensively considered, and economic indicators such as return on investment (ROI), net present value (NPV), and payback period can be used to quantify the overall economic feasibility and attractiveness of the maintenance strategies, thereby obtaining the final economic evaluation results.

[0093] This embodiment introduces quantitative calculations of projected costs and energy-saving benefits, transforming the economic evaluation of candidate maintenance strategies from a vague qualitative judgment into a precise quantitative analysis based on specific data and models. Specifically, obtaining accurate cost data from a cost database ensures the reliability of projected costs; calculating energy-saving benefits through energy consumption simulation allows for the scientific prediction of the actual impact of maintenance strategies on building energy consumption. The acquisition of these two key indicators provides a solid data foundation for subsequent economic evaluation, making the evaluation results more convincing and accurately reflecting the true economic value of the maintenance strategies.

[0094] To illustrate this technical solution more clearly, a specific example is used below. Suppose a building component (e.g., the exterior wall insulation layer) shows signs of aging. The system identifies two candidate maintenance strategies: Strategy A (partial repair) and Strategy B (complete replacement with new insulation material). First, the system calculates the estimated cost of Strategy A (e.g., material and labor costs totaling 50,000 yuan) and Strategy B (e.g., material and labor costs totaling 200,000 yuan) based on a cost database. Next, the system simulates energy consumption for both strategies. During the simulation, the improved insulation performance after repair or replacement, as well as local climate conditions and indoor set temperature, are considered. The simulation results show that Strategy A can save 10,000 yuan in energy costs annually, while Strategy B can save 50,000 yuan annually. Finally, the system evaluates the economic viability of both strategies based on the estimated costs and energy-saving benefits. For example, by calculating the return on investment (ROI): The ROI for Strategy A is (10,000 yuan / year) / 50,000 yuan = 20% / year; the ROI for Strategy B is (50,000 yuan / year) / 200,000 yuan = 25% / year. Based on this economic assessment, although Strategy B has a higher initial investment, its long-term economic benefits are superior, and therefore it may be identified as a more economical maintenance strategy. In this way, this embodiment can provide users with quantitative and comparable economic assessment results, thereby assisting users in selecting the optimal maintenance strategy.

[0095] Through the above technical solution, this embodiment can significantly improve the accuracy and reliability of economic evaluation of candidate maintenance strategies. By quantifying the expected costs and energy-saving benefits, the evaluation process becomes more transparent and objective, avoiding biases caused by subjective judgment. This not only helps managers to better understand the economic input and output of various maintenance strategies, thus making more informed decisions, but also effectively identifies maintenance strategies with genuine cost-effectiveness and energy-saving potential, providing a solid economic basis for achieving intelligent energy management in low-carbon building materials.

[0096] In some embodiments, step S202, performing energy consumption simulation on candidate maintenance strategies and calculating energy-saving benefits, may include, but is not limited to, the following steps:

[0097] Step S301: Obtain current building component status data, current environmental data, and historical meteorological data;

[0098] Step S302: Construct thermal response mapping relationship;

[0099] Step S303: Based on the thermal response mapping relationship, map the current building component status data and the current environmental data to the target thermal response parameters;

[0100] Step S304: Based on the target thermal response parameters, historical meteorological data and indoor environmental setpoints, perform energy consumption simulation on the candidate maintenance strategies to obtain the building energy consumption before and after maintenance.

[0101] Step S305: Calculate the energy-saving benefits based on the building energy consumption before and after maintenance.

[0102] In some embodiments, current building component status data, current environmental data, and historical meteorological data can be acquired first. Current building component status data refers to various physical parameters of the building components that are monitored or collected in real time, such as internal temperature, surface temperature, humidity, and heat flux density. Current environmental data refers to real-time environmental parameters outside the building, such as external temperature, external humidity, external wind speed, and light intensity. Historical meteorological data refers to meteorological records over a past period, which can be used to establish a basic model for energy consumption simulation and to predict future trends.

[0103] Then, a thermal response mapping relationship is constructed. A thermal response mapping relationship refers to a mathematical model or empirical relationship showing how the thermal properties (such as heat transfer coefficient, thermal resistance, and heat storage capacity) of a building component change under different environmental conditions. This relationship reflects the performance differences of the component material before and after aging, deterioration, or maintenance. Based on the thermal response mapping relationship, the current building component state data and current environmental data are mapped to target thermal response parameters. Specifically, the target thermal response parameters are key inputs for energy consumption simulation, such as the actual heat transfer coefficient and thermal resistance under the current state and environment. By substituting real-time data into the constructed thermal response mapping relationship, the thermal response parameters reflecting the current state of the component can be dynamically obtained.

[0104] Then, based on the target thermal response parameters, historical meteorological data, and indoor environmental setpoints, energy consumption simulations are performed on candidate maintenance strategies to obtain building energy consumption before and after maintenance. Energy consumption simulation utilizes specialized building energy consumption simulation software or algorithms (such as DesignBuilder, EnergyPlus, etc.) combined with the current thermal performance parameters of the components (target thermal response parameters), historical meteorological data (as climate input for simulation), and preset indoor environmental comfort requirements (indoor environmental setpoints, such as temperature and humidity ranges) to calculate building energy consumption under two scenarios: no maintenance strategy and implementation of candidate maintenance strategies. Specifically, the building energy consumption before maintenance refers to the predicted energy consumption value under the current component state without any maintenance; the building energy consumption after maintenance refers to the predicted energy consumption value assuming that the component performance is improved after implementing the candidate maintenance strategy.

