CN119721320A - A building carbon measurement method, device and medium based on edge computing - Google Patents

Abstract

The present invention discloses a building carbon metering method, device and medium based on edge computing. By deploying multiple sensors in the building, data such as electricity consumption, gas usage, ambient temperature and humidity, indoor and outdoor gas composition and equipment working status are collected in real time. The edge computing device is used to pre-process the collected data and calculate carbon emissions in real time, and at the same time, it is coordinated with the cloud to achieve long-term data storage and in-depth analysis. The system can intelligently optimize and make decisions, provide dynamic adjustment suggestions, and reduce the energy consumption and carbon emissions of the building. In addition, the method also includes a carbon emission monitoring and alarm mechanism, as well as strict security and data protection measures to ensure data security and privacy protection. Beneficial effects: Through this method, the carbon emissions of the building can be effectively monitored and managed, and real-time data support and optimization suggestions can be provided to building managers, thereby achieving the goal of intelligent building management and supporting sustainable development and environmental protection.

Description

Building carbon metering method, equipment and medium based on edge calculation

Technical Field

The invention relates to the field of double carbon, in particular to a building carbon metering method, equipment and medium based on edge calculation.

Background

With the serious challenges of global climate change, reducing building energy consumption and carbon emissions has become an indispensable part of pushing green sustainable development. The building industry is one of the main sources of energy consumption and greenhouse gas emission, and the energy efficiency improvement and low-carbon transformation are particularly important.

In the context of smart city construction, building carbon emission monitoring and management is becoming a hotspot for research and application. The smart city realizes energy conservation and emission reduction of various living scenes through digital transformation, wherein a building automatic control system (Bu i l d i ng Automat i on System, BAS) is used as a core component part of an intelligent building technology, and is an important tool for solving the problems of building energy consumption and carbon emission due to the high-efficiency, accurate and intelligent characteristics.

However, the prior art has some drawbacks and challenges in building carbon emission monitoring and management:

the limitation of data acquisition is that the traditional building management system often depends on limited sensors and monitoring equipment, and comprehensive and real-time data acquisition is difficult to realize, so that the energy consumption and carbon emission conditions of a building cannot be accurately estimated.

The existing building management system generally lacks of fine management on various areas and devices in a building, and cannot formulate personalized energy-saving and emission-reduction strategies for different areas and devices.

The data processing and analysis capabilities are inadequate, and although some building management systems are capable of collecting large amounts of energy consumption data, they lack efficient data processing and analysis capabilities and are unable to translate the data into valuable information and decision support.

The system integration level is low, the existing building management system cannot be integrated with other systems, such as meteorological data, energy price information and the like, and the potential of the system in the aspects of optimizing energy use and reducing carbon emission is limited.

The safety and privacy protection are insufficient, and the problems of data safety and privacy protection are increasingly prominent with the increase of the data volume in the building management system. Existing systems may lack efficient data encryption and access control mechanisms, and it may be difficult to prevent unauthorized access and data tampering.

The intelligent level is limited, and many existing building management systems do not fully utilize advanced technologies such as artificial intelligence and machine learning, and cannot realize intelligent energy consumption prediction, fault diagnosis and optimal control.

While edge computing is a distributed computing paradigm that deploys data processing, application execution, and intelligent services on devices or nodes that are close to the data source, rather than relying entirely on remote data centers or cloud platforms. Such architecture aims to reduce data transmission delay, improve data security, alleviate network bandwidth pressure, and support real-time decision-making and autonomous operation.

The use of edge computing presents great potential in terms of building energy consumption and carbon emission management. Through reasonable design, the real-time monitoring and analysis of building carbon emission can be realized in theory, the energy use is optimized, and the carbon emission is reduced.

Disclosure of Invention

In order to solve the technical problems, the invention provides a building carbon metering method, equipment and medium based on edge calculation.

The invention is realized by the following technical scheme:

a building carbon metering method based on edge calculation comprises the following steps:

Step 1, multipoint acquisition of building carbon emission data

A plurality of sensors are arranged at each key position of the building and used for collecting data affecting carbon emission in real time, such as power consumption, gas use, environment temperature and humidity, indoor and outdoor gas components, equipment working states and the like;

The sensor comprises an energy meter, a gas concentration detector, a temperature sensor and the like, and the data acquisition frequency can be set according to the actual application scene;

step 2, data preprocessing and edge equipment uploading

The data collected by the sensor is initially processed locally (sensor node), such as data denoising, data filtering and abnormal value detection, so that the accuracy and the integrity of the sensed data are ensured;

The preprocessed data is transmitted to the edge computing equipment through the local area network, so that all original data is prevented from being uploaded to the cloud, and bandwidth occupation is reduced;

Step 3, real-time carbon emission calculation of edge equipment

The edge computing equipment calculates the received sensing data in real time, and estimates the carbon emission of each area or equipment based on the information such as energy consumption, equipment state and the like;

Using a pre-established carbon emission formula or model, and carrying out partition calculation of carbon emission in a region by combining sensor data and equipment load of a specific region;

Step 4, analysis and feedback of partial carbon emissions

Performing carbon emission analysis on edge computing equipment, identifying high emission areas or high energy consumption equipment in a building, and identifying possible inefficiency or fault conditions of the equipment by analyzing energy efficiency data;

The edge equipment can generate local feedback information and provide optimization suggestions for a building management system, such as adjusting the running time of certain equipment, optimizing the temperature of an air conditioner and reducing the use of high-emission equipment;

Step 5, edge calculation and cloud cooperative processing

For long-term data storage and depth analysis, the edge computing device periodically uploads the processed data to the cloud server;

The cloud may perform more complex carbon emission trend analysis, device health monitoring, and provide higher level management decision support, such as predicting building carbon emissions for a time period in the future;

step 6, intelligent optimization and decision making

Based on the cooperative result of the edge computing equipment and cloud analysis, the system continuously optimizes a carbon emission computing model through a machine learning algorithm and provides dynamic optimization suggestions in a building management system;

real-time advice includes adjusting the operating strategy of high-energy consumption equipment, changing the operating mode of an air conditioning system, etc. to reduce carbon emission of the whole building;

step 7, monitoring and alarming carbon emission

The system continuously monitors the carbon emission condition of each area or device, and when abnormal high carbon emission is detected, the edge computing device can trigger an alarm in real time;

The system will record the exception event and recommend action to be taken by the manager, such as shutting down a particular device or adjusting regional environmental parameters;

Step 8, security and data protection measures

In the whole data transmission and processing process, the system adopts a data encryption and access control mechanism to ensure the safety and privacy protection of the data and prevent unauthorized access and data tampering;

The method is different from the traditional centralized processing in that the method combines low-delay processing and localized data optimization, and improves instantaneity, flexibility and data processing efficiency;

Through distributed data acquisition and application of edge intelligent equipment, carbon emission data can be accurately metered, and energy consumption of a building can be reduced through real-time analysis, so that an intelligent building management target is achieved.

