Carbon emission electric power monitoring system and control method

Abstract

The invention discloses a carbon emission electric power monitoring system and a control method, and relates to the technical field of carbon emission, and the indoor carbon emission electric power regulation and control process comprises the steps: S1, indoor configuration and networking; s2, performing personalized setting by a user; s3, acquiring indoor real-time data; s4, indoor data analysis; s5, controlling the indoor delayable equipment; s6, indoor adjustable power equipment is controlled; and S7, indoor non-interruptible equipment monitoring is carried out. According to the carbon emission electric power monitoring system and the control method, community public electricity utilization facilities and indoor private electricity utilization facilities are closely combined, a complete and comprehensive carbon emission electric power monitoring and control system is constructed from public charging piles to various indoor electricity utilization devices, all-around and systematic management of community electricity utilization is achieved, and the power utilization efficiency is improved. Carbon emission power monitoring and control with a community as a region can be refined to households, so that the carbon emission of the whole community can be controlled and regulated more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emissions, and in particular to a carbon emission power monitoring system and control method. Background Art

[0002] The application of carbon emission power monitoring systems in residential communities mainly focuses on carbon emission accounting for residents' electricity consumption, energy-saving guidance, and green community construction. Through data monitoring and management, it helps families reduce their carbon footprint, while providing support for carbon neutrality at the community level.

[0003] Existing carbon emission power monitoring systems used in residential communities mostly focus on overall energy scheduling in the community and unified management of public facilities such as charging stations and lighting. Household-based processing simply involves monitoring through smart meters, making it difficult to control household electrical facilities. This results in insufficiently detailed carbon emission power monitoring and control in the community. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a carbon emission power monitoring system and control method, which solves the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a carbon emission power monitoring system, the carbon emission power monitoring system including an indoor carbon emission power regulation process and a community public carbon emission power regulation process;

[0006] The specific process of indoor carbon emission power regulation is as follows:

[0007] S1. Indoor configuration and networking: After residents sign an electricity control agreement, they install a smart meter in their home distribution box to monitor total household electricity consumption. Each electrical device is connected to a smart socket. Users download a dedicated app, pair the device with the app via Bluetooth or Wi-Fi, and establish a communication connection between the home gateway and the community energy management gateway to achieve remote control and data transmission of indoor devices.

[0008] S2. User Personalization: Users set the operating preferences of indoor devices on the APP, including but not limited to air conditioning temperature and humidifier humidity;

[0009] S3. Indoor real-time data collection: Smart meters collect total household electricity consumption, and smart sockets monitor the switch status and real-time power consumption of each device. The data is first transmitted to the home gateway and then uploaded to the cloud platform;

[0010] S4. Indoor data analysis: The cloud platform calculates each household's carbon emissions in real time based on the collected electricity consumption data and the grid's carbon emission factors. It also uses historical data and machine learning algorithms to predict the household electricity load curve for the next 1-24 hours.

[0011] S5. Indoor Delayable Device Control: Automatically delays the start-up of delayable devices based on electricity consumption time, grid carbon intensity, and user preset preferences, allowing them to start and operate during grid off-peak hours.

[0012] S6. Indoor adjustable power device control: The system automatically adjusts the power of indoor adjustable power devices by combining indoor and outdoor environmental sensor data with the grid’s carbon intensity.

[0013] S7. Indoor non-interruptible equipment monitoring: Real-time monitoring of the operating status of non-interruptible equipment. When abnormal operating data is found, the alarm information will be immediately pushed through the APP, and the property maintenance personnel will be notified to handle the situation on site. Indoor non-interruptible equipment includes but is not limited to refrigerators and security systems.

[0014] Furthermore, in step S4, the carbon emissions of each household are calculated using the following formula: carbon emissions = electricity consumption × carbon emission factor, where the carbon emission factor is obtained from the grid database.

