Building carbon metering method and device based on edge calculation and medium

Abstract

The invention discloses a building carbon metering method, equipment and medium based on edge calculation, and the method comprises the steps: deploying a plurality of sensors in a building, and collecting the data of power consumption, gas use, environment temperature and humidity, indoor and outdoor gas components, and equipment working states in real time; an edge computing device is utilized to perform preprocessing and real-time carbon emission calculation on acquired data, and meanwhile, the edge computing device and a cloud end perform cooperative processing, so that long-term data storage and deep analysis are realized. The system can intelligently optimize and make decisions, provide dynamic adjustment suggestions, and reduce energy consumption and carbon emission of the building. In addition, the method further comprises a carbon emission monitoring and alarming mechanism and strict security and data protection measures, so that the security and privacy protection of data are ensured. The method has the beneficial effects that carbon emission of the building can be effectively monitored and managed through the method, and real-time data support and optimization suggestions are provided for building managers, so that an intelligent building management target is realized, and sustainable development and environmental protection are supported.

Description

Technical Field

[0001] The present invention relates to the field of dual carbon, and in particular to a building carbon metering method, device and medium based on edge computing. Background Art

[0002] With the severe challenge of global climate change, reducing building energy consumption and carbon emissions has become an indispensable part of promoting green and sustainable development. As one of the main sources of energy consumption and greenhouse gas emissions, the energy efficiency improvement and low-carbon transformation of the construction industry are particularly important.

[0003] In the context of smart city construction, building carbon emission monitoring and management has gradually become a hot topic in research and application. Smart cities achieve energy conservation and emission reduction in various life scenarios through digital transformation. Among them, the building automation system (BAS), as a core component of intelligent building technology, is becoming an important tool for addressing building energy consumption and carbon emission issues with its high efficiency, accuracy and intelligence.

[0004] However, existing technologies have some shortcomings and challenges in monitoring and managing building carbon emissions:

[0005] Limitations of data collection: Traditional building management systems often rely on limited sensors and monitoring equipment, making it difficult to achieve comprehensive and real-time data collection, resulting in an inability to accurately assess a building’s energy consumption and carbon emissions.

[0006] Lack of refined management: Existing building management systems usually lack refined management of various areas and equipment within the building, and are unable to formulate personalized energy-saving and emission reduction strategies for different areas and equipment.

[0007] Insufficient data processing and analysis capabilities: Although some building management systems are able to collect a large amount of energy consumption data, they lack efficient data processing and analysis capabilities and are unable to transform the data into valuable information and decision support.

[0008] Low system integration: Existing building management systems are often unable to integrate with other systems, such as meteorological data, energy price information, etc., limiting the system's potential in optimizing energy use and reducing carbon emissions.

[0009] Insufficient security and privacy protection: As the amount of data in building management systems increases, data security and privacy protection issues become increasingly prominent. Existing systems may lack effective data encryption and access control mechanisms, making it difficult to prevent unauthorized access and data tampering.

[0010] Limited level of intelligence: Many existing building management systems have not yet fully utilized advanced technologies such as artificial intelligence and machine learning to achieve intelligent energy consumption prediction, fault diagnosis and optimization control.

[0011] Edge computing is a distributed computing paradigm that deploys data processing, application running, and intelligent services on devices or nodes close to the data source, rather than relying entirely on remote data centers or cloud platforms. This architecture is designed to reduce data transmission latency, improve data security, reduce network bandwidth pressure, and support real-time decision-making and autonomous operations.

[0012] The application of edge computing shows great potential in building energy consumption and carbon emission management. Through reasonable design, it is theoretically possible to achieve real-time monitoring and analysis of building carbon emissions, optimize energy use, and reduce carbon emissions. Summary of the invention

[0013] In order to solve the above technical problems, the present invention provides a building carbon metering method, device and medium based on edge computing.

[0014] The present invention is achieved through the following technical solutions:

[0015] A building carbon measurement method based on edge computing includes the following steps:

[0016] Step 1: Multi-point collection of building carbon emission data

[0017] Install multiple sensors at key locations in the building to collect real-time data that affects carbon emissions, such as electricity consumption, gas usage, ambient temperature and humidity, indoor and outdoor gas composition, and equipment operating status;

[0018] Sensors include energy meters, gas concentration detectors, temperature sensors, etc. The data collection frequency can be set according to the actual application scenario;

[0019] Step 2: Data preprocessing and edge device upload

[0020] The data collected by the sensor is first processed locally (sensor node), such as data denoising, data filtering, and outlier detection, to ensure the accuracy and integrity of the sensor data;

[0021] The pre-processed data is transmitted to the edge computing device through the local area network, avoiding uploading all the original data to the cloud and reducing bandwidth usage;

[0022] Step 3: Real-time carbon emissions calculation on edge devices

[0023] The edge computing device performs real-time calculations on the received sensor data and estimates the carbon emissions of each area or device based on information such as energy consumption and device status;

[0024] Use pre-established carbon emission formulas or models, combined with sensor data and equipment loads in specific areas, to perform regional carbon emission calculations;

[0025] Step 4: Analysis and feedback of local carbon emissions

[0026] Conduct carbon emission analysis on edge computing devices to identify high-emission areas or high-energy-consuming equipment in the building, and identify possible inefficiencies or failures of equipment by analyzing energy efficiency data;

[0027] Edge devices generate local feedback information and provide optimization suggestions to the building management system, such as adjusting the operating time of a certain device, optimizing air conditioning temperature, and reducing the use of high-emission equipment;

[0028] Step 5: Edge computing and cloud co-processing

[0029] For long-term data storage and in-depth analysis, edge computing devices regularly upload processed data to cloud servers;

[0030] The cloud can perform more complex carbon emission trend analysis, equipment health monitoring, and provide higher-level management decision support, such as predicting building carbon emissions for a certain period of time in the future;

[0031] Step 6: Intelligent Optimization and Decision Making

[0032] Based on the collaborative results of edge computing devices and cloud analysis, the system will continuously optimize the carbon emission calculation model through machine learning algorithms and provide dynamic optimization suggestions in the building management system;

[0033] Real-time recommendations include adjusting the operation strategy of high-energy-consuming equipment and changing the operation mode of the air-conditioning system to reduce the overall building carbon emissions;

[0034] Step 7: Carbon emission monitoring and alarm

[0035] The system continuously monitors carbon emissions in various areas or equipment. When abnormally high carbon emissions are detected, edge computing devices can trigger alarms in real time.

[0036] The system will record the abnormal event and suggest that managers take measures, such as shutting down specific equipment or adjusting regional environmental parameters;

[0037] Step 8: Security and Data Protection Measures

[0038] During the entire data transmission and processing process, the system uses data encryption and access control mechanisms to ensure data security and privacy protection and prevent unauthorized access and data tampering;

[0039] Edge computing is used for real-time monitoring and feedback of carbon emissions. This method, which combines low-latency processing and localized data optimization, is different from traditional centralized processing and improves real-time performance, flexibility, and data processing efficiency.