Further, the multipoint collection of the building carbon emission data comprises the following steps:

1.1 determining the type and extent of data acquisition

The collected target data should be related to carbon emission, and relate to energy consumption and environmental factors, and specifically include:

The power consumption is used for monitoring the energy consumption of electrical equipment (such as air conditioners, illumination, office equipment and the like) in a building, and is one of the main sources of carbon emission;

The fuel gas is used, if natural gas or other fuels are used for heating or supplying power in a building, the fuel gas consumption needs to be monitored;

The equipment state information is used for monitoring the running state and the service time of equipment, such as the workload of high-power equipment such as an elevator, an air conditioner, a boiler, a generator and the like;

Environmental parameters including indoor and outdoor temperature, humidity, air flow conditions (wind speed, wind direction) and the like, and influence the energy efficiency performance of the equipment;

and detecting the gas content of the room such as carbon dioxide, methane and the like in the air inside and outside the building, and directly reflecting the carbon emission level.

1.2 Sensor types and layout design

Selecting proper sensors according to different requirements of data acquisition, and performing reasonable spatial arrangement to ensure the accuracy and representativeness of the acquired data;

The power sensor is arranged in a circuit of main equipment in a building by using a smart meter or a power sensor, and records the power consumption in real time;

The gas sensor is arranged at the inlet and outlet positions of the natural gas pipeline, monitors the usage amount of gas and is related to the operation condition of corresponding equipment;

The environmental sensor comprises a temperature and humidity sensor and a wind speed and direction sensor which can be distributed in different areas of a building, especially in the positions with larger influence on air flows such as an air outlet of an air conditioner, a roof, a basement and the like;

The gas detector is arranged in different areas inside and outside the room, especially places with dense people flow (such as meeting rooms, halls, parking lots and the like) and is used for monitoring the concentration of carbon dioxide, methane and other gases in the air.

1.3 Networking and data summarization of sensor nodes

Networking of sensor nodes, namely, various sensors form a Local Area Network (LAN) through wireless communication (such as Wi-Fi, zi gbee, loRa and the like) or wired network (such as Ethernet and RS 485) so as to transmit acquired data to edge computing equipment in real time;

wireless communication, namely, for areas which are difficult to wire (such as sensors on the outside of a building or on a ceiling), a wireless communication mode, such as low-power-consumption wide area network technology of LoRa, Z i gbee and the like, can be adopted, so that reliable communication of equipment in a longer distance can be ensured;

The wired communication, which is to adopt Ethernet connection for equipment with larger data volume or high communication requirement (such as equipment in a power sensor or a server room) to ensure high data transmission rate and stability;

The data summarization, namely each sensor node transmits the acquired data to local edge computing equipment through a network to form a preliminary data pool, wherein the process needs to set the frequency and uploading period of data acquisition, and ensures the real-time performance and effectiveness of the data;

the data acquisition frequency is set according to the equipment type and the monitoring requirement, for example, the acquisition frequency of power consumption can be once every minute, and the temperature and humidity data can be acquired once every 5 minutes.

1.4 Real-time monitoring and verification of data acquisition

The edge computing equipment is responsible for monitoring the running state of each sensor node and ensuring the real-time performance and stability of the data acquisition system, and once an abnormality (such as data loss and abnormal value) occurs in one sensor node, the system can automatically trigger an alarm or self-repairing mechanism;

Data verification, namely, for data acquired by a sensor, the system needs to perform preliminary verification, such as detecting the integrity and accuracy of the data, and removing noise data or unreasonable data points according to a certain rule, for example, when the sudden increase of the energy consumption of a certain device is detected but the state of the device is not changed, the system can judge that the false alarm of the sensor is possible.

1.5 Storage and processing preparation of data

The local storage is that all the collected real-time data are locally stored on the edge computing equipment, so that the data loss caused by the network problem is avoided;

the preparation of treatment, namely, the data is used as basic data for carbon emission calculation in the subsequent step after the data are subjected to preliminary cleaning and integration.

Aiming at the fine monitoring of various devices in a building, the step combines the data of multiple types of sensors, especially the direct detection of gas components, and provides a reliable data source for the accurate measurement of the carbon emission;

in addition, the networking modes of the sensor nodes are diversified, the requirements of different building structures are met, and the flexibility and the stability of data transmission are improved by combining a low-power wireless network with a wired network;

and finally, a real-time monitoring and data checking mechanism is added, so that the quality of sensor data is ensured, and a more reliable basis is provided for subsequent carbon emission calculation.

Further, the data preprocessing and the edge device uploading include the following steps:

2.1 data denoising and cleaning

Denoising, namely, due to the possible existence of factors such as signal interference, sensor aging and the like in the environment, the acquired data can contain noise, and unreasonable fluctuation is eliminated through a filtering algorithm (such as low-pass filtering and Kalman filtering), so that the smoothness and accuracy of the data are ensured;

Data cleansing, namely, eliminating invalid data points with obvious anomalies, such as:

Power values exceeding the normal operating range of the device;

The concentration of the collected gas changes extremely and severely, and the external conditions are unchanged;

The cleaning algorithm may formulate a rule according to a specific scenario, for example, if the power consumption value of a certain device is continuously far from the normal range multiple times, it is determined as abnormal.

2.2 Data compression and optimization

Because the data volume generated by the sensor is large, data compression is necessary to reduce network bandwidth occupation and storage pressure;

The compression method comprises differential coding (only storing the difference between the front acquisition point and the rear acquisition point) or sample thinning (reducing the acquisition frequency for stable and unchanged data segments);

in addition, the data aggregation technology is adopted to reduce multiple redundant data, for example, temperature information acquired by a plurality of sensors can be averaged by weighting to generate a unified environment temperature value in the area, so that the uploading data quantity is reduced.

2.3 Anomaly detection and local storage

Local abnormality detection, namely, carrying out localization abnormality detection before uploading the edge equipment, for example, triggering an alarm and generating a related abnormality report to prompt a manager or trigger an automatic process (such as turning off high-energy-consumption equipment) if the energy consumption or gas emission value of a certain equipment exceeds a normal threshold value;

Temporary data storage, in order to prevent data loss caused by network interruption or transmission failure, the sensor nodes can firstly cache the data on the local storage device and upload the data to the edge computing device in batches after the network is recovered.

2.4 Data encryption and upload

Data encryption, namely encrypting the acquired data before uploading to prevent the data from being stolen or tampered in the transmission process, wherein common encryption technologies such as AES (advanced encryption standard), RSA (rivest-Shamir-Adleman) encryption and the like are used for ensuring the privacy and the safety of the data;

And the data uploading is carried out by setting an uploading period, and the edge computing equipment acquires the processed data from each sensor node, wherein the process adopts high-efficiency data transmission protocols (such as MQTT, coAP and the like) to ensure low-delay and reliable data transmission.