[0015] Furthermore, in step S4, a machine learning algorithm is used to construct a household electricity load prediction model. First, hourly electricity consumption data of each household for the past year or more is extracted from the database, including electricity consumption in different seasons, weekdays and weekends, and different weather conditions, to form an initial data set. At the same time, corresponding environmental data such as temperature, humidity, light intensity, and date and time information such as day of the week and holidays are collected as input features of the model.

[0016] Preprocess the initial data to clean outliers and fill missing values. Then, use feature engineering techniques to transform and combine the raw data to enhance the correlation between data features and power load.

[0017] A long short-term memory network is used to build a prediction model. After inputting the processed feature data, the hidden layer within the model continuously learns the patterns and regularities in the data, training a model that can accurately predict future electricity load.

[0018] The trained and optimized model predicts the electricity load of each household for the next 1-24 hours at regular intervals every day, such as 2:00-3:00 in the morning when electricity consumption is low. The prediction results output by the model are hourly electricity load values.

[0019] Furthermore, in step S5, the indoor delayable devices include but are not limited to washing machines and dryers.

[0020] Furthermore, in step S5, the user can actively cancel the delay operation manually in the APP, but the power consumption caused by this operation needs to pay the peak period electricity price.

[0021] Furthermore, in step S6, the control of indoor adjustable power equipment includes but is not limited to air conditioning, electric heating, and fans.

[0022] Furthermore, in step S1, the user can view real-time electricity consumption data on the APP homepage, including current power, today's electricity consumption, carbon emissions, and the operating status of each device. At the same time, the APP will also display the electricity consumption of community public facilities.

[0023] Furthermore, the community public carbon emission power control process is specifically as follows:

[0024] S8. Hardware Deployment: Install a smart main meter in the residential power distribution room to monitor the community's overall electricity consumption in real time. Install smart controllers for public power facilities, including charging stations, lighting, and gates. Charging stations are equipped with metering and communication modules to monitor and remotely control charging status. Lights are connected to a smart dimming system, and gates are connected to a dual power switching device and UPS. All public facilities are connected to the community energy management gateway via LoRa or NB-IoT networks.

[0025] S9. Parameter setting: The administrator enters the basic information of the community on the cloud management platform, specifies the peak and off-peak hours of the power grid, and sets the real-time carbon emission factor of the power grid;

[0026] S10, Data Collection and Analysis: The smart main electricity meter collects the total electricity consumption of the community every minute; the charging pile monitors the connection status, charging power, and charged capacity of the charging vehicle in real time; the lighting provides feedback on the current brightness and power information; all public facility data is aggregated to the community energy management gateway and then uploaded to the cloud platform;

[0027] The cloud platform calculates carbon emissions in the community's public areas in real time based on collected electricity consumption data and combined with the grid's carbon emission factors. It also uses historical data and machine learning algorithms to predict the community's public electricity load curve for the next 1-24 hours.

[0028] S11. Charging Pile Usage Control: After each user in the community signs an electricity usage control agreement, when a vehicle is connected to a charging pile, if the system determines that it is during peak hours, a delayed charging prompt interface will immediately pop up, showing the user a comparison of electricity prices and carbon emissions during peak and off-peak hours. If the user chooses delayed charging, the charging pile remains connected but does not supply power, and the vehicle enters the off-peak charging queue. If the user chooses to charge immediately, the peak electricity price will be charged, and the corresponding carbon emissions will be recorded.

[0029] If a user needs to charge urgently during peak hours, the system will automatically unplug the vehicle with the most remaining power based on the charging time selected by the user, and guide the user to use it through voice commands. After the charging time is up, the user will be notified to reset the charging plug and reinsert it into the original vehicle.

[0030] If the user does not respond to the voice guidance for a long time, the system will notify the on-duty personnel to reset the charging plug.

[0031] S12. Lighting lamp time scheduling: Based on the community road map, during peak hours, ensure that the street lights on the path from each unit building to the main road maintain the preset power for lighting. For other road lighting, landscape lights, and decorative lights, turn off or reduce the power. During off-peak hours, each lamp either maintains the power and on / off status of the peak period, or resumes normal power operation.