[0040] Through distributed data collection and the application of edge intelligent devices, carbon emission data can not only be measured more accurately, but also reduce building energy consumption through real-time analysis, thereby achieving the goal of intelligent building management.

[0041] Furthermore, the multi-point collection of the above-mentioned building carbon emission data includes the following steps:

[0042] 1.1 Determine the type and scope of data collection

[0043] The target data collected should be related to carbon emissions, energy consumption and environmental factors, including:

[0044] Power consumption: used to monitor the energy consumption of electrical equipment in buildings (such as air conditioners, lighting, office equipment, etc.). Power consumption is one of the main sources of carbon emissions.

[0045] Gas usage: If natural gas or other fuels are used for heating or electricity in the building, gas consumption needs to be monitored;

[0046] Equipment status information: monitor the operating status and usage time of equipment, such as the workload of high-power equipment such as elevators, air conditioners, boilers, generators, etc.

[0047] Environmental parameters: including indoor and outdoor temperature, humidity, air flow (wind speed, wind direction), etc., which affect the energy efficiency performance of the equipment;

[0048] Gas composition detection: Detect the content of greenhouse gases such as carbon dioxide and methane in the air inside and outside the building, which directly reflects the level of carbon emissions.

[0049] 1.2 Sensor types and layout design

[0050] According to different data collection requirements, select appropriate sensors and arrange them in a reasonable space to ensure the accuracy and representativeness of the collected data;

[0051] Power sensors: Use smart meters or power sensors installed in the circuits of major equipment in the building to record power consumption in real time; key equipment includes: air conditioners, lighting systems, elevators, computer room servers, etc.

[0052] Gas sensor: installed at the entrance and exit of the natural gas pipeline to monitor the gas usage and associate it with the operation status of the corresponding equipment;

[0053] Environmental sensors: Temperature and humidity sensors, wind speed and direction sensors can be distributed in different areas of the building, especially in locations with greater airflow impact, such as air-conditioning outlets, rooftops, and basements;

[0054] Gas detectors: installed in different areas indoors and outdoors, especially in crowded places (such as conference rooms, lobbies, parking lots, etc.), to monitor the concentration of gases such as carbon dioxide and methane in the air.

[0055] 1.3 Sensor Node Networking and Data Aggregation

[0056] Networking of sensor nodes: Various sensors form a local area network (LAN) through wireless communication (such as Wi-Fi, Zigbee, LoRa, etc.) or wired network (such as Ethernet, RS485) to transmit the collected data to the edge computing device in real time;

[0057] Wireless communication: For areas where wiring is difficult (such as sensors outside buildings or on ceilings), wireless communication methods can be used, such as LoRa, Zigbee and other low-power wide area network technologies to ensure that devices can communicate reliably over long distances;

[0058] Wired communication: For devices with large data volumes or high communication requirements (such as power sensors or devices in server rooms), Ethernet connections are used to ensure high data transmission rates and stability;

[0059] Data aggregation: Each sensor node transmits the collected data to the local edge computing device through the network to form a preliminary data pool. This process requires setting the frequency of data collection and the upload cycle to ensure the real-time and validity of the data.

[0060] Data collection frequency: Set the collection frequency according to the device type and monitoring requirements. For example, the collection frequency of power consumption can be once per minute, while temperature and humidity data can be collected every 5 minutes.

[0061] 1.4 Real-time monitoring and verification of data collection

[0062] Real-time monitoring: The edge computing device is responsible for monitoring the operating status of each sensor node to ensure the real-time and stability of the data acquisition system. Once an abnormality occurs in a sensor node (such as data loss or abnormal value), the system will automatically trigger an alarm or self-repair mechanism.

[0063] Data verification: For the data collected by the sensor, the system needs to perform preliminary verification, such as detecting the integrity and accuracy of the data, and eliminating noise data or unreasonable data points according to certain rules; for example, when it is detected that the energy consumption of a certain device suddenly surges but the device status has not changed, it can be judged that it may be a false alarm of the sensor.

[0064] 1.5 Data storage and processing preparation

[0065] Local storage: On edge computing devices, all collected real-time data is stored locally to avoid data loss due to network problems. The storage method can be cyclical, and expired data is cleaned up regularly to ensure sustainable use of storage space.

[0066] Processing preparation: After preliminary cleaning and integration, the data will serve as the basic data for carbon emission calculation in subsequent steps.

[0067] This step is a detailed monitoring of various equipment in the building, combining data from multiple types of sensors, especially direct detection of gas composition, to provide a reliable data source for accurate measurement of carbon emissions;

[0068] In addition, the networking methods of sensor nodes are diversified to meet the needs of different building structures. The flexibility and stability of data transmission are improved by combining low-power wireless networks and wired networks.

[0069] Finally, a real-time monitoring and data verification mechanism was added to ensure the quality of sensor data, providing a more reliable basis for subsequent carbon emissions calculations.

[0070] Furthermore, the above data preprocessing and edge device uploading include the following steps:

[0071] 2.1 Data denoising and cleaning

[0072] De-noising: Due to factors such as signal interference and sensor aging in the environment, the collected data may contain noise. Filtering algorithms (such as low-pass filtering and Kalman filtering) are used to eliminate unreasonable fluctuations to ensure the smoothness and accuracy of the data.

[0073] Data cleaning: Remove invalid data points that are obviously abnormal, such as:

[0074] Power values ​​beyond the normal operating range of the equipment;

[0075] The concentration of the collected gas changes abnormally and dramatically, while the external conditions remain unchanged;

[0076] The cleaning algorithm can formulate rules based on specific scenarios. For example, if the power consumption value of a device is far away from the normal range for multiple consecutive times, it will be judged as abnormal.

[0077] 2.2 Data Compression and Optimization

[0078] Since the amount of data generated by sensors is large, data compression is necessary to reduce network bandwidth usage and storage pressure;

[0079] Compression methods include differential encoding (storing only the difference between two acquisition points) or sample sparsification (reducing the acquisition frequency for stable, little-changing data segments);

[0080] In addition, data aggregation technology is used to reduce redundant data; for example, the temperature information collected by multiple sensors can be weighted averaged to generate a unified ambient temperature value in the area, reducing the amount of uploaded data.

[0081] 2.3 Anomaly Detection and Local Storage

[0082] Local anomaly detection: Perform localized anomaly detection before uploading to edge devices; for example, if the energy consumption or gas emission value of a device exceeds the normal threshold, an alarm is triggered and a related anomaly report is generated to prompt managers or trigger automated processing (such as shutting down high-energy-consuming devices);

[0083] Temporary data storage: To prevent data loss caused by network interruption or transmission failure, sensor nodes can first cache data on local storage devices and upload them in batches to edge computing devices after the network is restored.

[0084] 2.4 Data encryption and upload

[0085] Data encryption: Before uploading, the collected data is encrypted to prevent the data from being stolen or tampered with during transmission; commonly used encryption technologies such as AES and RSA encryption ensure the privacy and security of the data;

[0086] Data upload: By setting the upload cycle, the edge computing device obtains the processed data from each sensor node; this process uses efficient data transmission protocols (such as MQTT, CoAP, etc.) to ensure low latency and reliable data transmission.

