LED induction control system and method based on human-computer interaction

Abstract

The present invention provides an LED sensing control system and method based on human-computer interaction, which belongs to the field of LED control technology. The present invention achieves a triple improvement in energy efficiency, experience and intelligence through data-driven dynamic lighting control and user feedback closed-loop optimization. On the one hand, it accurately matches personnel activities through real-time trajectory analysis and dynamic weight adjustment, and uses wireless networks to activate only necessary areas and adaptively adjust brightness, significantly reducing ineffective energy consumption; on the other hand, combined with predictive lighting and gradient dimming, it actively adapts to personnel behavior patterns and reduces visual discomfort, thereby improving user comfort; at the same time, the system automatically identifies key lighting nodes based on historical data, improves trajectory accuracy through multi-sensor fusion, and supports users to remotely customize coverage, taking into account flexibility, privacy protection and self-learning capabilities, and ultimately forming a sustainable lighting solution that optimizes energy efficiency, upgrades experience and coordinates intelligent management.

Description

Technical Field

[0001] The present invention relates to the technical field of LED control, and in particular to an LED induction control system and method based on human-computer interaction. Background Art

[0002] In traditional lighting control technologies and related application scenarios, problems such as energy waste, poor user experience, and insufficient intelligence have long existed, seriously hindering further improvements in lighting system energy efficiency, comfort, and management effectiveness. These problems are specifically reflected in the following aspects:

[0003] Most current lighting systems use zoned, centralized control or timed on / off modes, making it difficult to dynamically adjust lighting zones and brightness based on actual human activity. For example, in large office buildings, even if only a few areas on a floor are occupied, all lights remain on, consuming significant amounts of energy while illuminating unused areas. In shopping malls, aisle lights remain fully illuminated during non-business hours, even when no one is around, resulting in a significant increase in inefficient energy consumption.

[0004] Traditional brightness control relies on manual presets or simple light sensors, which are unable to adapt brightness in real time based on activity intensity and location. For example, in library reading areas, readers have different lighting requirements when walking between bookshelves and when reading at their desks. However, existing systems struggle to accurately identify and dynamically adjust brightness, resulting in over- or under-brightness in some areas and wasting energy.

[0005] Existing lighting systems are mostly passive and unable to anticipate human behavior and provide proactive lighting services. For example, when someone enters a hallway at night, the lights must be manually triggered or delayed, and sudden changes in brightness can easily cause visual discomfort. When changing scenes in a meeting room, the lighting mode must be manually switched, and it cannot automatically adjust to the meeting progress, affecting meeting efficiency and user experience.

[0006] Therefore, it is necessary to provide an LED sensing control system and method based on human-computer interaction to solve the above technical problems. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides an LED sensing control system and method based on human-computer interaction to solve the problems in existing lighting control technology, such as rough regional lighting control, lack of precision in brightness adjustment, and disconnection between lighting response and behavior pattern.

[0008] The LED induction control method based on human-computer interaction provided by the present invention includes the following steps:

[0009] Divide the lighting space into multiple lighting areas, obtain historical activity trajectory data of people in the lighting space, perform sequence identification on each lighting area, and obtain the sequence identification results of the lighting area;

[0010] Analyze the lighting correlation between each illumination area, and identify the illumination area with the correlation center from all illumination areas as the lighting node;

[0011] Based on the sequence identification results and lighting nodes, a lighting association network diagram between each illumination area is constructed, and the initial weight of each lighting node is set based on the lighting association network diagram;

[0012] Through the infrared sensor network, the current activity trajectory data of personnel is collected in real time, and the initial weights in the lighting association network diagram are dynamically adjusted according to the current activity trajectory data of personnel to obtain the adjusted priority weights;

[0013] Based on the adjusted priority weights, the lighting nodes are controlled in a linked manner;

[0014] Based on the linkage control results of the lighting nodes, the coverage of the lighting nodes is adjusted through the human-computer interaction terminal.

