Weak-current intelligent building control management system

Abstract

The invention discloses a weak current intelligent building control management system, which comprises a plurality of groups of different types of sensors, a data collection module, a data storage module, a data comparison and elimination module, an emergency management module and a control center. A plurality of groups of different types of sensors are distributed in each key area of a building, all-directional environment monitoring is ensured, preprocessing steps such as Kalman filtering and mean value filling are introduced to improve data quality, data storage adopts a block chain technology based on Hyperledger Fabric, data cannot be tampered and high security is realized, and the method is suitable for large-scale popularization and application. And the data comparison and elimination module verifies the sensor data in multiple dimensions by using transverse comparison, longitudinal comparison, logic verification and redundancy cross validation, so that the accuracy and consistency are improved, and the fault-tolerant and anti-interference capabilities of the system are enhanced.

Description

Technical Field

[0001] This invention relates to the field of low-voltage intelligent equipment technology, specifically a low-voltage intelligent building control and management system. Background Technology

[0002] Low-voltage intelligent buildings refer to buildings that integrate modern information technology, network communication technology, and automatic control technology to achieve automated management and control of various equipment and systems within the building. Such buildings not only improve the comfort and safety of living or working environments but also effectively save energy and reduce operating costs.

[0003] With the rapid development of smart building and Internet of Things (IoT) technologies, building automation systems are playing an increasingly important role in modern buildings. However, most building control systems still suffer from problems such as inaccurate data acquisition, poor storage security, imperfect anomaly detection mechanisms, and lack of traceability, which seriously restrict the system's intelligence level and operational efficiency.

[0004] First, in terms of sensor data acquisition, traditional systems often use a single sensor deployment method, which is prone to data distortion due to hardware failure or environmental interference.

[0005] Secondly, regarding data storage, existing building management systems generally rely on centralized databases for data recording. While this approach is convenient for management, it presents significant security risks. For example, data is easily tampered with, deleted, or forged, and lacks comprehensive audit trail capabilities, making it difficult to meet the demands of high-security and trustworthy application scenarios.

[0006] Furthermore, in terms of data comparison and anomaly detection, current systems mostly employ a single-dimensional threshold judgment method, identifying abnormal data solely by setting fixed upper and lower limits, ignoring the lateral correlation between sensors and historical trend changes. This judgment method is prone to false alarms or missed alarms, affecting the stability and accuracy of the system.

[0007] Furthermore, most systems lack redundant design for critical areas and have not established a sound cross-validation mechanism. When a sensor malfunctions or transmits abnormally, the system cannot quickly identify and eliminate erroneous data, which may trigger a chain reaction and affect the normal operation of the entire building's equipment.

[0008] Finally, from an overall architectural perspective, the existing building control system lacks clear modular division, has weak inter-system collaboration capabilities, and lacks a unified data management and emergency response mechanism. Especially in the face of emergencies, the system often fails to trigger alarms or activate linked devices in a timely manner, reducing the efficiency and reliability of emergency management.

[0009] Therefore, we propose a low-voltage intelligent building control and management system. Summary of the Invention

[0010] The purpose of this invention is to provide a low-voltage intelligent building control and management system to solve the problems that need to be solved in the background art.

[0011] To achieve the above objectives, the present invention provides the following technical solution: a low-voltage intelligent building control and management system, the low-voltage intelligent building control and management system comprising:

[0012] Sensors: The sensors are configured in multiple sets, and these multiple sets of sensors are installed inside the building to monitor the building's internal environmental parameters;

[0013] Data collection module: The data collection module is electrically connected to multiple sets of sensors. The data collection module is used to collect data collected by the sensors and convert environmental parameter data.

[0014] Data storage module: The data storage module is used to store the historical data of the sensors in the data collection module, and to build a blockchain network in the data storage module to store different data collected by multiple sensors into the blockchain network using corresponding storage nodes;

[0015] Data comparison and elimination module: The module compares historical data in the data storage module with environmental parameter data collected by the sensor, and uses cross-validation to verify the accuracy of the environmental parameter data collected by the sensor and eliminates erroneous data from the sensor.