[0087] The core of this step is to preliminarily process the raw data collected by the sensor to ensure the accuracy, completeness and compactness of the data, and then upload it to the edge computing device to prepare for subsequent carbon emissions calculations.

[0088] Furthermore, the real-time carbon emission calculation of the edge device includes the following steps:

[0089] 3.1 Establishment of carbon emission model

[0090] Establish a carbon emission calculation formula: Carbon emission models are usually calculated based on energy consumption and equipment operating load; for example:

[0091] The relationship between electricity consumption and carbon emissions can be converted through the carbon emission factor of the power grid (carbon emissions per unit of energy consumption), such as "electricity consumption × grid carbon emission factor = carbon emissions";

[0092] Carbon emissions from gas consumption are calculated through calorific value conversion: such as "gas usage × gas calorific value × carbon emission factor = carbon emissions";

[0093] Carbon emission factors for different equipment and energy types can be pre-set and flexibly adjusted based on the actual energy structure within the building.

[0094] 3.2 Calculation of carbon emissions by region

[0095] Regional division: Carbon emissions calculation is divided by region according to different functional areas of the building (such as office area, equipment room, restaurant, parking lot, etc.); this zoning calculation can accurately identify high-emission areas, facilitating subsequent management optimization;

[0096] Real-time calculation: Edge computing devices use predefined formulas to calculate carbon emissions in real time based on data from different regions; for example:

[0097] Perform weighted calculation on the total power consumption, gas emissions, temperature environment and other comprehensive information of a certain area, and output the carbon emission value of the area;

[0098] The calculation results of each area can be updated dynamically to reflect the current carbon emissions situation in real time.

[0099] 3.3 Equipment-level carbon emission analysis

[0100] Equipment classification and independent calculation: For important equipment in a building (such as air conditioners, generators and other high-energy-consuming equipment), their carbon emissions can be calculated independently;

[0101] The energy consumption data of each device is combined with its operating time to output the carbon emission value of the device in a specific time period;

[0102] In addition, for problems such as equipment aging and inefficient operation, edge computing devices can further analyze abnormalities in their carbon emissions;

[0103] Long-term tracking and trend prediction: By tracking the carbon emission performance of equipment over a long period of time, edge computing devices can identify equipment inefficiencies or potential failures, and promptly remind building managers to perform maintenance or replacements.

[0104] 3.4 Local Feedback Mechanism of Edge Devices

[0105] Feedback generation: Based on real-time calculation of carbon emissions, edge computing devices can generate instant feedback, such as suggesting shutting down some equipment during off-peak hours, adjusting air conditioning temperature, optimizing equipment operation strategies, etc., to reduce carbon emissions;

[0106] Alarm mechanism: When the carbon emissions of a certain area or equipment exceed the set threshold, the edge device will issue an alarm and notify the management system; for example, when the carbon emissions of an elevator or air conditioner suddenly increase sharply, it may be a signal of equipment failure, and the edge computing device will automatically issue a warning.

[0107] The core of this step is to process and analyze the collected data in real time through edge computing devices, calculate the carbon emissions in the building, and provide local carbon emissions assessment and management.

[0108] Furthermore, the above local carbon emission analysis and feedback includes the following steps:

[0109] 4.1 Local analysis of carbon emission data

[0110] Identification of high-emission areas: Edge computing devices analyze data from various collection points in real time to identify high-emission areas within the building. These areas may include high-energy-consuming areas such as air-conditioning rooms, elevator rooms, and heating equipment. By comparing carbon emission data from different areas, the system can quickly locate these high-emission areas.

[0111] Identification of high-energy-consuming equipment: In addition to analyzing the area, edge computing devices can also identify specific high-energy-consuming equipment in the building, such as air conditioning, lighting systems, heating systems, etc. If the carbon emission level of a certain device exceeds the preset benchmark, the system will record the device and mark it as a device that may need to be optimized.

[0112] 4.2 Equipment operation efficiency and fault analysis

[0113] Energy efficiency data analysis: Edge computing devices analyze the energy efficiency data of high-emission equipment to identify the equipment's operating efficiency. If it is found that the energy consumption of the equipment does not match its expected output effect (i.e., inefficient operation), the system will record this situation. For example, an air conditioner may increase energy consumption due to insufficient maintenance, but the cooling effect has not increased accordingly.

[0114] Fault warning: By analyzing the historical operation data of the device, the edge device can also identify possible fault conditions of the device. For example, the system may detect a sudden increase in the energy consumption of a device and a decrease in its output performance, and then infer that the device may need to be repaired or replaced.

[0115] 4.3 Local feedback and optimization suggestions

[0116] Feedback information generation: Based on the results of local carbon emissions and energy efficiency analysis, edge computing devices will generate local feedback information. For example, if carbon emissions in a certain area increase abnormally, the device will send an alarm message to the building management system to remind relevant personnel to pay attention to the situation in that area.

[0117] Generation of optimization suggestions: Edge computing devices can also provide instant optimization suggestions to the management system. Based on the carbon emission analysis of equipment and regions, the system may recommend adjusting the operating hours of certain equipment, reducing the frequency of use of high-emission equipment, or optimizing the temperature settings of air conditioning systems. For example, edge devices may recommend reducing the operating power of air conditioners during off-peak hours, or adjusting the brightness of lighting systems to reduce unnecessary energy consumption.

[0118] 4.4 Interaction with Building Management Systems

[0119] Instant adjustment and feedback: The feedback information generated by the edge device will be transmitted to the building management system in real time for the reference of the management personnel. At the same time, the building management system can adjust the operating status of the equipment according to the suggestions. For example, it can remotely shut down certain high-energy-consuming equipment, or dynamically adjust the temperature and lighting in the area.

[0120] Dynamic feedback and continuous optimization: The system will continuously track the implementation effect of feedback and adjust the operation strategy of the equipment in real time. For example, if the system recommends optimizing the air conditioning temperature in a certain area and carbon emissions decrease after implementation, the system will record this information and use it as a basis for future decision-making.

[0121] in:

[0122] Refine energy efficiency analysis standards: Different devices should have different energy efficiency standards. Edge computing devices can formulate personalized energy efficiency standards for different devices based on the device type and operating environment to ensure the accuracy of the analysis results.

[0123] Intelligent learning based on historical data: Edge computing devices can introduce machine learning algorithms based on historical data to predict the usage patterns of high-emission equipment and dynamically adjust the system's feedback suggestions based on its changes, making the system more adaptable.

[0124] Furthermore, the collaborative processing of edge computing and cloud computing includes the following steps:

[0125] 5.1 Data upload and storage

[0126] Regular data upload: The edge computing device will regularly upload the processed building carbon emission data to the cloud server. The frequency of upload can be adjusted according to the importance and time requirements of the data. For example, real-time data can be uploaded every few minutes, while historical data can be uploaded regularly every day or week.