[0015] Preferably, the steps of dividing the lighting space into a plurality of lighting areas, obtaining historical activity trajectory data of people in the lighting space, performing sequence identification on each lighting area, and obtaining sequence identification results of the lighting areas include:

[0016] Divide the target lighting space into multiple independent lighting areas according to physical layout or functional requirements;

[0017] Use infrared sensors or cameras to obtain historical activity trajectory data of people in the target lighting space, including movement paths, stay locations, and duration;

[0018] A unique sequence identifier is assigned to each irradiation area to form an area identifier set.

[0019] Preferably, the steps of analyzing the lighting correlation between the illumination areas and identifying the illumination area at the correlation center from all illumination areas as the lighting node include:

[0020] Based on historical trajectory data, the transfer frequency, i.e., correlation, between different irradiation areas is counted to construct a transfer probability matrix;

[0021] Based on the transition probability matrix, the illumination area with the strongest correlation is selected as the illumination node, and the illumination area excluding the illumination node is set as the common illumination area.

[0022] Preferably, the steps of constructing a lighting association network diagram between each illumination area based on the sequence identification result and the lighting nodes, and setting the initial weight of each lighting node based on the lighting association network diagram include:

[0023] With lighting nodes as vertices and the associations between lighting nodes as edges, a lighting association network diagram with a directed weighted graph structure is constructed;

[0024] According to historical data, each edge is assigned a transition probability value or normalized association strength, that is, an initial weight.

[0025] Preferably, the infrared sensor network is used to collect the current activity trajectory data of the personnel in real time, and the initial weights in the lighting association network diagram are dynamically adjusted according to the current activity trajectory data of the personnel to obtain the adjusted priority weights, which steps include:

[0026] Through the deployed infrared sensor network, the current location, movement direction and duration of stay of personnel are detected in real time;

[0027] According to the distance between the lighting node and the personnel's current location and the length of stay, the initial lighting weight is dynamically adjusted through the adjusted calculation formula to obtain the adjusted priority weight, where the adjusted calculation formula is: priority weight = α*(1 / distance)+β*length of stay.

[0028] Preferably, the steps of performing linkage control on the lighting nodes based on the adjusted priority weights include:

[0029] According to the adjusted priority weight, the nodes with priority weight higher than the preset threshold and their associated illumination areas are activated for linkage control, including lighting up the LEDs of the current lighting nodes and illumination areas;

[0030] Based on the result of starting linkage control, the brightness of each node LED is dynamically adjusted according to the priority weight ratio, that is, the higher the priority weight, the stronger the brightness.

[0031] Preferably, the steps of adjusting the coverage of the lighting nodes through the human-computer interaction terminal based on the linkage control results of the lighting nodes include:

[0032] The terminal connected via the wireless network receives user instructions, and uses the wireless network to feed back the user instructions to the control element of the lighting node, adjusts the physical parameters of the lighting node, and obtains the interactive control adjustment result;

[0033] The interactive control adjustment results are fed back to the associated network graph to synchronously update the initial weights.

[0034] An LED induction control system based on human-computer interaction, the control system includes:

[0035] The sequence identification module is used to divide the lighting space into multiple lighting areas, obtain the historical activity trajectory data of people in the lighting space, perform sequence identification on each lighting area, and obtain the sequence identification results of the lighting area;

[0036] A data analysis module is used to analyze the lighting correlation between the illumination areas and identify the illumination area with the correlation center from all illumination areas as the lighting node;

[0037] An association construction module is used to construct a lighting association network diagram between each illumination area based on the sequence identification result and the lighting nodes, and set the initial weight of each lighting node based on the lighting association network diagram;

[0038] The dynamic adjustment module is used to collect the current activity trajectory data of personnel in real time through the infrared sensor network, and dynamically adjust the initial weights in the lighting association network diagram according to the current activity trajectory data of personnel to obtain the adjusted priority weights;

[0039] A linkage control module, for performing linkage control on lighting nodes based on the adjusted priority weights;

[0040] The interactive adjustment module is used to adjust the coverage of the lighting nodes through the human-computer interaction terminal based on the linkage control results of the lighting nodes.

