LED induction lamp intelligent control method and system based on wireless networking

Abstract

The invention relates to the technical field of LED induction lamps, in particular to an LED induction lamp intelligent control method and system based on wireless networking, and the method comprises the following steps: S1, enabling a plurality of LED induction lamp nodes to be interconnected through a wireless Mesh network mode of a multi-node, self-organizing and self-healing distributed communication architecture; s2, performing linkage detection by using PIR and a microwave radar, and identifying activity information in the environment; s3, after the effective activity is sensed, lighting triggering and adjusting operation is executed locally; s4, the trigger node broadcasts a linkage instruction to other nodes in the group, so that the plurality of LED induction lamps respond synchronously; s5, in the synchronous response process of the multiple LED lamps, the working mode is dynamically adjusted according to the environment and activity conditions; according to the LED induction lamp intelligent control method and system based on the TSCH self-organizing self-healing Mesh network, through PIR and microwave dual-technology fusion (time difference and temperature compensation is utilized) and RSSI + TOA dual-distance filtering, false alarms and cross-regional linkage are greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of LED induction lamps, and in particular to an intelligent control method and system for LED induction lamps based on wireless networking. Background Art

[0002] With the development of the Internet of Things and smart lighting technology, LED sensor lights are widely used in scenes such as corridors, hallways, and parking lots;

[0003] In existing technologies, most systems use a single PIR (passive infrared) or microwave radar for human detection, and connect lamps to a centralized controller via a star or tree network to enable on / off or simple timed brightness adjustment. Some high-end solutions also use Bluetooth Mesh or ZigBee Mesh networks to support multi-lamp linkage and remote centralized management.

[0004] However, these solutions usually have the following drawbacks:

[0005] Single sensor has high false alarm and missed alarm rates: PIR is easily affected by airflow and thermal radiation, causing false alarms. Microwave radar is prone to "overshooting" or multipath reflections, making it difficult for a single detection method to achieve both accuracy and coverage.

[0006] Poor network topology stability: Star or tree networks cannot self-heal when nodes lose connection, and routing links are single. Typical mesh solutions lack clock synchronization and distance filtering, and cross-layer misconnections can still occur.

[0007] The conflict between energy consumption and responsiveness: Fixed heartbeat or timed wake-up methods cannot simultaneously meet the requirements of fast response during high-traffic periods and extremely low power consumption during low-traffic periods.

[0008] Lack of closed-loop optimization and remote operation and maintenance: Most existing system parameters are statically set, lacking a dynamic threshold adjustment mechanism based on on-site false alarm / missed alarm feedback, and unable to implement online fault diagnosis and secure OTA upgrades. Therefore, to address the above problems, a wireless networking-based intelligent control method and system for LED induction lamps are proposed. Summary of the Invention

[0009] The purpose of the present invention is to provide an intelligent control method and system for LED sensor lights based on wireless networking to solve the problems of PIR being easily affected by airflow and thermal radiation and causing false alarms, microwave radar being easily "out of range" or falsely triggered by multipath reflections, and a single detection method being unable to achieve both accuracy and coverage; star or tree networks being unable to self-heal when nodes lose connection, and having a single routing link; typical mesh solutions lacking clock synchronization and distance filtering, and still experiencing cross-layer false linkages; fixed heartbeat or timed wake-up methods being unable to simultaneously meet the requirements of rapid response during high-traffic periods and extremely low power consumption during low-traffic periods; and existing system parameters being mostly statically set, lacking a dynamic threshold adjustment mechanism based on on-site false alarm / missed alarm feedback, and unable to implement online fault diagnosis and secure OTA upgrades.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A wireless networking-based intelligent control method and system for LED induction lamps includes the following steps:

[0012] S1: Multiple LED sensor light nodes are interconnected through a wireless mesh network with a multi-node, self-organizing, and self-healing distributed communication architecture;

[0013] S2: Use PIR and microwave radar for joint detection to identify activity information in the environment;

[0014] S3: After sensing valid activities, local lighting triggering and adjustment operations are performed;

[0015] S4: The trigger node broadcasts a linkage instruction to other nodes in the group, so that the multiple LED sensor lights respond synchronously;

[0016] S5: During the synchronous response of the plurality of LED lights, the working mode is dynamically adjusted according to the environment and activity conditions;

[0017] S6: During operation, the perception parameters, linkage strategies, and network topology are updated and fault detection is performed through the edge or cloud platform.