The core of the step is that the primary processing is carried out on the original data acquired by the sensor, the accuracy, the integrity and the compactness of the data are ensured, and then the data are uploaded to edge computing equipment for preparing for subsequent carbon emission computation.

Further, the real-time carbon emission calculation of the edge device includes the following steps:

3.1 establishment of carbon emission model

Establishing a carbon emission calculation formula, the carbon emission model is generally calculated based on energy consumption and plant operating load, such as:

The relation of the electric power consumption and the carbon emission can be converted by the carbon emission factor (carbon emission amount of unit energy consumption) of the electric power grid, such as "electric power consumption x electric power grid carbon emission coefficient=carbon emission amount";

Carbon emissions generated by gas consumption are converted by heat value such as "gas usage amount×gas heat value×carbon emission factor=carbon emission amount";

The carbon emission factors of different equipment and energy types can be preset and flexibly adjusted based on the actual energy structure in the building.

3.2 Zone carbon emission calculations

The regional division is that the calculation of carbon emission is divided according to regions according to different functional regions (such as office areas, equipment rooms, restaurants, parking lots and the like) of the building, and the regional division calculation can accurately identify high emission regions, thereby facilitating the subsequent management and optimization;

Real-time calculation, the edge calculation device calculates the carbon emission amount in real time according to the data of different areas by using a predefined formula, for example:

weighting and calculating the comprehensive information such as total power consumption, gas emission, temperature environment and the like of a certain area, and outputting a carbon emission value of the area;

The calculation result of each area can be dynamically updated to reflect the current carbon emission condition in real time.

3.3 Plant-level carbon emission analysis

The equipment classification and independent calculation are that for important equipment (such as air conditioner, generator and other high energy consumption equipment) in the building, the carbon emission amount can be calculated independently;

the energy consumption data of each device is combined with the running time of the device, and the carbon emission value of the device in a specific time period is output;

In addition, aiming at the problems of equipment aging, low-efficiency operation and the like, the edge computing equipment can further analyze the abnormal condition of carbon emission;

And long-term tracking and trend prediction, namely, by long-term tracking the carbon emission performance of equipment, the edge computing equipment can identify the inefficiency or potential failure of the equipment and prompt a building manager to maintain or replace in time.

3.4 Local feedback mechanism for edge devices

Feedback generation, based on real-time calculation of carbon emissions, the edge computing device may generate immediate feedback, e.g., suggesting shut down of a portion of the devices during off-peak hours, adjusting air conditioning temperatures, optimizing device operating strategies, etc., to reduce carbon emissions;

An alarm mechanism that the edge device will give an alarm and inform the management system when the carbon emission of a certain area or device exceeds a set threshold, and that the edge computing device will automatically give a warning when, for example, the carbon emission of an elevator or air conditioner suddenly increases, possibly as a signal of a device failure.

The core of the step is that the collected data is processed and analyzed in real time through the edge computing equipment, the carbon emission in the building is calculated, and the local carbon emission assessment and management are provided.

Further, the analysis and feedback of the local carbon emissions include the steps of:

4.1 local analysis of carbon emission data

And (3) identifying the high-emission area, namely analyzing the data of each acquisition point by the edge computing equipment in real time, and identifying the high-emission area inside the building. These areas may include areas of high energy consumption such as air conditioning rooms, elevator rooms, heating equipment, etc. By comparing the carbon emission data for the different regions, the system is able to quickly locate these high emission regions.

High energy consumption device identification in addition to analyzing the area, the edge computing device can also identify specific high energy consumption devices in the building, such as air conditioning, lighting systems, heating systems, and the like. If the carbon emission level of a particular device exceeds a preset reference, the system will record the device and mark it as a device that may require optimization.

4.2 Device operation efficiency and failure analysis

And (3) analyzing the energy efficiency data, namely analyzing the energy efficiency data of the high-emission equipment by the edge computing equipment and identifying the operation efficiency of the equipment. If the energy consumption of the device is found to be mismatched (i.e., energy inefficient operation) with its intended output, the system will record this. For example, air conditioning may increase energy consumption due to insufficient maintenance, but the cooling effect is not improved accordingly.

And the fault early warning is that the edge equipment can also identify possible fault conditions of the equipment by analyzing the historical operation data of the equipment. For example, the system may detect a sudden increase in energy consumption of a device while its output performance decreases, thereby inferring that the device may need to be serviced or replaced.

4.3 Local feedback and optimization suggestions

Feedback information generation, wherein the edge computing device generates local feedback information according to the results of the local carbon emission and energy efficiency analysis. For example, if the carbon emissions in a certain area increase abnormally, the device will send an alarm message to the building management system, alerting relevant personnel to the situation in that area.

Generation of optimization suggestions the edge computing device may also provide immediate optimization suggestions to the management system. Depending on the carbon emissions analysis of the equipment and the area, the system may suggest adjusting the operating time of certain equipment, reducing the frequency of use of high emission equipment, or optimizing the temperature settings of the air conditioning system. For example, the edge device may suggest reducing the operating power of the air conditioner during off-peak hours, or adjusting the brightness of the lighting system to reduce unnecessary energy consumption.

4.4 Interaction with building management System

And (3) real-time adjustment and feedback, wherein feedback information generated by the edge equipment is transmitted to the building management system in real time for reference of management personnel. Meanwhile, the building management system can adjust the running state of the equipment according to the advice. For example, certain high energy consuming devices may be turned off remotely or the temperature and illumination in the area may be dynamically adjusted.

Dynamic feedback and continuous optimization, namely, the system continuously tracks the implementation effect of feedback and adjusts the operation strategy of the equipment in real time. For example, if the system suggests optimizing the air conditioning temperature in a region and carbon emissions drop after execution, the system will record this information and use it as a basis for future decisions.

Wherein:

The energy efficiency analysis standard is refined, namely different devices have different energy efficiency standards, and the edge computing device can formulate individualized energy efficiency standards for different devices according to different device types and different running environments, so that the accuracy of analysis results is ensured.

The edge computing device can introduce a machine learning algorithm based on historical data to predict the usage rule of the high-emission device and dynamically adjust the feedback suggestion of the system according to the change of the usage rule, so that the system has more adaptability.

Further, the cooperative processing of the edge calculation and the cloud end comprises the following steps:

5.1 data upload and store

And uploading the data periodically, namely uploading the processed building carbon emission data to a cloud server by the edge computing equipment periodically. The frequency of the uploading can be adjusted according to the importance of the data and the time requirement. For example, real-time data may be uploaded once every few minutes, while historical data may be uploaded periodically daily or weekly.

And the cloud server is responsible for long-term storage of the uploaded data, and an efficient database management system is used for ensuring the integrity, stability and safety of the data. The cloud has larger storage capacity, so that historical data of a plurality of years can be stored for future deep analysis.