[0127] Data storage management: The cloud server is responsible for long-term storage of uploaded data, using an efficient database management system to ensure data integrity, stability and security. The cloud has a large storage capacity, so it can store many years of historical data for future in-depth analysis.

[0128] 5.2 In-depth analysis of the cloud

[0129] Carbon emission trend analysis: By analyzing historical data and real-time data, the cloud can generate long-term carbon emission trend charts. For example, it can analyze the carbon emission patterns of a building in different seasons and on different working days, and identify possible carbon emission peaks.

[0130] Equipment health monitoring: The cloud can also monitor the health status of each device in the building, especially those that generate a lot of carbon emissions (such as air conditioning and heating systems). By analyzing the equipment operation data in the cloud, the maintenance needs of the equipment can be predicted, reducing the sudden increase in carbon emissions caused by equipment failure.

[0131] Senior management decision support: The cloud provides comprehensive data reports to help senior management make strategic adjustments. For example, through cloud analysis, managers can understand the carbon emissions comparison of each building and decide whether to replace high-energy-consuming equipment or adjust the energy structure.

[0132] 5.3 Collaborative Processing Mechanism

[0133] Data preprocessing and decision feedback: Edge computing devices perform preliminary processing on real-time data, such as filtering out abnormal data and classifying carbon emission data in different regions. These preprocessed data are then uploaded to the cloud, which conducts further in-depth analysis and then feeds back to edge devices for real-time adjustments.

[0134] Task allocation between cloud and edge computing: Edge computing devices handle time-sensitive tasks, such as immediate carbon emission detection and early warning, while the cloud handles more complex and large-scale data analysis tasks, such as long-term trend prediction of carbon emissions and equipment maintenance planning. Through this collaborative mechanism, the system's performance and response speed are optimized.

[0135] 5.4 Future Carbon Emissions Forecast

[0136] Time period prediction: Based on the historical carbon emission data of the building, the cloud can predict carbon emissions for a certain period of time in the future. For example, by analyzing the data of previous years and combining it with real-time energy consumption, weather and building usage, the cloud can predict carbon emissions for the next week or month and provide optimization suggestions to managers.

[0137] Decision support and intelligent optimization: The cloud can provide decision support based on the prediction results and recommend adjusting the operating time or power of certain devices to reduce carbon emissions. For example, the system can recommend reducing the operation of certain high-energy-consuming devices before the expected peak of carbon emissions, thereby controlling carbon emissions in advance.

[0138] Among them: In order to avoid the impact of data upload delays on system decisions, asynchronous communication can be adopted between the cloud and edge devices. When there is a problem with the network, the edge device will automatically cache the data and wait for the network to recover before uploading it in batches.

[0139] Data compression and encryption: To improve upload efficiency, data can be compressed before uploading. At the same time, to ensure data security, the uploaded data should be encrypted to prevent leakage during the intermediate transmission process.

[0140] Furthermore, the above intelligent optimization and decision-making includes the following steps:

[0141] 6.1 Global Data Aggregation and Fusion

[0142] Edge device data aggregation: Obtain carbon emission-related data from edge computing devices in various areas and different devices in the building, such as energy consumption data, equipment operating status, carbon emission concentration, etc.

[0143] Data fusion and correction: The central control system fuses and removes duplicate data from edge devices and corrects abnormal data; for example:

[0144] Correct sensor error readings and remove abnormal data points that are obviously unreasonable;

[0145] Adjust the weights of different data sources according to the actual situation of the building to make the global data more accurate.

[0146] 6.2 Intelligent Analysis Algorithm

[0147] Global carbon emission trend prediction: The central system predicts the overall carbon emission trend of the building through machine learning or deep learning models; for example:

[0148] Use historical data and real-time data to predict the peak of carbon emissions in a certain period of time in the future, so as to formulate response strategies in advance;

[0149] Scenario simulation and optimization analysis: The system optimizes the operation strategy of each device by simulating carbon emissions in different scenarios (such as peak and off-peak periods); for example:

[0150] Simulate the effects of different energy-saving strategies, evaluate the carbon reduction effect and impact on building comfort of each option, and select the best strategy.

[0151] 6.3 Optimizing Decisions

[0152] Dynamic strategy adjustment: The central system dynamically adjusts the building’s carbon emission control strategy based on the analysis results; for example:

[0153] When building traffic increases, the system automatically increases air conditioning efficiency but reduces the power output of other non-essential equipment to achieve a balance between carbon emissions and user comfort;

[0154] Equipment priority management: The system adjusts the priority of different equipment according to the equipment's energy consumption, usage frequency, and carbon emissions; for example:

[0155] Prioritize the control of equipment with high energy consumption, such as air conditioning, lighting, etc., to reduce their working time or power.

[0156] 6.4 Decision Feedback and Iterative Optimization

[0157] Real-time feedback mechanism: The system feeds back optimization decisions to each edge device and adjusts the device's operating parameters in real time; for example:

[0158] Reduce energy consumption by reducing the power of some equipment or shutting down non-essential equipment during peak hours;

[0159] Self-learning and optimization iteration: Through long-term operation data accumulation, the system self-learns the building carbon emission pattern and continuously optimizes the decision-making algorithm; for example:

[0160] The system automatically adjusts the equipment operation strategy based on historical operation data and external environmental changes (such as weather changes, seasonal influences, etc.), gradually improving the accuracy of decision-making.

[0161] This step mainly uses the system to aggregate data from various edge computing devices, conduct global analysis and make intelligent decisions to further optimize the building's carbon emission control.

[0162] Furthermore, the above carbon emission monitoring and alarming includes the following steps:

[0163] 7.1 Real-time carbon emission monitoring

[0164] Local analysis on edge devices: Edge computing devices perform preliminary analysis on the collected carbon emission data; for example:

[0165] When a rapid increase in carbon emissions is detected over a certain period of time, the edge device will immediately calculate and determine whether an alarm is needed.

[0166] 7.2 Carbon Emission Anomaly Detection

[0167] Abnormal threshold setting: Set reasonable carbon emission thresholds according to different areas and equipment in the building; for example:

[0168] The carbon emission threshold of the office area is lower, while the threshold of the dining area is relatively higher; the system sets different upper limits according to actual conditions;

[0169] Over-threshold alarm mechanism: When the carbon emissions of a certain area or equipment exceed the set threshold, the system will automatically trigger an alarm; for example:

[0170] When kitchen equipment malfunctions or is used improperly, causing a surge in carbon emissions, the system immediately sends out an alarm and notifies relevant managers to take action.

[0171] 7.3 Alarm feedback mechanism

[0172] Multi-level alarm mechanism: The system provides different levels of alarms according to the severity of the abnormal situation; for example:

[0173] When the limit is slightly exceeded, the system alerts managers via email or app;

[0174] If carbon emissions continue to exceed the standard or reach a serious level, the system will issue an emergency alarm, accompanied by voice notifications, mobile phone text messages and other channels to ensure that the alarm is received.

[0175] Automated emergency response: For serious over-limit situations, the system automatically triggers emergency measures; for example:

[0176] The system can automatically shut down or reduce the power of specific high-energy-consuming devices to quickly reduce carbon emissions.