[0016] Emergency Management Module: The emergency management module is used to manage abnormal data from the sensors. When the emergency management module receives sensor data that exceeds a set threshold, it defines it as abnormal data. When the emergency management module receives abnormal data, it uses the communication module to trigger the alarm to issue an alarm.

[0017] Control Center: The control center is connected to the emergency management module and interacts with building equipment.

[0018] With the rapid development of smart building and IoT technologies, building automation systems are playing an increasingly important role in modern buildings. However, most current building control systems still suffer from problems such as inaccurate data acquisition, poor storage security, imperfect anomaly detection mechanisms, and lack of traceability, severely restricting the system's intelligence level and operational efficiency. Firstly, in terms of sensor data acquisition, traditional systems often employ a single-sensor deployment approach, which is prone to data distortion due to hardware failures or environmental interference. Secondly, regarding data storage, existing building management systems generally rely on centralized databases for data recording. While this method is convenient for management, it presents significant security risks. For example, data is easily tampered with, deleted, or forged, and lacks comprehensive audit trail functionality, making it difficult to meet the demands of high-security and reliable application scenarios. Thirdly, in terms of data comparison and anomaly identification, current systems often use a single-dimensional threshold judgment method, identifying abnormal data only by setting fixed upper and lower limits, ignoring the horizontal correlation between sensors and historical trend changes. This judgment method is prone to false alarms or missed alarms, affecting the system's stability and accuracy. Furthermore, most systems lack redundant design for critical areas and have not established a sound cross-validation mechanism. When a sensor malfunctions or transmits abnormally, the system cannot quickly identify and remove erroneous data, which may trigger a chain reaction and affect the normal operation of the entire building's equipment. Finally, from the perspective of the overall architecture, the existing building control system is not clearly divided into modules, the coordination between subsystems is weak, and there is a lack of unified data management and emergency response mechanisms.Especially in the face of emergencies, the system often fails to trigger alarms or link devices in a timely manner, reducing the efficiency and reliability of emergency management. In this invention, multiple sets of sensors are distributed in key areas of the building, covering various types such as temperature and humidity, illuminance, CO2 concentration, smoke detection, and water immersion detection, ensuring comprehensive and multi-layered environmental monitoring capabilities. Furthermore, by introducing Kalman filters to remove noise, using mean-filling to fill missing values, and standardizing physical units and normalizing the data, the quality of the raw data is greatly improved. Secondly, blockchain network technology is introduced into the data storage module, constructing a Hyperledger-based system. Fabric's private blockchain architecture not only ensures data immutability and high security but also provides detailed operation records, guaranteeing accurate traceability of every sensor data point. After verification via the PBFT consensus mechanism, the data is written into the blockchain network and synchronized to all storage nodes, enhancing the system's attack resistance and transparency. The data comparison and elimination module in this invention employs multiple data analysis methods to compare and eliminate errors in sensor data, ensuring its accuracy. Furthermore, by utilizing a horizontal comparison module, a vertical comparison module, a logical verification module, and a redundant cross-verification module, the collaborative work of these four modules through multi-dimensional data comparison and verification improves the accuracy and consistency of sensor data, enhances the system's fault tolerance and anti-interference capabilities, ensures stable operation in various complex environments, achieves automated anomaly detection and processing, reduces the need for manual intervention, and improves management efficiency. Finally, the emergency management module can monitor sensor data in real time. Once abnormal data exceeding the set threshold is detected, it will immediately trigger the alarm through the communication module and notify relevant personnel to take measures. At the same time, the emergency management module is connected to the control center and can automatically execute preset emergency response strategies in emergency situations, such as turning off the air conditioner, starting the smoke exhaust fan, and turning on the fire sprinkler system, thereby improving the speed and effectiveness of emergency response.

[0019] As a further description of the above technical solution:

[0020] The sensors include, but are not limited to, temperature and humidity sensors, illuminance sensors, CO2 concentration sensors, smoke detectors, water immersion sensors, and sound sensors, with multiple sets of sensors installed at each location.