[0105] Finally, the energy-saving benefits are calculated based on the building's energy consumption before and after maintenance. Energy-saving benefits refer to the amount of energy consumption reduction that can be achieved by implementing candidate maintenance strategies, and are usually calculated as the difference between the building's energy consumption before and after maintenance.

[0106] This embodiment provides comprehensive input for energy consumption simulation by acquiring multi-dimensional data, including current building component status data, current environmental data, and historical meteorological data. By constructing a thermal response mapping relationship, it can accurately capture the dynamic thermal performance of building components under different environmental conditions and map it to target thermal response parameters, thereby making the energy consumption simulation more closely resemble reality. Based on this, combined with historical meteorological data and indoor environmental setpoints, it accurately simulates building energy consumption before and after implementing maintenance strategies, thus quantifying the energy-saving effect brought about by the maintenance strategies.

[0107] Through the above technical solution, this embodiment can perform a refined and dynamic evaluation of the energy-saving effect of candidate maintenance strategies. Specifically, by combining real-time data and thermal response models, the accuracy of energy consumption simulation is improved, making the calculated energy-saving benefits more reliable and providing a solid data foundation for subsequent economic evaluation, thereby helping to select maintenance strategies with real energy-saving potential.

[0108] In some embodiments, the construction of the thermal response mapping relationship in step S302 may include, but is not limited to, the following steps:

[0109] Acquire historical building component status data and historical environmental data. Historical building component status data includes internal component temperature data, component surface temperature data, and component heat flow data. Historical environmental data includes external temperature, external humidity, external air pressure, external wind speed, and light intensity.

[0110] Based on historical environmental data, identify environmental change events, including sudden drops in temperature, prolonged periods of high humidity, or intense sunlight.

[0111] Based on environmental change events, identify component thermal response parameters, including surface temperature change rate, heat flow change magnitude, thermal resistance, and heat transfer coefficient.

[0112] Based on historical building component status data, historical environmental data, and component thermal response parameters, a thermal response mapping relationship is constructed using nonlinear regression analysis.

[0113] In some embodiments, historical building component status data and historical environmental data can be acquired first. Historical building component status data refers to monitoring records of the internal and surface thermodynamic state and heat transfer of building components over a past period. This data includes internal temperature data, surface temperature data, and heat flow data. Internal temperature data can be acquired in real time using temperature sensors embedded within the building component; surface temperature data can be obtained using infrared thermal imagers or temperature sensors mounted on the component surface; and heat flow data is typically measured using heat flow meters. The acquisition of this data aims to comprehensively and meticulously reflect the thermodynamic behavior and heat exchange processes of building components under different historical conditions. Historical environmental data refers to long-term records of various physical parameters related to the external environment in which the building component is located. This data includes external temperature, external humidity, external air pressure, external wind speed, and light intensity. This data can be continuously monitored and stored using weather stations and environmental sensor networks deployed outside or around the building. This historical environmental data forms the basis for analyzing the thermal response characteristics of building components and understanding their interaction mechanisms with the external environment.

[0114] Then, based on historical environmental data, environmental change events are identified. These events refer to sudden or persistent anomalies in external environmental conditions that significantly impact the thermal performance of building components. Environmental change events include sudden drops in temperature, prolonged periods of high humidity, or intense sunlight. For example, a sudden drop in temperature may lead to a sharp increase in the temperature difference between the inside and outside of a component; prolonged high humidity may affect the hygroscopicity and thermal conductivity of component materials; and intense sunlight will cause a significant increase in the surface temperature and heat load of the component. Identifying these environmental change events helps in the targeted analysis of the thermal response behavior of building components under specific or extreme conditions.

[0115] Then, based on environmental change events, the thermal response parameters of the components are identified. These parameters are key indicators for quantifying the degree to which building components respond to environmental changes. These parameters include the surface temperature change rate, the magnitude of heat flow change, thermal resistance, and heat transfer coefficient. The surface temperature change rate reflects how quickly the component's surface temperature changes over time and is an important parameter for assessing the component's thermal inertia; the magnitude of heat flow change indicates the intensity of heat transfer within the component; thermal resistance measures a material's or component's ability to impede heat transfer; and the heat transfer coefficient comprehensively reflects the overall heat transfer performance of the component. The identification and extraction of these parameters aim to transform the complex thermal behavior of components into quantifiable and analyzable indicators.

[0116] Finally, based on historical building component status data, historical environmental data, and component thermal response parameters, a thermal response mapping relationship is constructed using nonlinear regression analysis. Nonlinear regression analysis is a statistical method aimed at establishing a nonlinear relationship model between one or more independent and dependent variables. Historical building component status data and historical environmental data serve as independent variables, while component thermal response parameters serve as dependent variables. By applying nonlinear regression analysis, the complex and nonlinear physical processes present in the thermal behavior of building components can be captured, thereby constructing a more realistic and accurate thermal response mapping relationship. The thermal response mapping relationship refers to a mathematical model or algorithm whose function is to predict or calculate the corresponding component thermal response parameters based on input building component status data and environmental data. This mapping relationship is the core of energy consumption simulation; it abstracts complex physical processes into a computable model, providing a foundation for subsequent energy consumption prediction.

