Intelligent building integrated management system and method

Abstract

The invention relates to the technical field of intelligent building integrated management, in particular to an intelligent building integrated management system and method. A data acquisition layer, an edge computing layer, a cloud platform layer and an application layer are integrated through a unified architecture, and compatibility of different sensor data formats and communication protocols is realized by using a protocol conversion unit; a hierarchical decision-making mechanism is adopted, an edge computing layer processes a time delay sensitive task, a cloud platform layer executes resource intensive optimization computing, and data transmission delay and bandwidth occupation are remarkably reduced; introducing a decision engine unit which realizes adaptive learning and dynamic strategy generation based on a depth deterministic strategy gradient algorithm; the BIM data and the real-time sensor data are fused through a digital twin model unit, a building 3D visual model is constructed, and a prediction-based dynamic energy management strategy is generated in combination with an algorithm; and finally, the application layer provides a visual configuration interface unit and an emergency linkage unit, and supports a user to configure a management strategy individually.

Description

Technical Field

[0001] The present invention relates to the technical field of smart building integrated management, and in particular to a smart building integrated management system and method. Background Art

[0002] With the deep integration of Internet of Things technology and building intelligence, traditional building management systems are gradually developing towards integration and automation. Existing technologies have achieved real-time monitoring and basic regulation of environmental parameters, energy consumption and security status within buildings by deploying sensor networks, automated control equipment and central monitoring platforms. Such systems have effectively improved building operation efficiency, reduced the cost of manual intervention, and realized automated linkage control of some equipment through preset rule engines.

[0003] However, existing technologies still have significant technical defects: first, each subsystem usually adopts an independent architecture, and the data format and communication protocol are incompatible with each other, resulting in serious information island phenomenon and difficulty in achieving cross-domain collaborative optimization; second, traditional systems rely on centralized cloud computing, with high data transmission latency and large bandwidth usage, which cannot meet scenarios with high real-time requirements; third, there is a lack of adaptive learning mechanism, and the rule engine threshold needs to be manually preset, which makes it difficult to cope with complex dynamic environments; fourth, the energy management strategy is extensive and can only be used for post-analysis through total statistics, and cannot achieve dynamic optimization based on prediction; fifth, the user interaction interface has single functions and lacks the ability to link personalized configuration with emergency scenarios. Summary of the Invention

[0004] The purpose of the present invention is to provide a smart building integrated management system and method, aiming to solve the significant technical defects of the existing technology: first, each subsystem usually adopts an independent architecture, and the data format and communication protocol are incompatible with each other, resulting in serious information island phenomenon and difficulty in achieving cross-domain collaborative optimization; second, the traditional system relies on centralized cloud computing, with high data transmission delay and large bandwidth occupancy, which cannot meet scenarios with high real-time requirements; third, there is a lack of adaptive learning mechanism, and the rule engine threshold needs to be manually preset, which makes it difficult to cope with complex dynamic environments; fourth, the energy management strategy is extensive and can only be analyzed post-analyzed through total statistics, and dynamic optimization based on prediction cannot be achieved; fifth, the user interaction interface has a single function and lacks the technical problem of personalized configuration and emergency scenario linkage capabilities.

[0005] To achieve the above objectives, the present invention adopts an intelligent building integrated management system, including a data acquisition layer, an edge computing layer, a cloud platform layer and an application layer, wherein the data acquisition layer includes an environmental parameter sensor unit and a device status sensor unit, the edge computing layer includes a data preprocessing unit and a protocol conversion unit, the environmental parameter sensor unit and the device status sensor unit are both connected to the edge computing layer, the data preprocessing unit and the protocol conversion unit are both connected to the cloud platform layer, and the cloud platform layer is connected to the application layer;

[0006] The environmental parameter sensor unit and the device status sensor unit are used to continuously collect building data and transmit the original data to the edge computing layer via wired / wireless means;

[0007] The data pre-processing unit cleans and compresses the raw data, the protocol conversion unit converts the data into a unified format, marks the timestamp and device ID, and sends the processed data to the cloud platform layer via 5G / Ethernet;

[0008] After receiving the data, the cloud platform layer combines historical data with preset rules to generate control instructions through algorithms, and sends the control instructions to the edge computing layer. The edge layer then converts the instructions into a format recognizable by the device through a protocol conversion unit and sends them to the corresponding device;

[0009] The application layer obtains real-time data and statistical results from the cloud platform and displays them to users through a visual interface. At the same time, the application layer synchronizes new strategies to the cloud platform, and the cloud platform updates the global control logic.

