Green building information integrated management method and system based on BIM

Abstract

The invention relates to the technical field of building information models, in particular to a BIM-based green building information integrated management method and system, and the method comprises the following steps: constructing a building information model based on the specification information of a building project through recognizing various building elements in various target buildings, mapping a plurality of devices in the building into the model, and constructing a BIM-based building information model; and obtaining a model construction result. According to the method, the building information model is constructed, a plurality of devices in the building are mapped into the model, accurate tracking and management of utilization of power consumption, water resources and solar energy of the electric devices of the building are achieved, time sequence analysis is combined, a resource consumption mode is recognized, energy demands are predicted, and the method is suitable for popularization and application. Problems in energy efficiency and resource recycling are effectively identified and handled, and long-term operation and maintenance of a building are effectively supported by adjusting working parameters of building energy consumption equipment, optimizing the energy use efficiency and enhancing the initiative of energy management.

Description

Technical Field

[0001] The present invention relates to the technical field of building information modeling, and in particular to a green building information integrated management method and system based on BIM. Background Art

[0002] The field of building information modeling technology focuses on managing data throughout the entire life cycle of a building by creating a digital building information model, including a three-dimensional visual representation of the building, data related to the building's design, performance, and maintenance, to help building professionals make design decisions, simulate building performance, and evaluate life cycle costs, including energy analysis, space utilization, and material consumption. It also helps predict and identify signature issues in building operation and maintenance during the design phase, and plan solutions in advance to optimize the sustainability and operational efficiency of the building.

[0003] Among them, the green building information integration management method aims to improve the efficiency and sustainability of green building projects. By integrating green standards and performance indicators related to buildings, including energy efficiency, environmental impact and material sustainability, it can achieve efficient data utilization and management in the design and operation stages, helping the construction team to evaluate the impact of different schemes on the environment in the early stages of design and select the best building materials and technologies. It can also achieve maximum energy efficiency, real-time monitoring and performance evaluation during the life of the building, ensure that the building meets green building standards during its service life, and achieve sustainable development goals for the environment, economy and society.

[0004] Traditional green building information integration management technology has deficiencies in real-time data integration, dynamic monitoring and instant optimization. It lacks a mechanism to feed real-time monitoring data back to the model for dynamic adjustment, resulting in low energy management efficiency during the building operation stage and difficulty in achieving precise regulation of building energy efficiency and resource use. In terms of data integration and practicality, it is usually difficult to support complex data processing requirements, limiting the actual application value of data in design and operation. The lack of effective data integration tools and strategies makes it difficult for building teams to comprehensively evaluate the environmental impact of solutions in the early stages of design and to adjust strategies in a timely manner during the building's life to adapt to changing operating conditions. This limits the potential of building projects to achieve optimal energy efficiency and resource utilization, and affects the contribution of building projects to environmental, economic and social sustainable development goals. Summary of the invention

[0005] In order to solve the technical problems existing in the prior art, the embodiment of the present invention provides a green building information integrated management method and system based on BIM. The technical solution is as follows:

[0006] On the one hand, a green building information integration management method based on BIM is provided, the method comprising:

[0007] S1: Based on the building project specification information, a building information model is constructed by identifying multiple building elements in multiple target buildings, and multiple devices in the building are mapped into the model to obtain the model construction result;

[0008] S2: Using the model construction results, the power consumption of multiple electrical equipment in the target building is monitored in real time, and the water resource recovery and solar energy resource utilization data are recorded to evaluate the energy efficiency level and resource recycling rate of the target building, and obtain real-time energy consumption monitoring data;

[0009] S3: Based on the real-time energy consumption monitoring data, extract energy and resource usage data, identify resource consumption patterns, perform trend analysis on target data, calculate energy demand forecast values ​​at multiple time points, and generate energy consumption forecast data;

[0010] S4: Based on the energy consumption prediction data, identify abnormal energy consumption data and map it to the building model, adjust the working parameters of the building's lighting equipment, air conditioning equipment, windows and ventilation equipment, optimize energy use efficiency, and generate energy use management parameters;

[0011] S5: Based on the energy use management parameters, the relationship between various devices and building elements is analyzed, and the interdependence and structural relationship of building elements are utilized to adjust the storage tags of various data, optimize data query efficiency, and complete the information integration management results.

[0012] As a further solution of the present invention, the model construction results include building element identification information, element location information, and equipment mapping information; the real-time energy consumption monitoring data include equipment power records, water resource recovery rate, and solar energy utilization efficiency indicators; the energy consumption prediction data include time series energy demand data sets, resource consumption patterns, and prediction error ranges; the energy use management parameters include lighting equipment control settings, air conditioning temperature control parameters, and ventilation equipment adjustment parameters; the information integration management results include index tag addition records, query response efficiency, and dependency analysis results between building elements.