[0018] As a further optimized content of the present invention, the process of interconnecting the multiple LED sensor lamp nodes through a wireless mesh network is as follows:

[0019] S11: Multiple LED sensor light nodes are powered on and local networking and sensing parameters are loaded;

[0020] The sensing parameters include: node identification, group ID, Mesh routing table, PIR-microwave dual identification time difference threshold , basic lighting duration T, basic brightness L and delay protection t;

[0021] S12: Each node automatically joins the wireless mesh network according to the group ID, establishes a two-way route with the adjacent node, and maintains heartbeat synchronization with the edge gateway;

[0022] S13: The node enters low-power sleep mode according to the preset heartbeat interval H during the standby phase, and enters detection preparation mode when it wakes up at a scheduled time or receives a heartbeat signal from a neighbor.

[0023] As a further optimization of the present invention, the PIR and microwave radar linkage detection process is as follows: the PIR and microwave radar sensors are activated synchronously, the detection window is opened, and the trigger timestamps of the two are recorded. 、 , and determine whether there is human activity based on the human activity rules and the two recorded trigger timestamps;

[0024] The rules for human activities are: When the compound trigger occurs N times in succession, human activity is recognized.

[0025] As a further optimized content of the present invention, wherein: the dual identification time difference threshold The digital temperature sensor on the node measures the ambient temperature Tamb dynamically and corrects it regularly. Within the range, The correction formula is:

[0026]

[0027] Where, is the initial threshold, γ is the compensation coefficient, which is obtained by factory calibration or on-site debugging. are the upper and lower thresholds, set based on system experimental results. The reference temperature is 25°C.

[0028] As a further optimization of the present invention, in S3, the lighting triggering and adjustment operation is executed locally after dynamically calculating the lighting duration and brightness based on the activity frequency and the ambient light.

[0029] As a further optimization of the present invention, in S4, the linkage instruction is a "Group_ON" message, and the receiving node responds synchronously and resets the lighting timer after filtering according to the TOA time difference and the RSSI threshold.

[0030] As a further optimization of the present invention, in S5, the dynamic adjustment of the working mode according to the environment and activity conditions is specifically: adaptively adjusting the heartbeat wake-up interval according to the day and night mode and the average trigger frequency in the previous statistical period, and limiting it to a preset range.

[0031] As a further optimization of the present invention, in S6, the update and fault detection process is: the edge gateway regularly uploads the node operation log to the cloud platform, and the cloud platform securely sends the sensing threshold, delay parameters and networking strategy through OTA based on the machine learning model and actual false alarm / missed alarm feedback, and performs fault diagnosis and alarm at the same time.

[0032] As further optimized content of the present invention, including:

[0033] Several LED sensor light nodes, each node integrates a PIR sensor, microwave radar, ambient light sensor, temperature sensor and a low-power MCU. The MCU runs:

[0034] The networking module is used to self-organize and self-heal to join the wireless mesh network through the TSCH time slot channel hopping protocol according to the node identifier and group ID;

[0035] The sensing module is used to synchronously activate the PIR and microwave radar within the detection window, record the trigger timestamp, and determine human activity based on dual identification and temperature compensation thresholds;

[0036] The control module dynamically calculates the lighting duration and brightness based on activity frequency and ambient light, and performs lighting / dimming locally;

[0037] The communication module is used to broadcast the "Group_ON" linkage command and respond synchronously to the received multicast message after distance filtering based on the TOA time difference and RSSI dual thresholds;

[0038] A power management module, which is used to adaptively adjust the heartbeat wake-up interval according to the day and night mode and trigger frequency and implement hierarchical sleep wake-up; an edge gateway, which communicates bidirectionally with the LED sensor light node through the Mesh network, is responsible for aggregating node operation logs and maintaining a connection with the cloud platform;

[0039] A cloud platform with machine learning and device management subsystems deployed to:

[0040] Receive and analyze the false alarm / missed alarm rate, voltage, current and communication quality data uploaded by the edge gateway;

[0041] Generate new sensing thresholds, delay parameters and networking strategies, and securely send them to the edge gateway via OTA;

[0042] When network packet loss rate or node voltage anomalies are detected, fault diagnosis and alarm information are pushed to the operation and maintenance terminal.