[0010] Wherein, the data collection layer further includes a personnel behavior recognition unit, and the personnel behavior recognition unit is connected to the edge computing layer;

[0011] The personnel behavior recognition unit counts the flow of people in the area and tracks their trajectories based on the Wi-Fi fingerprint positioning and computer vision fusion algorithm.

[0012] The edge computing layer further includes a local decision-making unit, which is connected to the cloud platform layer;

[0013] The local decision-making unit performs immediate response to emergency events based on a lightweight rule engine.

[0014] The cloud platform layer includes a digital twin model unit and a decision engine unit, and both the digital twin model unit and the decision engine unit are connected to the application layer;

[0015] The digital twin model unit is used to construct a 3D visual model of the building by fusing BIM data with real-time sensor data;

[0016] The decision engine unit is used to generate a control strategy for the optimization target using a deep deterministic policy gradient algorithm.

[0017] Wherein, the application layer includes a visual configuration interface unit and an emergency linkage unit;

[0018] The visual configuration interface unit is used to display real-time data and statistical results to users through a visual interface, and facilitate customers to adjust management strategies based on the interface;

[0019] The emergency linkage unit is used to integrate the plan library and can trigger the coordinated action of multiple devices with one click.

[0020] Among them, the collaborative computing architecture from the edge computing layer to the cloud platform layer adopts a hierarchical decision-making mechanism, wherein the edge layer processes delay-sensitive tasks and the cloud platform layer performs resource-intensive optimization calculations.

[0021] The present invention also provides a smart building integrated management method, which is applied to the smart building integrated management system described above, and includes the following steps:

[0022] The environmental parameter sensor unit, the device status sensor unit, and the personnel behavior recognition unit continuously collect building environment data, device status data, and personnel trajectory data, and transmit them to the edge computing layer via wired / wireless means; the data preprocessing unit cleans, compresses, and extracts features from the raw data, and the protocol conversion unit converts the data into a unified format and marks it with a timestamp and device ID;

[0023] The edge computing layer uploads the pre-processed data to the cloud platform layer; the digital twin model unit integrates BIM data and real-time sensor data to construct a 3D visual model of the building; the decision engine unit generates a global optimization control strategy based on a deep reinforcement learning algorithm and sends instructions to the edge computing layer;

[0024] Then, the local decision-making unit responds to the emergency immediately based on the lightweight rule engine; the protocol conversion unit converts the control instructions issued by the cloud into a format recognizable by the device and sends it to the corresponding device for execution;

[0025] When the device executes the control instruction, the real-time status data is pre-processed by the edge computing layer and uploaded to the cloud platform layer to update the building operation status and digital twin model;

[0026] Finally, based on the application layer, real-time data, historical trends and device status are displayed through the visual configuration interface unit, and users can adjust management strategies based on the interface; the emergency linkage unit integrates the plan library and supports one-click triggering of multi-device collaborative actions; the application layer synchronizes the new strategy adjusted by the user to the cloud platform layer, updates the global control logic, and realizes closed-loop optimization.

[0027] A smart building integrated management system and method of the present invention integrates the data acquisition layer, the edge computing layer, the cloud platform layer and the application layer through a unified architecture, and uses the protocol conversion unit to achieve compatibility between different sensor data formats and communication protocols, breaking information silos and supporting cross-domain collaborative optimization; adopts a hierarchical decision-making mechanism, the edge computing layer processes delay-sensitive tasks, and the cloud platform layer performs resource-intensive optimization calculations, significantly reducing data transmission delay and bandwidth occupancy, and meeting real-time requirements; introduces the decision engine unit, which implements adaptive learning and dynamic strategy generation based on a deep deterministic policy gradient algorithm, replacing manual preset rules and adapting to complex dynamic environments; then, through the digital twin model unit, BIM data and real-time sensor data are integrated to construct a 3D visual model of the building, and a dynamic energy management strategy based on prediction is generated in combination with the algorithm, replacing extensive post-analysis; finally, the application layer provides the visual configuration interface unit and the emergency linkage unit, supports user personalized configuration management strategies, and integrates the plan library to realize one-click triggering of multi-device collaborative actions, thereby improving user interaction experience and emergency response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 It is a schematic diagram of the principle of the intelligent building integrated management system of the present invention.