5.2 Deep analysis of cloud

And C, analyzing the carbon emission trend, namely, analyzing historical data and real-time data, and generating a long-term carbon emission trend graph by the cloud. For example, the carbon emission patterns of a building in different seasons and different workdays can be analyzed, and the possible carbon emission peak periods can be identified.

The cloud end can also monitor the health status of various devices in the building, especially those devices that generate a large amount of carbon emissions (such as air conditioning and heating systems). Through the analysis of the cloud to the equipment operation data, the maintenance requirement of the equipment can be predicted, and the carbon emission sudden increase caused by equipment faults is reduced.

And the high-level management decision support is that the cloud provides a comprehensive data report to help high-level management personnel to carry out strategy adjustment. For example, through cloud analysis, a manager can know the carbon emission contrast condition of each building and determine whether to replace high-energy-consumption equipment or adjust the energy structure.

5.3 Synergistic processing mechanism

And the data preprocessing and decision feedback are that the edge computing equipment performs preliminary processing on the real-time data, such as filtering abnormal data and classifying carbon emission data of different areas. And uploading the preprocessed data to a cloud end, and further carrying out depth analysis by the cloud end, and feeding back the data to the edge equipment for real-time adjustment after the completion.

Cloud and edge computing task allocation, in which edge computing devices handle time-sensitive tasks such as real-time carbon emission detection and early warning, while cloud processes more complex and large-scale data analysis tasks such as long-term trend prediction of carbon emission and equipment maintenance planning. Through the cooperative mechanism, the performance and the response speed of the system are optimized.

5.4 Future carbon emission prediction

And the time period prediction is that the cloud end can predict the carbon emission of a certain time period in the future based on the historical carbon emission data of the building. For example, by analyzing the data from the previous years, in combination with real-time energy consumption, weather and building usage, the cloud can predict the carbon emissions for the next week or month and provide optimization advice to the manager.

Decision support and intelligent optimization, the cloud can give decision support according to the prediction result, and the cloud can recommend to adjust the running time or power of some equipment to reduce carbon emission. For example, the system may recommend reducing the operation of certain high energy consuming devices prior to the predicted peak carbon emission period, thereby controlling the carbon emissions in advance.

In order to avoid the influence of data uploading delay on system decision, an asynchronous communication mode can be adopted between the cloud end and the edge equipment, when a network has a problem, the edge equipment automatically caches data, and the data is uploaded in batches after the network is recovered.

Data compression and encryption, in order to improve uploading efficiency, data can be compressed before uploading. Meanwhile, in order to ensure the security of the data, the uploaded data should be encrypted, so as to prevent leakage in the middle transmission process.

Further, the intelligent optimization and decision comprises the following steps:

6.1 Global data summarization and fusion

The data summarization of the edge equipment comprises the steps of acquiring carbon emission related data from edge computing equipment of different equipment and each area of a building, and obtaining energy consumption data, equipment running state, carbon emission concentration and the like;

The data fusion and correction, wherein the central control system fuses and de-duplicates the data of the edge equipment and corrects the abnormal data, for example:

Correcting the sensor error reading, and eliminating obvious unreasonable abnormal data points;

the weights of different data sources are adjusted according to the actual conditions of the building, so that the global data are more accurate.

6.2 Intelligent analysis Algorithm

Global carbon emission trend prediction, in which a central system predicts the overall carbon emission trend of a building through a machine learning or deep learning model, for example:

Predicting a carbon emission peak value of a certain period of time by utilizing historical data and data acquired in real time, so as to make a coping strategy in advance;

scene simulation and optimization analysis, wherein the system optimizes the operation strategy of each device by simulating carbon emission conditions (such as peak period and low peak period) under different conditions, for example:

And simulating the effects of different energy-saving strategies, evaluating the carbon emission reduction effect and the influence on building comfort level of each scheme, and selecting the optimal strategy.

6.3 Optimization decisions

Dynamic strategy adjustment, namely, the central system dynamically adjusts the carbon emission control strategy of the building according to the analysis result, for example:

When the people flow rate of the building is increased, the system automatically improves the air conditioning efficiency, but reduces the power output of other unnecessary equipment so as to achieve the purpose of balancing carbon emission and user comfort;

device priority management, wherein the system adjusts the priorities of different devices according to the energy consumption, the use frequency and the carbon emission of the devices, for example:

devices with greater energy consumption, such as air conditioners, lighting, etc., are preferentially controlled to reduce their operating time or power.

6.4 Decision feedback and iterative optimization

Real-time feedback mechanism, in which the system feeds back the optimized decision to each edge device and adjusts the operation parameters of the device in real time, for example:

reducing energy consumption during peak power consumption by reducing part of the equipment power or shutting down unnecessary equipment;

self-learning and optimization iteration, namely, through long-term operation data accumulation, the system self-learns the building carbon emission mode and continuously optimizes a decision algorithm, for example:

The system automatically adjusts the equipment operation strategy, and gradually improves the decision accuracy based on historical operation data and external environment changes (such as weather changes, seasonal influences and the like).

The data of all edge computing devices are summarized through the system, global analysis is carried out, intelligent decision is made, and carbon emission control of the building is further optimized.

Further, the carbon emission monitoring and alarming method comprises the following steps:

7.1 real-time carbon emission monitoring

Edge device local analysis, the edge computing device performs preliminary analysis on the collected carbon emission data, for example:

when the rapid rise of carbon emission in a certain period is detected, the edge equipment immediately calculates and judges whether an alarm is required.

7.2 Abnormal carbon emission detection

Abnormal threshold setting, namely setting reasonable carbon emission thresholds according to different areas and equipment of a building, for example:

the threshold of carbon emission in office areas is low, while the threshold in dining areas is relatively high; the system sets different upper limits according to actual conditions;

Super-threshold alarm mechanism, when the carbon emission of a certain area or equipment exceeds a set threshold, the system automatically triggers an alarm, for example:

When kitchen equipment fails or improper use causes carbon emissions to proliferate, the system immediately alerts and notifies the relevant manager to take action.

7.3 Alarm feedback mechanism

Multi-level alarm mechanism, the system provides different levels of alarm according to the severity of abnormal conditions, for example:

When the standard is slightly exceeded, the system reminds the manager through an email or an application program;

If the carbon emission is continuously out of standard or reaches the severity, the system can send out an emergency alarm, and various channels such as voice notification, mobile phone short messages and the like are accompanied to ensure that the alarm is received.

Automated emergency response-for severe out-of-standard conditions, the system automatically triggers emergency actions, such as:

The system may automatically shut down or reduce the power of certain high energy consumption devices to quickly mitigate carbon emissions.