[0032] Furthermore, in step S8, the charging pile is a public delayable device, the lighting lamp is a public adjustable power device, and the gate is an uninterruptible device.

[0033] A control method for a carbon emission power monitoring system, which is applied to the above-mentioned carbon emission power monitoring system, comprises the following steps:

[0034] Step 1: Install smart controllers for the community's public power facilities to enable remote control, and connect all household electrical appliances to smart sockets and pair them with the app;

[0035] Step 2: Users set operating preferences for each indoor device. Based on real-time data on the power and electricity consumption of each device, future electricity consumption is predicted. For delayed devices, they are automatically set to operate during off-peak hours. For adjustable power devices, real-time adjustments are made based on indoor and outdoor environmental parameters and the carbon intensity of the grid.

[0036] Step 3: For charging piles in community public facilities, after the user plugs the charging plug into the vehicle and agrees to delay charging, the vehicle will be charged during off-peak hours. If emergency charging is needed during peak hours, the system will allocate the plug of the vehicle with the most remaining power to the emergency vehicle and guide the user to reset the plug after the charging time is completed.

[0037] Step 4: For public lighting, ensure that the lighting power from the entrances and exits of each unit building to the main road remains unchanged during peak hours, and reduce or turn off the power of other lighting lamps. During off-peak hours, keep the status unchanged or turn it back on and restore normal power.

[0038] The present invention provides a carbon emission power monitoring system and control method, which has the following beneficial effects:

[0039] 1. This carbon emission power monitoring system and control method closely integrates the community's public power facilities and private household power facilities. From the smart main meter in the distribution room to the smart meter in the household distribution box, from public charging piles to various household electrical equipment, it builds a complete and comprehensive carbon emission power monitoring and control system, realizing all-round and systematic management of community electricity consumption. It enables the carbon emission power monitoring and control of the community area to be refined to the household, thereby more accurately controlling and regulating the carbon emissions of the entire community.

[0040] 2. This carbon emission power monitoring system and control method provides real-time charging and delayed charging based on user selection at charging piles in public facilities, and opens an emergency charging strategy for emergency charging vehicles that subsequently enter the charging location during peak hours, avoiding charging piles being occupied during peak hours but idle due to users choosing delayed charging. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of the indoor carbon emission power regulation process of a carbon emission power monitoring system and control method of the present invention;

[0042] Figure 2 This is a schematic diagram of the community public carbon emission power regulation process of a carbon emission power monitoring system and control method of the present invention. DETAILED DESCRIPTION

[0043] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0044] like Figure 1 As shown, the present invention provides a technical solution: a carbon emission power monitoring system, the carbon emission power monitoring system includes an indoor carbon emission power regulation process and a community public carbon emission power regulation process;

[0045] The specific process of indoor carbon emission power regulation is as follows:

[0046] S1. Indoor configuration and networking: After residents sign an electricity control agreement, they install a smart meter in their home distribution box to monitor total household electricity consumption. Each electrical device is connected to a smart socket. Users download a dedicated app, pair the device with the app via Bluetooth or Wi-Fi, and establish a communication connection between the home gateway and the community energy management gateway to achieve remote control and data transmission of indoor devices.

[0047] Users can view real-time electricity consumption data on the APP homepage, including current power, today's electricity consumption, carbon emissions, and the operating status of each device. At the same time, the APP will also display the electricity consumption of community public facilities;

[0048] S2. User Personalization: Users can set operating preferences for indoor devices on the app, including but not limited to air conditioner temperature and humidifier humidity. For example, they can set the air conditioner temperature adjustment range (24-28°C) and delay the prohibited operation time of devices (e.g., not allowing the washing machine to start during work hours).

[0049] S3. Indoor real-time data collection: Smart meters collect total household electricity consumption, and smart sockets monitor the switch status and real-time power consumption of each device. The data is first transmitted to the home gateway and then uploaded to the cloud platform;

[0050] S4. Indoor data analysis: The cloud platform calculates each household's carbon emissions in real time based on the collected electricity consumption data and the grid's carbon emission factors. It also uses historical data and machine learning algorithms to predict the household electricity load curve for the next 1-24 hours.