[0021] As a further description of the above technical solution:

[0022] When the sensor collects environmental parameters inside the building, it performs data preprocessing, which includes:

[0023] Data cleaning: Random noise in sensor signals is removed using Kalman filtering;

[0024] Filling in missing values: When sensor data is lost, the mean value method is used to fill in the missing data points;

[0025] Data conversion: unifying data collected by different sensors into the same physical unit;

[0026] Normalization: Scaling the sensor data to make it fall within a specific range;

[0027] Data integration: When acquiring data from multiple different types of sensors, integrate the sensor data information to form a more complete view.

[0028] As a further description of the above technical solution:

[0029] The data preprocessing also includes:

[0030] Anomaly detection and handling: Use support vector mechanisms to identify outliers and correct, delete, or mark abnormal data in sensor data;

[0031] Time synchronization: For sensor data without an external time source, an internal clock synchronization algorithm is used to estimate and adjust the clock offset between time nodes by using message passing between time nodes.

[0032] Data dimensionality reduction: Principal component analysis is used to reduce the dimensionality of data from multiple sets and types of sensor data.

[0033] As a further description of the above technical solution:

[0034] The data comparison and elimination module includes:

[0035] Horizontal comparison module: The horizontal comparison is used to compare data from sensors of the same type. When the difference between one set of sensor data and the other sensor data exceeds a predetermined threshold, the sensor data is marked as abnormal data.

[0036] Longitudinal comparison module: The collected sensor data is compared with historical sensor data from the same period. The moving average method and time series prediction model are used to predict future values ​​and compare them with the actual values ​​to determine whether the sensor data deviates from the predicted future value range. When the current sensor data exceeds the predicted future value, the sensor data is classified as abnormal data.

[0037] As a further description of the above technical solution:

[0038] The data comparison and elimination module also includes:

[0039] Logic verification module: It performs correlation matching on multiple groups of sensors, compares and analyzes sensor data by utilizing the correlation between sensors, and classifies sensor data with logical errors as abnormal data.

[0040] As a further description of the above technical solution:

[0041] The data comparison and elimination module also includes a redundant cross-validation module:

[0042] Key monitoring areas are defined, and multiple sensors are set up within these areas to create redundancy. The final result is determined by a majority vote or a weighted average to identify abnormal data.

[0043] As a further description of the above technical solution:

[0044] The blockchain network is a private blockchain architecture based on Hyperledger Fabric, which includes at least one consensus node and multiple storage nodes. After the sensor data is processed by the data collection module, it is packaged into transactions by the blockchain nodes, verified by the PBFT consensus mechanism, written into the blockchain network, and synchronized to all storage nodes.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. In this invention, multiple sets of sensors are distributed in key areas of the building and cover various types such as temperature and humidity, illuminance, CO2 concentration, smoke detection, and water immersion detection, ensuring comprehensive and multi-level environmental monitoring capabilities. In addition, by introducing Kalman filters to remove noise, using mean filling to fill missing values, unifying physical units, and normalizing the data, the quality of the original data is greatly improved.

[0047] 2. Secondly, blockchain network technology was introduced into the data storage module, and a private chain architecture based on Hyperledger Fabric was built. This architecture not only achieves the immutability and high security of the data, but also provides detailed operation records to ensure that every piece of sensor data can be accurately traced. After being verified by the PBFT consensus mechanism, it is written into the blockchain network and synchronized to all storage nodes, which enhances the system's anti-attack capability and transparency.

[0048] 3. Furthermore, the data comparison and elimination module in this invention employs multiple data analysis methods to compare and eliminate errors in the sensor data, ensuring that the sensor data is error-free:

[0049] Furthermore, by utilizing the horizontal comparison module, vertical comparison module, logical verification module, and redundant cross-verification module, and through the collaborative work of these four modules, the accuracy and consistency of sensor data are improved through multi-dimensional data comparison and verification. This enhances the system's fault tolerance and anti-interference capabilities, ensuring stable operation in various complex environments. It also achieves automated anomaly detection and processing, reduces the need for manual intervention, and improves management efficiency.