7.4 Alarm Log recording and analysis

Alarm event logging, in which each alarm event is logged by the system for subsequent analysis and improvement, for example:

recording the exceeding time, exceeding equipment, triggering reasons and solutions;

Alarm data analysis and optimization, wherein the system analyzes areas and equipment frequently suffering from carbon emission problems according to historical alarm data, and further optimizes an alarm mechanism, for example:

And identifying the problem of exceeding the standard of long-term carbon emission of a certain device or region through alarm data, and carrying out device updating or adjusting strategies.

The method mainly monitors the carbon emission of the building in real time by an intelligent monitoring means, ensures that a timely alarm is given when abnormality occurs, and ensures the high-efficiency and stable operation of the system.

Further, the security and data protection measures include the following steps:

8.1 data encryption and Transmission Security

Data encryption storage, namely all carbon emission data in the system need to be encrypted for storage so as to prevent unauthorized access, for example:

encrypting the data by adopting main stream encryption technologies such as AES (advanced encryption Standard) and the like, so as to ensure the security of sensitive data;

Secure transport protocol-securing carbon emission data during transport by encrypted transport protocol (e.g., TLS/SSL), for example:

When the system transmits data, the TLS protocol is used for preventing the data from being intercepted or tampered in the transmission process.

8.2 Rights management and Access control

Multilevel rights control-the system sets different rights for different levels of users (e.g., administrators, equipment maintenance personnel, etc.):

Only the system administrator has access to the global carbon emission data and the optimization strategy, and the equipment maintainer can only view the equipment state data of the relevant area;

two-factor authentication, namely adding a two-factor authentication mechanism for key operation and access of sensitive data by the system, and improving security, for example:

When accessing the system management background, the user is required to input a password and a dynamic verification code.

8.3 Data privacy protection

Data anonymization processing, namely, when sensitive data (such as user activity data) is processed, the system performs anonymization processing on related data, for example:

the information which can directly identify the user is removed or hidden, so that the data privacy is ensured to be protected;

and (3) user data access audit, namely recording and auditing the behavior of the user for accessing the data by the system, and preventing unauthorized data access or data leakage.

8.4 System protection and attack detection

Firewall and intrusion detection systems deploy firewalls and Intrusion Detection Systems (IDS) to prevent network attacks, such as:

the firewall can filter unsafe network requests, and the intrusion detection system monitors and alarms possible hacking;

Periodic security audit and bug repair, which is to periodically perform security audit on a system to discover and repair system bugs, for example:

And the software of the edge equipment and the central system is updated regularly, so that the system is ensured to have the latest safety protection capability.

The step aims to ensure the data safety, privacy protection and network safety of the building carbon metering system and ensure the running reliability of the system.

A marginal computing based building carbon metering device for performing the method, the device comprising:

the multi-point carbon emission data acquisition module is used for installing sensors at a plurality of key positions of a building and acquiring data affecting carbon emission in real time, wherein the data comprise power consumption, gas use, environment temperature and humidity, indoor and outdoor gas components and equipment working states;

the data preprocessing module is connected with the sensor and is used for carrying out preliminary processing on the acquired sensing data, wherein the preliminary processing comprises data denoising, data filtering and abnormal value detection, so that the accuracy and the integrity of the sensing data are ensured;

The edge calculation module is used for carrying out real-time carbon emission calculation on the preprocessed sensing data, estimating the carbon emission of each region or device based on energy consumption and device state information, and carrying out regional carbon emission calculation by using a preset carbon emission formula or model and combining the sensing data and the device load of a specific region;

The system comprises a carbon emission analysis and feedback module, a building management system, a control module and a control module, wherein the carbon emission analysis and feedback module is used for carrying out carbon emission analysis on edge computing equipment, identifying high-emission areas or high-energy consumption equipment in a building, and identifying the inefficiency or failure condition of the equipment by combining energy efficiency data analysis;

The edge computing and cloud cooperative processing module is used for periodically uploading data processed by the edge computing equipment to a cloud server, and the cloud server performs long-term data storage and deep analysis and provides carbon emission trend analysis, equipment health monitoring and higher-level management decision support;

The intelligent optimization and decision module is used for optimizing the carbon emission calculation model through a machine learning algorithm based on the cooperative result of the edge calculation equipment and the cloud analysis, and providing dynamic optimization suggestions, including adjusting the operation strategy of high-energy equipment and changing the operation mode of an air conditioning system so as to reduce the carbon emission of the whole building;

The carbon emission monitoring and alarming module is used for continuously monitoring carbon emission conditions of all areas or equipment, triggering an alarm in real time and recording an abnormal event when abnormal high carbon emission is detected, and sending a prompt to a manager to recommend measures including closing specific equipment or adjusting environmental parameters of the areas;

and the security and data protection module is used for ensuring the security and privacy protection of data and preventing unauthorized access and data tampering by adopting a data encryption and access control mechanism in the data transmission and processing process.

A readable storage medium having stored thereon a computer program which when executed by a processor implements the above-described edge calculation based building carbon metering method.

The invention has the beneficial effects that:

The comprehensive and accurate data acquisition is improved, and the real-time monitoring of electric power, fuel gas, environmental parameters and the like is realized by deploying various sensors at key positions of a building, so that accurate data is provided for carbon emission calculation.

The real-time processing capability of the data is enhanced, the data preprocessing and the real-time calculation are performed by utilizing the edge calculation, the dependence on a central data center is reduced, and the data transmission delay is reduced.

The method realizes the fine energy management, provides detailed energy consumption and carbon emission conditions for building managers by carrying out partition calculation and analysis on the carbon emission of different areas and devices, and is beneficial to making more effective energy-saving and emission-reducing measures.

The integration level and the cooperative efficiency of the system are improved, and the cooperative working mode of edge calculation and cloud is realized, so that the system can process more complex data analysis tasks and provide higher-level management decision support.

The data security and privacy protection are enhanced, and the data security and privacy protection in the data transmission and processing process are ensured by adopting a data encryption and access control mechanism.

The intelligent level of the building management system is optimized, the carbon emission calculation model is optimized through a machine learning algorithm, and dynamic optimization suggestions are provided, so that the building management system can respond to environmental changes more intelligently.

The sustainable development goal is supported, and the goal of environmental protection and sustainable development is supported by reducing the energy consumption and carbon emission of the building.

Drawings

FIG. 1 is a schematic flow chart of the method of the invention;

Detailed Description

The invention is further described with reference to the drawings and detailed description which follow:

Embodiment as shown in fig. 1, a building carbon metering method based on edge calculation comprises the following steps:

Step 1, multipoint acquisition of building carbon emission data

Sensors are installed at strategic locations in the building, and the selection of these locations is based on detailed analysis of building energy consumption and environmental impact. For example, power consumption sensors are installed in the main switchboard and in the branch circuits of critical electrical equipment to monitor real-time power usage. A gas usage sensor is installed at a gas inlet of a building to monitor gas consumption. The environmental temperature and humidity sensor is installed in the air outlet, the air return and the personnel-intensive area of the air conditioner so as to monitor the indoor air quality. Gas composition sensors are installed indoors and outdoors to monitor the concentration of greenhouse gases such as carbon dioxide and methane. The equipment operating state sensor is installed at key equipment such as an elevator, a boiler, an air conditioning unit and the like to monitor the operating state and energy consumption of the equipment.