[0051] The carbon emissions calculation formula for each household is as follows: Carbon emissions = electricity consumption × carbon emission factor. The carbon emission factor is obtained from the grid database.

[0052] A household electricity load forecasting model is constructed using a machine learning algorithm. First, hourly electricity consumption data for each household over the past year or more is extracted from the database, including electricity consumption in different seasons, weekdays and weekends, and under different weather conditions, to form an initial dataset. Furthermore, corresponding environmental data such as temperature, humidity, light intensity, and date and time information (such as days of the week and holidays) are collected as input features for the model.

[0053] Preprocess the initial data to clean outliers and fill missing values. Then, use feature engineering techniques to transform and combine the raw data to enhance the correlation between data features and power load.

[0054] A long short-term memory network is used to build a prediction model. After inputting the processed feature data, the hidden layer within the model continuously learns the patterns and regularities in the data, training a model that can accurately predict future electricity load.

[0055] The trained and optimized model predicts the electricity load of each household for the next 1-24 hours during the low-consumption period, such as 2:00-3:00 a.m. The model outputs the hourly electricity load value.

[0056] S5. Indoor Delayable Appliance Control: Automatically delays the start of delayable appliances based on electricity consumption time, grid carbon intensity, and user preferences, allowing them to start and operate during off-peak hours. For example, if a user starts a washing machine at 7:00 PM, the system will determine that it is during peak hours and has high carbon intensity, delaying its start until 11:00 PM. The app will also display the reason for the delay, the estimated carbon emissions savings, and the start-up time.

[0057] Indoor appliances that can be delayed include but are not limited to washing machines and dryers. Users can manually cancel the delay operation in the app, but the power consumption caused by this operation will be subject to peak electricity prices;

[0058] S6. Indoor Adjustable Power Device Control: The system combines indoor and outdoor environmental sensor data (temperature, humidity, and light) with the grid's carbon intensity to automatically adjust the power of indoor adjustable power devices. Indoor adjustable power device control includes but is not limited to air conditioning, electric heating, and fans. For example, during peak hours, when the outdoor temperature is 35°C and the indoor temperature is 28°C, the air conditioner will automatically increase the user-set temperature from 24°C to 26°C, reducing power by 20%, and simultaneously push notifications to the app.

[0059] S7. Indoor non-interruptible equipment monitoring: Real-time monitoring of the operating status of non-interruptible equipment. When abnormal operating data is found, the app will immediately push an alarm message and simultaneously notify the property maintenance personnel to handle the situation on site. Indoor non-interruptible equipment includes but is not limited to refrigerators and security systems.

[0060] like Figure 2 As shown in the figure, the specific process of community public carbon emission power regulation is as follows:

[0061] S8. Hardware Deployment: Install a smart main meter in the residential power distribution room to monitor the community's overall electricity consumption in real time. Install smart controllers for public power facilities, including charging stations, lighting, and gates. Charging stations are equipped with metering and communication modules to monitor and remotely control charging status. Lights are connected to a smart dimming system, and gates are connected to a dual power switching device and UPS. All public facilities are connected to the community energy management gateway via LoRa or NB-IoT networks.

[0062] Charging piles are public delayable devices, lighting is public adjustable power devices, and gates are non-interruptible devices;

[0063] S9. Parameter setting: The administrator enters the basic information of the community on the cloud management platform, specifies the peak hours (e.g. 18:00-22:00) and off-peak hours (23:00-7:00) of the power grid, and sets the real-time carbon emission factor of the power grid (updated hourly);

[0064] S10, Data Collection and Analysis: The smart main electricity meter collects the total electricity consumption of the community every minute; the charging pile monitors the connection status, charging power, and charged capacity of the charging vehicle in real time; the lighting provides feedback on the current brightness and power information; all public facility data is aggregated to the community energy management gateway and then uploaded to the cloud platform;

[0065] The cloud platform calculates carbon emissions in the community's public areas in real time based on collected electricity consumption data and combined with the grid's carbon emission factors. It also uses historical data and machine learning algorithms to predict the community's public electricity load curve for the next 1-24 hours.