[0043] As a further optimized content of the present invention, a computer program is stored thereon, and when the computer program is executed by a processor, the steps in the method as described in any one of claims 1 to 8 are executed.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] In the present invention, the intelligent control method and system of LED sensor lights based on the TSCH self-organizing and self-healing Mesh network greatly reduces false alarms and cross-zone linkage through the fusion of PIR and microwave dual identification (using time difference and temperature compensation) and RSSI + TOA dual distance filtering; combines the trigger frequency with the ambient light closed-loop adjustment of lighting duration and brightness to achieve adaptive heartbeat management with rapid response during peak periods and extremely low power consumption during trough periods; and through the closed-loop machine learning and secure OTA mechanism of the edge gateway / cloud platform, dynamically sends sensing thresholds, delay parameters and network partitioning strategies, and supports online fault diagnosis and alarms. While improving perception reliability and energy-saving effects, the system also significantly reduces on-site maintenance costs and enhances the scalability and maintainability of the overall deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of an intelligent control method for LED induction lamps based on wireless networking according to the present invention. DETAILED DESCRIPTION

[0047] See also Figure 1 , the present invention provides a technical solution:

[0048] A wireless networking-based intelligent control method and system for LED induction lamps includes the following steps:

[0049] S1: Multiple LED sensor light nodes are interconnected through a wireless mesh network with a multi-node, self-organizing, and self-healing distributed communication architecture;

[0050] S2: Use PIR and microwave radar for joint detection to identify activity information in the environment;

[0051] S3: After sensing valid activities, local lighting triggering and adjustment operations are performed;

[0052] S4: The trigger node broadcasts linkage instructions to other nodes in the group, making multiple LED sensor lights respond synchronously;

[0053] S5: During the process of multiple LED lights responding synchronously, the working mode is dynamically adjusted according to the environment and activity conditions;

[0054] S6: During operation, the perception parameters, linkage strategies, and network topology are updated and fault detection is performed through the edge or cloud platform.

[0055] Example 1:

[0056] The process of interconnecting multiple LED sensor light nodes through a wireless mesh network is as follows:

[0057] S11: Multiple LED sensor light nodes are powered on and local networking and sensing parameters are loaded;

[0058] Among them, the sensing parameters include: node identification, group ID, Mesh routing table, PIR-microwave dual detection time difference threshold , basic lighting duration T, basic brightness L and delay protection t;

[0059] S12: Each node automatically joins the wireless mesh network according to the group ID, establishes two-way routing with adjacent nodes, and maintains heartbeat synchronization with the edge gateway;

[0060] S13: The node enters low-power sleep according to the preset heartbeat interval H during the standby phase, and enters detection preparation when it wakes up regularly or receives the neighbor's heartbeat signal. The PIR and microwave radar linkage detection process is as follows: the PIR and microwave radar sensors are activated synchronously, the detection window is opened, and the trigger timestamps of both are recorded. 、 , and determine whether there is human activity based on the human activity rules and the two recorded trigger timestamps;

[0061] The rules for human activities are: When N times of compound triggering are repeated, human activity is recognized and the dual-detection time difference threshold is set. The digital temperature sensor on the node measures the ambient temperature Tamb dynamically and corrects it regularly. Within the range, The correction formula is:

[0062]

[0063] Where, is the initial threshold, γ is the compensation coefficient, which is obtained by factory calibration or on-site debugging. are the upper and lower thresholds, set based on system experimental results. The reference temperature is 25℃;

[0064] The triggering time difference threshold of PIR and microwave radar according to the ambient temperature Tamb Dynamic correction is performed to compensate for the drift of sensor response speed in high or low temperature environments. The linear temperature compensation factor γ can maintain the stability of dual-detection judgment at different temperatures to avoid false alarms or missed alarms at extreme temperatures. At the same time, the clamp is limited to range to ensure that the parameters will not become unstable due to temperature changes.

[0065] Example 2:

[0066] In S3, the lighting triggering and adjustment operations are executed locally, and the lighting duration T and brightness L are dynamically calculated based on the activity frequency and ambient light.

[0067] Among them, the calculation formula of lighting duration T is:

[0068]

[0069] Where, is the basic duration (15 s), k is the activity coefficient (5 s / time), is a 10-minute statistical window, 60s;

[0070] And the lighting time T is limited to no more than ;

[0071] By setting the basic lighting duration This is calculated by linearly superposing the number of compound triggers n per unit time, giving the system the ability to automatically extend or shorten the lighting duration based on traffic density. Meanwhile, min{⋅,Tmax} is used to ensure that the maximum safe lighting duration is not exceeded. This method, unlike fixed-duration solutions, can improve user experience during peak periods and maximize energy savings during off-peak periods.