Step 2, data preprocessing and edge equipment uploading

The data collected by the sensor is subjected to preliminary processing locally, and the method comprises the following steps:

Data denoising-using digital filtering techniques, such as moving average filtering or wavelet transformation, to eliminate data fluctuations caused by electrical interference or sensor noise.

Data filtering, namely, applying a threshold detection algorithm to remove data points beyond the normal operating range of the equipment, for example, the power consumption of an air conditioning unit suddenly and abruptly rises to twice the normal range, which can indicate sensor faults or equipment anomalies.

Outlier detection using statistical methods such as standard deviation analysis or box plot to identify and process outliers.

The processed data is transmitted to the edge computing device via the local area network. The data transmission uses the MQTT protocol, which is a lightweight publish/subscribe message transmission protocol suitable for the environment of the Internet of things.

Step 3, real-time carbon emission calculation of edge equipment

The edge computing device performs real-time carbon emission calculations using the following method:

And (3) power consumption, namely calculating the carbon emission according to the power consumption and the average carbon emission factor of the power grid. For example, if the electricity consumption of a building is 1000 kilowatt-hours and the carbon emission factor of the grid is 0.9 kg carbon dioxide/kilowatt-hour, the carbon emission is 900 kg carbon dioxide.

And (3) fuel gas consumption, namely calculating the carbon emission according to the fuel gas consumption, the heat value of the fuel gas and the carbon emission factor. For example, if the fuel gas consumption is 100 cubic meters, the heat value of the fuel gas is 35.8 megajoules/cubic meter, and the carbon emission factor is 2.75 kg carbon dioxide/megajoules, the carbon emission is 2.75 kg carbon dioxide/megajoules×35.8 megajoules/cubic meter×100 cubic meters= 983.5 kg carbon dioxide.

Partition calculation, namely dividing a building into a plurality of areas, such as an office area, a conference room, a machine room and the like, and calculating the carbon emission of each area according to the energy consumption and the equipment use condition of each area.

Step 4, analysis and feedback of partial carbon emissions

The edge computing device performs carbon emission analysis, identifies high emission areas or devices, and analyzes the energy efficiency data to identify inefficiency or failure conditions. For example, if the carbon emissions from one zone continue to be higher than other zones, the system may recommend checking whether the air conditioning system of that zone is operating inefficiently. The system generated local feedback information may include adjusting the operating time of the device, optimizing the air conditioning temperature, or reducing the use of high emission devices.

Step 5, edge calculation and cloud cooperative processing

The edge computing equipment periodically uploads the processed data to a cloud server, and the cloud performs long-term data storage and deep analysis. Cloud servers use big data analysis tools, such as Apache Hadoop or Spark, to process and analyze large amounts of data. Cloud analysis may provide carbon emission trend analysis, device health monitoring, and higher level management decision support, such as predicting building carbon emissions for a time period in the future.

Step 6, intelligent optimization and decision making

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

Step 7, monitoring and alarming carbon emission

The system continuously monitors the carbon emissions of various areas or devices and when abnormally high carbon emissions are detected, the edge computing device may trigger an alarm in real time. The alarm can be sent out through an e-mail, a short message or an interface of a building management system, and an abnormal event is recorded to give a prompt to a manager, and measures including closing specific equipment or adjusting regional environment parameters are suggested to be taken.

Step 8, security and data protection measures

In the whole data transmission and processing process, the system adopts a data encryption and access control mechanism to ensure the safety and privacy protection of the data. The data is encrypted by AES before transmission, so that the safety of the data in the transmission process is ensured. The system also enforces strict access control, and only authorized personnel can access sensitive data. In addition, the system deploys a firewall and an intrusion detection system to prevent network attacks.

A marginal computing based building carbon metering device for performing the above method, comprising:

and the multipoint carbon emission data acquisition module is responsible for installing and maintaining the sensor, and ensures the accuracy and real-time performance of the data.

And the data preprocessing module is responsible for preliminary processing and uploading of data and ensures the quality and availability of the data.

And the edge calculation module is responsible for real-time carbon emission calculation and provides quick response and processing capacity.

And the carbon emission analysis and feedback module is responsible for analyzing carbon emission data and providing feedback to help management staff make decisions.

And the edge calculation and cloud cooperative processing module is responsible for uploading data and cloud analysis and provides long-term data storage and depth analysis.

And the intelligent optimization and decision module is responsible for providing optimization suggestions and helping management personnel to optimize the energy use and carbon emission of the building.

And the carbon emission monitoring and alarming module is responsible for monitoring carbon emission and triggering an alarm so as to ensure the safe and stable operation of the system.

And the security and data protection module is responsible for protecting the security and privacy of the data and preventing unauthorized access and data tampering.

The invention further comprises a readable storage medium having stored thereon a computer program which, when executed by a processor, enables the above-described edge calculation based building carbon metering method to be implemented. Such a storage medium may be a hard disk, a solid state disk, a USB drive, or other type of storage device.

Through the embodiment, the invention provides the high-efficiency and real-time building carbon emission monitoring and management method, which is beneficial to building managers to optimize energy use, reduce carbon emission and realize sustainable development targets.

It should be noted that the foregoing description is only a preferred embodiment of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood that modifications, equivalents, improvements and modifications to the technical solution described in the foregoing embodiments may occur to those skilled in the art, and all modifications, equivalents, and improvements are intended to be included within the spirit and principle of the present invention.

Claims (10)

1. The building carbon metering method based on edge calculation is characterized by comprising the following steps of:

The method comprises the steps of multipoint acquisition of building carbon emission data, mounting sensors at a plurality of key positions of a building, and acquiring data affecting carbon emission in real time, wherein the data comprise power consumption, gas use, environment temperature and humidity, indoor and outdoor gas components and equipment working states;

The data preprocessing and the edge equipment uploading are carried out, the sensor data acquired by the sensor is firstly subjected to preliminary processing at the sensor node, the preliminary processing comprises data denoising, data filtering and abnormal value detection, and the accuracy and the integrity of the sensor data are ensured;

calculating the carbon emission of the edge equipment in real time, calculating the received sensing data in real time by the edge calculating equipment, and estimating the carbon emission of each area or equipment based on energy consumption and equipment state information;

Analyzing and feeding back local carbon emission, carrying out carbon emission analysis on edge computing equipment, identifying high-emission areas or high-energy consumption equipment in a building, and identifying possible low-efficiency or fault conditions of the equipment by analyzing energy efficiency data;

The edge computing device can periodically upload processed data to the cloud server for long-term data storage and deep analysis, wherein the cloud server can perform more complex carbon emission trend analysis, device health monitoring and higher-level management decision support to predict building carbon emission in a certain time period in the future;

Based on the cooperative result of the edge computing equipment and cloud analysis, the system continuously optimizes a carbon emission computing model through a machine learning algorithm and provides dynamic optimization suggestions in a building management system, wherein the dynamic optimization suggestions comprise adjusting the operation strategy of high-energy-consumption equipment and changing the operation mode of an air conditioning system so as to reduce the carbon emission of the whole building;

The system continuously monitors the carbon emission condition of each area or equipment, and when the abnormally high carbon emission is detected, the edge computing equipment can trigger an alarm in real time;

and in the whole data transmission and processing process, the system adopts a data encryption and access control mechanism to ensure the security and privacy protection of the data and prevent unauthorized access and data tampering.