[0050] 4. Finally, the emergency management module can monitor sensor data in real time. Once abnormal data exceeding the set threshold is detected, it will immediately trigger the alarm through the communication module and notify relevant personnel to take measures. At the same time, the emergency management module is connected to the control center and can automatically execute preset emergency response strategies in emergency situations, such as turning off the air conditioner, starting the smoke exhaust fan, and turning on the fire sprinkler system, thereby improving the speed and effectiveness of emergency response. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the system principle of the present invention;

[0052] Figure 2 This is a schematic diagram of the data comparison and elimination module process of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1:

[0055] Please see Figure 1-2 This invention provides a technical solution: a low-voltage intelligent building control and management system, the low-voltage intelligent building control and management system comprising:

[0056] Sensors: The sensors are configured in multiple sets, which are installed inside the building to monitor the building's internal environmental parameters. The sensors include, but are not limited to, temperature and humidity sensors, illuminance sensors, CO2 concentration sensors, smoke detectors, water immersion sensors, and sound sensors. Multiple sets of the sensors are installed at each location.

[0057] Data collection module: The data collection module is electrically connected to multiple sets of sensors. The data collection module is used to collect data collected by the sensors and convert environmental parameter data.

[0058] Data storage module: The data storage module is used to store the historical data of the sensors in the data collection module, and to build a blockchain network in the data storage module to store different data collected by multiple sensors into the blockchain network using corresponding storage nodes;

[0059] Data comparison and elimination module: The module compares historical data in the data storage module with environmental parameter data collected by the sensor, and uses cross-validation to verify the accuracy of the environmental parameter data collected by the sensor and eliminates erroneous data from the sensor.

[0060] Emergency Management Module: The emergency management module is used to manage abnormal data from the sensors. When the emergency management module receives sensor data that exceeds a set threshold, it defines it as abnormal data. When the emergency management module receives abnormal data, it uses the communication module to trigger the alarm to issue an alarm.

[0061] Control Center: The control center is connected to the emergency management module and interacts with building equipment.

[0062] Multiple sensors are distributed across key areas of the building, covering various types of sensors including temperature and humidity, illuminance, CO2 concentration, smoke detection, and water immersion detection, ensuring comprehensive and multi-layered environmental monitoring capabilities. Furthermore, preprocessing steps such as introducing Kalman filters to remove noise, using mean-filling to fill missing values, standardizing physical units, and normalizing the data significantly improve the quality of the raw data. Secondly, blockchain network technology is incorporated into the data storage module to ensure accurate traceability of every sensor data point. Additionally, the data comparison and elimination module employs multiple data analysis methods to compare and eliminate errors in the sensor data, ensuring its accuracy. Finally, the emergency management module monitors sensor data in real time. Upon detecting abnormal data exceeding a set threshold, it immediately triggers an alarm via the communication module and notifies relevant personnel to take action. Simultaneously, this emergency management module is connected to the control center and can automatically execute preset emergency response strategies in emergencies, such as shutting down air conditioning, starting smoke exhaust fans, and activating fire sprinklers, improving the speed and effectiveness of emergency response.

[0063] Example 2:

[0064] When the sensor collects environmental parameters inside the building, it performs data preprocessing, which includes:

[0065] Data cleaning: Random noise in sensor signals is removed using Kalman filtering;

[0066] Filling in missing values: When sensor data is lost, the mean value method is used to fill in the missing data points;

[0067] Data conversion: unifying data collected by different sensors into the same physical unit;

[0068] Normalization: Scaling sensor data to make it fall within a specific range; this helps improve the performance of certain machine learning models.

[0069] Data integration: When acquiring data from multiple different types of sensors, integrate the sensor data information to form a more complete view.

[0070] The data preprocessing also includes:

[0071] Anomaly detection and handling: Use support vector mechanisms to identify outliers and correct, delete, or mark abnormal data in sensor data;

[0072] Time synchronization: For sensor data without an external time source, an internal clock synchronization algorithm is used to estimate and adjust the clock offset between time nodes by using message passing between time nodes; this ensures that data from different sensors are aligned in time, which is especially important for applications that require precise timestamps;

[0073] Data dimensionality reduction: Principal component analysis (PCA) can be used to reduce the dimensionality of data from multiple sets and types of sensors. In some cases, especially when the data dimensionality is high, techniques such as PCA and t-SNE can be used to reduce the data dimensionality while preserving important information as much as possible.

