Green building operation digital monitoring system

Abstract

The invention discloses a green building operation digital monitoring system, and relates to the technical field of building monitoring, and the system comprises a data collection module, and the data collection module is used for collecting different types of data through a plurality of sensors arranged at all key positions of a green building; the data processing module is used for receiving various data from the data acquisition module and preprocessing the data; the data storage module is used for storing the processed data; the data analysis module is used for carrying out deep analysis on the stored data; and the control and optimization module controls and optimizes the operation of the building in real time according to the result of the data analysis module. The system has the advantages that the system is comprehensive, multi-aspect data, including energy, environment and equipment operation data, in the operation process of the green building can be collected and analyzed, and a comprehensive solution is provided for overall optimization of the green building.

Description

Technical Field

[0001] The present invention belongs to the technical field of building monitoring, and particularly relates to a digital monitoring system for the operation of green buildings. Background Art

[0002] With the increasing emphasis on environmental protection and sustainable development by people, green buildings have received more and more attention. Green buildings aim to reduce the impact on the environment and improve energy utilization efficiency by adopting environmentally friendly materials, energy-saving equipment, and sustainable design concepts.

[0003] However, there are many deficiencies in the existing building monitoring systems in monitoring green buildings. On the one hand, the data collection of traditional monitoring systems is often not comprehensive enough to cover various key information required for green buildings, such as the utilization efficiency of renewable energy, indoor environmental quality, etc.; on the other hand, there is a lack of in-depth analysis and integration of the collected data, making it difficult to provide effective decision-making support for the operation optimization of green buildings based on the data.

[0004] Therefore, it is of great practical significance to develop a digital monitoring system for the operation of green buildings that is comprehensive, efficient, and intelligent. Summary of the Invention

[0005] The object of the present invention is to provide a digital monitoring system for the operation of green buildings in view of the problems that the data collection of the existing building monitoring system is not comprehensive enough and cannot provide effective decision-making support.

[0006] The present invention is achieved through the following technical solutions:

[0007] A digital monitoring system for the operation of green buildings, comprising:

[0008] A data collection module, which arranges a variety of sensors at various key positions of the green building to collect different types of data and stores them in a database;

[0009] A data processing module, which receives various data from the data collection module and preprocesses them;

[0010] A data storage module, which is used to store the processed data, stores the data in different storage areas according to the type and timestamp of the data for quick retrieval and call; the stored data includes historical data and real-time data, providing rich data resources for subsequent analysis and evaluation;

[0011] A data analysis module, which is used to deeply analyze the stored data;

[0012] A control and optimization module that, based on the results of the data analysis module, performs real-time control and optimization of the operation of the building.

[0013] A user interface module that provides an operation interface for users, facilitating their viewing of monitoring data and the operating status of the system.

[0014] Preferably, the sensors include, but are not limited to:

[0015] Energy consumption sensors: Installed on energy-consuming devices in the power system, heating, ventilation, and air conditioning (HVAC) system, and lighting system, for monitoring energy consumption.

[0016] Environmental sensors: Distributed in different indoor and outdoor areas, including temperature sensors, humidity sensors, carbon dioxide sensors, illuminance sensors, and air quality sensors, for monitoring environmental parameters such as indoor and outdoor temperature, humidity, carbon dioxide concentration, illuminance, and air quality, to evaluate the comfort of the indoor and outdoor environment and air quality, and provide data support for adjusting the indoor environment.

[0017] Device operating status sensors: Set on various building devices.

[0018] Preferably, the sensors communicate with the data processing unit in a wired or wireless manner to ensure stable data transmission.

[0019] Preferably, the data processing module converts data in different formats into a unified data format, and at the same time, identifies and processes abnormal data to ensure the high accuracy and reliability of the stored data.

[0020] Preferably, the data processing module also performs preliminary analysis on the collected data, calculates the average energy consumption over different time periods and the fluctuation range of environmental parameters, providing basic data for subsequent in-depth analysis.

