Intelligent building control system

Abstract

The invention discloses an intelligent building control system, and relates to the field of intelligent buildings, the intelligent building control system comprises a sensing layer, a network layer, a platform layer and an application layer, the sensing layer uses a multi-source sensor to realize acquisition of related physical information in a building, preprocessing of acquired data, screening of the acquired data, removal of invalid data, and acquisition of the acquired data; the method comprises the following steps of: storing the collected data, reserving effective data, converting the format of the effective data into a data format matched with a platform layer and an application layer, classifying the multi-source data of which the data format is matched with the platform layer, dividing the multi-source data into key data and non-key data, and storing the collected data converted into the uniform format. According to the invention, the engine is cooperatively controlled through the multi-protocol edge gateway and the equipment, cross-system seamless linkage of air conditioners, illumination and elevators is realized, and the equipment response delay is shortened. And an energy consumption optimization engine based on an LSTM algorithm is combined with renewable energy source cooperative scheduling, so that real-time monitoring and dynamic regulation and control of energy consumption are realized, and the total energy consumption of the building is reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent buildings, and in particular to an intelligent building control system. Background Technology

[0002] With the advancement of smart city construction, the application of intelligent building technologies is constantly being upgraded. However, the industry still faces significant pain points in equipment collaboration and energy consumption management, which restrict the improvement of building intelligence levels and operational efficiency. I. Poor equipment interoperability and insufficient system compatibility Existing smart buildings mostly adopt a "segmented system construction" model, with core equipment such as air conditioning, lighting, and elevators provided by different manufacturers with independent control systems. The communication protocols used are incompatible, creating "information silos." For example, after the lighting system is preset to turn off during the off-hours, the air conditioning system cannot simultaneously obtain the "no one in the area" signal and continues to operate normally, requiring manual intervention to turn it off or adjust it, resulting in wasted manpower and energy. Elevator operation status is disconnected from building pedestrian flow data; during the morning rush hour, the inability to dynamically schedule elevators based on floor pedestrian density leads to a poor user experience. Insufficient system compatibility has become a core bottleneck affecting building operational efficiency.

[0003] II. Inefficient energy management and serious energy waste Currently, building energy consumption monitoring largely remains at the "post-event statistics" level, only able to obtain monthly energy consumption data through traditional metering devices such as electricity and water meters, failing to achieve real-time dynamic monitoring and precise control. For example, in office areas, lighting and air conditioning may not be turned off in time due to temporary meeting adjustments or early departures, resulting in ineffective energy consumption; central air conditioning systems adopt a "uniform temperature control" mode, failing to dynamically adjust based on the flow of people and temperature differences in different areas (such as meeting rooms, corridors, and offices), resulting in low energy utilization efficiency. Furthermore, existing technologies lack a coordinated scheduling mechanism for renewable energy sources (such as building solar power systems and wind energy storage equipment) and traditional energy sources, leading to insufficient utilization of clean energy and further exacerbating energy waste. Summary of the Invention

[0004] This application proposes an intelligent building control system with the following advantages: improved equipment linkage efficiency, breaking down information silos, achieving seamless cross-system linkage of air conditioning, lighting, and elevators through a multi-protocol edge gateway and equipment collaborative control engine, and shortening equipment response latency; precise energy consumption control and reduced operating costs, achieving real-time energy consumption monitoring and dynamic control based on an LSTM algorithm-based energy consumption optimization engine combined with renewable energy collaborative scheduling, reducing total building energy consumption, and solving the technical problems mentioned in the background.

[0005] To achieve the above objectives, this application adopts the following technical solution: an intelligent building control system, comprising a four-layer architecture of a perception layer, a network layer, a platform layer, and an application layer. The perception layer utilizes multi-source sensors to collect relevant physical information within the building, preprocesses the collected data, filters the collected data to remove invalid data and retain valid data, converts the valid data format into a data format compatible with the platform layer and the application layer, classifies the multi-source data whose data format is compatible with the platform layer, divides the multi-source data into key data and non-key data, and stores the collected data converted into a unified format. The network layer is used to achieve efficient data transmission between the perception layer and the platform layer, and between the platform layer and the perception layer. It also encrypts the data and dynamically adjusts the data transmission rate to ensure communication efficiency. The network layer includes an edge gateway, a data transmission unit, and a security encryption unit. The data transmission unit adopts a "local edge computing + cloud collaboration" transmission mode and controls the data transmission rate according to the data type and the data processing efficiency of the platform layer. The platform layer is used to store, process, analyze, manage, and support decision-making for the data collected by the perception layer, and to coordinate the linkage and optimization control between various subsystems. The platform layer integrates a device collaborative control engine structure and an energy consumption dynamic optimization engine architecture. The device collaborative control engine architecture is based on fuzzy algorithms to build a device linkage rule base. The energy consumption dynamic optimization engine architecture combines a long short-term memory network algorithm to predict and analyze building energy consumption data, and generates personalized energy consumption optimization schemes by learning from historical energy consumption data.