[0013] As a further solution of the present invention, based on the construction project specification information, by identifying multiple building elements in multiple target buildings, building a building information model, and mapping multiple electrical equipment in the building to the model, the steps of obtaining the model construction result are specifically as follows:

[0014] S101: Based on the construction project specification information, extract information of various construction elements in the target building through the construction project specification document, including the location and attributes of the construction elements, and generate construction element identification data;

[0015] S102: Based on the building element recognition data, a building information model is constructed using the positions and connection relationships of various building elements, including windows, doors, stairs, pipes, and cables;

[0016] S103: Based on the building information model, analyze and identify the locations of multiple devices in the building, including lighting equipment, air-conditioning equipment, ventilation equipment, rainwater recycling equipment, and solar energy equipment, and map the equipment to the building model to generate a model construction result.

[0017] As a further solution of the present invention, the model construction results are used to monitor the power consumption of multiple electrical equipment in the target building in real time, and record the water resource recovery and solar energy resource utilization data, evaluate the energy efficiency level and resource recycling rate of the target building, and obtain the real-time energy consumption monitoring data in the following steps:

[0018] S201: Using the model construction result, collecting power consumption data of multiple electrical equipment in the building in real time to generate power consumption collection data;

[0019] S202: Based on the power consumption collection data, monitor and record data on water resource recovery and solar energy utilization, evaluate the utilization rate of rainwater and solar energy, and generate resource recovery and utilization information;

[0020] S203: Based on the resource recycling information, the power consumption data and water consumption data of multiple devices are mapped to the building model in real time to generate real-time energy consumption monitoring data.

[0021] As a further solution of the present invention, based on the real-time energy consumption monitoring data, energy and resource usage data are extracted, resource consumption patterns are identified, and trend analysis is performed on target data to calculate energy demand forecast values ​​at multiple time points. The steps of generating energy consumption forecast data are specifically as follows:

[0022] S301: extracting energy and resource usage data of a target building based on the real-time energy consumption monitoring data, and constructing a time series data set using time information to generate a building energy consumption data set;

[0023] S302: Based on the building energy consumption data set, using time series analysis, identifying the frequency and pattern of energy use in the target building, recording the peak and valley periods of energy use and the corresponding power consumption, and generating a resource consumption pattern analysis result;

[0024] S303: Utilizing the resource consumption pattern analysis result, calculate energy demand forecast values ​​at multiple time points, and generate energy consumption forecast data.

[0025] As a further solution of the present invention, the specific formula for calculating the predicted energy demand values ​​at multiple time points is:

[0026]

[0027] Among them, P t represents the predicted energy consumption at time point t, d t-1 represents the actual energy consumption at time point t-1, d t-2 represents the actual energy consumption at time t-2, d t-3 represents the actual energy consumption at time t-3, w1 is the value of d t-1 The weight of w2 is d t-2 The weight of w3 is the weight of d t-3 The weight of t represents the time point currently being considered, t-1 represents a time point before the current time point, t-2 represents two time points before the current time point, and t-3 represents three time points before the current time point.

[0028] As a further solution of the present invention, according to the energy consumption forecast data, the abnormal energy consumption data is identified, the working parameters of the lighting equipment, air conditioning equipment, windows and ventilation equipment of the building are adjusted, the energy utilization efficiency is optimized, and the steps of generating energy utilization management parameters are specifically as follows:

[0029] S401: Analyze the energy consumption prediction data, detect abnormal data points by comparing the energy consumption prediction value with the actual monitoring value in real time, and map them to the building model to generate abnormal energy consumption identification information;

[0030] S402: Based on the abnormal energy consumption identification information, adjusting the operating parameters of the lighting and air conditioning systems, including brightness and temperature parameters, configuring the control parameters of windows and ventilation equipment, and generating equipment usage configuration parameters;

[0031] S403: Apply the device usage configuration parameters to configure the working parameters of multiple devices, combine the real-time energy consumption data, evaluate the adjustment effect, optimize energy usage, and generate energy usage management parameters.

[0032] As a further solution of the present invention, the specific formula for detecting abnormal data points is:

[0033]

[0034] Among them, P represents the predicted energy consumption value, A represents the actual monitored energy consumption value, and σ P represents the standard deviation of the predicted values, σ A represents the standard deviation of the actual value, and E represents the normalized anomaly index, which is used to quantify the degree of deviation between the predicted value and the actual value.

[0035] As a further solution of the present invention, based on the energy use management parameters, the relationship between various devices and building elements is analyzed, and the interdependence and structural relationship of the building elements are utilized to adjust the storage tags of various data, optimize the data query efficiency, and complete the steps of information integration management results:

[0036] S501: Based on the energy use management parameters, analyzing the correlation of multiple equipment building elements in the building, including windows, floors, and doors, and generating element correlation data;

[0037] S502: Based on the element association data and according to the dependency relationship between the equipment and the building elements, the storage tags of the power consumption data of the multiple equipment are adjusted to generate a search tag adding record;

[0038] S503: Apply the search tag to add records, adjust the storage and query structure of the database, optimize the response time and processing speed of the query request, and generate information integration management results.