[0117] This embodiment first acquires comprehensive historical building component status data and historical environmental data, providing a solid data foundation for subsequent thermal response analysis. Given the significant impact of environmental factors on the thermal performance of building components, identifying specific environmental change events allows for targeted analysis of component behavior under different operating conditions. Based on this, component thermal response parameters are further identified, quantifying the complex thermal behavior of the components into actionable indicators. Finally, nonlinear regression analysis is used to correlate these historical data and identified parameters, thereby constructing a thermal response mapping relationship that accurately reflects the thermodynamic characteristics of building components. This systematic process of data acquisition, event identification, parameter quantification, and model building enables subsequent energy consumption simulations to be based on an accurate and reliable component thermal response model, thus improving the accuracy of energy consumption prediction.

[0118] Through the above technical solution, this embodiment can establish a thermal response mapping relationship based on actual historical data, capable of capturing complex nonlinear relationships. This mapping relationship not only considers the internal temperature data, surface temperature data, and heat flow data of the components, but also fully integrates various external environmental influencing factors such as external temperature, external humidity, external air pressure, external wind speed, and light intensity. It can also identify key environmental change events such as sudden drops in temperature, prolonged periods of high humidity, or intense sunlight. The mapping relationship constructed by this embodiment using nonlinear regression analysis can more accurately simulate the thermal behavior of building components in dynamic environments, thereby providing more accurate and reliable basic data for subsequent energy consumption simulation, significantly improving the accuracy of energy consumption prediction and the effectiveness of maintenance strategies.

[0119] In some embodiments, after constructing the thermal response mapping relationship in step S302, the method may further include, but is not limited to, the following steps:

[0120] Acquire historical component microstructure data, current component microstructure data, historical moisture accumulation data, and current moisture accumulation data;

[0121] The degree of improvement in the component's microstructure is assessed based on historical and current component microstructure data.

[0122] Assess the degree of improvement in water accumulation based on historical and current water accumulation data;

[0123] The thermal response mapping relationship is adjusted based on the degree of improvement in the component's microstructure and the degree of improvement in moisture accumulation.

[0124] In some embodiments, the actual thermal response characteristics of building components are affected by changes in their internal microstructure and moisture accumulation. These factors may cause deviations between the pre-built thermal response mapping and the actual state of the components, thereby affecting the accuracy of energy consumption simulation.

[0125] To this end, we can first acquire historical component microstructure data, current component microstructure data, historical moisture accumulation data, and current moisture accumulation data. Historical and current component microstructure data refer to the internal material structure information of building components obtained at different time points using non-destructive testing techniques (such as scanning electron microscopy, X-ray diffraction, and ultrasonic testing), including porosity, grain size, fiber distribution, and crack density. Their purpose is to reflect the deterioration or repair status of the component material. Historical and current moisture accumulation data refer to the moisture content and distribution inside or on the surface of building components monitored at different time points using devices such as humidity sensors, infrared thermal imagers, and dielectric constant measurements. Their purpose is to reflect the degree of moisture absorption or dryness of the components.

[0126] Then, based on historical and current component microstructure data, the degree of improvement in component microstructure is assessed. Changes in component material properties, such as reduced porosity, increased density, and reduced cracks, can be quantified by comparing historical and current microstructure data. This is typically achieved by setting thresholds or establishing an evaluation model.

[0127] Then, based on historical and current moisture accumulation data, the degree of improvement in moisture accumulation is assessed. By comparing historical and current moisture accumulation data, changes in the internal moisture condition of components can be quantified, such as a decrease in moisture content or an increase in drying speed. The purpose is to reflect the impact of maintenance measures on the moisture-proof performance of components.

[0128] Finally, the thermal response mapping relationship is adjusted based on the degree of improvement in the component's microstructure and moisture accumulation. The original thermal response mapping relationship can be modified by adjusting parameters or reconstructing the model based on the assessed degrees of improvement in the component's microstructure and moisture accumulation. For example, if the improvement in microstructure indicates increased material density, the heat transfer coefficient can be lowered accordingly; if the improvement in moisture accumulation indicates dryness of the component, the thermal resistance can be increased to more accurately reflect the actual thermal performance of the component after maintenance.

[0129] This embodiment dynamically captures the actual physical changes of building components before and after maintenance by introducing monitoring and evaluation of the microstructure and moisture accumulation of the components. When the microstructure of a component changes due to aging or maintenance, or when its internal moisture content fluctuates, these changes directly affect the component's thermal performance. By acquiring historical and current component microstructure and moisture accumulation data, and evaluating the degree of improvement in microstructure and moisture accumulation, the impact of these physical changes on the component's thermal performance can be quantified. Therefore, the original thermal response mapping relationship can be adjusted to more accurately reflect the actual thermal response characteristics of the component in its current state. This dynamic adjustment mechanism ensures that the input parameters for energy consumption simulation remain consistent with the actual physical state of the component, thereby improving the accuracy and reliability of the energy consumption simulation results and effectively solving the problem of the thermal response mapping relationship potentially being out of sync with the actual state of the component.

[0130] To illustrate this technical solution more clearly, a specific example is used below. Suppose that before maintenance, historical microstructure data analysis of a building component (e.g., an external wall insulation layer) reveals increased porosity and fiber breakage in its insulation material, while historical moisture accumulation data indicates localized dampness in the area. After implementing a maintenance strategy (e.g., locally repairing the insulation layer and adding a moisture barrier), re-obtaining current microstructure and moisture accumulation data reveals a significant reduction in porosity, restored fiber structure, and a substantial decrease in moisture content. The system then adjusts the component's thermal resistance parameter upwards and the heat transfer coefficient downwards based on these improvements, for example, by adjusting the thermal response mapping relationship. The adjusted thermal response mapping relationship is used for subsequent energy consumption simulations, thereby more accurately predicting the actual energy-saving benefits of the maintenance strategy and avoiding evaluation biases caused by unconsidered changes in material properties.