[0006] An energy consumption optimization engine based on the LSTM algorithm, combined with renewable energy collaborative scheduling, enables real-time monitoring and dynamic control of energy consumption, reducing the total energy consumption of buildings. By processing data through the perception layer, uploaded data can be directly used by the platform layer or application layer, improving the response efficiency of the platform layer and application layer, thereby improving the response efficiency of the system and ensuring the quality of data received by the platform layer and application layer, thus improving the reliability of the system.

[0007] Preferably, the sensing layer includes a data acquisition unit, a data preprocessing unit, and a data storage unit; wherein: the data acquisition unit includes a device status acquisition unit and an environment and energy consumption acquisition unit.

[0008] Preferably, the data storage unit has an interface adapted to the platform layer and the application layer, and both the platform layer and the application layer can directly retrieve valid data collected by the sensor from the data storage unit through the adapted interface.

[0009] Preferably, the data preprocessing unit is used to screen and preprocess the data, and the data preprocessing unit counts the proportion of invalid data collected by the sensor. When the proportion of invalid data reaches or exceeds a set threshold, the data preprocessing unit marks the sensor as a faulty sensor, removes the data collected by the faulty sensor, and uploads the unique identifier of the faulty sensor to the application layer.

[0010] Preferably, the network layer integrates the data transmission rate data and the data upload timestamp into the data to be transmitted. However, if the transmission rate of the data to be transmitted remains constant during transmission, it is not necessary to repeatedly add the data transmission rate data and the data upload timestamp to the data to be transmitted.

[0011] Preferably, the platform layer includes a data caching module, a data processing and analysis module, and a device management and control module. The data caching module receives data uploaded from the network layer, decrypts encrypted data, and uploads the decrypted data to the data processing and calculation module according to the platform layer's data processing efficiency. The data processing and analysis module processes and analyzes the received data, generates control commands based on the device collaborative control engine structure and energy consumption dynamic optimization engine architecture. The device management and control module monitors the status of equipment within the building, can remotely modify equipment parameters, receive upper-layer commands, and accurately send them to target devices.

[0012] Preferably, the data caching module continuously monitors its own memory usage. When it determines that the data caching module has insufficient memory, the data receiving rate is lower than the platform layer's data processing rate, or the data receiving frequency is higher than the platform layer's data processing rate, it sends real-time data feedback to the network layer and controls the feedback data transmission rate based on the data transmission distance and data receiving interval between the network layer and the platform layer.

[0013] Preferably, the application layer provides various application software and visual interfaces for users, managers and maintenance personnel to achieve convenient operation, real-time monitoring and intelligent services; the application layer includes a user interaction module and an energy consumption management module.

[0014] Preferably, the step of the network layer dynamically adjusting the data transmission rate according to the platform layer's data processing rate is as follows: the network layer dynamically calculates the data transmission distance L between the network layer and the platform layer through real-time data feedback from the platform layer; when data accumulation occurs in the platform layer, the network layer calculates the time t required for the platform layer to process the accumulated data, and the ratio of L to t is the data transmission rate of the network layer; when the data receiving rate of the platform layer is lower than the platform processing rate, the network layer gradually increases the data sending rate according to a specific ratio to make the sending rate approach the platform processing rate; if the data receiving rate of the platform layer is higher than the platform processing rate, the network layer dynamically reduces the data sending rate to make the sending rate approach the platform processing rate.

[0015] Preferably, the network layer adjusts the data transmission rate according to the data type as follows: for critical data information with high timeliness requirements, high-efficiency priority transmission is adopted, and the data is transmitted to the local edge node through the network to achieve millisecond-level response; for non-real-time energy consumption statistics and non-critical data, low-power transmission is adopted, and the data is transmitted to the cloud platform through the network to reduce communication costs and dynamically adjust the communication protocol and bandwidth allocation.

