Smart park environment monitoring and regulation system based on Internet of Things

Abstract

The invention provides a smart park environment monitoring and regulation system based on the Internet of Things, and the system comprises a sensing layer which is used for collecting park environment data, personnel state data and equipment operation state data; the decision-making layer is in communication connection with the sensing layer, receives the data acquired by the sensing layer, analyzes and processes the data, and generates a regulation and control instruction and a maintenance instruction; the execution layer is in communication connection with the decision-making layer, receives the regulation and control instruction generated by the decision-making layer and executes the corresponding regulation and control operation. According to the system, monitoring and regulation and control integration is achieved through the same Internet of Things, multiple sensors at the monitoring end cooperatively capture environment, equipment and personnel states, data are transmitted in real time, and a sensing blind area is eliminated; the regulation and control end dynamically triggers scene adjustment according to monitoring data in the Internet of Things, air conditioner illumination is accurately controlled according to needs, linkage maintenance scheduling is carried out when equipment is abnormal, and invalid energy consumption and fault influences are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart park, in particular to a smart park environment monitoring and regulation system based on Internet of Things. BACKGROUND

[0002] A smart park is a new development model that uses Internet of Things, big data, artificial intelligence, and other new-generation information technologies to achieve intelligent management, service, and operation of the park. By integrating intelligent security, energy monitoring, intelligent transportation, facility management, and other systems, it improves resource utilization efficiency, optimizes service experience, and reduces operating costs. The core goal is to create a green, efficient, and innovative digital ecosystem to facilitate collaborative development and promote industrial upgrading. Typical applications include face recognition access control, intelligent parking, environmental monitoring, and other services that provide convenient, safe, and sustainable work and living environments for tenants and employees.

[0003] The existing smart park environment monitoring and regulation system has significant pain points: data silos caused by system fragmentation, environmental monitoring, device control, security, and other subsystems built by different vendors, using heterogeneous communication protocols, data cannot be fused across systems, resulting in personnel data captured by cameras cannot be linked in real time to air conditioning adjustment, environmental regulation lags far behind actual needs; control strategies are static and rely on preset fixed thresholds, lack of edge computing layer for local rapid response, cannot dynamically optimize based on personnel flow and device status, leading to frequent start-stop or continuous idling of devices, resulting in wasted energy consumption; maintenance response is passive, relying on manual inspection or post-alarm, not using real-time data such as current and vibration for AI health assessment, unable to trigger predictive maintenance when devices are in sub-health state, fault risk and maintenance cost are high, the root cause is that the traditional architecture breaks the "perception-decision-execution" closed-loop link, monitoring data is only used for visualization rather than real-time driving control, the decision-making process lacks a rule engine and dynamic optimization algorithm, ultimately leading to resource waste and inefficient maintenance. SUMMARY

[0004] (I) Technical problems solved

[0005] To address the shortcomings of the prior art, the present application provides a smart park environment monitoring and regulation system based on Internet of Things, which solves the problem of data silos caused by system fragmentation in existing smart park systems, environmental monitoring, device control, security, and other subsystems built by different vendors, using heterogeneous communication protocols, and data cannot be fused across systems.

[0006] (II) Technical solutions

[0007] To achieve the above purpose, the present application is implemented by the following technical solutions: a smart park environment monitoring and regulation system based on Internet of Things, comprising:

[0008] A perception layer for collecting park environment data, personnel state data and equipment operation state data;

[0009] A decision layer in communication connection with the perception layer, receiving the data collected by the perception layer and performing analysis and processing to generate control instructions and maintenance instructions;

[0010] An execution layer in communication connection with the decision layer, receiving the control instructions generated by the decision layer and performing corresponding control operations;

[0011] The perception layer, the decision layer and the execution layer realize data interaction and centralized control through a unified Internet of Things architecture, forming a closed-loop control link of collection, decision and execution.

[0012] Preferably, the perception layer comprises:

[0013] A plurality of parameter sensors deployed in a grid manner for collecting temperature and humidity, illumination and PM2.5 environment data;

[0014] Special monitoring equipment, including a CO2 sensor deployed in a conference room, a noise sensor in an equipment room and a weather station on a roof, for collecting CO2 concentration, noise value and outdoor environment parameters, respectively;

[0015] Equipment state sensors, including a clamp current sensor and a vibration sensor, for collecting current data of air conditioners and lighting main lines and vibration data of water pumps and fans, respectively;

[0016] A camera with AI analysis function for collecting regional personnel density data.