[0131] Through the above technical solution, this embodiment can overcome the problem of inaccurate thermal response mapping relationships in energy consumption simulation caused by factors such as aging of building component materials, changes in microstructure, or moisture accumulation. By acquiring and analyzing the microstructure data and moisture accumulation data of the components in real time or periodically, and dynamically adjusting the thermal response mapping relationship accordingly, the energy consumption simulation can more accurately reflect the actual thermal performance of the components. This not only improves the accuracy of energy consumption simulation results and provides more reliable data support for the economic evaluation of maintenance strategies, but also helps to more effectively identify and implement maintenance measures that truly have energy-saving effects, thereby further optimizing the energy consumption management of low-carbon building materials and achieving more precise energy-saving goals.

[0132] In some embodiments, in step S203, an economic evaluation is performed on the candidate maintenance strategies based on the expected costs and energy-saving benefits to obtain the economic evaluation results, which may include, but is not limited to, the following steps:

[0133] Step S401: Obtain building materials market price data;

[0134] Step S402: Based on building materials market price data, predict the price fluctuation range and price probability distribution;

[0135] Step S403: Update the estimated cost based on the price fluctuation range and price probability distribution;

[0136] Step S404: Identify the resource competition relationships between different maintenance strategies in the maintenance strategy library;

[0137] Step S405: Identify the correlation of repair effects between different maintenance strategies in the maintenance strategy library;

[0138] Step S406: Based on resource competition, repair effect correlation, energy saving benefits, and expected cost after the upgrade, conduct an economic evaluation of the candidate maintenance strategies to obtain the economic evaluation results.

[0139] In some embodiments, factors such as fluctuations in building material market prices, competition for maintenance resources, and potential correlations in repair effectiveness between different maintenance strategies can significantly impact the accuracy and comprehensiveness of the economic assessment. If these dynamic and interacting factors are not fully considered, the resulting economic assessment may fail to accurately reflect the actual economic benefits and feasibility of the maintenance strategies, thereby affecting the optimization level of the final maintenance recommendations.

[0140] To achieve this, we can first obtain building material market price data. This can be done through real-time data interfaces, historical database queries, or market research to collect market transaction price information for various building materials related to building component maintenance. This data can include prices from different suppliers, brands, and specifications of building materials, as well as their trends over time. Based on the building material market price data, we can predict the range of price fluctuations and the probability distribution of price levels. Statistical methods (such as time series analysis and Monte Carlo simulation) can be used to predict the possible range of building material price changes over a future period, assess the probability of different price levels occurring, and obtain the range of price fluctuations and the probability distribution of price levels. The purpose is to quantify the impact of market uncertainty on maintenance costs.

[0141] Then, based on the price fluctuation range and price probability distribution, the projected cost is updated. The uncertainty of predicted market prices can be incorporated into the original projected cost calculation, for example, through expected value calculations or risk adjustment, so that the projected cost can more accurately reflect future actual expenditures.

[0142] Further, identify the resource competition relationships among different maintenance strategies in the maintenance strategy library. This allows analysis of the shared resource requirements (human, material, and financial) of different maintenance strategies during implementation. For example, multiple maintenance strategies may simultaneously require scarce specialized equipment or skilled workers, thus creating resource competition. Also, identify the correlation of repair effects among different maintenance strategies in the library. This allows assessment of the potential positive or negative impacts of implementing one maintenance strategy on other strategies. For example, maintaining a component may improve the performance of adjacent components, or conversely, it may negatively impact other components. The aim is to comprehensively consider the synergistic effects or conflicts of maintenance strategies.

[0143] Finally, based on resource competition, the correlation between repair effects, energy-saving benefits, and the expected cost after replacement, an economic evaluation is conducted on the candidate maintenance strategies to obtain the economic evaluation results. These multi-dimensional factors can be integrated into a comprehensive economic evaluation model, such as using multi-objective decision analysis, analytic hierarchy process (AHP), or weighted scoring method, to obtain more comprehensive and accurate economic evaluation results and guide the selection of the optimal maintenance strategy.

[0144] This embodiment incorporates building material market price data and predicts its fluctuations, ensuring that the calculation of estimated costs fully considers market uncertainties and avoids economic assessment biases caused by drastic price fluctuations. Simultaneously, by identifying resource competition relationships between different maintenance strategies, it effectively avoids maintenance plan delays or cost increases due to resource shortages or improper allocation, ensuring the feasibility of maintenance strategies. Furthermore, by identifying correlations in repair effects, this embodiment can assess the synergistic effects or potential conflicts between maintenance strategies, thus incorporating more comprehensive influencing factors into the economic assessment and avoiding the situation where local optimization ignores overall benefits. This comprehensive consideration of multi-dimensional factors makes the economic assessment results more realistic, providing a more solid data foundation for subsequent selection of target maintenance strategies.