[0066] S11. Charging Pile Usage Control: After each user in the community signs an electricity usage control agreement, when a vehicle is connected to a charging pile, if the system determines that it is during peak hours, a delayed charging prompt interface will immediately pop up, showing the user a comparison of electricity prices and carbon emissions during peak and off-peak hours. If the user chooses delayed charging, the charging pile remains connected but does not supply power, and the vehicle enters the off-peak charging queue. If the user chooses to charge immediately, the peak electricity price will be charged, and the corresponding carbon emissions will be recorded.

[0067] If a user needs to charge urgently during peak hours, the system will automatically unplug the vehicle with the most remaining power based on the charging time selected by the user, and guide the user to use it through voice commands. After the charging time is up, the user will be notified to reset the charging plug and reinsert it into the original vehicle.

[0068] If the user does not respond to the voice guidance for a long time, the system will notify the on-duty personnel to reset the charging plug.

[0069] S12. Lighting Time Scheduling: Based on the residential area road map, during peak hours, ensure that streetlights on the paths from each apartment building to the main road maintain preset power levels. Turn off or reduce the power of other road lights, landscape lights, and decorative lights. During off-peak hours, lights may maintain peak power levels and on / off status, or resume normal power operation.

[0070] When the power grid issues a peak-shaving signal or the community's public power load exceeds a threshold, the system sends a pause charging command to all charging piles (except for emergency charging vehicles) and simultaneously shuts down non-essential electrical equipment in public areas, such as gym equipment and public water dispenser heating functions, to quickly reduce the power load in public areas. Demand response notifications are pushed to residents via the app to encourage them to participate in energy-saving operations for indoor equipment, such as shutting down non-essential appliances and reducing air conditioning power.

[0071] Based on the above description, the present invention closely integrates the community's public power facilities and private household power facilities. From the smart main meter in the distribution room to the smart meter in the household distribution box, from public charging piles to various household electrical devices, a complete and comprehensive carbon emission power monitoring and control system is built. This achieves all-round and systematic management of community electricity consumption, allowing carbon emission power monitoring and control within the community to be refined down to the household level, thereby more accurately controlling and regulating carbon emissions throughout the community.

[0072] The present invention is based on the fact that charging piles in public facilities provide real-time charging and delayed charging based on user selection, and open an emergency charging strategy for emergency charging vehicles that subsequently enter the charging position during peak hours, avoiding the situation where charging piles are occupied during peak hours but idle due to users choosing delayed charging.

[0073] In summary, a control method for a carbon emission power monitoring system is provided, which is applied to the above-mentioned carbon emission power monitoring system. The control method includes the following steps:

[0074] Step 1: Install smart controllers for the community's public power facilities to enable remote control, and connect all household electrical appliances to smart sockets and pair them with the app;

[0075] Step 2: Users set operating preferences for each indoor device. Based on real-time data on the power and electricity consumption of each device, future electricity consumption is predicted. For delayed devices, they are automatically set to operate during off-peak hours. For adjustable power devices, real-time adjustments are made based on indoor and outdoor environmental parameters and the carbon intensity of the grid.

[0076] Step 3: For charging piles in community public facilities, after the user plugs the charging plug into the vehicle and agrees to delay charging, the vehicle will be charged during off-peak hours. If emergency charging is needed during peak hours, the system will allocate the plug of the vehicle with the most remaining power to the emergency vehicle and guide the user to reset the plug after the charging time is completed.

[0077] Step 4: For public lighting, ensure that the lighting power from the entrances and exits of each unit building to the main road remains unchanged during peak hours, and reduce or turn off the power of other lighting lamps. During off-peak hours, keep the status unchanged or turn it back on and restore normal power.