[0039] On the other hand, a green building information integrated management system based on BIM is provided, and the system is applied to a green building information integrated management method based on BIM, and the system includes:

[0040] The building model building module identifies various building elements in the target building, including windows, doors, stairs, pipes, and cables, based on the building project specification information, builds a building information model of the target building, and maps multiple devices in the building to the model to generate a model building result;

[0041] The building real-time monitoring module uses the model construction results to monitor the power consumption and resource recovery of multiple power-consuming devices in real time, record data and perform energy efficiency evaluation, and generate real-time energy consumption monitoring data;

[0042] The energy consumption data analysis module extracts energy usage data based on the real-time energy consumption monitoring data, analyzes the energy consumption pattern of the target building, predicts energy demand in combination with time series analysis, and generates energy consumption prediction data;

[0043] The abnormal data detection module identifies abnormal energy consumption data based on the energy consumption prediction data and maps it to the building model to generate abnormal energy consumption detection results;

[0044] The equipment parameter adjustment module adjusts the control parameters of lighting, air conditioning, and windows based on the abnormal energy consumption detection results, optimizes energy efficiency, and generates energy use management parameters;

[0045] The retrieval tag management module uses the energy use management parameters to analyze the dependencies between devices and building elements, add index tags to the energy consumption data of multiple devices, optimize the efficiency of data retrieval and storage, and generate information integration management results.

[0046] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0047] By constructing a building information model and mapping multiple devices in the building to the model, it is possible to accurately track and manage the power consumption of the building's electrical equipment, the use of water resources and solar energy. Combined with time series analysis, resource consumption patterns can be identified, and energy demand can be predicted. Problems in energy efficiency and resource recycling can be effectively identified and addressed. By adjusting the working parameters of the building's energy-consuming equipment, energy utilization efficiency can be optimized, the initiative of energy management can be enhanced, and the long-term operation and maintenance of the building can be effectively supported. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0059] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0061] The embodiment of the present invention provides a green building information integrated management method based on BIM, such as Figure 1 The flowchart of the green building information integration management method based on BIM is shown in the figure. The processing flow of the method may include the following steps:

[0062] S1: Based on the building project specification information, a building information model is constructed by identifying multiple building elements in multiple target buildings, and multiple devices in the building are mapped into the model to obtain the model construction result;

[0063] S2: Using the model construction results, the power consumption of multiple electrical equipment in the target building is monitored in real time, and the water resource recovery and solar energy resource utilization data are recorded to evaluate the energy efficiency level and resource recycling rate of the target building, and obtain real-time energy consumption monitoring data;

[0064] S3: Based on real-time energy consumption monitoring data, extract energy and resource usage data, identify resource consumption patterns, perform trend analysis on target data, calculate energy demand forecast values ​​at multiple time points, and generate energy consumption forecast data;

[0065] S4: Based on the energy consumption forecast data, identify abnormal energy consumption data and map it to the building model, adjust the working parameters of the building's lighting equipment, air conditioning equipment, windows and ventilation equipment, optimize energy efficiency, and generate energy use management parameters;

[0066] S5: Based on the energy use management parameters, analyze the relationship between various devices and building elements, and use the interdependence and structural relationship of building elements to adjust the storage tags of various data, optimize data query efficiency, and complete the information integration management results.

[0067] The model building results include building element identification information, element location information, and equipment mapping information. Real-time energy consumption monitoring data include equipment power records, water resource recovery rate, and solar energy utilization efficiency indicators. Energy consumption forecasting data include time series energy demand data sets, resource consumption patterns, and forecast error ranges. Energy use management parameters include lighting equipment control settings, air conditioning temperature control parameters, and ventilation equipment adjustment parameters. Information integration management results include index tag addition records, query response efficiency, and dependency analysis results between building elements.

[0068] See also Figure 2 Based on the building project specification information, a building information model is constructed by identifying multiple building elements in multiple target buildings, and multiple electrical equipment in the building is mapped to the model. The steps to obtain the model construction result are as follows:

[0069] S101: Based on the construction project specification information, extract information of various construction elements in the target building through the construction project specification document, including the location and attributes of the construction elements, and generate construction element identification data;

[0070] In sub-step S101, based on the construction project specification document, the document content is analyzed and various building element data in the target building are extracted. The parameters involved in the process include the size, location coordinates and type attributes of the elements. The description in the specification book is structured using text parsing technology, including the use of regular expressions to identify and classify the keywords and values ​​of the building elements, determine the specific location of each building element and its spatial relationship with other elements, and generate accurate building element identification data. The target data includes the spatial coordinates of the elements, the connection method, and the contact points with other building structures, which provides the necessary basic data for subsequent model construction and lays the foundation for the accuracy and reliability of the building information model.