[0145] To illustrate this technical solution more clearly, a specific example is used below. Suppose maintenance is needed on the walls and roof of a building. First, the system obtains price data for insulation and waterproofing materials in the current building materials market and uses historical data and machine learning models to predict the price fluctuation range and probability distribution for the next three months. For example, the price of a certain insulation board is expected to fluctuate between 50-60 yuan per square meter, with 55 yuan being the most probable price. Based on this, the original estimated cost is adjusted to an updated estimated cost that considers market risk. Simultaneously, the system identifies that both wall and roof maintenance strategies may require high-altitude work equipment and professional waterproofing teams. If the quantity of these resources in the maintenance resource pool is limited, the system will determine that there is resource competition and adjust the resource allocation order according to preset priorities (e.g., roof leakage has higher priority than wall insulation). Furthermore, the system also identifies that roof insulation maintenance may indirectly improve the wall temperature of the top-floor rooms, thus positively impacting the energy-saving effect of the wall maintenance strategy. Conversely, if a maintenance strategy may negatively affect adjacent components, this will also be identified and considered. Finally, by integrating updated estimated costs, energy-saving benefits, resource competition, and the correlation between repair effects, a multi-criteria decision-making model is used to evaluate the economic viability of the two candidate strategies: wall maintenance and roof maintenance. For example, a weighted average method is used, assigning different weights to cost, energy saving, resource feasibility, and related benefits, to calculate the comprehensive economic score for each strategy, thereby selecting the maintenance strategy with the highest economic viability as the target maintenance strategy.

[0146] Through the above technical solutions, this embodiment can significantly improve the accuracy and comprehensiveness of economic assessment. Specifically, by dynamically updating the projected costs, the risks brought about by market price fluctuations can be effectively addressed, making cost predictions more accurate; by considering resource competition, resource allocation can be optimized, maintenance efficiency can be improved, and potential resource bottleneck risks can be reduced; by evaluating the correlation of repair effects, synergistic effects or mutual constraints between maintenance strategies can be identified, thereby avoiding isolated decisions and maximizing maintenance benefits. These improvements make the generated maintenance recommendations not only superior in energy-saving benefits but also more advantageous in economic feasibility and resource utilization efficiency, ultimately contributing to the intelligent management of building energy consumption and the effective utilization of low-carbon building materials.

[0147] In some embodiments, step S405, identifying the correlation of repair effects between different maintenance strategies in the maintenance strategy library, may include, but is not limited to, the following steps:

[0148] Obtain the physical proximity and material properties of adjacent building components;

[0149] Based on physical proximity and material properties, assess the performance impact of each maintenance strategy on adjacent building components;

[0150] Based on the performance impact, determine the intensity and scope of the repair effect;

[0151] Based on physical proximity, material properties, repair effect intensity, and repair effect influence range, a repair effect correlation is constructed.

[0152] In some embodiments, the physical proximity and material properties of adjacent building components can be obtained first. Adjacent building components refer to building components that are physically close to or in direct contact with each other, and their maintenance or performance changes may affect each other. Physical proximity refers to the distance between components, connection methods, or shared structures. Material properties may include, but are not limited to, the material of the component, thermal conductivity, density, hygroscopicity, etc.

[0153] Then, based on physical proximity and material properties, the performance impact of each maintenance strategy on adjacent building components is assessed. For example, waterproofing a component may affect the humidity environment or thermal performance of its neighboring components. Performance impacts can be quantified as changes in temperature, humidity, structural stress, etc.

[0154] Next, based on the performance impact, determine the intensity and extent of the repair effect. The intensity of the repair effect refers to the degree to which the maintenance strategy improves the performance of the target component and its adjacent components. The extent of the repair effect refers to the spatial area where the maintenance strategy affects surrounding components. For example, during waterproofing maintenance, if the maintenance strategy reduces the humidity of adjacent building components by 10%, then the intensity of the repair effect is 10%, and the area where the humidity reduction is only within a specific range is the extent of the repair effect.

[0155] Finally, repair effect correlations are constructed based on physical proximity, material properties, repair effect intensity, and repair effect impact range. These correlations can be established by creating models or rule bases to describe the chain reactions of different maintenance strategies on different components. Alternatively, physical proximity, material properties, repair effect intensity, and repair effect impact range can be integrated to form a comprehensive repair effect correlation.

[0156] This embodiment identifies potential interactions between adjacent building components by acquiring their physical proximity and material properties. Based on this, the performance impact of each maintenance strategy on adjacent components is evaluated, quantifying the nature and extent of this impact. Furthermore, the strength and scope of the repair effect are determined, leading to a more comprehensive and accurate assessment of the overall effectiveness of the maintenance strategies. Finally, repair effect correlations are constructed based on this information, ensuring that the synergistic effects of maintenance strategies are fully considered during economic evaluations, avoiding localized optimization that leads to overall performance degradation or the neglect of potential synergistic gains.

[0157] Through the above technical solution, this embodiment can more comprehensively and accurately identify the correlation of repair effects between different maintenance strategies in the maintenance strategy library. This helps to consider not only the maintenance effect of a single component during the economic evaluation process, but also the chain reaction and synergistic effect of the maintenance strategy on adjacent building components, thereby avoiding negative impacts on the overall building performance due to local maintenance, or missing opportunities to achieve greater energy-saving benefits through collaborative maintenance. As a result, more accurate and optimized economic evaluation results can be obtained, leading to more instructive maintenance recommendations.

[0158] In some embodiments, step S404, identifying the resource competition relationship between different maintenance strategies in the maintenance strategy library, may include, but is not limited to, the following steps:

[0159] Step S501: Obtain the resource requirement type and resource requirement quantity corresponding to different maintenance strategies;

[0160] Step S502: Monitor and maintain the real-time resource availability in the resource pool;

[0161] Step S503: Set the resource demand fluctuation range;

[0162] Step S504: Based on the type of resource requirement, determine whether there is any overlap in resource requirements between different maintenance strategies;

[0163] Step S505: If there is an overlap in resource requirements among different maintenance strategies, the resource allocation order shall be adjusted according to the resource requirement quantity, real-time resource availability, resource requirement fluctuation range and maintenance strategy priority sequence.