[0016] In summary, the present invention has the following beneficial effects: 1. Improve equipment linkage efficiency and break down information silos: Through multi-protocol edge gateways and equipment collaborative control engines, seamless linkage between air conditioning, lighting, and elevator systems is achieved, and equipment response latency is shortened.

[0017] 2. Precisely regulate energy consumption and reduce operating costs: The energy consumption optimization engine based on the LSTM algorithm, combined with the coordinated scheduling of renewable energy, enables real-time monitoring and dynamic regulation of energy consumption, thereby reducing the total energy consumption of the building.

[0018] 3. The perception layer has functions such as data screening, data processing, data classification and storage, and sensor fault diagnosis, which enables the uploaded data to be directly used by the platform layer or application layer, improving the response efficiency of the platform layer and application layer, thereby improving the response efficiency of the system, ensuring the quality of data received by the platform layer and application layer, and improving the reliability of the system. Attached Figure Description

[0019] Figure 1 This is a structural block diagram of an intelligent building control system proposed in this invention; Figure 2 This is a structural block diagram of the perception layer; Figure 3 This is a platform layer structure diagram; Figure 4 This is a flowchart of the network layer regulating data transmission speed. Detailed Implementation

[0020] like Figures 1 to 4 An intelligent building control system comprises a four-layer architecture: a perception layer, a network layer, a platform layer, and an application layer. It focuses on the core needs of device collaboration and energy consumption optimization, with each layer working collaboratively. The perception layer is used to collect physical information generated by various devices, the environment, and personnel within the building, serving as the data source for the entire system. By deploying various sensors within the building, the perception layer collects data on the status of equipment, the physical environment, and personnel behavior, providing raw input for subsequent data transmission, analysis, control, and decision-making. It also preprocesses the data, enabling it to be directly used and improving system response efficiency.

[0021] The perception layer includes a data acquisition unit, a data preprocessing unit, and a data storage unit; among which: the data acquisition unit includes an equipment status acquisition unit and an environment and energy consumption acquisition unit; the equipment status acquisition unit: deploys intelligent sensors compatible with multiple protocols to collect real-time operating data of equipment such as air conditioners, lighting, and elevators.

[0022] Environmental and energy consumption acquisition unit: Equipped with infrared human body sensors, temperature and humidity sensors, as well as smart meters and solar power system data acquisition devices that support real-time metering and data uploading, to obtain basic information linking "regional population flow - environmental parameters - energy consumption data" in real time.

[0023] Data preprocessing unit: Receives data from the equipment acquisition unit and the environment and energy consumption acquisition unit, and first filters the data, removing invalid data and retaining valid data. This ensures data quality while allowing the data preprocessing unit to preprocess only valid data, reducing the amount of data processing required by the data preprocessing unit and thus improving its data processing efficiency.

[0024] The data preprocessing unit processes the effective data in the following ways: First, it converts the different data formats collected by various sensors into data formats that are compatible with the platform layer and application layer. For example, it converts heterogeneous data from devices from different manufacturers into a standardized JSON format, thus completely solving the "information silo" problem. Second, it classifies the multi-source data whose data formats are compatible with the platform layer, dividing the multi-source data into key data and non-key data.

[0025] In addition, the data preprocessing unit counts the proportion of invalid data collected by the sensors. When the proportion of invalid data reaches or exceeds a set threshold, the data preprocessing unit marks the sensor as a faulty sensor and uploads the unique identifier of the faulty sensor to the application layer to realize human-computer interaction, so that staff can respond to faults and perform maintenance in a timely manner. Furthermore, the data preprocessing unit removes the data collected by the faulty sensor to prevent the data collected by the faulty sensor from being used by the platform layer, thus ensuring the reliability of the system.

[0026] The data storage unit stores multidimensional sensor data converted into a unified format. It also features interfaces compatible with both the platform and application layers. Both layers can directly retrieve valid sensor data from the data storage unit via these interfaces, improving data reuse efficiency and reducing redundant data acquisition. The data storage unit then uploads the data compatible with the platform layer to the platform layer via the network layer.