[0078] The embodiments of the present invention are presented for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described in order to better illustrate the principles of the invention and its practical application and to enable those skilled in the art to understand the invention and design various embodiments with various modifications as suited for specific applications.

Claims

1. A carbon emission power monitoring system, characterized by: The carbon emission power monitoring system includes an indoor carbon emission power regulation process and a community public carbon emission power regulation process; The specific process of indoor carbon emission power regulation is as follows: S1. Indoor configuration and networking: After residents sign an electricity control agreement, they install a smart meter in their home distribution box to monitor total household electricity consumption. Each electrical device is connected to a smart socket. Users download a dedicated app, pair the device with the app via Bluetooth or Wi-Fi, and establish a communication connection between the home gateway and the community energy management gateway to achieve remote control and data transmission of indoor devices. S2. User Personalization: Users set the operating preferences of indoor devices on the APP, including but not limited to air conditioning temperature and humidifier humidity; S3. Real-time data collection indoors: Smart meters collect total household electricity consumption, and smart sockets monitor the on / off status and real-time power consumption of each device. The data is first transmitted to the home gateway and then uploaded to the cloud platform. S4. Indoor data analysis: The cloud platform calculates each household's carbon emissions in real time based on the collected electricity consumption data and the grid's carbon emission factors. It also uses historical data and machine learning algorithms to predict the household electricity load curve for the next 1-24 hours. S5. Indoor Delayable Device Control: Automatically delays the start-up of delayable devices based on electricity consumption time, grid carbon intensity, and user preset preferences, allowing them to start and operate during grid off-peak hours. S6. Indoor adjustable power device control: The system automatically adjusts the power of indoor adjustable power devices by combining indoor and outdoor environmental sensor data with the grid’s carbon intensity. S7. Indoor non-interruptible equipment monitoring: Real-time monitoring of the operating status of non-interruptible equipment. When abnormal operating data is found, the alarm information will be immediately pushed through the APP, and the property maintenance personnel will be notified to handle the situation on site. Indoor non-interruptible equipment includes but is not limited to refrigerators and security systems.

2. A carbon emission power monitoring system according to claim 1, characterized in that: In step S4, the carbon emissions of each household are calculated using the following formula: carbon emissions = electricity consumption × carbon emission factor, where the carbon emission factor is obtained from the grid database.

3. The carbon emission power monitoring system according to claim 1, characterized in that: In step S4, a machine learning algorithm is used to construct a household electricity load prediction model. First, hourly electricity consumption data of each household for the past year or more is extracted from the database, including electricity consumption in different seasons, weekdays and weekends, and different weather conditions, to form an initial data set. At the same time, corresponding environmental data such as temperature, humidity, light intensity, and date and time information such as day of the week and holidays are collected as input features of the model. Preprocess the initial data to clean outliers and fill missing values. Then, use feature engineering techniques to transform and combine the raw data to enhance the correlation between data features and power load. A long short-term memory network is used to build a prediction model. After inputting the processed feature data, the hidden layer within the model continuously learns the patterns and regularities in the data, training a model that can accurately predict future electricity load. The trained and optimized model predicts the electricity load of each household for the next 1-24 hours at regular intervals every day, such as 2:00-3:00 in the morning when electricity consumption is low. The prediction results output by the model are hourly electricity load values.

4. The carbon emission power monitoring system according to claim 1, characterized in that: In step S5, the indoor delayed devices include but are not limited to washing machines and dryers.

5. The carbon emission power monitoring system according to claim 1, characterized in that: In step S5, the user can manually cancel the delay operation in the APP, but the power consumption caused by this operation needs to pay the peak period electricity price.

6. The carbon emission power monitoring system according to claim 1, characterized in that: In step S6, the control of indoor adjustable power equipment includes but is not limited to air conditioning, electric heating, and fans.

7. The carbon emission power monitoring system according to claim 1, characterized in that: In step S1, the user can view real-time electricity consumption data on the APP homepage, including current power, today's electricity consumption, carbon emissions, and the operating status of each device. At the same time, the APP will also display the electricity consumption of community public facilities.