[0074] Among these methods, Kalman filtering and other techniques are used to remove random noise from sensor signals, ensuring smoother and more accurate data acquisition. This improves the reliability of subsequent analysis results, reduces data fluctuations caused by external environmental interference or hardware issues, and makes the monitored environmental parameters closer to the true values. Using mean filling or other interpolation methods to fill missing data points avoids analysis interruptions or biases due to data loss, ensures the continuity of time series data, and facilitates trend analysis and anomaly detection using methods such as moving averages and time series forecasting. Unifying data from different sensors into the same physical unit simplifies the fusion process of multi-source data, facilitating subsequent integrated analysis. It ensures that data from different types of sensors can be compared on the same benchmark, improving the effectiveness of cross-type data analysis. Scaling sensor data to fall within a specific range (e.g., between 0 and 1) can improve the performance of many machine learning algorithms, especially those sensitive to the scale of input variables. Acquiring data from multiple different types of sensors and integrating it into a more complete view helps to comprehensively understand the building's internal environmental conditions, providing a basis for developing precise control strategies.

[0075] Furthermore, by using support vectors to identify outliers, potential risks can be detected early in the problem, allowing timely measures to prevent the situation from worsening. Correcting, deleting, or marking outlier data ensures that the dataset entering the next step of analysis is clean and reliable, reducing the impact of erroneous information on the final results.

[0076] Finally, for sensor data without an external time source, the time synchronization setting uses an internal clock synchronization algorithm to adjust the clock offset between each other, ensuring that the timestamps of all data are consistent. This is especially important for applications that rely on time series analysis, which is conducive to carrying out joint analysis across devices and regions, improving the coordination and efficiency of the overall system. Using methods such as principal component analysis (PCA) to reduce data dimensionality and complexity makes the constructed model more concise and efficient.

[0077] Example 3:

[0078] The data comparison and elimination module includes:

[0079] Horizontal comparison module: The horizontal comparison is used to compare data from sensors of the same type. When the difference between one set of sensor data and the other sensor data exceeds a predetermined threshold, the sensor data is marked as abnormal data.

[0080] If the value of a certain sensor differs from that of other sensors by more than a set threshold, it is marked as a suspected anomaly.

[0081] Example: If four out of five temperature and humidity sensors read 25°C, while one reads 60°C, the sensor data may be abnormal.

[0082] Longitudinal comparison module: The collected sensor data is compared with historical sensor data from the same period. The moving average method and time series prediction model are used to predict future values ​​and compare them with the actual values ​​to determine whether the sensor data deviates from the predicted future value range. When the current sensor data exceeds the predicted future value, the sensor data is classified as abnormal data.

[0083] Logic verification module: performs correlation matching on multiple groups of sensors, compares and analyzes sensor data based on the correlation between sensors, and classifies sensor data with logical errors as abnormal data;

[0084] Verify the reasonable relationship between different parameters;

[0085] Example:

[0086] When the temperature rises, the relative humidity usually decreases;

[0087] When air quality deteriorates (PM2.5 increases), CO2 concentration may also increase;

[0088] If a combination of elements defies common sense, it may indicate a sensor malfunction or data anomaly.

[0089] The data comparison and elimination module also includes a redundant cross-validation module:

[0090] Define key monitoring areas, set up multiple sensors within these areas to create redundancy, and use majority voting or weighted average to determine the final result and obtain abnormal data; improve fault tolerance and avoid single point of failure.

[0091] The blockchain network is a private chain architecture based on Hyperledger Fabric, which includes at least one consensus node and multiple storage nodes. After the sensor data is processed by the data collection module, it is packaged into transactions by the blockchain nodes, verified by the PBFT consensus mechanism, written into the blockchain network, and synchronized to all storage nodes to achieve data immutability and traceability.

[0092] The system employs a private blockchain architecture based on Hyperledger Fabric, ensuring data immutability and high security, making it suitable for scenarios with extremely high requirements for data privacy and integrity. The PBFT consensus mechanism verifies transaction validity, guaranteeing stable system operation and high-speed transaction confirmation, making it suitable for enterprise-level applications. Each transaction is meticulously recorded, providing comprehensive audit trail capabilities and enhancing transparency and trust. These benefits collectively constitute an efficient, reliable, and intelligent building control system, improving not only data accuracy, security, and availability but also enhancing system stability and responsiveness, providing strong support for the intelligent management of modern buildings.