[0017] Preferably, the perception layer transmits the collected data to the decision layer through LoRa wireless communication technology, and the camera realizes data interaction with the decision layer through a data interface.

[0018] Preferably, the decision layer comprises an edge computing box and a park cloud platform, the edge computing box integrates a data fusion engine and a control decision algorithm for receiving the perception layer data and performing real-time analysis and local rapid response, and the park cloud platform is used for realizing global data aggregation and strategy optimization, forming a double-layer decision mode of local rapid response and cloud global optimization.

[0019] Preferably, the edge computing box is built-in with a unified rule engine, and the rule engine is solidified with monitoring thresholds, energy-saving strategies and equipment linkage logic.

[0020] Preferably, the execution layer comprises an air conditioner intelligent controller, a lighting dimming module and a fresh air actuator, and the execution layer receives the control instructions of the decision layer through a Zigbee wireless communication protocol to perform temperature regulation, brightness control and fresh air volume adjustment operations.

[0021] Preferably, a data-driven regulation triggering mechanism is formed between the decision layer and the perception layer, including:

[0022] Environmental monitoring data linkage: when the CO2 concentration collected by the perception layer exceeds the standard or the temperature and humidity deviates from the comfortable interval, the decision layer generates corresponding fresh air volume adjustment instructions or air conditioning temperature adjustment instructions;

[0023] Personnel state linkage: the decision layer fuses the personnel density data collected by the camera and the access control face recognition information to generate regional pre-adjustment instructions or off-site energy-saving instructions;

[0024] Device state linkage: the decision layer performs health assessment analysis on the data collected by the current sensor and the vibration sensor, and generates maintenance instructions when the device state is abnormal.

[0025] Preferably, the regional pre-adjustment instruction is an instruction for starting the air conditioner to the comfortable temperature in advance when the target area is about to be used; and the off-site energy-saving instruction includes an instruction for gradually dimming the lighting system to off and an instruction for slowly increasing the set temperature of the air conditioning system to the energy-saving interval.

[0026] When the decision layer generates the maintenance instruction, maintenance resources are dynamically scheduled in combination with the schedule, emergency maintenance instructions are generated in priority if there is an important activity the next day, and regular maintenance instructions are generated if there is no important activity.

[0027] (Three) beneficial effects

[0028] The present application provides a smart park environment monitoring and regulation system based on Internet of Things. It has the following beneficial effects:

[0029] 1. The present application realizes monitoring and regulation integration relying on the same Internet of Things, the monitoring end cooperates with multiple sensors to capture the environment, device and personnel state, and transmits data in real time to eliminate the perception blind area; the regulation end dynamically triggers scene adjustment according to the monitoring data in the Internet of Things, the air conditioning and lighting are accurately controlled on demand, and the maintenance scheduling is linked when the device is abnormal, thereby reducing invalid energy consumption and fault influence. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0031] Embodiment:

[0032] The embodiment of the present application provides a smart park environment monitoring and regulation system based on Internet of Things, which comprises:

[0033] The perception layer is used to collect park environment data, personnel state data and equipment operation state data. In the office area of the park, a temperature and humidity sensor of model SHT30, a BH1750 light sensor and a PMS5003 laser PM2.5 sensor are installed in the ceiling, the above-mentioned sensors are integrated in the same shell to form a multi-parameter sensing node, the sensing node communicates with the edge computing box LoRa gateway through the LoRa module, and the environment data is uploaded once every 5 minutes. The transmission data packet adopts the AES-128 encryption algorithm to ensure data security;

[0034] The decision layer is in communication connection with the perception layer, receives the data collected by the perception layer and analyzes and processes the data to generate control instructions and maintenance instructions.

[0035] The execution layer is in communication connection with the decision layer, receives the control instructions generated by the decision layer and executes the corresponding control operation.

[0036] Among them, the perception layer, the decision layer and the execution layer realize data interaction and centralized control through the unified Internet of Things architecture, forming a closed-loop control link of collection, decision and execution.

[0037] Special monitoring equipment configuration:

[0038] Conference room: SCD30 CO2 sensor is installed in the ceiling keel, connected with the edge computing box through the RS485 interface, and the concentration data is output in real time;

[0039] Equipment room: MS10 noise sensor is fixed on the wall 1.5m away from the ground, with built-in LoRa transmission module, which automatically sends alarm frame when the detection value exceeds 65dB;

[0040] Roof: Deploy a small weather station, and synchronize outdoor temperature and humidity, ultraviolet intensity data to the park cloud platform through 5G wireless network bridge.