[0030] Figure 2 It is a flowchart of the steps of a smart building integrated management method of the present invention.

[0031] 1-Data acquisition layer, 2-Edge computing layer, 3-Cloud platform layer, 4-Application layer, 5-Environmental parameter sensor unit, 6-Equipment status sensor unit, 7-Data preprocessing unit, 8-Protocol conversion unit, 9-Emergency linkage unit, 10-Personnel behavior recognition unit, 11-Local decision unit, 12-Digital twin model unit, 13-Decision engine unit, 14-Visual configuration interface unit. DETAILED DESCRIPTION

[0032] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0033] See also Figure 1 The present invention provides an integrated management system for a smart building, comprising a data acquisition layer 1, an edge computing layer 2, a cloud platform layer 3, and an application layer 4. The data acquisition layer 1 comprises an environmental parameter sensor unit 5 and a device status sensor unit 6. The edge computing layer 2 comprises a data preprocessing unit 7 and a protocol conversion unit 8. The environmental parameter sensor unit 5 and the device status sensor unit 6 are both connected to the edge computing layer 2. The data preprocessing unit 7 and the protocol conversion unit 8 are both connected to the cloud platform layer 3. The cloud platform layer 3 is connected to the application layer 4.

[0034] The environmental parameter sensor unit 5 and the device status sensor unit 6 are used to continuously collect building data and transmit the original data to the edge computing layer 2 via wired / wireless means;

[0035] The data pre-processing unit 7 cleans and compresses the raw data, and the protocol conversion unit 8 converts the data into a unified format, marks the timestamp and device ID, and sends the processed data to the cloud platform layer 3 via 5G / Ethernet;

[0036] After receiving the data, the cloud platform layer 3 combines historical data with preset rules to generate control instructions through algorithms, and sends the control instructions to the edge computing layer 2. The edge layer then converts the instructions into a format recognizable by the device through the protocol conversion unit 8 and sends them to the corresponding device;

[0037] The application layer 4 obtains real-time data and statistical results from the cloud platform and displays them to the user through a visual interface. At the same time, the application layer 4 synchronizes new strategies to the cloud platform, and the cloud platform updates the global control logic.

[0038] In this embodiment, the data acquisition layer 1, the edge computing layer 2, the cloud platform layer 3 and the application layer 4 are integrated through a unified architecture to achieve centralized management and cross-layer collaboration of multi-source heterogeneous data. The environmental parameter sensor unit 5 and the device status sensor unit 6 in the data acquisition layer 1 continuously collect building data, wherein the environmental parameter sensor unit 5 is a temperature sensor, a humidity sensor, an air quality sensor, a light sensor, a wind speed sensor, a smoke sensor, a fire hose water pressure sensor and a water leakage detection sensor, and the device status sensor unit 6 is a pressure sensor, a flow sensor, a liquid level sensor, a position sensor, an acceleration sensor and an access control sensor. The data preprocessing unit 7 and the protocol conversion unit 8 of the edge computing layer 2 clean, compress and convert the data format, significantly reducing the data transmission bandwidth occupancy and improving the transmission efficiency. The cloud platform layer 3 generates control instructions based on the data in a unified format and sends them to the device through the edge computing layer 2, realizing global optimization and efficient control of the building system and solving the problems of data islands and poor coordination in traditional systems.

[0039] Furthermore, the data collection layer 1 further includes a personnel behavior recognition unit 10, and the personnel behavior recognition unit 10 is connected to the edge computing layer 2;

[0040] The personnel behavior recognition unit 10 counts the flow of people in the area and tracks their trajectory based on the Wi-Fi fingerprint positioning and computer vision fusion algorithm.

[0041] In this embodiment, this unit, by connecting to the edge computing layer 2, transmits human behavior data to the cloud in real time, providing dynamic crowd distribution information for building management. The application of the human behavior recognition unit 10 enhances the intelligent level of the building system, supports energy consumption optimization and safety warning based on crowd density, and solves the problem that traditional systems lack the ability to perceive human behavior.

[0042] Furthermore, the edge computing layer 2 further includes a local decision-making unit 11, and the local decision-making unit 11 is connected to the cloud platform layer 3;

[0043] The local decision-making unit 11 performs immediate response to emergency events based on a lightweight rule engine.