[0071] S102: Based on the building element recognition data, the building information model is constructed using the positions and connection relationships of various building elements, including windows, doors, stairs, pipes, and cables;

[0072] In sub-step S102, the construction of the building information model is executed based on the building element identification data. The process uses the modeling function in the computer-aided design software to accurately layout the building elements according to the extracted data. The parameter types used in the process include the three-dimensional coordinates of each element and the predefined connection parameters. In the model construction, attention is paid to the joints and load-bearing capacity between the elements. The parametric design method is used to automatically adjust the element position to optimize the space usage and structural stability, improve the model construction efficiency, and ensure the accuracy of the model in practical applications. The generated building information model reflects the actual physical structure and functional layout of the building, and provides a basis for equipment mapping and overall energy efficiency analysis.

[0073] S103: Based on the building information model, analyze and identify the locations of multiple devices in the building, including lighting equipment, air conditioning equipment, ventilation equipment, rainwater recycling equipment, and solar energy equipment, and map the equipment to the building model to generate a model construction result;

[0074] In sub-step S103, based on the constructed building information model, the equipment location analysis and mapping process is performed. In the process, the parameter types used include the power requirements, operating frequency and environmental impact factors of the equipment. The spatial analysis algorithm is used to determine the optimal location of each device in the building to ensure that the spatial efficiency of the equipment installation point is maximized while reducing energy loss, including lighting simulation of lighting equipment, airflow dynamics analysis of air-conditioning and ventilation equipment, and environmental benefit assessment of rainwater recycling and solar energy equipment. Through target technical means, the equipment location and parameters are mapped to the building model. The generated model construction results record the layout of the equipment, optimize the energy efficiency of equipment operation, and provide data support for the long-term operation of the building and a decision-making basis for energy management.

[0075] See also Figure 3 , using the model construction results, real-time monitoring of the power consumption of multiple electrical equipment in the target building, and recording the water resource recovery and solar energy resource utilization data, evaluating the energy efficiency level and resource recycling rate of the target building. The specific steps to obtain real-time energy consumption monitoring data are as follows:

[0076] S201: Using the model construction result, collecting power consumption data of multiple power-consuming devices in the building in real time to generate power consumption collection data;

[0077] In sub-step S201, based on the constructed building information model, a real-time data acquisition system, such as smart meters and sensor networks, is used to continuously monitor and record the energy consumption of each electrical device. The parameters in the process include current, voltage, power and timestamp to ensure the accuracy and real-time nature of the data. To process the target data, data acquisition algorithms, such as time series analysis, are applied to format and preliminarily analyze the collected power consumption data. Through this method, the generated power consumption collection data includes the power consumption curve and total power consumption of each device. The target data provides an empirical basis for subsequent energy management and efficiency optimization. Real-time data acquisition not only enhances the transparency of building energy management, but also provides the necessary information to support the implementation and verification of energy efficiency improvement measures.

[0078] S202: Based on the power consumption data collection, monitor and record the data of water resource recovery and solar energy utilization, evaluate the utilization rate of rainwater and solar energy, and generate resource recovery and utilization information;

[0079] In sub-step S202, based on the power consumption data collected from the intelligent sensor system, the monitoring scope is expanded to include the performance data of the rainwater recycling system and the solar panels. The technologies used include flow meters and photovoltaic efficiency sensors. The parameters involve water flow, water quality parameters, solar energy conversion efficiency and ambient light conditions. By comprehensively analyzing the target data and using resource utilization evaluation models, such as efficiency calculation formulas, the utilization rates of rainwater and solar energy are quantitatively evaluated. The generated resource recycling information includes the collection efficiency of each resource and the environmental impact assessment results, which provides a scientific basis for sustainable energy utilization and helps building managers adjust resource allocation and optimize the overall energy utilization efficiency of the building.

[0080] S203: Based on the resource recycling information, the power consumption data and water consumption data of multiple devices are mapped to the building model in real time to generate real-time energy consumption monitoring data;

[0081] In sub-step S203, the collected electricity and water consumption data, solar energy and rainwater recycling information are mapped to the building information model in real time. The process involves data integration technology. The parameter types include energy consumption, resource recovery data and time series. By using geographic information system and building information model technology, combined with real-time data mapping algorithms such as data fusion and visualization technology, it is ensured that all data are accurately represented in the building model. The generated real-time energy consumption monitoring data includes the usage of various energy and resources, and reflects the impact of target usage on the overall energy efficiency of the building in real time, providing dynamic support for real-time energy efficiency management and decision-making, and enhancing the responsiveness and sustainability of building operations.