[0177] 7.4 Alarm log recording and analysis

[0178] Alarm event log recording: Each alarm event will be recorded in the log by the system for subsequent analysis and improvement; for example:

[0179] Record the time when the standard is exceeded, the equipment that exceeds the standard, the triggering cause and the solution;

[0180] Alarm data analysis and optimization: Based on historical alarm data, the system analyzes areas and equipment where carbon emission problems frequently occur, and further optimizes the alarm mechanism; for example:

[0181] Through alarm data, we can identify the problem of long-term excessive carbon emissions in a certain device or area, and then update the equipment or adjust the strategy.

[0182] This step mainly involves real-time monitoring of building carbon emissions through intelligent monitoring methods, ensuring timely alarms when abnormalities occur and ensuring efficient and stable operation of the system.

[0183] Furthermore, the above security and data protection measures include the following steps:

[0184] 8.1 Data encryption and transmission security

[0185] Data encryption storage: All carbon emission data in the system must be encrypted and stored to prevent unauthorized access; for example:

[0186] Use mainstream encryption technologies such as AES (Advanced Encryption Standard) to encrypt data to ensure the security of sensitive data;

[0187] Secure transmission protocol: Encrypted transmission protocols (such as TLS / SSL) are used to ensure the security of carbon emission data during transmission; for example:

[0188] When the system transmits data, the TLS protocol is used to prevent the data from being intercepted or tampered with during transmission.

[0189] 8.2 Permission Management and Access Control

[0190] Multi-level authority control: The system sets different authorities for users of different levels (such as administrators, equipment maintenance personnel, etc.); for example:

[0191] Only system administrators have access to global carbon emission data and optimization strategies, while equipment maintenance personnel can only view equipment status data in relevant areas;

[0192] Two-factor authentication: The system adds a two-factor authentication mechanism for access to key operations and sensitive data to improve security; for example:

[0193] When accessing the system management backend, users are required to enter a password and a dynamic verification code.

[0194] 8.3 Data Privacy Protection

[0195] Data anonymization: When processing sensitive data (such as user activity data), the system anonymizes the relevant data; for example:

[0196] Remove or hide information that can directly identify users to ensure data privacy is protected;

[0197] User data access audit: The system records and audits user data access behavior to prevent unauthorized data access or data leakage.

[0198] 8.4 System Protection and Attack Detection

[0199] Firewalls and Intrusion Detection: The system deploys firewalls and intrusion detection systems (IDS) to prevent network attacks; for example:

[0200] Firewalls can filter unsafe network requests, and intrusion detection systems monitor and alert potential hacker attacks;

[0201] Regular security audits and vulnerability patching: Regularly conduct security audits on the system to discover and patch system vulnerabilities; for example:

[0202] Regularly upgrade the software of edge devices and central systems to ensure that the system has the latest security protection capabilities.

[0203] This step aims to ensure the data security, privacy protection and network security of the building carbon metering system and guarantee the reliability of system operation.

[0204] A building carbon metering device based on edge computing for executing the above method, the device comprising:

[0205] The multi-point carbon emission data collection module is used to install sensors at several key locations in the building to collect data affecting carbon emissions in real time, including power consumption, gas usage, ambient temperature and humidity, indoor and outdoor gas composition, and equipment working status; the sensors include energy meters, gas concentration detectors, and temperature sensors;

[0206] A data preprocessing module is connected to the above-mentioned sensor and is used to perform preliminary processing on the collected sensor data. The above-mentioned preliminary processing includes data denoising, data filtering and outlier detection to ensure the accuracy and integrity of the sensor data; the processed data is transmitted to the edge computing device through the local area network to reduce the bandwidth occupation caused by uploading the original data;

[0207] An edge computing module is used to perform real-time carbon emission calculations on the pre-processed sensor data, and estimate the carbon emissions of each region or device based on energy consumption and device status information; the edge computing module uses a preset carbon emission formula or model, combined with sensor data and device load in a specific region, to perform zoned carbon emission calculations;

[0208] Carbon emission analysis and feedback module, which is used to perform carbon emission analysis on edge computing devices, identify high-emission areas or high-energy consumption equipment in the building, and identify inefficiencies or failures of equipment in combination with energy efficiency data analysis; the above module generates local feedback information and provides optimization suggestions to the building management system, including adjusting equipment operating time and optimizing air conditioning temperature;

[0209] The edge computing and cloud collaborative processing module is used to regularly upload the data processed by the edge computing device to the cloud server. The cloud server performs long-term data storage and in-depth analysis, provides carbon emission trend analysis, equipment health monitoring and higher-level management decision support;

[0210] Intelligent optimization and decision-making module, which is used to optimize the carbon emission calculation model through machine learning algorithms based on the collaborative results of edge computing devices and cloud analysis, and provide dynamic optimization suggestions, including adjusting the operation strategy of high-energy-consuming equipment and changing the operation mode of the air-conditioning system to reduce the carbon emissions of the entire building;

[0211] The carbon emission monitoring and alarm module is used to continuously monitor the carbon emissions of each area or equipment. When abnormally high carbon emissions are detected, an alarm is triggered in real time and abnormal events are recorded, and managers are reminded to take measures including shutting down specific equipment or adjusting regional environmental parameters;

[0212] The security and data protection module is used to adopt data encryption and access control mechanisms during data transmission and processing to ensure data security and privacy protection and prevent unauthorized access and data tampering.

[0213] A readable storage medium stores a computer program, which, when executed by a processor, implements the building carbon metering method based on edge computing.

[0214] The beneficial effects of the present invention are:

[0215] The comprehensiveness and accuracy of data collection have been improved. By deploying a variety of sensors at key locations in buildings, real-time monitoring of electricity, gas, environmental parameters, etc. can be achieved, providing accurate data for carbon emission calculations.

[0216] It enhances the real-time data processing capability, uses edge computing for data preprocessing and real-time computing, reduces dependence on central data centers, and reduces data transmission delays.

[0217] It realizes refined energy management. By calculating and analyzing the carbon emissions of different areas and equipment, it provides building managers with detailed information on energy consumption and carbon emissions, which helps to formulate more effective energy-saving and emission-reduction measures.

[0218] The system's integration and collaborative efficiency have been improved. The collaborative working mode of edge computing and cloud enables the system to handle more complex data analysis tasks and provide higher-level management decision support.

[0219] Strengthened data security and privacy protection, adopted data encryption and access control mechanisms to ensure data security and privacy protection during data transmission and processing.

[0220] The intelligence level of the building management system has been optimized, the carbon emission calculation model has been optimized through machine learning algorithms, and dynamic optimization suggestions have been provided, so that the building management system can respond to environmental changes more intelligently.