2. The method for metering building carbon based on edge calculation as set forth in claim 1, wherein the multipoint acquisition of the building carbon emission data comprises the following steps:

Step 1, determining the type and range of data acquisition, wherein the acquired target data are related to carbon emission, and relate to energy consumption and environmental factors, including power consumption, gas consumption, running state, work load and service time of high-power equipment, and environmental parameters including indoor and outdoor temperature, humidity, wind speed and wind direction, and carbon dioxide and methane contents in air inside and outside a building;

step 2, sensor type and layout design, according to different requirements of data acquisition, selecting an adaptive sensor, including:

The power sensor is arranged in a circuit of main equipment in a building by using a smart meter or a power sensor, wherein the main equipment comprises an air conditioner, a lighting system, an elevator and a machine room server;

the gas sensor is arranged at the inlet and outlet positions of the natural gas pipeline and used for monitoring the consumption of gas;

The environment sensor comprises a temperature and humidity sensor and a wind speed and direction sensor which are distributed in different areas of the building, wherein the temperature and humidity sensor and the wind speed and direction sensor comprise air conditioner air outlets, roofs and basements with larger air flow influence;

the gas detector is arranged in different areas inside and outside the room, especially in places with dense people flow, and is used for monitoring the concentration of carbon dioxide and methane gas in the air;

Step 3, networking and data summarization of sensor nodes, wherein various sensors form a local area network through wireless communication or a wired network so as to transmit collected data to edge computing equipment in real time;

Wireless communication, namely, for the area difficult to wire, a wireless communication mode can be adopted to ensure that the equipment can reliably communicate in a longer distance;

The wired communication, which is to adopt Ethernet connection for equipment with larger data quantity or high communication requirement, so as to ensure high data transmission rate and stability;

Data summarization, namely each sensor node transmits acquired data to local edge computing equipment through a network to form a preliminary data pool;

The data acquisition frequency is set according to the equipment type and the monitoring requirement, the acquisition frequency of the power consumption is once per minute, and the temperature and humidity data are acquired once per 5 minutes;

step 4, monitoring and checking data acquisition in real time;

the edge computing equipment is responsible for monitoring the running state of each sensor node, ensuring the real-time performance and stability of the data acquisition system, and once an abnormality occurs to one sensor node, the system automatically triggers an alarm or starts a self-repairing mechanism;

Data verification, namely, carrying out preliminary verification on data acquired by a sensor, wherein the system comprises the steps of detecting the integrity and the accuracy of the data, and eliminating noise data or unreasonable data points;

Step 5, preparing local storage for data storage and processing;

On edge computing equipment, carrying out localized storage on all acquired real-time data;

the preparation of treatment, namely, the data is used as basic data for carbon emission calculation in the subsequent step after the data are subjected to preliminary cleaning and integration.

3. The method for metering building carbon based on edge computing as set forth in claim 1, wherein the data preprocessing and the uploading of the edge device comprises the following steps:

denoising basic data acquired by a sensor, and using a low-pass filtering algorithm or a Kalman filtering algorithm to eliminate noise in the data and ensure the smoothness and accuracy of the data;

performing data cleaning, and eliminating data points including but not limited to power values exceeding the normal working range of the equipment and collected abnormal and severe changes of the gas concentration;

Compressing and optimizing data, adopting a differential coding or sample sparsification method, and reducing the data volume by using a data aggregation technology;

The method comprises the steps of carrying out localization abnormality detection before uploading an edge device, generating an abnormality report if detecting that an energy consumption or gas emission value exceeds a normal threshold value;

encryption processing is carried out on the data before uploading, and AES or RSA encryption technology is used to ensure the privacy and security of the data;

And uploading the processed data to the edge computing device through an efficient MQTT or CoAP data transmission protocol.

4. The method for measuring building carbon based on edge calculation according to claim 1, wherein the real-time carbon emission calculation of the edge equipment comprises the following steps:

Establishing a carbon emission calculation model, and converting energy consumption and equipment operation load into carbon emission by using a formula, wherein the carbon emission of power consumption is converted by a power grid carbon emission coefficient, and the carbon emission of fuel gas consumption is converted by a fuel gas heat value and a carbon emission factor;

Carrying out regional carbon emission calculation on different functional areas in the building, and carrying out real-time calculation according to energy consumption, gas emission and environmental parameters of each area so as to identify a high-emission area;

performing equipment-level carbon emission analysis on important equipment in a building, calculating a carbon emission value in a specific time period by combining energy consumption data and running time of the equipment, and analyzing abnormal carbon emission caused by equipment aging or low-efficiency running;

Implementing long-term tracking and trend prediction to identify inefficiency or potential faults of equipment and timely remind building managers of maintenance or replacement;

based on the real-time carbon emission calculation result, generating instant feedback, recommending an optimizing equipment operation strategy to reduce carbon emission;

when the carbon emissions exceed a set threshold, an alarm mechanism is triggered and the management system is notified.

5. The method for measuring building carbon based on edge calculation according to claim 1, wherein the analysis and feedback of the local carbon emission comprises the following steps:

Analyzing the data of each acquisition point in real time to identify a high emission area in the building, wherein the high emission area comprises an air conditioner room, an elevator room and heating equipment;

specific high-energy-consumption equipment in the building is identified, wherein the equipment comprises an air conditioner, a lighting system and a heating system, and equipment exceeding a preset reference is recorded;

Analyzing the energy efficiency data of the high-emission equipment, identifying the operation efficiency of the equipment, and recording the equipment with low energy efficiency operation;

by analyzing the historical operation data of the equipment, the possible fault condition of the equipment is identified, and fault early warning is provided;

generating local feedback information according to the results of the analysis of the local carbon emission and the energy efficiency, and sending alarm information to a building management system;

Providing immediate optimization suggestions for the management system, including adjusting the operating time of the equipment, reducing the frequency of use of high emission equipment, or optimizing the temperature setting of the air conditioning system;

Transmitting feedback information to the building management system in real time, and adjusting the running state of the equipment according to the advice;

continuously tracking the implementation effect of feedback, and adjusting the operation strategy of the equipment in real time;

Establishing personalized energy efficiency standards according to different equipment types and operating environments, and ensuring the accuracy of analysis results;

And introducing a machine learning algorithm based on historical data, predicting the usage rule of the high-emission equipment, and dynamically adjusting the feedback proposal of the system according to the change of the usage rule.