[0164] Step S506: Construct resource competition relationships based on resource demand type, resource demand quantity, and resource allocation order.

[0165] In some embodiments, the resource requirement types and quantities corresponding to different maintenance strategies can be obtained first. Specific resource information required for each maintenance strategy can be extracted from the maintenance strategy library. Resource requirement types can cover human resources (e.g., professional technicians, general workers), equipment resources (e.g., cranes, testing instruments), and material resources (e.g., specific types of building materials, auxiliary consumables), etc. Resource quantity refers to the specific amount required for each type of resource, such as the number of engineers required, the quantity of specific types of equipment, the usage of a certain type of building material, etc. This information is usually pre-stored in the maintenance strategy library or related resource management database as the basis for evaluation. Simultaneously, the real-time resource availability in the maintenance resource pool is monitored. The actual quantity of all currently available maintenance resources can be continuously tracked and updated. The maintenance resource pool refers to the collection of all currently available maintenance resources, including personnel, equipment, materials, etc. Real-time resource availability refers to the actual available quantity of each resource type at a specific point in time. This is typically obtained through data interaction with enterprise resource planning (ERP) systems, equipment management systems, or human resource management systems to ensure accurate understanding and dynamic updates of resource status.

[0166] Then, a resource demand fluctuation range is set to provide flexibility in resource allocation. The resource demand fluctuation range allows resource demand to fluctuate within a certain range during resource allocation to cope with uncertainties or unforeseen circumstances in actual operation. For example, a fluctuation range of ±5% or ±10% can be set to increase the flexibility of system scheduling and the ability to respond to emergencies.

[0167] Next, based on the type of resource requirement, it is determined whether there is overlap in resource requirements between different maintenance strategies, aiming to identify whether different maintenance strategies require the same type of resources. For example, if two or more maintenance strategies require specific types of lifting equipment or specifically qualified professional technicians, then there is an overlap in resource requirements between these strategies. This judgment is a key prerequisite for identifying resource competition relationships.

[0168] If resource requirements overlap between different maintenance strategies, the resource allocation order is adjusted based on the quantity of resource requirements, real-time resource availability, resource requirement fluctuation range, and maintenance strategy priority sequence. The maintenance strategy priority sequence can be determined based on the urgency of building components, the importance of maintenance effectiveness, the impact on building energy consumption, or preset business rules. When resource requirements overlap and resources are limited, the resource allocation order can be dynamically adjusted based on these factors, prioritizing the resource requirements of higher-priority strategies and optimizing allocation within a set fluctuation range to maximize overall efficiency.

[0169] Finally, resource competition relationships are constructed based on resource demand type, resource demand quantity, and resource allocation order. This resource competition relationship reflects the competition for shared resources among different maintenance strategies, as well as the priority and order of resource allocation. The resource demand type, resource demand quantity, and resource allocation order can be integrated to obtain the resource competition relationship. This can be represented by a resource allocation table, a resource conflict diagram, or a resource scheduling plan, clearly showing which strategies compete for which resources and how to coordinate and allocate them.

[0170] This embodiment first obtains the specific resource requirements of each maintenance strategy and monitors the availability of the resource pool in real time, laying a data foundation for subsequent competition analysis. Furthermore, by setting a floating range for resource requirements, the flexibility and robustness of resource scheduling are increased. Based on this, the system can identify whether there is overlap in resource requirements between different maintenance strategies, which is crucial for determining competition relationships. Once overlap is identified, the system intelligently adjusts the resource allocation order by combining the resource requirements, real-time availability, floating range, and preset priority sequence of each strategy. This adjustment mechanism ensures that, under limited resource conditions, the execution of critical maintenance strategies is prioritized, and overall resource utilization efficiency is optimized. Finally, by integrating this information, a clear resource competition relationship is constructed, providing a comprehensive resource constraint perspective for economic evaluation.

[0171] Through the above technical solution, this embodiment can identify and quantify the resource competition relationships between different maintenance strategies in a detailed and systematic manner. This allows for the consideration of not only the cost and benefits of a single strategy during economic evaluation, but also the potential resource conflicts and bottlenecks that may arise when multiple strategies are executed in parallel or sequentially. Consequently, maintenance delays or cost overruns caused by improper resource allocation can be avoided, improving the accuracy and reliability of economic evaluation results. This enables more effective selection of the most economically optimal maintenance strategy under actual resource constraints, ensuring the practicality and feasibility of the intelligent energy consumption management method for low-carbon building materials.

[0172] In some embodiments, setting the resource demand fluctuation range in step S503 may include, but is not limited to, the following steps:

[0173] Retrieve historical maintenance data;

[0174] Extract the resource consumption fluctuation range of the same type of resource from historical maintenance data;

[0175] Based on the fluctuation range of resource consumption, a fluctuation range for resource demand is set.

[0176] In some embodiments, historical maintenance data may be acquired first. Various types of data generated during past maintenance of building components are collected and stored. Historical maintenance data may include, but is not limited to: actual resource consumption for different maintenance tasks, duration of maintenance tasks, unexpected situations encountered during maintenance, and corresponding resource adjustment records. Historical maintenance data is an important basis for resource demand forecasting and risk assessment.