[0027] As described above, the perception layer has functions such as data screening, data processing, data classification and storage, and sensor fault diagnosis. This allows uploaded data to be directly used by the platform layer or application layer, improving the response efficiency of the platform layer and application layer, thereby improving the response efficiency of the system and ensuring the quality of data received by the platform layer and application layer, thus improving the reliability of the system.

[0028] The network layer is used to enable efficient data transmission between the perception layer and the platform layer, as well as between the platform layer and the perception layer. It also encrypts the data and dynamically adjusts the data transmission rate to ensure communication efficiency.

[0029] The network layer is a multi-protocol compatible communication module, including an edge gateway, a data transmission unit, and a security encryption unit; among which, the edge gateway: sets up a multi-protocol edge computing gateway, supports wired and wireless communication methods, and realizes unified access and protocol conversion of data from sensing layer devices.

[0030] Data transmission unit: Employing a "local edge computing + cloud collaboration" transmission mode, it controls the data transmission speed based on data type and platform-level data processing efficiency, integrating data transmission speed data and data upload timestamps into the data to be transmitted. However, if the transmission speed of the data to be transmitted remains constant throughout the transmission process, there is no need to repeatedly add data transmission speed data and data upload timestamps to the data to be transmitted. This allows the platform layer to understand the real-time data transmission status, reducing unnecessary data redundancy, improving transmission efficiency, and avoiding excessively large data packet sizes due to repeated data addition, which could lead to wasted network resources and reduced transmission performance.

[0031] For example, critical data information with high timeliness requirements, such as device linkage commands and real-time energy consumption data, adopts high-efficiency priority transmission and is transmitted to local edge nodes via the network to achieve millisecond-level response; non-real-time energy consumption statistics and non-critical data adopt low-power transmission and are transmitted to the cloud platform via the network to reduce communication costs, dynamically adjust communication protocols and bandwidth allocation, balance real-time performance and energy saving, and improve the response efficiency of the platform layer.

[0032] Based on the platform layer's data processing efficiency, the data transmission speed is controlled as follows: The network layer dynamically calculates the data transmission distance L between the network layer and the platform layer through real-time data feedback from the platform layer. Combined with the platform layer's data processing efficiency, it adjusts the data transmission speed. When data backlog occurs at the platform layer, the network layer calculates the time t required for the platform layer to process the backlog. The ratio of L to t is the network layer's data transmission speed, achieving a precise match between the transmission rate and the platform's processing capacity. This prevents data backlog at the platform layer, avoids data loss, and improves the system's stability and efficiency. When the platform layer's data reception rate is lower than its processing rate, the network layer gradually increases the transmission rate according to a specific ratio (e.g., increasing the transmission rate by 10%~20% per round) to increase the data inflow rate and bring the transmission rate closer to the platform's processing rate. If the platform layer's data reception rate is higher than its processing rate, the network layer dynamically reduces the data transmission rate to bring it closer to the platform's processing rate. If the platform layer's buffer status returns to normal (e.g., sufficient remaining capacity, shortened queue length), the transmission rate is maintained or slightly adjusted to avoid over-transmission.

[0033] Security encryption unit: It uses encryption protocols to encrypt transmitted data, ensuring the security of equipment control commands and energy consumption data transmission.

[0034] As described above, the network layer can dynamically adjust the data transmission speed according to actual needs, reduce network congestion, and ensure data timeliness by guaranteeing the transmission speed of critical data, thereby ensuring the reliability of the system.

[0035] The platform layer is used for storing, processing, analyzing, managing, and providing decision support for data collected by the perception layer, as well as coordinating the linkage and optimization control between various subsystems. The platform layer integrates a device collaborative control engine structure and an energy consumption dynamic optimization engine architecture.

[0036] Equipment Collaborative Control Engine: Based on fuzzy algorithms, a rule base for equipment linkage is constructed. For example, when an infrared human body sensor detects that no one is in an office area on a certain floor for more than 10 minutes, the engine automatically matches the "energy-saving rule for unoccupied areas" and simultaneously sends instructions to the lighting controller (turn off the lights) and the air conditioning controller (adjust to energy-saving mode: 28℃ in summer and 20℃ in winter) in that area. During the morning peak hours, the engine dynamically schedules elevator operation strategies (such as increasing the frequency of stops on high-traffic floors and adjusting elevator operation priorities) based on the "number of people waiting for elevators" data collected by infrared human body sensors on each floor, thereby shortening the waiting time.