[0021] Preferably, the data analysis module includes an energy consumption analysis sub-module, an environmental analysis sub-module, and a device operation analysis sub-module;

[0022] The energy consumption analysis sub-module, by analyzing energy consumption data over different time periods, can identify peaks and valleys of energy consumption, analyze the energy consumption contribution of different devices, calculate energy utilization efficiency, and predict future energy consumption trends based on the building's usage pattern and environmental conditions, providing a basis for formulating energy-saving strategies.

[0023] Environmental analysis sub-module: Based on the data of environmental sensors, evaluates the quality and comfort of the indoor environment. For example, by analyzing indoor and outdoor temperature and humidity, carbon dioxide concentration, and air quality, an environmental quality index is obtained, providing a basis for intelligent adjustment of environmental parameters.

[0024] Meanwhile, the ecological performance of green buildings can also be evaluated, such as analyzing the impact of green vegetation on the microclimate, the effectiveness of natural lighting and ventilation, etc.

[0025] Equipment operation analysis sub-module: By analyzing the data from the sensors of the equipment operation status, evaluate the operation status of the equipment, establish a health model of the equipment, and predict the failure risk of the equipment in advance.

[0026] Using machine learning algorithms, based on the historical operation data and real-time operation data of the equipment, predict the possible failures of the equipment, and give corresponding maintenance suggestions to extend the service life of the equipment.

[0027] Preferably, the user interface module can display data in the form of a graphical interface, including real-time energy consumption curves, environmental quality indicators, equipment operation status and other information, enabling users to intuitively understand the operation of green buildings.

[0028] Meanwhile, users can set the parameters of the system through this interface, such as setting the target value of energy consumption, the standard range of environmental quality, etc., and receiving the alarm information of the system, such as equipment failure alarms, energy consumption exceeding the standard alarms, etc.

[0029] The present invention has the following advantages compared with the prior art:

[0030] 1. The advantage of the present invention lies in its comprehensiveness, which can collect and analyze various data during the operation of green buildings, including energy, environment and equipment operation data, providing a comprehensive solution for the overall optimization of green buildings.

[0031] 2. By using deep learning and machine learning algorithms, in-depth analysis of data is achieved, which can predict equipment failures and energy consumption trends in advance, improving the intelligence and forward-looking of the system.

[0032] 3. Through the automated control and optimization module, real-time regulation of building operation is achieved, which can significantly improve energy utilization efficiency and environmental quality, while reducing the maintenance cost of equipment.

[0033] 4. The system has good scalability and can easily add new sensors and functional modules to meet the needs of different types and scales of green buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a structural block diagram of a digital monitoring system for green building operation;

[0035] Figure 2 is a structural block diagram of an environmental sensor in a digital monitoring system for green building operation;

[0036] Figure 3 It is a flowchart showing the specific steps for the equipment operation analysis sub-module to predict equipment failures. Specific implementation manners

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment, please refer to Figures 1 - 3 , the digital monitoring system for the operation of green buildings of the present invention includes the following main parts:

[0039] 1) Data acquisition module. This module collects different types of data through a variety of sensors arranged at various key positions in the green building. These sensors include but are not limited to: energy consumption sensors, environmental sensors, and equipment operation status sensors.

[0040] Energy consumption sensors are installed on major energy-consuming equipment such as power systems, heating, ventilation, and air conditioning (HVAC) systems, and lighting systems, and are used to monitor the energy consumption situation. For example, electricity meters, water meters, and gas meters can collect the usage amounts of electricity, water, and gas in the building in real time and transmit the data to the data processing unit.

[0041] Environmental sensors are distributed in different indoor and outdoor areas, including temperature sensors, humidity sensors, carbon dioxide sensors, illuminance sensors, air quality sensors, etc., and are used to monitor indoor and outdoor environmental parameters such as indoor and outdoor temperature, humidity, carbon dioxide concentration, illuminance, and air quality, so as to evaluate the comfort of the indoor and outdoor environment and air quality and provide data support for adjusting the indoor environment.