[0072] The calculation formula of lighting brightness L is:

[0073]

[0074] Where, is the base brightness (30%), is the environmental compensation coefficient (0.1–0.3), The standard for aisles is 80 lux. is the real-time ambient illumination, =10%, =100%;

[0075] And L ;

[0076] According to the real-time environmental illumination With reference standard illuminance The difference between the two values ​​is used to dynamically adjust the light brightness L and is limited to range, ensuring that the output can be reduced when the environment is bright enough and automatically increased when the environment is dim, so as to achieve a balance between closed-loop performance and comfort.

[0077] Example 3:

[0078] In S4, the linkage command is a "Group_ON" message, and the receiving node responds synchronously and resets the lighting timer after filtering based on the TOA time difference and RSSI threshold. Through TOA + RSSI dual filtering, it can accurately identify physically adjacent nodes in the same group, avoid cross-zone false triggering, and achieve high accuracy and stability of lighting linkage;

[0079] In S5, the working mode is dynamically adjusted according to the environment and activity conditions. Specifically, the heartbeat wake-up interval is adaptively adjusted according to the day and night mode and the average trigger frequency in the previous statistical cycle, and is limited to a preset range. The wake-up frequency can be automatically optimized according to the traffic density and time period. This can maintain a fast response during peak hours and significantly reduce power consumption during off-peak hours or at night, extending the life of the device.

[0080] In S6, the update and fault detection process is as follows: the edge gateway regularly uploads the node operation log to the cloud platform. The cloud platform securely sends the sensing threshold, delay parameters and networking strategy through OTA based on the machine learning model and actual false alarm / missed alarm feedback, and performs fault diagnosis and alarm at the same time, realizing closed-loop adaptive optimization and real-time remote operation and maintenance. It can continuously improve the perception accuracy during operation, proactively warn of faults, and reduce on-site maintenance costs.

[0081] Example 4:

[0082] Includes: Several LED sensor light nodes, each node integrates a PIR sensor, microwave radar, ambient light sensor, temperature sensor and low-power MCU, running on the MCU:

[0083] The networking module is used to self-organize and self-heal to join the wireless mesh network through the TSCH time slot channel hopping protocol according to the node identifier and group ID;

[0084] The sensing module is used to synchronously activate the PIR and microwave radar within the detection window, record the trigger timestamp, and determine human activity based on dual identification and temperature compensation thresholds;

[0085] The control module dynamically calculates the lighting duration and brightness based on activity frequency and ambient light, and performs lighting / dimming locally;

[0086] The communication module is used to broadcast the "Group_ON" linkage command and respond synchronously to the received multicast message after distance filtering based on the TOA time difference and RSSI dual thresholds;

[0087] The power management module is used to adaptively adjust the heartbeat wake-up interval according to the day and night mode and trigger frequency, and implement hierarchical sleep wake-up. The edge gateway communicates bidirectionally with the LED sensor light node through the mesh network, is responsible for aggregating node operation logs and maintaining a connection with the cloud platform.

[0088] A cloud platform with machine learning and device management subsystems deployed to:

[0089] Receive and analyze false alarm / missed alarm rate, voltage, current and communication quality data uploaded by the edge gateway;

[0090] Generate new sensing thresholds, delay parameters, and networking strategies, and securely send them to the edge gateway via OTA.

[0091] When abnormal network packet loss or node voltage is detected, fault diagnosis and alarm information are pushed to the operation and maintenance terminal. The modular design of hardware and software realizes closed-loop management of the entire link from sensing to decision-making to operation and maintenance, ensuring flexible system deployment, strong scalability and easy maintenance.

[0092] A computer program is stored thereon. When the computer program is executed by the processor, the steps of any method as claimed in claims 1 to 8 are performed. The software-defined method is adopted, and the control logic can be quickly iterated by updating the program, and it is compatible with different hardware platforms, thereby improving the system's upgradeability and future scalability.

[0093] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method of the present invention and its core ideas. The above is only a preferred implementation method of the present invention. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of the present invention.

Claims

1. A wireless networking-based intelligent control method for LED induction lamps, characterized in that: The following steps are involved: S1: Multiple LED sensor light nodes are interconnected through a wireless mesh network with a multi-node, self-organizing, and self-healing distributed communication architecture; S2: Use PIR and microwave radar for joint detection to identify activity information in the environment; S3: After sensing valid activities, local lighting triggering and adjustment operations are performed; S4: The trigger node broadcasts a linkage instruction to other nodes in the group, so that the multiple LED sensor lights respond synchronously; S5: During the synchronous response of the plurality of LED lights, the working mode is dynamically adjusted according to the environment and activity conditions; S6: During operation, the perception parameters, linkage strategies, and network topology are updated and fault detection is performed through the edge or cloud platform.