[0044] In this embodiment, the unit processes delay-sensitive tasks locally and can quickly trigger emergency response mechanisms without relying on cloud computing, significantly reducing system response delays.

[0045] Furthermore, the cloud platform layer 3 includes a digital twin model unit 12 and a decision engine unit 13, and both the digital twin model unit 12 and the decision engine unit 13 are connected to the application layer 4;

[0046] The digital twin model unit 12 is used to construct a 3D visual model of the building by fusing BIM data with real-time sensor data;

[0047] The decision engine unit 13 is used to generate a control strategy for the optimization target by using a deep deterministic policy gradient algorithm.

[0048] In this implementation, a 3D visual model of the building is constructed by integrating BIM data with real-time sensor data, and a deep deterministic policy gradient algorithm is used to generate a dynamic control strategy.

[0049] Furthermore, the application layer 4 includes a visual configuration interface unit 14 and an emergency linkage unit 9;

[0050] The visual configuration interface unit 14 is used to display real-time data and statistical results to users through a visual interface, and facilitate customers to adjust management strategies based on the interface;

[0051] The emergency linkage unit 9 is used to integrate the plan library and can trigger the coordinated action of multiple devices with one click.

[0052] In this embodiment, real-time data and statistical results are presented to users through an intuitive visual interface, and users are supported to adjust management strategies based on the interface.

[0053] Furthermore, the collaborative computing architecture from the edge computing layer 2 to the cloud platform layer 3 adopts a hierarchical decision-making mechanism, wherein the edge layer processes delay-sensitive tasks and the cloud platform layer 3 performs resource-intensive optimization calculations.

[0054] In this embodiment, reasonable allocation of computing resources is achieved through task division, which not only ensures the real-time requirements of the system but also supports complex global optimization analysis. The hierarchical decision-making mechanism significantly reduces data transmission delay and bandwidth occupancy, improves the overall operation efficiency of the system, and solves the problems of poor real-time performance and high bandwidth pressure caused by traditional systems relying on centralized cloud computing.

[0055] Furthermore, the digital twin model unit 12 introduces a spatiotemporal convolutional network to achieve prediction of environmental parameters for the next 24 hours.

[0056] Furthermore, the visual configuration interface unit 14 is embedded with a natural language interaction module to support voice command generation control logic.

[0057] Furthermore, the visual configuration interface unit 14 supports multiple interaction methods, including mobile applications, web pages, voice control, etc. Users can view building information, set personalized parameters, receive alarm notifications, etc. anytime and anywhere, and can provide customized service recommendations based on user habits and preferences.

[0058] Based on the present invention, please refer to Figure 2 The present invention also provides a smart building integrated management method, which is applied to the above-mentioned smart building integrated management system and includes the following steps:

[0059] S1: The environmental parameter sensor unit 5, the device status sensor unit 6, and the personnel behavior recognition unit 10 continuously collect building environment data, device status data, and personnel trajectory data, and transmit them to the edge computing layer 2 via wired / wireless means; the data preprocessing unit 7 cleans, compresses, and extracts features from the raw data, and the protocol conversion unit 8 converts the data into a unified format and marks it with a timestamp and device ID;

[0060] S2: The edge computing layer 2 uploads the pre-processed data to the cloud platform layer 3; the digital twin model unit 12 integrates BIM data and real-time sensor data to construct a 3D visual model of the building; the decision engine unit 13 generates a global optimization control strategy based on a deep reinforcement learning algorithm and sends the instruction to the edge computing layer 2;

[0061] S3: The local decision unit 11 then responds to the emergency immediately based on the lightweight rule engine; the protocol conversion unit 8 converts the control instructions issued by the cloud into a format recognizable by the device and sends it to the corresponding device for execution;

[0062] S4: After the device executes the control instruction, the real-time status data is pre-processed by the edge computing layer 2 and uploaded to the cloud platform layer 3 to update the building operation status and digital twin model;

[0063] S5: Finally, based on the application layer 4, the visual configuration interface unit 14 displays real-time data, historical trends and device status, and the user can adjust the management strategy based on the interface; the emergency linkage unit 9 integrates the plan library and supports one-click triggering of multi-device collaborative actions; the application layer 4 synchronizes the new strategy adjusted by the user to the cloud platform layer 3, updates the global control logic, and realizes closed-loop optimization.