[0042] Equipment operation status sensors are set on various building equipment such as elevators, ventilation equipment, and water supply and drainage equipment, and are used to monitor the operation parameters of the equipment. For example, speed sensors, pressure sensors, and vibration frequency sensors are used to detect the speed, pressure, vibration frequency, etc. of the equipment to determine whether the equipment is operating normally, and at the same time, potential equipment failure hazards can be warned in advance.

[0043] The energy consumption sensors, environmental sensors, and equipment operation status sensors can communicate with the data processing unit in a wired or wireless manner to ensure the stable transmission of data.

[0044] 2) Data processing module. The data processing module receives various data from the data acquisition module and preprocesses it, including operations such as data cleaning, filtering, and conversion.

[0045] It converts data in different formats into a unified data format for subsequent storage and analysis. Meanwhile, it identifies and processes abnormal data to ensure the high accuracy and reliability of the stored data.

[0046] In addition, the data processing module conducts preliminary analysis on the collected data, such as calculating the average energy consumption over different time periods and the fluctuation range of environmental parameters, providing basic data for subsequent in-depth analysis.

[0047] 3) The data storage module is used to store the processed data. A distributed storage system can be adopted to ensure data security and scalability.

[0048] The data storage module stores data in different storage areas according to the data type and timestamp for quick retrieval and invocation. The stored data includes historical data and real-time data, providing rich data resources for subsequent analysis and evaluation.

[0049] 4) The data analysis module, which is the core part of the system, conducts in-depth analysis based on the stored data. It includes an energy consumption analysis sub-module, an energy consumption analysis sub-module, and an energy consumption analysis sub-module.

[0050] Among them, the energy consumption analysis sub-module: Analyzes the energy consumption data over different time periods through statistical analysis and machine learning algorithms, can identify the peaks and valleys of energy consumption, analyze the energy consumption contributions of different devices, calculate the energy utilization efficiency, and predict future energy consumption trends based on the building's usage patterns and environmental conditions, providing a basis for formulating energy-saving strategies.

[0051] For example, based on historical data and real-time data, combined with the building's usage schedule, it analyzes the time periods with the highest energy consumption, the devices with relatively high energy consumption, and the factors affecting energy consumption, thus providing data support for adjusting the device operation time or optimizing the device performance.

[0052] The energy consumption analysis sub-module: Evaluates the quality and comfort of the indoor environment according to the data of environmental sensors. For example, by analyzing the indoor and outdoor temperature and humidity, carbon dioxide concentration, and air quality, it obtains environmental quality indicators, providing a basis for intelligent adjustment of environmental parameters.

[0053] Meanwhile, it can also evaluate the ecological performance of green buildings, such as analyzing the impact of green vegetation on the microclimate, the effects of natural lighting and ventilation, etc.

[0054] The device operation analysis sub-module: Evaluates the operation status of devices by analyzing the data of device operation status sensors, establishes a device health model, and predicts the device failure risk in advance.

[0055] Using machine learning algorithms, based on the historical operation data and real-time operation data of the device, predict the possible failures of the device, and give corresponding maintenance suggestions to extend the service life of the device.

[0056] The specific steps are as follows:

[0057] S01. Data collection and preprocessing

[0058] Data collection: Collect the operation data of the device from various data sources, including but not limited to sensors, device logs, monitoring systems, etc. The data should cover multiple aspects of the device, such as the operation status of the device (such as on / off, operation mode), operation parameters (such as temperature, pressure, rotation speed, current, voltage, vibration frequency, etc.), environmental conditions (such as ambient temperature, humidity), and performance indicators of the device (such as output power, efficiency), etc.

[0059] At the same time, collect the failure records of the device, including the time of failure occurrence, failure type, and maintenance information, so as to use them as labeled data to provide training basis for supervised learning algorithms.

[0060] Data preprocessing:

[0061] Handle missing values. For the missing values in the data, they can be filled with the mean, median, mode, or interpolation methods (such as linear interpolation, polynomial interpolation) to ensure the integrity of the data. For example, if there are some missing values in the temperature sensor data, they can be filled according to the average value of the previous and subsequent data points or the linear trend.