[0037] Energy Consumption Dynamic Optimization Engine: Combining Long Short-Term Memory (LSTM) network algorithms, this engine predicts and analyzes building energy consumption data. By learning from historical energy consumption data (such as weekday / weekend patterns, seasonal variations, and pedestrian traffic patterns), it generates personalized energy consumption optimization plans. For example, if the peak electricity consumption period is predicted to be 9:00-11:00 the next day, and the solar power system is expected to generate 50kWh, the engine automatically generates a plan—adjusting the lighting brightness in public areas on floors 1-3 to 70% (to meet basic lighting needs), while simultaneously scheduling solar energy storage devices to power the air conditioning system, reducing the load on the power grid. It also monitors energy consumption data in real time; if actual energy consumption exceeds the predicted value by 10%, it further shuts down redundant electrical equipment such as advertising light boxes in non-core areas to ensure energy consumption remains within the optimized range.

[0038] The platform layer includes a data caching module, a data processing and analysis module, and a device management and control module. The data caching module receives data uploaded from the network layer, decrypts encrypted data, and uploads the decrypted data to the data processing and calculation module based on the platform layer's data processing efficiency. Furthermore, the data caching module continuously monitors its memory usage, setting reasonable thresholds. When it determines that the data caching module's memory is insufficient, the data reception rate is lower than the platform layer's data processing rate, or the data reception frequency is higher than the platform layer's data processing rate, it sends real-time data feedback to the network layer. During this process, the data caching module determines the data transmission distance between the network layer and the platform layer based on the data reception interval and data transmission speed. Based on the data transmission distance and data reception interval, it controls the feedback data transmission rate, enabling the network layer to adjust the data upload speed in a timely manner. The real-time feedback data includes the data transmission speed and the data upload timestamp, preventing data backlog.

[0039] The data processing and analysis module processes and analyzes the received data, and generates control commands based on the equipment collaborative control engine structure and energy consumption dynamic optimization engine architecture; the equipment management and control module monitors the status of equipment in the building and can remotely modify equipment parameters, such as air conditioning temperature thresholds and sensor sampling frequencies; it receives upper-level commands and accurately sends them to the target equipment.

[0040] As described above, the platform layer's energy consumption optimization engine, based on the LSTM algorithm and combined with renewable energy collaborative scheduling, enables real-time monitoring and dynamic control of energy consumption, thereby reducing the building's total energy consumption.

[0041] The application layer provides various application software and visual interfaces for users, managers, and maintenance personnel, enabling convenient operation, real-time monitoring, and intelligent services. The application layer includes a user interaction module and an energy management module.

[0042] User interaction module: Develop a web-based management platform and a mobile app. Property managers can view the real-time status of equipment linkage and energy consumption data reports, and remotely issue equipment control commands. Users can view the real-time operating status of elevators and reserve elevators through the app, improving the user experience.

[0043] Energy management module: Automatically generates daily / weekly / monthly energy consumption reports, compares and analyzes the differences in energy consumption before and after optimization, and intuitively displays the energy-saving effect; supports dividing energy consumption statistics dimensions by equipment type (air conditioning, lighting, elevator) and floor, accurately locating high-energy-consuming links.

[0044] In summary, the intelligent building control system proposed in this invention collects multi-source data within the building through the application layer, analyzes the collected data using the platform layer, and generates and issues control commands based on the device collaborative control engine and energy consumption dynamic optimization engine, combined with renewable energy collaborative scheduling. These commands are precisely sent to target devices, achieving seamless cross-system linkage between air conditioning, lighting, and elevators within the building. This reduces device response latency and lowers the building's overall energy consumption. The analysis results from the platform layer are displayed through a user interaction module. Simultaneously, property management personnel can view the real-time device linkage status and energy consumption data reports, and remotely issue device control commands through the user interaction module. Users can also view the real-time elevator operating status and reserve elevators through the user interaction module, enhancing the user experience.