[0041] Power distribution cabinet: Hall current sensor is clamped on the main air conditioning cable, and the output current analog signal is converted by AD and transmitted to the edge box through LoRa, to monitor the current fluctuation in real time;

[0042] Water pump / fan: MEMS vibration sensor is pasted on the surface of the equipment shell, with a sampling frequency of 100Hz, which communicates with the edge box through Bluetooth low power consumption protocol and uploads vibration spectrum data.

[0043] Personnel state perception optimization: Reuse the original Hikvision DS-2CD3T47FWD-LS network camera in the park, load the human detection AI model based on YOLOv5 algorithm through firmware upgrade, real-time identify the personnel contour in the picture, output the regional personnel quantity and moving track data, and transmit to the edge computing box through Ethernet interface.

[0044] The perception layer includes:

[0045] Multi-parameter sensors deployed in a grid manner for collecting temperature, humidity, light, and PM2.5 environmental data;

[0046] Special monitoring equipment, including CO2 sensors deployed in conference rooms, noise sensors in equipment rooms, and weather stations on rooftops, for collecting CO2 concentration, noise values, and outdoor environmental parameters, respectively;

[0047] Device status sensors, including clamp current sensors and vibration sensors, for collecting current data of air conditioners and lighting main lines and vibration data of water pumps and fans, respectively;

[0048] Cameras with AI analysis function for collecting regional personnel density data.

[0049] The perception layer transmits the collected data to the decision layer through LoRa wireless communication technology, and the cameras interact with the decision layer through a data interface.

[0050] The decision layer includes edge computing boxes and a park cloud platform. The edge computing boxes integrate data fusion engines and control and decision algorithms for receiving perception layer data and performing real-time analysis and local rapid response. The park cloud platform is used to realize global data aggregation and strategy optimization, forming a double-layer decision-making mode of local rapid response and cloud global optimization.

[0051] The edge computing boxes are built-in with a unified rule engine, which has monitoring thresholds, energy-saving strategies, and device linkage logic.

[0052] The execution layer includes air conditioner intelligent controllers, lighting dimming modules, and fresh air actuators. The execution layer receives control instructions from the decision layer through Zigbee wireless communication protocol and performs temperature regulation, brightness control, and fresh air volume adjustment operations.

[0053] A data-driven control triggering mechanism is formed between the decision layer and the perception layer, including:

[0054] Environmental monitoring data linkage: When the CO2 concentration collected by the perception layer exceeds the standard or the temperature and humidity deviates from the comfort interval, the decision layer generates corresponding fresh air volume adjustment instructions or air conditioner temperature adjustment instructions;

[0055] Personnel state linkage: The decision layer fuses the personnel density data collected by the camera and the access control face recognition information to generate regional pre-adjustment instructions or off-site energy-saving instructions;

[0056] Device state linkage: The decision layer performs health assessment analysis on the data collected by the current sensor and the vibration sensor, and generates maintenance instructions when the device state is abnormal.

[0057] The area pre-conditioning instruction is an instruction for starting the air conditioner to a comfortable temperature in advance when the target area is about to be used. The off-site energy-saving instruction includes an instruction for gradually dimming the lighting system to off and an instruction for slowly raising the set temperature of the air conditioning system to an energy-saving interval.

[0058] When generating maintenance instructions, the decision layer dynamically schedules maintenance resources in combination with the schedule. If there is an important activity the next day, an emergency maintenance instruction is generated first. If there is no important activity, a regular maintenance instruction is generated.

[0059] In use, when the access control face recognition is successful and matches the area reservation information, the decision layer sends instructions to the air conditioner and lighting execution equipment in advance, and adjusts the environment to a comfortable state before the person enters (for example, the air conditioner in the conference room is started to 24℃ in advance, and the lighting is adjusted to 80% brightness).

[0060] (1) Dynamic environment maintenance:

[0061] If the CO2 concentration exceeds the standard, the decision layer sends a wind volume raising instruction to the fresh air actuator, and adjusts the gradient to the standard concentration. When the temperature and humidity deviate from the interval, the air conditioning controller receives a precise temperature adjustment instruction (0.5℃ precision) to maintain the stability of the environment.