[0062] Handle outliers. Use statistical methods (such as the Z-score method) or box plot-based methods to identify and handle outliers. Outliers may be the result of measurement errors or abnormal device operation. For example, for the voltage measurement value of a certain device, if it exceeds the normal range (calculated from historical data), it can be replaced with the average value of adjacent normal data or a predicted value based on historical data.

[0063] Feature engineering:

[0064] Extract time features. Extract useful information from the timestamp, such as hour, day, week, month, season, etc., because the operation and failure of the device may be related to time factors. For example, some devices are more likely to fail in high-load seasons or specific time periods of the day.

[0065] Calculate statistical features. Calculate the statistical information of each feature, such as mean, variance, maximum value, minimum value, slope, kurtosis, skewness, etc., to reflect the statistical characteristics of the device operation data. For example, calculate the mean and variance of the temperature of the device in the past hour to reflect the temperature stability of the device.

[0066] Feature combination: According to domain knowledge, different features are combined to form new features to capture the complex relationships in the operation of the device. For example, for a motor device, current and voltage can be combined to calculate power, as power is an important indicator of the device's operating state.

[0067] S02. Data partitioning

[0068] The processed data is partitioned into a training set, a validation set, and a test set. The partitioning is done according to a ratio of 70:15:15 or 80:10:10. The training set is used to train the model, the validation set is used to adjust the hyperparameters of the model, and the test set is used to evaluate the final performance of the model. Ensure that the data distribution remains consistent during partitioning to avoid data bias.

[0069] S03. Model training

[0070] Select decision tree - based algorithms for classification and regression problems. They can handle non - linear relationships between features and can output feature importances, helping us understand which features have the greatest impact on device failures.

[0071] Use the training set to train the model and optimize the model's parameters by minimizing the loss function. For example, according to the historical operation data of the device and the corresponding fault labels, optimize the splitting rules of the decision tree to minimize the error between the prediction result and the true label.

[0072] S04. Model evaluation and hyperparameter tuning

[0073] Evaluation metrics:

[0074] For classification tasks (predicting fault types), use metrics such as accuracy, precision, recall, F1 - score, and confusion matrix to evaluate the model performance. For example, precision can tell us the proportion of samples predicted as faults that are actually faults; recall can tell us the proportion of actual fault samples that are predicted.

[0075] For regression tasks (predicting the time of fault occurrence or the remaining life of the device), metrics such as mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) can be used. For example, RMSE can reflect the deviation degree between the predicted fault time and the actual fault time.

[0076] For anomaly detection, use metrics such as precision, recall, F1 - score, and at the same time, evaluate it in combination with the false positive rate and false negative rate. A false positive is predicting normal data as abnormal, and a false negative is predicting abnormal data as normal.

[0077] Hyperparameter tuning: Use the validation set to adjust the hyperparameters of the model to optimize the model performance.

[0078] Hyperparameter search can be performed using Grid Search, Random Search, or more advanced Bayesian optimization methods to find the optimal combination of hyperparameters.

[0079] S05, Fault Prediction and Maintenance Recommendation Generation

[0080] Fault Prediction: For new real-time operation data, input it into the trained model, and the model will output the fault prediction result. For example, if a classification model is used, it will output whether the device will fail and the possible types of faults; if it is a regression model, it will output the time when the device is expected to fail or the remaining life of the device.

[0081] Maintenance Recommendation Generation: Based on the predicted fault type and time, combined with the operation manual and maintenance experience of the device, corresponding maintenance recommendations are generated.

[0082] For different fault types, there can be different maintenance recommendations. For example, if it is predicted that the fault is caused by too high motor temperature, it can be recommended to check the cooling system, clean the radiator fins, or change the lubricant; if it is predicted that the device is about to reach its service life, it can be recommended to arrange for early replacement of the device or conduct a comprehensive maintenance inspection.