[0221] It supports the sustainable development goals and supports the goals of environmental protection and sustainable development by reducing energy consumption and carbon emissions in buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0222] Figure 1 : Schematic diagram of the method flow of the present invention; DETAILED DESCRIPTION

[0223] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments:

[0224] Example: Figure 1 As shown, a building carbon measurement method based on edge computing includes the following steps:

[0225] Step 1: Multi-point collection of building carbon emission data

[0226] Sensors are installed at key locations in the building, and the selection of these locations is based on a detailed analysis of the building's energy consumption and environmental impact. For example, power consumption sensors are installed in the main distribution board and branch circuits of key electrical equipment to monitor real-time power usage. Gas usage sensors are installed at the gas inlet of the building to monitor gas consumption. Ambient temperature and humidity sensors are installed at air-conditioning outlets, return air outlets, and crowded areas to monitor indoor air quality. Gas composition sensors are installed indoors and outdoors to monitor the concentration of greenhouse gases such as carbon dioxide and methane. Equipment working status sensors are installed at key equipment such as elevators, boilers, air-conditioning units, etc. to monitor the operating status and energy consumption of the equipment.

[0227] Step 2: Data preprocessing and edge device upload

[0228] The data collected by the sensors is initially processed locally, including:

[0229] Data Denoising: Use digital filtering techniques, such as moving average filtering or wavelet transforms, to remove data fluctuations caused by electrical interference or sensor noise.

[0230] Data filtering: Apply threshold detection algorithms to remove data points that are outside the normal operating range of the equipment. For example, a sudden spike in power consumption of an air conditioning unit to twice the normal range could indicate a sensor failure or equipment anomaly.

[0231] Outlier detection: Use statistical methods, such as standard deviation analysis or boxplots, to identify and handle outliers.

[0232] The processed data is transmitted to the edge computing device through the local area network. Data transmission uses the MQTT protocol, which is a lightweight publish / subscribe message transmission protocol suitable for the IoT environment.

[0233] Step 3: Real-time carbon emissions calculation on edge devices

[0234] Edge computing devices perform real-time carbon emissions calculations using the following methods:

[0235] Electricity consumption: Carbon emissions are calculated based on electricity consumption and the average carbon emission factor of the power grid. For example, if the building consumes 1,000 kWh of electricity and the carbon emission factor of the power grid is 0.9 kg CO2 / kWh, the carbon emissions are 900 kg CO2.

[0236] Gas consumption: Carbon emissions are calculated based on gas consumption, gas calorific value and carbon emission factor. For example, if gas consumption is 100 cubic meters, gas calorific value is 35.8 MJ / m3, and carbon emission factor is 2.75 kg CO2 / MJ, then carbon emissions are 2.75 kg CO2 / MJ × 35.8 MJ / m3 × 100 cubic meters = 983.5 kg CO2.

[0237] Partition calculation: Divide the building into multiple areas, such as office areas, conference rooms, computer rooms, etc., and calculate the carbon emissions of each area based on the energy consumption and equipment usage of each area.

[0238] Step 4: Analysis and feedback of local carbon emissions

[0239] Edge computing devices perform carbon emissions analysis, identify high-emission areas or equipment, and analyze energy efficiency data to identify inefficiencies or failures. For example, if carbon emissions in one area are consistently higher than in other areas, the system may recommend checking whether the air conditioning system in that area is operating inefficiently. Local feedback information generated by the system may include adjusting the operating hours of equipment, optimizing air conditioning temperatures, or reducing the use of high-emission equipment.

[0240] Step 5: Edge computing and cloud co-processing

[0241] Edge computing devices regularly upload processed data to cloud servers for long-term data storage and in-depth analysis. Cloud servers use big data analysis tools, such as Apache Hadoop or Spark, to process and analyze large amounts of data. Cloud analysis can provide carbon emission trend analysis, equipment health monitoring, and higher-level management decision support, such as predicting building carbon emissions for a certain period of time in the future.

[0242] Step 6: Intelligent Optimization and Decision Making

[0243] The system continuously optimizes the carbon emission calculation model through machine learning algorithms and provides dynamic optimization suggestions in the building management system. For example, the system may learn that at a certain time of the day, carbon emissions can be significantly reduced by reducing the operation of the air conditioning system with minimal impact on indoor comfort.

[0244] Step 7: Carbon emission monitoring and alarm

[0245] The system continuously monitors carbon emissions in various areas or equipment. When abnormally high carbon emissions are detected, edge computing devices can trigger alarms in real time. Alarms can be sent via email, text messages, or the building management system interface, and abnormal events are recorded, alerting managers and suggesting measures such as shutting down specific equipment or adjusting regional environmental parameters.

[0246] Step 8: Security and Data Protection Measures

[0247] During the entire data transmission and processing process, the system uses data encryption and access control mechanisms to ensure data security and privacy protection. Data is encrypted using AES before transmission to ensure data security during transmission. The system also implements strict access control so that only authorized personnel can access sensitive data. In addition, the system also deploys firewalls and intrusion detection systems to prevent network attacks.

[0248] A building carbon metering device based on edge computing for executing the above method comprises:

[0249] Multi-point carbon emission data collection module: responsible for installing and maintaining sensors to ensure the accuracy and real-time nature of data.

[0250] Data preprocessing module: responsible for the preliminary processing and uploading of data to ensure the quality and availability of data.

[0251] Edge computing module: responsible for real-time carbon emissions calculation, providing rapid response and processing capabilities.

[0252] Carbon emission analysis and feedback module: responsible for analyzing carbon emission data and providing feedback to help managers make decisions.

[0253] Edge computing and cloud collaborative processing module: responsible for data uploading and cloud analysis, providing long-term data storage and in-depth analysis.

[0254] Intelligent optimization and decision-making module: responsible for providing optimization suggestions to help managers optimize the building's energy use and carbon emissions.

[0255] Carbon emission monitoring and alarm module: responsible for monitoring carbon emissions and triggering alarms to ensure the safe and stable operation of the system.

[0256] Security and data protection module: responsible for data security and privacy protection, preventing unauthorized access and data tampering.

[0257] In addition, the present invention also includes a readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement the above-mentioned building carbon measurement method based on edge computing. Such storage medium can be a hard disk, a solid state drive, a USB drive or other types of storage devices.

[0258] Through the above implementation modes, the present invention provides an efficient and real-time building carbon emission monitoring and management method, which helps building managers optimize energy use, reduce carbon emissions, and achieve sustainable development goals.