[0082] See also Figure 4,Based on real-time energy consumption monitoring data, extract energy and resource usage data, identify resource consumption patterns, and perform trend analysis on target data, calculate energy demand forecast values ​​at multiple time points, and the steps for generating energy consumption forecast data are as follows:

[0083] S301: Based on the real-time energy consumption monitoring data, the energy and resource usage data of the target building are extracted, and the time series data set is constructed using the time information to generate a building energy consumption data set;

[0084] In sub-step S301, based on the real-time energy consumption monitoring data, data extraction and sorting operations are performed. The operations include extracting the power usage records and environmental resource data of each device from the monitoring system, such as the utilization of water and solar energy. The types of data used involve energy consumption, timestamps, device identification and environmental parameters. By applying database query languages ​​and data processing scripts, such as SQL and Python Pandas library, the target data is integrated to construct a time series data set of energy consumption and resource usage with a time dimension. The data set is arranged in chronological order, and each record reflects the energy usage details at a specific time point. The generated building energy consumption data set provides rich historical data and real-time data for subsequent analysis, making energy management more accurate and dynamic.

[0085] S302: Based on the building energy consumption data set, using time series analysis, identify the frequency and pattern of energy use in the target building, record the peak and valley periods of energy use and the corresponding power consumption, and generate resource consumption pattern analysis results;

[0086] In sub-step S302, based on the constructed building energy consumption data set, time series analysis methods, such as the autoregressive moving average model, are used to analyze and identify the frequency and pattern of building energy use. The process parameters include time window, autoregressive parameters and moving average parameters. The target parameters are adjusted through historical data to match the actual dynamics of energy use. Through model analysis, the peak and valley periods of energy use and their corresponding electricity consumption are determined. The generated resource consumption pattern analysis results record the energy consumption in different time periods, providing a scientific basis for adjusting energy allocation and optimizing energy strategies. The analysis results help building managers to conduct more targeted energy scheduling and cost control.

[0087] S303: Calculate energy demand forecast values ​​at multiple time points using the resource consumption pattern analysis results to generate energy consumption forecast data;

[0088] The specific formula for calculating the predicted energy demand values ​​at multiple time points is:

[0089]

[0090] Among them, P trepresents the predicted energy consumption at time point t, d t-1 represents the actual energy consumption at time point t-1, d t-2 represents the actual energy consumption at time t-2, d t-3 represents the actual energy consumption at time t-3, w1 is the value of d t-1 The weight of w2 is d t-2 The weight of w3 is the weight of d t-3 The weight of t represents the time point currently being considered, t-1 represents a time point before the current time point, t-2 represents two time points before the current time point, and t-3 represents three time points before the current time point.

[0091] formula:

[0092]

[0093] Detailed explanation of the formula and the process of formula calculation and derivation:

[0094] The formula is used to calculate the predicted energy consumption at time point t, which helps to optimize energy management and plan resource allocation;

[0095] Parameter meaning and setting value:

[0096] d t-1 d t-2 d t-3 Represents the actual energy consumption at time points t-1, t-2, and t-3, respectively. Assume that the energy consumption collected at time point t-1 is 450kWh, t-2 is 430kWh, and t-3 is 420kWh:

[0097] w1, w2, and W3 are the weights of each time point, assuming w1 = 0.6, W2 = 0.3, and W3 = 0.1;

[0098] Substitute the parameters into the formula for calculation:

[0099]

[0100] The calculated P t =441 indicates that at time point t, the expected energy consumption is 441 kWh. The result is used to adjust the energy supply to ensure the optimization of energy use, and to adjust the operating parameters of related equipment according to the predicted data to reduce energy waste and optimize energy efficiency.

[0101] See also Figure 5 ,According to the energy consumption forecast data, abnormal energy consumption data is identified, the working parameters of the building's lighting equipment, air conditioning equipment, windows and ventilation equipment are adjusted to optimize energy efficiency. The specific steps for generating energy use management parameters are as follows:

[0102] S401: Analyze energy consumption forecast data, detect abnormal data points by comparing the energy consumption forecast value with the actual monitoring value in real time, and map them to the building model to generate abnormal energy consumption identification information;

[0103] The specific formula for detecting abnormal data points is:

[0104]

[0105] Among them, P represents the predicted energy consumption value, A represents the actual monitored energy consumption value, and σ P represents the standard deviation of the predicted values, σ A represents the standard deviation of the actual value, and E represents the normalized anomaly index, which is used to quantify the degree of deviation between the predicted value and the actual value.