[0083] For different predicted remaining lives, different maintenance plans can be formulated. For example, if it is predicted that the remaining life of the device is short, more frequent inspections and maintenance can be carried out; if the remaining life is long, regular preventive maintenance can be carried out.

[0084] S06, Model Update and Continuous Learning

[0085] Regularly add the newly collected device operation data and fault records to the training dataset and retrain the model to maintain the accuracy and adaptability of the model.

[0086] Adopt incremental learning methods so that the model can learn new data patterns without forgetting old knowledge and has better adaptability to newly emerging fault types or changes in device operation modes.

[0087] S07, Deployment and Integration

[0088] Deploy the trained model to the actual device monitoring system, integrate it with the data acquisition module and the control module to achieve real-time fault prediction and output of maintenance recommendations.

[0089] Establish a feedback mechanism to feedback the actual faults and maintenance results to the model for further optimizing the model performance.

[0090] 5) Control and Optimization Module: Based on the results of the Data Analysis Module, the Control and Optimization Module conducts real-time control and optimization of the building's operation, including an Energy Management Sub-module, an Environment Regulation Sub-module, and a Facility Management Sub-module.

[0091] Among them, the Energy Management Sub-module can automatically adjust the operating status of energy facilities. For example, according to the energy consumption analysis results and real-time environmental data, it can automatically adjust the temperature setting of the HVAC system, the brightness and on-time of the lighting system, etc., to achieve optimal utilization of energy.

[0092] When it is detected that there are fewer people indoors, it automatically reduces the cooling or heating power of the air conditioner, or dims the light brightness, thereby reducing energy consumption.

[0093] The Environment Regulation Sub-module: According to the environmental analysis results, it automatically controls ventilation equipment, sunshade systems, etc., to regulate the indoor environmental quality.

[0094] When the indoor carbon dioxide concentration is too high, it automatically turns on the ventilation equipment; when the light intensity is too high, it automatically adjusts the opening degree of the sunshade system to maintain a comfortable indoor environment.

[0095] The Facility Management Sub-module: According to the equipment operation analysis results, it conducts optimal scheduling and maintenance arrangements for the equipment.

[0096] It arranges maintenance in advance for equipment that may malfunction, or adjusts the operating parameters of the equipment according to the operating efficiency of the equipment. For example, it optimizes the elevator scheduling algorithm to reduce waiting time and operating energy consumption.

[0097] 6) User Interface Module: The User Interface Module provides an operation interface for users, facilitating users to view monitoring data and the operating status of the system.

[0098] It can display data in the form of a graphical interface, including real-time energy consumption curves, environmental quality indicators, equipment operating status, etc. information, enabling users to intuitively understand the operation of the green building.

[0099] At the same time, users can set system parameters through this interface, such as setting target values for energy consumption, standard ranges for environmental quality, etc., and receiving system alarm information, such as equipment failure alarms, energy consumption over-limit alarms, etc.

[0100] The working steps of the present invention:

[0101] First, various sensors in the Data Acquisition Module start to work, collecting real-time energy consumption data, environmental data, and equipment operation data in the green building.

[0102] The collected data is transmitted to the Data Processing Module through wired or wireless communication methods.

[0103] The data processing module receives data from the data acquisition module, cleans it, removes invalid data, and standardizes the data format.

[0104] Mark and process abnormal data, and store the processed data in the data storage module.

[0105] The data analysis module extracts data from the data storage module and conducts in-depth analysis.

[0106] The energy consumption analysis sub-module uses statistical analysis and machine learning algorithms to analyze energy consumption data, calculate energy utilization efficiency, and predict energy consumption trends. The environmental analysis sub-module evaluates environmental quality and comfort based on environmental data and analyzes the ecological performance of green buildings. The equipment operation analysis sub-module uses equipment operation data to establish an equipment health model and predict equipment failure risks.