2. The method for intelligently controlling an LED induction lamp based on wireless networking according to claim 1, characterized in that: The process of interconnecting the multiple LED sensor light nodes through a wireless mesh network is as follows: S11: Multiple LED sensor light nodes are powered on and local networking and sensing parameters are loaded; The sensing parameters include: node identification, group ID, Mesh routing table, PIR-microwave dual identification time difference threshold , basic lighting duration T, basic brightness L and delay protection t; S12: Each node automatically joins the wireless mesh network according to the group ID, establishes a two-way route with the adjacent node, and maintains heartbeat synchronization with the edge gateway; S13: The node enters low-power sleep mode according to the preset heartbeat interval H during the standby phase, and enters detection preparation mode when it wakes up at a scheduled time or receives a heartbeat signal from a neighbor.

3. The method for intelligently controlling LED induction lamps based on wireless networking according to claim 1, characterized in that: The PIR and microwave radar linkage detection process is as follows: the PIR and microwave radar sensors are activated synchronously, the detection window is opened, and the trigger timestamps of both are recorded. 、 , and determine whether there is human activity based on the human activity rules and the two recorded trigger timestamps; The rules for human activities are: When the compound trigger occurs N times in succession, human activity is recognized.

4. The method for intelligently controlling an LED induction lamp based on wireless networking according to claim 2, wherein: The dual identification time difference threshold The digital temperature sensor on the node measures the ambient temperature Tamb dynamically and corrects it regularly. Within the range, The correction formula is: Where, is the initial threshold, γ is the compensation coefficient, which is obtained by factory calibration or on-site debugging. are the upper and lower thresholds, set based on system experimental results. The reference temperature is 25°C.

5. The method for intelligently controlling LED induction lamps based on wireless networking according to claim 1, characterized in that: In S3, lighting triggering and adjustment operations are executed locally, and the lighting duration and brightness are dynamically calculated based on the activity frequency and ambient light.

6. The method for intelligently controlling LED induction lamps based on wireless networking according to claim 1, characterized in that: In S4, the linkage instruction is a "Group_ON" message, and the receiving node responds synchronously and resets the lighting timer after filtering according to the TOA time difference and RSSI threshold.

7. The method for intelligently controlling LED induction lamps based on wireless networking according to claim 1, characterized in that: In S5, the dynamic adjustment of the working mode according to the environment and activity conditions is specifically: adaptively adjusting the heartbeat wake-up interval according to the day and night mode and the average trigger frequency in the previous statistical period, and limiting it to a preset range.

8. The method for intelligently controlling LED induction lamps based on wireless networking according to claim 1, characterized in that: In S6, the update and fault detection process is as follows: the edge gateway regularly uploads the node operation log to the cloud platform. The cloud platform securely sends the sensing threshold, delay parameters and networking strategy through OTA based on the machine learning model and actual false positive / missed negative feedback, and performs fault diagnosis and alarm at the same time.

9. The LED sensor light intelligent control system based on wireless networking according to claim 1, characterized in that: include: Several LED sensor light nodes, each node integrates a PIR sensor, microwave radar, ambient light sensor, temperature sensor and a low-power MCU. The MCU runs: The networking module is used to self-organize and self-heal to join the wireless mesh network through the TSCH time slot channel hopping protocol according to the node identifier and group ID; The sensing module is used to synchronously activate the PIR and microwave radar within the detection window, record the trigger timestamp, and determine human activity based on dual identification and temperature compensation thresholds; The control module dynamically calculates the lighting duration and brightness based on activity frequency and ambient light, and performs lighting / dimming locally; The communication module is used to broadcast the "Group_ON" linkage command and respond synchronously to the received multicast message after distance filtering based on the TOA time difference and RSSI dual thresholds; Power management module, used to adaptively adjust the heartbeat wake-up interval according to the day and night mode and trigger frequency and realize graded sleep wake-up; The edge gateway communicates bidirectionally with the LED sensor light node through the Mesh network, is responsible for aggregating node operation logs and maintaining a connection with the cloud platform; A cloud platform with machine learning and device management subsystems deployed to: Receive and analyze the false alarm / missed alarm rate, voltage, current and communication quality data uploaded by the edge gateway; Generate new sensing thresholds, delay parameters and networking strategies, and securely send them to the edge gateway via OTA; When network packet loss rate or node voltage anomalies are detected, fault diagnosis and alarm information are pushed to the operation and maintenance terminal.

10. The LED sensor light intelligent control system based on wireless networking according to claim 1, wherein a computer program is stored thereon, characterized in that When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 8 are executed.

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