[0177] Then, the resource consumption fluctuation range of the same type of resource is extracted from historical maintenance data. The acquired historical maintenance data can be analyzed to identify the difference between the actual consumption and planned consumption of a specific type of resource (e.g., a specific material, manpower for a specific job, or a specific type of equipment) in different maintenance tasks, and to quantify the fluctuation range of this difference. For example, statistical analysis methods can be used to calculate the maximum, minimum, average consumption, and standard deviation of a certain material in past maintenance projects, thereby determining its consumption fluctuation range.

[0178] Next, based on the fluctuation range of resource consumption, a fluctuation range for resource demand can be set. Based on the extracted fluctuation range of resource consumption for similar resources, a reasonable upper and lower fluctuation range can be set for the expected demand of that resource in future maintenance tasks. This fluctuation range aims to reflect the uncertainty of resource consumption and provide some flexibility in resource allocation. For example, if historical data shows that the consumption of a certain material typically fluctuates within ±10% of the expected amount, the fluctuation range for resource demand can be set to ±10% of the expected amount.

[0179] This embodiment acquires historical maintenance data and extracts the resource consumption fluctuation range of similar resources, thereby enabling the setting of a resource demand fluctuation range based on actual historical experience. This method ensures that the setting of the resource demand fluctuation range is no longer based on subjective estimation but is supported by objective data. Because this fluctuation range more accurately reflects the actual uncertainty of resource consumption, it allows for a more precise assessment of the possibility and degree of overlapping resource demands when subsequently identifying resource competition relationships between different maintenance strategies, thereby optimizing the resource allocation sequence.

[0180] Through the above technical solution, this embodiment can scientifically and objectively set the fluctuation range of resource demand based on historical data, thereby improving the accuracy and reliability of resource demand forecasting. This helps to more realistically reflect the actual resource availability and consumption when identifying resource competition relationships between maintenance strategies, avoiding resource shortages or waste caused by inaccurate resource demand estimates. Furthermore, by considering historical consumption fluctuations, it can provide more reasonable flexibility for resource allocation, enhancing the system's ability to cope with uncertainty, thereby improving the robustness and efficiency of the entire intelligent energy consumption management method for low-carbon building materials.

[0181] The beneficial effects of implementing the embodiments of the present invention include: First, the embodiment of this application obtains the attribute information of building components, selects a maintenance strategy from the maintenance strategy library as a candidate maintenance strategy, then performs an economic evaluation on the candidate maintenance strategy to obtain the economic evaluation result, and then, based on the economic evaluation result, selects the maintenance strategy with the highest economic efficiency in the maintenance strategy library as the target maintenance strategy, and finally generates maintenance suggestions based on the target maintenance strategy, the attribute information of building components, and the economic evaluation result. This enables the generation of maintenance suggestions by combining the attribute information of building components and the economic evaluation, thereby realizing building energy consumption management and improving accuracy and reliability.

[0182] like Figure 2 As shown in the figure, this embodiment of the invention also provides a low-carbon building materials building energy consumption intelligent management system, including:

[0183] The information acquisition module 601 is used to acquire the attribute information of building components, including unique identifiers, material types, geometric dimensions and locations.

[0184] The candidate maintenance strategy selection module 602 is used to select a maintenance strategy from the maintenance strategy library as a candidate maintenance strategy based on the building component attribute information.

[0185] The economic evaluation module 603 is used to perform an economic evaluation on the candidate maintenance strategies and obtain the economic evaluation results.

[0186] The target maintenance strategy determination module 604 is used to select the most economical maintenance strategy in the maintenance strategy library as the target maintenance strategy based on the economic evaluation results.

[0187] The maintenance suggestion generation module 605 is used to generate maintenance suggestions based on the target maintenance strategy, building component attribute information, and economic assessment results.

[0188] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0189] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A method for intelligent management of energy consumption in low-carbon building materials, characterized in that, Includes the following steps: Obtain the attribute information of building components, which includes a unique identifier, material type, geometric dimensions, and location; Based on the building component attribute information, a maintenance strategy is selected from the maintenance strategy library as a candidate maintenance strategy; An economic evaluation is performed on the candidate maintenance strategies to obtain the economic evaluation results; Based on the economic evaluation results, the most economical maintenance strategy in the maintenance strategy library will be selected as the target maintenance strategy. Based on the target maintenance strategy, the building component attribute information, and the economic assessment results, maintenance recommendations are generated. The step of conducting an economic evaluation of the candidate maintenance strategies to obtain the economic evaluation results includes: Calculate the estimated cost of the candidate maintenance strategy based on the cost database; Energy consumption simulations were performed on the candidate maintenance strategies to calculate energy-saving benefits; Based on the estimated costs and energy-saving benefits, an economic evaluation is performed on the candidate maintenance strategies to obtain the economic evaluation results; The step of performing energy consumption simulation on the candidate maintenance strategies and calculating energy-saving benefits includes: Acquire current building component status data, current environmental data, and historical meteorological data; Construct a thermal response mapping relationship; Based on the thermal response mapping relationship, the current building component status data and the current environmental data are mapped to target thermal response parameters; Based on the target thermal response parameters, the historical meteorological data, and the indoor environmental setpoints, energy consumption simulation is performed on the candidate maintenance strategies to obtain the building energy consumption before and after maintenance. The energy-saving benefits are calculated based on the building energy consumption before and after maintenance. The construction of the thermal response mapping relationship includes: Acquire historical building component status data and historical environmental data. The historical building component status data includes internal component temperature data, component surface temperature data, and component heat flow data. The historical environmental data includes external temperature, external humidity, external air pressure, external wind speed, and light intensity. Based on the historical environmental data, environmental change events are identified, including sudden drops in temperature, prolonged periods of high humidity, or intense sunlight. Based on the environmental change events, identify the component thermal response parameters, which include the surface temperature change rate, the magnitude of heat flow change, thermal resistance, and heat transfer coefficient. Based on the historical building component status data, the historical environmental data, and the component thermal response parameters, the thermal response mapping relationship is constructed using nonlinear regression analysis.