[0062] (2) Off-site energy-saving regulation:

[0063] After the camera detects that the person has left, the decision layer starts the gradient energy-saving rule: the lighting is gradually dimmed to off within a few minutes, and the air conditioner is slowly warmed to the energy-saving interval to avoid frequent start-stop of the equipment.

[0064] (3) When the device health evaluation value is lower than the threshold, the decision layer judges the priority in combination with the next day's schedule: if there is an important activity, an emergency work order is immediately pushed to the maintenance terminal; if there is no important arrangement, it is included in the night maintenance queue, realizing the dynamic matching of device state monitoring and maintenance resources.

[0065] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart park environmental monitoring and control system based on the Internet of Things, characterized in that, include: The perception layer is used to collect data on the park environment, personnel status, and equipment operating status. The decision-making layer communicates with the perception layer, receives data collected by the perception layer, analyzes and processes it, and generates control and maintenance instructions. The execution layer is communicatively connected to the decision-making layer, receives control instructions generated by the decision-making layer, and executes corresponding control operations. The perception layer, decision-making layer, and execution layer achieve data interaction and centralized control through a unified Internet of Things architecture, forming a closed-loop control link of data collection, decision-making, and execution.

2. The smart park environmental monitoring and control system based on the Internet of Things according to claim 1, characterized in that: The sensing layer includes: A grid-deployed multi-parameter sensor is used to collect environmental data on temperature, humidity, light intensity, and PM2.

5. Specialized monitoring equipment includes CO2 sensors deployed in the conference room, noise sensors in the equipment room, and a weather station on the roof, which are used to collect CO2 concentration, noise levels, and outdoor environmental parameters, respectively. Equipment status sensors, including clamp-on current sensors and vibration sensors, are used to collect current data of air conditioning and lighting main lines and vibration data of water pumps and fans, respectively. Cameras equipped with AI analysis capabilities are used to collect data on population density in the area.

3. The smart park environmental monitoring and control system based on the Internet of Things according to claim 2, characterized in that: The perception layer transmits the collected data to the decision layer via LoRa wireless communication technology, and the camera interacts with the decision layer via a data interface.

4. The smart park environmental monitoring and control system based on the Internet of Things according to claim 1, characterized in that: The decision-making layer includes an edge computing box and a campus cloud platform. The edge computing box integrates a data fusion engine and a control decision algorithm to receive data from the perception layer and perform real-time analysis and local rapid response. The campus cloud platform is used to realize global data aggregation and strategy optimization, forming a two-layer decision-making mode of local rapid response and cloud-based global optimization.

5. The smart park environmental monitoring and control system based on the Internet of Things according to claim 4, characterized in that: The edge computing box has a built-in unified rule engine, which contains monitoring thresholds, energy-saving strategies, and device linkage logic.

6. The smart park environmental monitoring and control system based on the Internet of Things according to claim 1, characterized in that: The execution layer includes an air conditioning intelligent controller, a lighting dimming module, and a fresh air actuator. The execution layer receives the control commands from the decision layer via the Zigbee wireless communication protocol and performs temperature adjustment, brightness control, and fresh air volume adjustment operations.

7. The smart park environmental monitoring and control system based on the Internet of Things according to claim 1, characterized in that: A data-driven regulation triggering mechanism is formed between the decision-making layer and the perception layer, including: Environmental monitoring data linkage: When the CO2 concentration collected by the sensing layer exceeds the standard or the temperature and humidity deviate from the comfortable range, the decision layer generates corresponding fresh air volume adjustment instructions or air conditioning temperature adjustment instructions. Personnel status linkage: The decision-making layer integrates personnel density data collected by cameras with facial recognition information from access control to generate area pre-adjustment instructions or departure energy-saving instructions; Equipment status linkage: The decision-making layer performs health assessment and analysis on the data collected by the current sensor and vibration sensor, and generates maintenance instructions when the equipment status is abnormal.

8. The smart park environmental monitoring and control system based on the Internet of Things according to claim 7, characterized in that: The pre-adjustment instruction for the area is an instruction to start the air conditioning to a comfortable temperature in advance when the target area is about to be used; the energy-saving instructions for leaving the site include instructions to gradually dim the lighting system to turn it off and instructions to slowly raise the set temperature of the air conditioning system to the energy-saving range. When the decision-making level generates maintenance instructions, it dynamically schedules maintenance resources based on the schedule. If there are important events the next day, emergency maintenance instructions are generated first; otherwise, regular maintenance instructions are generated.