6. The method for metering building carbon based on edge calculation according to claim 1, wherein the cooperative processing of the edge calculation and the cloud comprises the following steps:

Uploading building carbon emission data processed by the edge computing equipment to a cloud server at regular intervals, and adjusting uploading frequency according to importance and time requirements of the data;

The cloud server uses the high-efficiency database management system to store the uploaded data for a long time, so that the integrity, stability and safety of the data are ensured;

The cloud performs deep analysis to generate a long-term carbon emission trend graph, analyzes carbon emission modes of different seasons and workdays, and identifies carbon emission peak periods;

The cloud monitors the health state of each device in the building, predicts the maintenance requirement of the device, and reduces the carbon emission surge caused by the device fault;

The cloud provides a comprehensive data report, and supports decision-making personnel to perform strategy adjustment, including replacement of high-energy-consumption equipment or energy structure adjustment;

implementing a collaborative processing mechanism of data preprocessing and decision feedback, and uploading data to a cloud after preliminary processing by edge computing equipment, and feeding back to the edge equipment for real-time adjustment after deep analysis by the cloud;

Implementing a task allocation mechanism of cloud and edge computing, and processing time-sensitive tasks by edge computing equipment, wherein the cloud processes complex and large-scale data analysis tasks;

the cloud end predicts carbon emission in a certain time period in the future based on the historical carbon emission data and provides optimization suggestions;

The cloud provides decision support according to the prediction result, and recommends to adjust the running time or power of the equipment to reduce carbon emission;

An asynchronous communication mode is adopted between the cloud end and the edge equipment, the edge equipment automatically caches data when a network has a problem, and the data are uploaded in batches after the network is recovered;

And the data is compressed and encrypted before uploading, so that the uploading efficiency is improved and the data security is ensured.

7. The method for metering building carbon based on edge calculation according to claim 1, wherein the intelligent optimization and decision comprises the following steps:

Summarizing and fusing data of edge computing equipment from various areas of a building and different equipment, wherein the data comprises energy consumption data, equipment running states and carbon emission concentration;

Performing de-duplication and abnormal data correction on summarized data, and adjusting weights of different data sources to improve data accuracy;

Predicting the overall carbon emission trend of the building by using a machine learning or deep learning model, and predicting the carbon emission peak value of a certain period in the future by using historical data and real-time data;

optimizing operation strategies of all equipment by simulating carbon emission conditions in different environments, and evaluating carbon emission reduction effects of all schemes and influence on building comfort;

Dynamically adjusting a carbon emission control strategy of the building according to an analysis result, wherein the carbon emission control strategy comprises the steps of automatically improving the air conditioning efficiency and reducing the power output of unnecessary equipment when the flow of people is increased;

The priority of different equipment is adjusted according to the energy consumption, the use frequency and the carbon emission of the equipment, and the equipment with larger energy consumption is controlled preferentially;

Implementing a real-time feedback mechanism, feeding back an optimization decision to each edge device, and adjusting the operation parameters of the devices in real time to reduce energy consumption;

By long-term operation data accumulation, the system self-learns the building carbon emission mode, continuously optimizes the decision algorithm, and improves the decision accuracy.

8. The method for measuring carbon in a building based on edge calculation of claim 1, wherein the carbon emission monitoring and alarming comprises the following steps:

the system continuously monitors the carbon emission of each area or device;

the edge computing equipment performs preliminary analysis on the collected carbon emission data, immediately computes and judges whether an alarm is required when the rapid rise of the carbon emission is detected;

setting reasonable carbon emission thresholds according to different areas and equipment of a building, and automatically triggering an alarm when the carbon emission exceeds the set thresholds;

Providing a multi-level alarm mechanism, and sending out an alarm through different channels including an email, an application program, a voice notification and a mobile phone short message according to the severity of an abnormal situation;

For severe out-of-standard conditions, the system automatically triggers emergency measures, including shutting down or reducing the power of certain high-energy-consumption equipment to quickly mitigate carbon emissions;

Recording detailed information of each alarm event, including the exceeding time, exceeding equipment, triggering reasons and solutions, and storing the detailed information in a system log;

and according to the historical alarm data, analyzing areas and equipment with frequent carbon emission problems, and optimizing an alarm mechanism and a carbon emission control strategy of the building.

9. An edge computing based building carbon metering device according to any of claims 1 to 8, comprising:

The multi-point carbon emission data acquisition module is used for installing sensors at a plurality of key positions of a building and acquiring data affecting carbon emission in real time, wherein the data comprise power consumption, gas use, environment temperature and humidity, indoor and outdoor gas components and equipment working states;

The data preprocessing module is connected with the sensor and is used for carrying out preliminary processing on the acquired sensing data, wherein the preliminary processing comprises data denoising, data filtering and abnormal value detection, so that the accuracy and the integrity of the sensing data are ensured;

The edge calculation module is used for carrying out real-time carbon emission calculation on the preprocessed sensing data, and estimating the carbon emission of each area or equipment based on energy consumption and equipment state information;

the system comprises a carbon emission analysis and feedback module, a building management system, a control module and a control module, wherein the carbon emission analysis and feedback module is used for carrying out carbon emission analysis on edge computing equipment, identifying high-emission areas or high-energy consumption equipment in a building, and identifying the inefficiency or failure condition of the equipment by combining energy efficiency data analysis;

the edge computing and cloud cooperative processing module is used for periodically uploading data processed by the edge computing equipment to a cloud server, wherein the cloud server performs long-term data storage and deep analysis and provides carbon emission trend analysis, equipment health monitoring and higher-level management decision support;

The intelligent optimization and decision module is used for optimizing the carbon emission calculation model through a machine learning algorithm based on the cooperative result of the edge calculation equipment and the cloud analysis, and providing dynamic optimization suggestions, including adjusting the operation strategy of high-energy equipment and changing the operation mode of an air conditioning system so as to reduce the carbon emission of the whole building;

The carbon emission monitoring and alarming module is used for continuously monitoring carbon emission conditions of all areas or equipment, triggering an alarm in real time and recording an abnormal event when abnormal high carbon emission is detected, and sending a prompt to a manager to recommend measures including closing specific equipment or adjusting environmental parameters of the areas;

and the security and data protection module is used for ensuring the security and privacy protection of data and preventing unauthorized access and data tampering by adopting a data encryption and access control mechanism in the data transmission and processing process.

10. A readable storage medium having stored thereon a computer program, which when executed by a processor implements the edge computing based building carbon metering method of any of claims 1-8.