[0106] formula:

[0107]

[0108] Detailed explanation of the formula and the process of formula calculation and derivation:

[0109] The formula is used to calculate the normalized anomaly index between the predicted and actual values, and the result is used to identify abnormal energy consumption data points;

[0110] Parameter meaning and setting value:

[0111] P is the predicted energy consumption value, assumed to be 200 kWh, reflecting the energy demand predicted based on historical data;

[0112] A is the actual monitored energy consumption value, assumed to be 230 kWh, reflecting the actual energy usage recorded during the monitoring period;

[0113] σ P is the standard deviation of the predicted value, assuming it is 10 kWh, indicating the fluctuation range of the predicted data;

[0114] σ A is the standard deviation of the actual value, assumed to be 15 kWh, indicating the fluctuation range of the actual energy usage data;

[0115] Substitute the parameters into the formula for calculation:

[0116]

[0117] The result E=1.664 shows that there is a large difference between the predicted value and the actual value. The index is used to identify energy consumption data that does not conform to the normal pattern, helping the energy consumption management system to adjust relevant parameters and optimize energy efficiency.

[0118] S402: Based on the abnormal energy consumption identification information, adjust the working parameters of the lighting and air conditioning systems, including brightness and temperature parameters, configure the control parameters of windows and ventilation equipment, and generate equipment usage configuration parameters;

[0119] In sub-step S402, based on the abnormal energy consumption identification information, the operating parameters of the lighting and air-conditioning systems are adjusted. The process involves analyzing the identified abnormal energy consumption data. The parameter types include lighting brightness level and air-conditioning temperature setting value. Adaptive control technology, such as fuzzy logic controller, is used to automatically adjust the lighting brightness and air-conditioning temperature to adapt to the energy consumption demand and occupancy conditions in different time periods to ensure maximum energy efficiency. At the same time, the control parameters of windows and ventilation equipment are configured, and environmental sensor data, such as CO2 concentration and indoor temperature monitoring, are used to adjust the ventilation rate and window opening frequency, and generate equipment usage configuration parameters, which improves the indoor environmental quality, reduces energy waste, and provides a more comfortable and energy-efficient operating environment for the building.

[0120] S403: Apply device usage configuration parameters, configure working parameters of multiple devices, combine real-time energy consumption data, evaluate adjustment effects, optimize energy usage, and generate energy usage management parameters;

[0121] In sub-step S403, the newly adjusted equipment usage configuration parameters are applied to configure the parameters of lighting, air conditioning, windows and ventilation equipment, etc., and real-time data analysis and equipment feedback control systems are adopted. The parameter types cover the operating time, power usage, and response threshold of the equipment. Combined with the collected real-time energy consumption data, data analysis models such as regression analysis are used to evaluate the energy usage differences before and after the equipment parameter adjustment, and optimize the operation strategy to reduce peak load and improve energy efficiency. The generated energy usage management parameters reflect the optimized state of equipment operation, match the actual usage of the building, and ensure that energy consumption is minimized while maintaining the required indoor comfort standards.

[0122] See also Figure 6 ,Based on the energy use management parameters, the relationship between various equipment and building elements is analyzed, and the interdependence and structural relationship of building elements are used to adjust the storage tags of various data, optimize the data query efficiency, and complete the information integration management results. The specific steps are:

[0123] S501: Based on the energy use management parameters, analyze the correlation of multiple equipment building elements in the building, including windows, floors, and doors, and generate element correlation data;

[0124] In sub-step S501, based on the energy use management parameters, a correlation analysis is performed on the internal elements of the building. The analysis involves identifying and recording the spatial and functional relationships between various building elements such as windows, floors and doors. Parameters such as element location, functional classification and energy consumption data are used, and relationship analysis techniques such as network topology analysis are used to visualize the target relationship to reveal the mutual influence of energy use patterns and building structures. The generated element correlation data provides insights for building design optimization and helps facility managers better understand the impact of element configuration on energy efficiency. The target data is used to adjust building operation strategies to maximize energy efficiency.

[0125] S502: Based on the element association data and the dependency relationship between the equipment and the building elements, the storage tags of the power consumption data of multiple equipment are adjusted to generate a search tag addition record;

[0126] In sub-step S502, the dependency relationships between equipment and related building elements are systematically marked based on element association data. Data labeling technology is used in the process. By setting multiple labels, such as "door lighting" and "hall air conditioning", target labels are applied to corresponding equipment and building elements to make data retrieval more efficient and facilitate subsequent data analysis and report generation. Through this systematic data labeling, the generated retrieval label addition records provide more accurate input data for the subsequent intelligent building management system and support more complex query and analysis requirements.

[0127] S503: Apply search tags to add records, adjust the storage and query structure of the database, optimize the response time and processing speed of query requests, and generate information integration management results;

[0128] In sub-step S503, records are added using retrieval tags, and the storage structure and query mechanism of the database are optimized. The optimization includes adjusting the database index, optimizing the query algorithm, and adjusting the data table structure. The parameter types involve query frequency, data size, and response time. Database management technologies, such as index optimization and query optimization technologies, are used to ensure that data access efficiency is maximized, query delays are reduced, data processing speed is improved, and system load is reduced. The generated information integration management results show that the response time and processing speed of query requests are significantly improved, supporting efficient building information management and real-time energy consumption monitoring.