[0259] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A building carbon measurement method based on edge computing, characterized in that: The following steps are involved: Multi-point collection of building carbon emission data: sensors are installed at several key locations in the building to collect data that affects carbon emissions in real time, including power consumption, gas usage, ambient temperature and humidity, indoor and outdoor gas composition, and equipment working status; the sensors include energy meters, gas concentration detectors, and temperature sensors; Data preprocessing and edge device upload: The sensor data collected by the sensor is first processed in the sensor node. The preliminary processing includes data denoising, data filtering, and outlier detection to ensure the accuracy and integrity of the sensor data. The preliminarily processed data is transmitted to the edge computing device through the local area network to avoid uploading all the original data to the cloud and reduce bandwidth usage. Real-time carbon emission calculation of edge devices. Edge computing devices perform real-time calculations on the received sensor data and estimate the carbon emissions of each area or device based on energy consumption and device status information. They use pre-established carbon emission formulas or models, combined with sensor data and device loads in a specific area, to perform regional carbon emission calculations. Analysis and feedback of local carbon emissions: Carbon emissions analysis is performed on edge computing devices to identify high-emission areas or high-energy-consuming equipment in the building, and by analyzing energy efficiency data, identify possible inefficiencies or failures in the equipment. The edge device will generate local feedback information and provide optimization suggestions to the building management system, including adjusting the operating time of a certain device, optimizing the air conditioning temperature, and reducing the use of high-emission equipment. Edge computing and cloud collaborative processing, for long-term data storage and in-depth analysis, edge computing devices will regularly upload processed data to cloud servers; The cloud server can perform more complex carbon emission trend analysis, equipment health monitoring, and provide higher-level management decision support to predict building carbon emissions in a certain period of time in the future; Intelligent optimization and decision-making: Based on the collaborative results of edge computing devices and cloud analysis, the system will continuously optimize the carbon emission calculation model through machine learning algorithms and provide dynamic optimization suggestions in the building management system; dynamic optimization suggestions include adjusting the operation strategy of high-energy-consuming equipment and changing the operation mode of the air-conditioning system to reduce the carbon emissions of the entire building; Carbon emission monitoring and alarm: The system continuously monitors the carbon emissions of each area or equipment. When abnormally high carbon emissions are detected, the edge computing device can trigger an alarm in real time. The system will record the abnormal event and recommend that managers take measures, including shutting down specific equipment or adjusting regional environmental parameters. Security and data protection measures: During the entire data transmission and processing process, the system adopts data encryption and access control mechanisms to ensure data security and privacy protection and prevent unauthorized access and data tampering.

2. A building carbon measurement method based on edge computing according to claim 1, characterized in that: The multi-point collection of building carbon emission data includes the following steps: Step 1: Determine the type and scope of data collection. The target data to be collected should be related to carbon emissions, involving energy consumption and environmental factors, including: electricity consumption, gas usage, operating status, workload and usage time of high-power equipment, environmental parameters including indoor and outdoor temperature, humidity, wind speed, wind direction, and carbon dioxide and methane content in the air inside and outside the building; Step 2: Sensor type and layout design. Select the appropriate sensors according to different data collection requirements, including: Power sensor: Use smart meters or power sensors installed in the circuits of major equipment in the building; major equipment includes: air conditioners, lighting systems, elevators, and computer room servers; Gas sensor: installed at the entrance and exit of the natural gas pipeline to monitor the gas usage; Environmental sensors: Temperature and humidity sensors, wind speed and direction sensors are distributed in different areas of the building, including air-conditioning outlets, rooftops, and basements where airflow has a greater impact; Gas detectors: installed in different areas indoors and outdoors, especially in crowded places, to monitor the concentration of carbon dioxide and methane in the air; Step 3: Networking and data aggregation of sensor nodes. Various sensors form a local area network through wireless communication or wired network to transmit the collected data to the edge computing device in real time; Wireless communication: For areas where wiring is difficult, wireless communication can be used to ensure that devices can communicate reliably over long distances; Wired communication: For devices with large data volumes or high communication requirements, Ethernet connection is used to ensure high data transmission rate and stability; Data aggregation: Each sensor node transmits the collected data to the local edge computing device through the network to form a preliminary data pool; Data collection frequency: Set the collection frequency according to the equipment type and monitoring requirements. The collection frequency of power consumption is once per minute, while the temperature and humidity data are collected every 5 minutes. Step 4: Real-time monitoring and verification of data collection; Real-time monitoring: The edge computing device is responsible for monitoring the operating status of each sensor node to ensure the real-time and stability of the data acquisition system. Once a sensor node is abnormal, the system will automatically trigger an alarm or start a self-repair mechanism. Data verification: For the data collected by the sensor, the system needs to perform preliminary verification, including testing the integrity and accuracy of the data, and eliminating noise data or unreasonable data points; Step 5: Data storage and processing Prepare local storage; On edge computing devices, all collected real-time data is stored locally; Processing preparation: After preliminary cleaning and integration, the data will serve as the basic data for carbon emission calculation in subsequent steps.

3. A building carbon measurement method based on edge computing according to claim 1, characterized in that: The data preprocessing and edge device uploading include the following steps: De-noising the basic data collected by the sensor, using a low-pass filter algorithm or a Kalman filter algorithm to eliminate noise in the data and ensure the smoothness and accuracy of the data; Perform data cleaning to remove data points including but not limited to power values ​​that exceed the normal operating range of the equipment and data points with abnormal and drastic changes in the collected gas concentration; Compress and optimize data, use differential coding or sample sparsification methods, and use data aggregation technology to reduce the amount of data; Perform localized anomaly detection before uploading to edge devices. If energy consumption or gas emission values ​​are detected to exceed normal thresholds, an anomaly report is generated. The data is temporarily stored in a local storage device and uploaded to the edge computing device in batches after the network is restored. Encrypt data before uploading, using AES or RSA encryption technology to ensure data privacy and security; Upload the processed data to the edge computing device through the efficient MQTT or CoAP data transmission protocol.

4. The building carbon measurement method based on edge computing according to claim 1 is characterized in that: The real-time carbon emission calculation of the edge device includes the following steps: Establish a carbon emission calculation model and use a formula to convert energy consumption and equipment operating load into carbon emissions. The carbon emissions from electricity consumption are converted through the carbon emission coefficient of the power grid, and the carbon emissions from gas consumption are converted through the calorific value of the gas and the carbon emission factor. Calculate carbon emissions for different functional areas in the building, and perform real-time calculations based on energy consumption, gas emissions and environmental parameters in each area to identify high-emission areas; Conduct equipment-level carbon emission analysis on important equipment in the building, combine the energy consumption data and operating time of the equipment, calculate the carbon emission value within a specific time period, and analyze abnormal carbon emissions caused by equipment aging or inefficient operation; Implement long-term tracking and trend forecasting to identify equipment inefficiencies or potential failures, and promptly alert building managers to perform maintenance or replacements; Generate instant feedback based on real-time carbon emission calculation results and recommend optimized equipment operation strategies to reduce carbon emissions; When carbon emissions exceed the set threshold, the alarm mechanism is triggered and the management system is notified.

5. The building carbon measurement method based on edge computing according to claim 1 is characterized in that: The analysis and feedback of local carbon emissions include the following steps: Analyze data from various collection points in real time to identify high-emission areas within buildings, including air-conditioning rooms, elevator rooms, and heating equipment; Identify specific high-energy-consuming equipment in the building, including air conditioning, lighting systems, and heating systems, and record equipment that exceeds a preset benchmark; Analyze energy efficiency data of high-emission equipment to identify equipment operating efficiency and record equipment operating inefficiently; By analyzing the historical operation data of the equipment, possible faults of the equipment can be identified and fault warnings can be provided; Generate local feedback information based on the results of local carbon emissions and energy efficiency analysis, and send alarm information to the building management system; Provide instant optimization suggestions to the management system, including adjusting the operating hours of equipment, reducing the frequency of use of high-emission equipment, or optimizing the temperature settings of air conditioning systems; Send feedback information to the building management system in real time and adjust the operating status of the equipment according to the suggestions; Continuously track the implementation effect of feedback and adjust the operation strategy of the equipment in real time; Develop personalized energy efficiency standards based on different equipment types and operating environments to ensure the accuracy of analysis results; Introduce a machine learning algorithm based on historical data to predict the usage patterns of high-emission equipment and dynamically adjust the system's feedback suggestions based on its changes.