[0093] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low-voltage intelligent building control and management system, characterized in that: The low-voltage intelligent building control and management system includes: Sensors: The sensors are configured in multiple sets, and these multiple sets of sensors are installed inside the building to monitor the building's internal environmental parameters; Data collection module: The data collection module is electrically connected to multiple sets of sensors. The data collection module is used to collect data collected by the sensors and convert environmental parameter data. Data storage module: The data storage module is used to store the historical data of the sensors in the data collection module, and to build a blockchain network in the data storage module to store different data collected by multiple sensors into the blockchain network using corresponding storage nodes; Data comparison and elimination module: It compares historical data in the data storage module with environmental parameter data collected by the sensor, uses cross-validation to verify the accuracy of the environmental parameter data collected by the sensor, and eliminates erroneous data from the sensor. Emergency Management Module: The emergency management module is used to manage abnormal data from the sensors. When the emergency management module receives sensor data that exceeds a set threshold, it defines it as abnormal data. When the emergency management module receives abnormal data, it uses the communication module to trigger the alarm to issue an alarm. Control Center: The control center is connected to the emergency management module and interacts with building equipment.

2. The low-voltage intelligent building control and management system according to claim 1, characterized in that: The sensors include, but are not limited to, temperature and humidity sensors, illuminance sensors, CO2 concentration sensors, smoke detectors, water immersion sensors, and sound sensors, with multiple sets of sensors installed at each location.

3. The low-voltage intelligent building control and management system according to claim 2, characterized in that: When the sensor collects environmental parameters inside the building, it performs data preprocessing, which includes: Data cleaning: Random noise in sensor signals is removed using Kalman filtering; Filling in missing values: When sensor data is lost, the mean value method is used to fill in the missing data points; Data conversion: unifying data collected by different sensors into the same physical unit; Normalization: Scaling the sensor data to make it fall within a specific range; Data integration: When acquiring data from multiple different types of sensors, integrate the sensor data information to form a more complete view.

4. The low-voltage intelligent building control and management system according to claim 3, characterized in that: The data preprocessing also includes: Anomaly detection and handling: Use support vector mechanisms to identify outliers and correct, delete, or mark abnormal data in sensor data; Time synchronization: For sensor data without an external time source, an internal clock synchronization algorithm is used to estimate and adjust the clock offset between time nodes by using message passing between time nodes. Data dimensionality reduction: Principal component analysis is used to reduce the dimensionality of data from multiple sets and types of sensor data.

5. The low-voltage intelligent building control and management system according to claim 4, characterized in that: The data comparison and elimination module includes: Horizontal comparison module: The horizontal comparison is used to compare data from sensors of the same type. When the difference between one set of sensor data and the other sensor data exceeds a predetermined threshold, the sensor data is marked as abnormal data. Longitudinal comparison module: The collected sensor data is compared with historical sensor data from the same period. The moving average method and time series prediction model are used to predict future values ​​and compare them with the actual values ​​to determine whether the sensor data deviates from the predicted future value range. When the current sensor data exceeds the predicted future value, the sensor data is classified as abnormal data.

6. The low-voltage intelligent building control and management system according to claim 5, characterized in that: The data comparison and elimination module also includes: Logic verification module: It performs correlation matching on multiple groups of sensors, compares and analyzes sensor data by utilizing the correlation between sensors, and classifies sensor data with logical errors as abnormal data.

7. The low-voltage intelligent building control and management system according to claim 6, characterized in that: The data comparison and elimination module also includes a redundant cross-validation module: Key monitoring areas are defined, and multiple sensors are set up within these areas to create redundancy. The final result is determined by a majority vote or a weighted average to identify abnormal data.

8. The low-voltage intelligent building control and management system according to claim 7, characterized in that: The blockchain network is a private blockchain architecture based on Hyperledger Fabric, which includes at least one consensus node and multiple storage nodes. After the sensor data is processed by the data collection module, it is packaged into transactions by the blockchain nodes, verified by the PBFT consensus mechanism, written into the blockchain network, and synchronized to all storage nodes.

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