2. The method according to claim 1, characterized in that, After constructing the thermal response mapping relationship, the method further includes: Acquire historical component microstructure data, current component microstructure data, historical moisture accumulation data, and current moisture accumulation data; The degree of improvement in the component's microstructure is assessed based on the historical component microstructure data and the current component microstructure data. The degree of improvement in water accumulation is assessed based on the historical water accumulation data and the current water accumulation data. The thermal response mapping relationship is adjusted based on the degree of improvement in the microstructure of the component and the degree of improvement in moisture accumulation.

3. The method according to claim 1, characterized in that, The step of conducting an economic evaluation of the candidate maintenance strategies based on the estimated costs and energy-saving benefits, and obtaining the economic evaluation results, includes: Obtain building materials market price data; Based on the building materials market price data, predict the price fluctuation range and price probability distribution; The estimated cost is updated based on the price fluctuation range and the price probability distribution; Identify the resource competition relationships between different maintenance strategies in the maintenance strategy library; Identify the correlation of repair effects between different maintenance strategies in the maintenance strategy library; Based on the resource competition relationship, the correlation of repair effect, the energy saving benefit, and the expected cost after the update, the economic evaluation of the candidate maintenance strategy is carried out to obtain the economic evaluation result.

4. The method according to claim 3, characterized in that, The identification of the correlation of repair effects between different maintenance strategies in the maintenance strategy library includes: Obtain the physical proximity and material properties of adjacent building components; Based on the physical proximity and the material properties, assess the performance impact of each maintenance strategy on the adjacent building components; Based on the aforementioned performance impact, determine the intensity and scope of the repair effect; The repair effect correlation is constructed based on the physical proximity relationship, the material properties, the repair effect intensity, and the repair effect influence range.

5. The method according to claim 3, characterized in that, The identification of resource competition relationships between different maintenance strategies in the maintenance strategy library includes: Obtain the resource requirement types and quantities corresponding to different maintenance strategies; Monitor and maintain the real-time resource availability in the resource pool; Set a fluctuation range for resource requirements; Based on the resource requirement type, determine whether there is any overlap in resource requirements between different maintenance strategies; If there is an overlap in resource requirements among different maintenance strategies, the resource allocation order shall be adjusted according to the quantity of resource requirements, the real-time availability of resources, the fluctuation range of resource requirements, and the priority sequence of maintenance strategies. The resource competition relationship is constructed based on the resource demand type, the resource demand quantity, and the resource allocation order.

6. The method according to claim 5, characterized in that, The set resource demand fluctuation range includes: Retrieve historical maintenance data; Extract the resource consumption fluctuation range of the same type of resource from the historical maintenance data; Based on the fluctuation range of resource consumption, the fluctuation range of resource demand is set.

7. A smart management system for low-carbon building material energy consumption, characterized in that, include: The information acquisition module is used to acquire the attribute information of building components, including unique identifiers, material types, geometric dimensions, and locations. The candidate maintenance strategy selection module is used to select maintenance strategies as candidate maintenance strategies from the maintenance strategy library based on the building component attribute information. An economic evaluation module is used to perform an economic evaluation on the candidate maintenance strategies and obtain the economic evaluation results. The target maintenance strategy determination module is used to select the most economical maintenance strategy from the maintenance strategy library as the target maintenance strategy based on the economic evaluation results. The maintenance suggestion generation module is used to generate maintenance suggestions based on the target maintenance strategy, the building component attribute information, and the economic evaluation results. The step of conducting an economic evaluation of the candidate maintenance strategies to obtain the economic evaluation results includes: Calculate the estimated cost of the candidate maintenance strategy based on the cost database; Energy consumption simulations were performed on the candidate maintenance strategies to calculate energy-saving benefits; Based on the estimated costs and energy-saving benefits, an economic evaluation is performed on the candidate maintenance strategies to obtain the economic evaluation results; The step of performing energy consumption simulation on the candidate maintenance strategies and calculating energy-saving benefits includes: Acquire current building component status data, current environmental data, and historical meteorological data; Construct a thermal response mapping relationship; Based on the thermal response mapping relationship, the current building component status data and the current environmental data are mapped to target thermal response parameters; Based on the target thermal response parameters, the historical meteorological data, and the indoor environmental setpoints, energy consumption simulation is performed on the candidate maintenance strategies to obtain the building energy consumption before and after maintenance. The energy-saving benefits are calculated based on the building energy consumption before and after maintenance. The construction of the thermal response mapping relationship includes: Acquire historical building component status data and historical environmental data. The historical building component status data includes internal component temperature data, component surface temperature data, and component heat flow data. The historical environmental data includes external temperature, external humidity, external air pressure, external wind speed, and light intensity. Based on the historical environmental data, environmental change events are identified, including sudden drops in temperature, prolonged periods of high humidity, or intense sunlight. Based on the environmental change events, identify the component thermal response parameters, which include the surface temperature change rate, the magnitude of heat flow change, thermal resistance, and heat transfer coefficient. Based on the historical building component status data, the historical environmental data, and the component thermal response parameters, the thermal response mapping relationship is constructed using nonlinear regression analysis.

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