[0129] See also Figure 7 , a green building information integrated management system based on BIM, the green building information integrated management system based on BIM is used to execute the above-mentioned green building information integrated management method based on BIM, the system includes:

[0130] The building model building module identifies various building elements in the target building, including windows, doors, stairs, pipes, and cables, based on the building project specification information, builds a building information model of the target building, and maps multiple devices in the building to the model to generate a model building result;

[0131] The building real-time monitoring module uses the model construction results to monitor the power consumption and resource recovery of multiple power-consuming devices in real time, record data and conduct energy efficiency evaluation, and generate real-time energy consumption monitoring data;

[0132] The energy consumption data analysis module extracts energy usage data based on real-time energy consumption monitoring data, analyzes the energy consumption pattern of the target building, predicts energy demand in combination with time series analysis, and generates energy consumption forecast data;

[0133] The abnormal data detection module identifies abnormal energy consumption data based on energy consumption forecast data and maps it to the building model to generate abnormal energy consumption detection results;

[0134] The equipment parameter adjustment module adjusts the control parameters of lighting, air conditioning, and windows based on the abnormal energy consumption detection results, optimizes energy efficiency, and generates energy use management parameters;

[0135] The retrieval tag management module uses energy use management parameters to analyze the dependencies between devices and building elements, add index tags to the energy consumption data of multiple devices, optimize the efficiency of data retrieval and storage, and generate information integration management results.

[0136] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0137] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0138] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0139] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0140] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0141] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0142] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0143] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0145] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0146] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A green building information integration management method based on BIM, characterized in that: The method comprises: Based on the building project specification information, a building information model is constructed by identifying multiple building elements in multiple target buildings, and multiple devices in the building are mapped into the model to obtain a model construction result; By using the model construction results, the power consumption of multiple electrical equipment in the target building is monitored in real time, and the water resource recovery and solar energy resource utilization data are recorded to evaluate the energy efficiency level and resource recycling rate of the target building, and obtain real-time energy consumption monitoring data; Based on the real-time energy consumption monitoring data, extract energy and resource usage data, identify resource consumption patterns, and perform trend analysis on target data, calculate energy demand forecast values ​​at multiple time points, and generate energy consumption forecast data; Based on the energy consumption forecast data, identify abnormal energy consumption data and map it to the building model, adjust the working parameters of the building's lighting equipment, air conditioning equipment, windows and ventilation equipment, optimize energy use efficiency, and generate energy use management parameters; Based on the energy use management parameters, the relationship between various devices and building elements is analyzed, and the interdependence and structural relationship of building elements are utilized to adjust the storage tags of various data, optimize data query efficiency, and complete the information integration management result.

2. The BIM-based green building information integrated management method according to claim 1, characterized in that: The model building results include building element identification information, element location information, and equipment mapping information; the real-time energy consumption monitoring data include equipment power records, water resource recovery rate, and solar energy utilization efficiency indicators; the energy consumption prediction data include time series energy demand data sets, resource consumption patterns, and prediction error ranges; the energy use management parameters include lighting equipment control settings, air conditioning temperature control parameters, and ventilation equipment adjustment parameters; the information integration management results include index tag addition records, query response efficiency, and dependency analysis results between building elements.

3. The BIM-based green building information integrated management method according to claim 1, characterized in that: Based on the building project specification information, by identifying multiple building elements in multiple target buildings, building a building information model, and mapping multiple electrical equipment in the building to the model, the steps to obtain the model construction result are as follows: Based on the building project specification information, extract the information of various building elements in the target building through the building project specification document, including the location and attributes of the building elements, and generate building element identification data; Based on the building element identification data, a building information model is constructed using the positions and connection relationships of various building elements, including windows, doors, stairs, pipes, and cables; Based on the building information model, the locations of multiple devices in the building are analyzed and identified, including lighting equipment, air-conditioning equipment, ventilation equipment, rainwater recycling equipment, and solar energy equipment, and the devices are mapped to the building model to generate a model construction result.

4. The BIM-based green building information integrated management method according to claim 1, characterized in that: Using the model construction results, the power consumption of multiple electrical equipment in the target building is monitored in real time, and the water resource recovery and solar energy resource utilization data are recorded to evaluate the energy efficiency level and resource recycling rate of the target building. The specific steps for obtaining real-time energy consumption monitoring data are as follows: Using the model construction results, power consumption data of multiple electrical equipment in the building are collected in real time to generate power consumption collection data; Based on the power consumption data collected, water resource recovery and solar energy utilization data are monitored and recorded, the utilization rate of rainwater and solar energy is evaluated, and resource recovery and utilization information is generated; Based on the resource recycling information, the electricity consumption data and water consumption data of multiple devices are mapped to the building model in real time to generate real-time energy consumption monitoring data.