[0064] The above disclosure is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of the rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A smart building integrated management system, characterized in that: It includes a data acquisition layer, an edge computing layer, a cloud platform layer and an application layer, wherein the data acquisition layer includes an environmental parameter sensor unit and a device status sensor unit, the edge computing layer includes a data preprocessing unit and a protocol conversion unit, the environmental parameter sensor unit and the device status sensor unit are both connected to the edge computing layer, the data preprocessing unit and the protocol conversion unit are both connected to the cloud platform layer, and the cloud platform layer is connected to the application layer; The environmental parameter sensor unit and the device status sensor unit are used to continuously collect building data and transmit the original data to the edge computing layer via wired / wireless means; The data pre-processing unit cleans and compresses the raw data, the protocol conversion unit converts the data into a unified format, marks the timestamp and device ID, and sends the processed data to the cloud platform layer via 5G / Ethernet; After receiving the data, the cloud platform layer combines historical data with preset rules to generate control instructions through algorithms, and sends the control instructions to the edge computing layer. The edge layer then converts the instructions into a format recognizable by the device through a protocol conversion unit and sends them to the corresponding device; The application layer obtains real-time data and statistical results from the cloud platform and displays them to users through a visual interface. At the same time, the application layer synchronizes new strategies to the cloud platform, and the cloud platform updates the global control logic.

2. The smart building integrated management system according to claim 1, characterized in that: The data collection layer further includes a personnel behavior recognition unit, which is connected to the edge computing layer; The personnel behavior recognition unit counts the flow of people in the area and tracks their trajectories based on the Wi-Fi fingerprint positioning and computer vision fusion algorithm.

3. The intelligent building integrated management system according to claim 2, characterized in that: The edge computing layer further includes a local decision-making unit, which is connected to the cloud platform layer; The local decision-making unit performs immediate response to emergency events based on a lightweight rule engine.

4. The intelligent building integrated management system according to claim 3, characterized in that: The cloud platform layer includes a digital twin model unit and a decision engine unit, and both the digital twin model unit and the decision engine unit are connected to the application layer; The digital twin model unit is used to construct a 3D visual model of the building by fusing BIM data with real-time sensor data; The decision engine unit is used to generate a control strategy for the optimization target using a deep deterministic policy gradient algorithm.

5. The intelligent building integrated management system according to claim 4, characterized in that: The application layer includes a visual configuration interface unit and an emergency linkage unit; The visual configuration interface unit is used to display real-time data and statistical results to users through a visual interface, and facilitate customers to adjust management strategies based on the interface; The emergency linkage unit is used to integrate the plan library and can trigger the coordinated action of multiple devices with one click.

6. The intelligent building integrated management system according to claim 5, characterized in that: The collaborative computing architecture from the edge computing layer to the cloud platform layer adopts a hierarchical decision-making mechanism, wherein the edge layer processes latency-sensitive tasks and the cloud platform layer performs resource-intensive optimization calculations.

7. A smart building integrated management method, applied to the smart building integrated management system according to claim 6, characterized in that: The steps include: The environmental parameter sensor unit, the device status sensor unit, and the personnel behavior recognition unit continuously collect building environment data, device status data, and personnel trajectory data, and transmit them to the edge computing layer via wired / wireless means; the data preprocessing unit cleans, compresses, and extracts features from the raw data, and the protocol conversion unit converts the data into a unified format and marks it with a timestamp and device ID; Uploading the pre-processed data to the cloud platform layer based on the edge computing layer; The digital twin model unit integrates BIM data and real-time sensor data to construct a 3D visual model of the building; the decision engine unit generates a global optimization control strategy based on a deep reinforcement learning algorithm and sends instructions to the edge computing layer; Then, the local decision-making unit immediately responds to the emergency based on a lightweight rule engine; The protocol conversion unit converts the control instructions issued by the cloud into a format recognizable by the device and sends it to the corresponding device for execution; When the device executes the control instruction, the real-time status data is pre-processed by the edge computing layer and uploaded to the cloud platform layer to update the building operation status and digital twin model; Finally, based on the application layer, real-time data, historical trends and device status are displayed through the visual configuration interface unit, and users can adjust management strategies based on the interface; The emergency linkage unit integrates a plan library and supports one-click triggering of multi-device collaborative actions; the application layer synchronizes the new strategy adjusted by the user to the cloud platform layer, updates the global control logic, and realizes closed-loop optimization.

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