[0107] The control and optimization module generates corresponding control instructions according to the results of data analysis. The energy management sub-module adjusts the operation status of energy equipment according to the energy consumption analysis results, such as adjusting the heating, ventilation, and air conditioning system, lighting system, etc. The environmental regulation sub-module controls ventilation equipment, shading systems, etc. according to the environmental analysis results. The equipment management sub-module conducts maintenance scheduling and adjusts operation parameters of equipment according to the equipment operation analysis results.

[0108] Users can view the monitoring data and operation status of the system through the user interface module, and can set system parameters through the user interface module and receive alarm information from the system.

[0109] The present invention can collect and analyze multi-faceted data during the operation of green buildings, including energy, environment, and equipment operation data, and provides a comprehensive solution for the overall optimization of green buildings. By using deep learning and machine learning algorithms, in-depth analysis of data is achieved, and equipment failures and energy consumption trends can be predicted in advance, improving the intelligence and forward-looking of the system. Through the automated control and optimization module, real-time regulation of building operation is realized, which can significantly improve energy utilization efficiency and environmental quality, while reducing the maintenance cost of equipment. The system has good scalability and can easily add new sensors and functional modules to meet the needs of different types and scales of green buildings.

[0110] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0111] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A green building operation digital monitoring system, characterized in that: include: The data acquisition module includes an energy consumption sensor, an environmental sensor and an equipment operation status sensor. The energy consumption sensor is arranged on the energy consumption equipment of the green building and is used to collect energy consumption data. The environmental sensor is distributed indoors and outdoors of the green building and is used to collect environmental parameters. The equipment operation status sensor is arranged on the building equipment and is used to collect equipment operation data. A data processing module, which is in communication with the data acquisition module and is used to receive, process and preliminarily analyze the acquired data; A data storage module, connected to the data processing module for storing processed data; A data analysis module, which is in communication with the data storage module and includes an energy consumption analysis submodule, an environment analysis submodule and an equipment operation analysis submodule, wherein the energy consumption analysis submodule is used to analyze energy consumption data, the environment analysis submodule is used to analyze environmental parameters, and the equipment operation analysis submodule is used to analyze equipment operation data; A control and optimization module, which is in communication with the data analysis module and includes an energy management submodule, an environment regulation submodule and an equipment management submodule. The energy management submodule controls energy equipment according to energy consumption analysis results, the environment regulation submodule controls environment regulation equipment according to environment analysis results, and the equipment management submodule manages equipment according to equipment operation analysis results; The user interface module is used by users to view system data and operate the system.

2. A green building operation digital monitoring system according to claim 1, characterized in that: The sensor in the data acquisition mode communicates with the data processing module via wired or wireless means.

3. A green building operation digital monitoring system according to claim 1, characterized in that: The data processing module cleans, filters, converts and processes abnormal data on the collected data.

4. A green building operation digital monitoring system according to claim 1, characterized in that: The data storage module uses a distributed storage system to store data, and stores the data in different storage areas according to data types and timestamps.

5. A green building operation digital monitoring system according to claim 1, characterized in that: The energy consumption analysis submodule analyzes energy consumption data through statistical analysis and machine learning algorithms, calculates energy utilization efficiency and predicts energy consumption trends.

6. A green building operation digital monitoring system according to claim 1, characterized in that: The environmental analysis submodule uses fuzzy logic algorithm to evaluate environmental quality and comfort.

7. A green building operation digital monitoring system according to claim 1, characterized in that: The equipment operation analysis submodule uses a machine learning algorithm to establish an equipment health model and predict equipment failure risks.

8. A green building operation digital monitoring system according to claim 1, characterized in that: The energy management submodule automatically adjusts the operating status of the HVAC system and the lighting system according to real-time environmental data and energy consumption analysis results.

9. A green building operation digital monitoring system according to claim 1, characterized in that: The environmental adjustment submodule automatically controls the ventilation equipment and the sunshade system according to the environmental analysis results.

10. A green building operation digital monitoring system according to claim 1, characterized in that: The equipment management submodule arranges equipment maintenance and adjusts equipment operation parameters according to the equipment operation analysis results.

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