Claims

1. An intelligent building control system comprising a four-layer architecture of a perception layer, a network layer, a platform layer, and an application layer, characterized in that: The perception layer utilizes multi-source sensors to collect relevant physical information in the building, pre-process the collected data, remove invalid data and retain valid data, convert the valid data format into a format suitable for the platform layer and the application layer, classify the multi-source data in the format suitable for the platform layer into key data and non-key data, and store the collected data in a unified format; The network layer is used for data transmission between the perception layer and the platform layer, and between the platform layer and the application layer; The platform layer is used for storing, processing, analyzing, managing and decision-making support of the collected data of the perception layer, and coordinating linkage and optimization control among subsystems; the platform layer integrates a device collaborative control engine structure and an energy consumption dynamic optimization engine architecture; the device collaborative control engine structure is based on a fuzzy algorithm and constructs a device linkage rule library; the energy consumption dynamic optimization engine architecture combines a long short-term memory network algorithm to predict and analyze building energy consumption data, and generates a personalized energy consumption optimization scheme by learning historical energy consumption data.

2. The intelligent building control system of claim 1, wherein: The perception layer includes a data collection unit, a data preprocessing unit and a data storage unit; the data collection unit includes a device state collection unit and an environment and energy consumption collection unit.

3. The intelligent building control system of claim 2, wherein: The data storage unit has an interface suitable for the platform layer and the application layer, and the platform layer and the application layer can directly call the valid data collected by the sensors from the data storage unit through the adaptive interface.

4. The intelligent building control system of claim 2, wherein: The data preprocessing unit is used for screening and preprocessing data, and the data preprocessing unit counts the proportion of invalid data collected by the sensors; when the proportion of invalid data reaches or exceeds a set threshold, the data preprocessing unit marks the sensor as a faulty sensor, removes the data collected by the faulty sensor, and uploads the unique identifier of the faulty sensor to the application layer.

5. The intelligent building control system of claim 1, wherein: The network layer integrates data transmission rate data and data upload timestamp into the data to be transmitted, but if the transmission rate of the data to be transmitted remains constant during transmission, the data transmission rate data and the data upload timestamp do not need to be repeatedly added to the data to be transmitted.

6. The intelligent building control system of claim 1, wherein: The platform layer includes a data caching module, a data processing and analysis module, and a device management and control module; the data caching module is used to receive network layer uploaded data, decrypt encrypted data, and upload decrypted data to the data processing and calculation module according to the data processing efficiency of the platform layer; the data processing and analysis module processes and analyzes the received data, produces control instructions based on the device collaborative control engine structure and the energy consumption dynamic optimization engine architecture; the device management and control module monitors the device state in the building, can remotely modify device parameters, receives upper-layer instructions, and accurately issues the instructions to target devices.

7. The intelligent building control system of claim 6, wherein: The data cache module continuously monitors its memory usage, and when it is determined that the data cache module is insufficient in memory, the receiving data rate is lower than the platform layer data processing rate, or the receiving data frequency is higher than the platform layer data processing rate, real-time data feedback is sent to the network layer, and the feedback data transmission rate is controlled based on the data transmission distance between the network layer and the platform layer and the receiving data interval.

8. The intelligent building control system of claim 1, wherein: The application layer provides various user-oriented, manager-oriented and operation and maintenance personnel-oriented application software and visual interfaces, realizes convenient operation, real-time monitoring and intelligent service; the application layer includes a user interaction module and an energy consumption management module.

9. The intelligent building control system of claim 6, wherein: The network layer includes an edge gateway, a data transmission unit and a security encryption unit; the data transmission unit adopts a transmission mode of "local edge computing + cloud cooperation", and controls the data transmission rate according to the data type and the data processing efficiency of the platform layer; The steps for the network layer to dynamically regulate the data transmission rate according to the platform layer data processing rate are as follows: the network layer dynamically calculates the data transmission distance L between the network layer and the platform layer through the real-time data feedback of the platform layer; when the platform layer has data accumulation, the network layer calculates the time t required for the platform layer to process the accumulated data, and the ratio of L to t is the data transmission rate of the network layer; when the platform layer has a data receiving rate lower than the platform processing rate, the network layer gradually increases the data sending rate by a certain proportion, so that the sending rate approaches the platform processing rate; if the platform layer has a data receiving rate higher than the platform processing rate, the data sending rate is dynamically reduced, so that the sending rate approaches the platform processing rate.

10. The intelligent building control system of claim 9, wherein: The network layer controls the data transmission rate according to the data type, that is, for key data information with high timeliness requirement, efficient priority transmission is adopted, and the data is transmitted to the local edge node through the network to realize millisecond-level response; for non-real-time energy consumption statistical non-key data, low-power transmission is adopted, and the data is transmitted to the cloud platform through the network to reduce communication cost and dynamically adjust communication protocol and bandwidth allocation.

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