8. The carbon emission power monitoring system according to claim 1, characterized in that: The specific process of regulating the public carbon emission electricity in the community is as follows: S8. Hardware Deployment: Install a smart main meter in the residential power distribution room to monitor the community's overall electricity consumption in real time. Install smart controllers for public power facilities, including charging stations, lighting, and gates. Charging stations are equipped with metering and communication modules to monitor and remotely control charging status. Lights are connected to a smart dimming system, and gates are connected to a dual power switching device and UPS. All public facilities are connected to the community energy management gateway via LoRa or NB-IoT networks. S9. Parameter setting: The administrator enters the basic information of the community on the cloud management platform, specifies the peak and off-peak hours of the power grid, and sets the real-time carbon emission factor of the power grid; S10, Data Collection and Analysis: The smart total electricity meter collects the total electricity consumption of the community every minute; the charging pile monitors the connection status, charging power, and charged capacity of the charging vehicle in real time; Lighting lamps provide feedback on current brightness and power, and all public facility data is aggregated to the community energy management gateway and then uploaded to the cloud platform. The cloud platform calculates carbon emissions in the community's public areas in real time based on collected electricity consumption data and combined with the grid's carbon emission factors. It also uses historical data and machine learning algorithms to predict the community's public electricity load curve for the next 1-24 hours. S11. Charging Pile Usage Control: After each user in the community signs an electricity usage control agreement, when a vehicle is connected to a charging pile, if the system determines that it is during peak hours, a delayed charging prompt interface will immediately pop up, showing the user a comparison of electricity prices and carbon emissions during peak and off-peak hours. If the user chooses delayed charging, the charging pile remains connected but does not supply power, and the vehicle enters the off-peak charging queue. If the user chooses to charge immediately, the peak electricity price will be charged, and the corresponding carbon emissions will be recorded. If a user needs to charge urgently during peak hours, the system will automatically unplug the vehicle with the most remaining power based on the charging time selected by the user, and guide the user to use it through voice commands. After the charging time is up, the user will be notified to reset the charging plug and reinsert it into the original vehicle. If the user does not respond to the voice guidance for a long time, the system will notify the on-duty personnel to reset the charging plug. S12. Lighting lamp time scheduling: Based on the community road map, during peak hours, ensure that the street lights on the path from each unit building to the main road maintain the preset power for lighting. For other road lighting, landscape lights, and decorative lights, turn off or reduce the power. During off-peak hours, each lamp either maintains the power and on / off status of the peak period, or resumes normal power operation.

9. The carbon emission power monitoring system according to claim 8, characterized in that: In step S8, the charging pile is a public delayable device, the lighting lamp is a public power-adjustable device, and the gate is an uninterruptible device.

10. A control method for a carbon emission power monitoring system, applied to a carbon emission power monitoring system according to any one of claims 1 to 9, characterized in that: The control method comprises the following steps: Step 1: Install smart controllers for the community's public power facilities to enable remote control, and connect all household electrical appliances to smart sockets and pair them with the app; Step 2: Users set operating preferences for each indoor device. Based on real-time data on the power and electricity consumption of each device, future electricity consumption is predicted. For delayed devices, they are automatically set to operate during off-peak hours. For adjustable power devices, real-time adjustments are made based on indoor and outdoor environmental parameters and the carbon intensity of the grid. Step 3: For charging piles in community public facilities, after the user plugs the charging plug into the vehicle and agrees to delay charging, the vehicle will be charged during off-peak hours. If emergency charging is needed during peak hours, the system will allocate the plug of the vehicle with the most remaining power to the emergency vehicle and guide the user to reset the plug after the charging time is completed. Step 4: For public lighting, ensure that the lighting power from the entrances and exits of each unit building to the main road remains unchanged during peak hours, and reduce or turn off the power of other lighting lamps. During off-peak hours, keep the status unchanged or turn it back on and restore normal power.

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