6. The building carbon measurement method based on edge computing according to claim 1 is characterized in that: The collaborative processing of edge computing and cloud computing includes the following steps: Regularly upload the building carbon emission data processed by the edge computing device to the cloud server, and the upload frequency is adjusted according to the importance and time requirements of the data; The cloud server uses an efficient database management system to store uploaded data for a long time, ensuring the integrity, stability and security of the data; In-depth analysis is performed in the cloud to generate long-term carbon emission trend charts, analyze carbon emission patterns in different seasons and weekdays, and identify carbon emission peak periods; The cloud monitors the health status of each device in the building, predicts the maintenance needs of the equipment, and reduces the sudden increase in carbon emissions caused by equipment failure; The cloud provides comprehensive data reports to support decision-makers in making strategic adjustments, including replacing high-energy-consuming equipment or adjusting energy structure; Implement a collaborative processing mechanism for data preprocessing and decision feedback. The edge computing device uploads the data to the cloud after preliminary processing. The cloud performs in-depth analysis and then feeds back to the edge device for real-time adjustment. Implement a task allocation mechanism between cloud and edge computing, with edge computing devices handling time-sensitive tasks and the cloud handling complex and large-scale data analysis tasks; The cloud predicts carbon emissions for a certain period of time in the future based on historical carbon emissions data and provides optimization suggestions; The cloud provides decision support based on the prediction results and recommends adjusting the equipment operating time or power to reduce carbon emissions; Asynchronous communication is used between the cloud and edge devices. When network problems occur, edge devices automatically cache data and upload in batches after the network is restored. Data is compressed and encrypted before uploading to improve upload efficiency and ensure data security.

7. The building carbon measurement method based on edge computing according to claim 1 is characterized in that: The intelligent optimization and decision-making includes the following steps: Aggregate and integrate data from edge computing devices in various areas and devices of the building, including energy consumption data, equipment operating status, and carbon emission concentration; Remove duplicates and correct abnormal data from the aggregated data, and adjust the weights of different data sources to improve data accuracy; Use machine learning or deep learning models to predict the overall carbon emission trend of a building, and use historical data and real-time data to predict the peak carbon emission in a certain period of time in the future; By simulating carbon emissions under different scenarios, we can optimize the operation strategies of various devices and evaluate the carbon reduction effect of each solution and its impact on building comfort. Dynamically adjust the building’s carbon emission control strategies based on the analysis results, including automatically increasing air conditioning efficiency and reducing the power output of non-essential equipment when traffic increases; Adjust the priority of different equipment according to the equipment's energy consumption, usage frequency and carbon emissions, and give priority to controlling equipment with higher energy consumption; Implement a real-time feedback mechanism to feed back optimization decisions to each edge device and adjust the device's operating parameters in real time to reduce energy consumption; Through long-term accumulation of operating data, the system self-learns the building carbon emission patterns, continuously optimizes the decision-making algorithm, and improves the accuracy of decision-making.

8. The building carbon measurement method based on edge computing according to claim 1 is characterized in that: The carbon emission monitoring and alarming comprises the following steps: The system continuously monitors the carbon emissions of each area or equipment; The edge computing device performs a preliminary analysis on the collected carbon emission data, and immediately calculates and determines whether an alarm is needed when a rapid increase in carbon emissions is detected; Set reasonable carbon emission thresholds according to different areas and equipment in the building, and automatically trigger an alarm when carbon emissions exceed the set thresholds; Provides a multi-level alarm mechanism, which sends out alarms through different channels including email, application, voice notification, and mobile phone text messages according to the severity of the abnormal situation; In the event of serious overshooting, the system automatically triggers emergency measures, including shutting down or reducing the power of specific high-energy-consuming equipment to quickly reduce carbon emissions; Record detailed information of each alarm event, including the time of exceeding the limit, the exceeding device, the triggering reason and the solution, and store them in the system log; Based on historical alarm data, analyze areas and equipment where carbon emission problems frequently occur, and optimize alarm mechanisms and building carbon emission control strategies.

9. A building carbon metering device based on edge computing according to any one of claims 1 to 8, characterized in that: include: Multi-point carbon emission data collection module, used to install sensors at several key locations of the building to collect data affecting carbon emissions in real time, including power consumption, gas usage, ambient temperature and humidity, indoor and outdoor gas composition, and equipment working status; the sensors include energy meters, gas concentration detectors, and temperature sensors; A data preprocessing module, connected to the sensor, for performing preliminary processing on the collected sensor data, wherein the preliminary processing includes data denoising, data filtering and outlier detection to ensure the accuracy and integrity of the sensor data; The processed data is transmitted to the edge computing device through the local area network, reducing the bandwidth usage caused by uploading the original data; An edge computing module is used to perform real-time carbon emission calculation on the preprocessed sensor data, and estimate the carbon emission of each area or device based on energy consumption and device status information; The edge computing module uses a preset carbon emission formula or model, combined with sensor data and equipment load in a specific area, to perform zoned carbon emission calculations; A carbon emission analysis and feedback module is used to perform carbon emission analysis on edge computing devices, identify high emission areas or high energy consumption equipment in the building, and identify inefficiencies or failures of equipment in combination with energy efficiency data analysis; the module generates local feedback information and provides optimization suggestions to the building management system, including adjusting equipment operating time, optimizing air conditioning temperature, etc.; The edge computing and cloud collaborative processing module is used to regularly upload the data processed by the edge computing device to the cloud server, which performs long-term data storage and in-depth analysis, provides carbon emission trend analysis, equipment health monitoring and higher-level management decision support; Intelligent optimization and decision-making module, which is used to optimize the carbon emission calculation model through machine learning algorithms based on the collaborative results of edge computing devices and cloud analysis, and provide dynamic optimization suggestions, including adjusting the operation strategy of high-energy-consuming equipment and changing the operation mode of the air-conditioning system to reduce the carbon emissions of the entire building; The carbon emission monitoring and alarm module is used to continuously monitor the carbon emissions of each area or equipment. When abnormally high carbon emissions are detected, an alarm is triggered in real time and abnormal events are recorded, and managers are reminded to take measures including shutting down specific equipment or adjusting regional environmental parameters; The security and data protection module is used to adopt data encryption and access control mechanisms during data transmission and processing to ensure data security and privacy protection and prevent unauthorized access and data tampering.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the building carbon metering method based on edge computing according to any one of claims 1 to 8 is implemented.

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