5. The BIM-based green building information integrated management method according to claim 1, characterized in that: Based on the real-time energy consumption monitoring data, extracting energy and resource usage data, identifying resource consumption patterns, and performing trend analysis on target data, calculating energy demand forecast values ​​at multiple time points, and generating energy consumption forecast data are specifically as follows: Based on the real-time energy consumption monitoring data, extract the energy and resource usage data of the target building, and use the time information to construct a time series data set to generate a building energy consumption data set; Based on the building energy consumption data set, using time series analysis, identifying the frequency and pattern of energy use in the target building, recording the peak and valley periods of energy use and the corresponding electricity consumption, and generating resource consumption pattern analysis results; The resource consumption pattern analysis results are used to calculate energy demand forecast values ​​at multiple time points to generate energy consumption forecast data.

6. The BIM-based green building information integrated management method according to claim 5 is characterized in that: The specific formula for calculating the predicted energy demand values ​​at multiple time points is: Among them, P t represents the predicted energy consumption at time point t, d t-1 represents the actual energy consumption at time point t-1, d t-2 represents the actual energy consumption at time t-2, d t-3 represents the actual energy consumption at time t-3, w1 is the value of d t-1 The weight of w2 is d t-2 The weight of w3 is the weight of d t-3 The weight of t represents the time point currently being considered, t-1 represents a time point before the current time point, t-2 represents two time points before the current time point, and t-3 represents three time points before the current time point.

7. The BIM-based green building information integrated management method according to claim 1, characterized in that: The steps of identifying abnormal energy consumption data according to the energy consumption forecast data, adjusting the working parameters of the building's lighting equipment, air conditioning equipment, windows and ventilation equipment, optimizing energy efficiency, and generating energy use management parameters are as follows: Analyze the energy consumption forecast data, detect abnormal data points by comparing the energy consumption forecast value with the actual monitored value in real time, and map them to the building model to generate abnormal energy consumption identification information; Based on the abnormal energy consumption identification information, adjust the operating parameters of the lighting and air conditioning systems, including brightness and temperature parameters, configure the control parameters of windows and ventilation equipment, and generate equipment usage configuration parameters; Apply the device usage configuration parameters, configure the working parameters of multiple devices, combine with real-time energy consumption data, evaluate the adjustment effect, optimize energy usage, and generate energy usage management parameters.

8. The BIM-based green building information integrated management method according to claim 7, characterized in that: The specific formula for detecting abnormal data points is: Among them, P represents the predicted energy consumption value, A represents the actual monitored energy consumption value, and σ P represents the standard deviation of the predicted values, σ A represents the standard deviation of the actual value, and E represents the normalized anomaly index, which is used to quantify the degree of deviation between the predicted value and the actual value.

9. The BIM-based green building information integrated management method according to claim 1, characterized in that: Based on the energy use management parameters, the relationship between various devices and building elements is analyzed, and the interdependence and structural relationship of building elements are utilized to adjust the storage tags of various data, optimize the data query efficiency, and complete the information integration management results. The specific steps are: Based on the energy use management parameters, analyzing the correlation of multiple equipment building elements in the building, including windows, floors, and doors, and generating element correlation data; Based on the element association data, according to the dependency relationship between the equipment and the building elements, the storage tags of the power consumption data of multiple equipment are adjusted to generate a retrieval tag addition record; The search tags are applied to add records, adjust the storage and query structure of the database, optimize the response time and processing speed of the query request, and generate information integration management results.

10. A green building information integrated management system based on BIM, characterized in that: According to the BIM-based green building information integrated management method according to any one of claims 1 to 9, the system comprises: The building model building module identifies various building elements in the target building, including windows, doors, stairs, pipes, and cables, based on the building project specification information, builds a building information model of the target building, and maps multiple devices in the building to the model to generate a model building result; The building real-time monitoring module uses the model construction results to monitor the power consumption and resource recovery of multiple power-consuming devices in real time, record data and perform energy efficiency evaluation, and generate real-time energy consumption monitoring data; The energy consumption data analysis module extracts energy usage data based on the real-time energy consumption monitoring data, analyzes the energy consumption pattern of the target building, predicts energy demand in combination with time series analysis, and generates energy consumption prediction data; The abnormal data detection module identifies abnormal energy consumption data based on the energy consumption prediction data and maps it to the building model to generate abnormal energy consumption detection results; The equipment parameter adjustment module adjusts the control parameters of lighting, air conditioning, and windows based on the abnormal energy consumption detection results, optimizes energy efficiency, and generates energy use management parameters; The retrieval tag management module uses the energy use management parameters to analyze the dependencies between devices and building elements, add index tags to the energy consumption data of multiple devices, optimize the efficiency of data retrieval and storage, and generate information integration management results.

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