[0041] Compared with related technologies, the LED induction control system and method based on human-computer interaction provided by the present invention has the following beneficial effects:

[0042] This invention achieves a triple improvement in energy efficiency, user experience, and intelligence through data-driven dynamic lighting control and user feedback closed-loop optimization. On the one hand, it accurately matches human activities through real-time trajectory analysis and dynamic weight adjustment, and uses wireless networks to activate only necessary areas and adaptively adjust brightness, significantly reducing ineffective energy consumption; on the other hand, it combines predictive lighting and gradient dimming to actively adapt to human behavior patterns and reduce visual discomfort, thereby improving user comfort; at the same time, the system automatically identifies key lighting nodes based on historical data, improves trajectory accuracy through multi-sensor fusion, and supports users to remotely customize coverage, taking into account flexibility, privacy protection, and self-learning capabilities, ultimately forming a sustainable lighting solution that optimizes energy efficiency, upgrades experience, and coordinates intelligent management. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the flow of the LED induction control method based on human-computer interaction of the present invention;

[0044] Figure 2 This is a system block diagram of the LED induction control system based on human-computer interaction of the present invention. DETAILED DESCRIPTION

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0046] Example 1

[0047] like Figure 1 As shown, the LED induction control method based on human-computer interaction includes the following steps:

[0048] S1. Divide the lighting space into multiple lighting areas, obtain historical activity trajectory data of people in the lighting space, perform sequence identification on each lighting area, and obtain sequence identification results for the lighting areas;

[0049] S2. Analyze the lighting correlation between the illumination areas, and identify the illumination area with the correlation center from all illumination areas as the lighting node;

[0050] S3. Based on the sequence identification results and the lighting nodes, construct a lighting association network diagram between the illumination areas, and set the initial weight of each lighting node based on the lighting association network diagram;

[0051] S4. Collect the current activity trajectory data of personnel in real time through the infrared sensor network, and dynamically adjust the initial weights in the lighting association network diagram based on the current activity trajectory data of personnel to obtain the adjusted priority weights;

[0052] S5. Based on the adjusted priority weights, the lighting nodes are controlled in a linked manner;

[0053] S6. Based on the linkage control results of the lighting nodes, adjust the coverage of the lighting nodes through the human-computer interaction terminal.

[0054] In the specific implementation process, step S1 includes:

[0055] S1.1. Divide the target lighting space into multiple independent lighting areas according to physical layout or functional requirements.

[0056] Specifically, the physical layout data of the target lighting space is obtained, including the type of room and partition conditions, and the principle of dividing the lighting area is determined, that is, it is determined according to the type of room and partition conditions. For example, if the room type is an office, the lighting area is divided according to the distribution of desks, the location and partition conditions of the meeting room, etc.; if the room type is a corridor, the lighting area is divided according to the length, turning points, etc.

[0057] In this example, the room is a large, rectangular office with multiple partitions creating different work areas and a small conference room. Based on the arrangement of the desks, each desk area is divided into an independent lighting zone, for a total of 10 work zones. The conference room is also divided into a separate zone. Furthermore, near the office entrance and exit, where people frequently enter and exit, a zone is also designated, for a total of 12 lighting zones.

[0058] S1.2. Use infrared sensors or cameras to obtain historical activity trajectory data of people in the target lighting space, including movement paths, stay locations, and duration.

[0059] Specifically, the infrared sensor network and the camera network are connected, and the infrared sensor or camera is started to collect the activity data of people in the lighting space in the previous cycle (a cycle of half a month or a month). The infrared sensor or camera will record the movement path of people in real time, that is, the trajectory of people moving from one position to another; at the same time, the position and duration of people's stay at different locations are recorded, and the collected historical activity trajectory data is stored in a database or storage device for subsequent analysis and processing.

[0060] S1.3. Assign a unique sequence identifier to each irradiation area to form an area identifier set.

[0061] Specifically, the areas are numbered according to their division order and functional characteristics, and each irradiation area is assigned a unique serial identifier. The identifier can be a number, letter, or a combination of numbers and letters. To ensure uniqueness and readability, the serial identifiers of all irradiation areas are aggregated to form a regional identifier set.

[0062] In this example, among the 12 light-illuminated areas of a large office, the area near the entrance is labeled "1," followed by the 10 office areas labeled "2-11," and the conference room labeled "12," forming a set of area identifiers {1, 2, 3, ..., 12}. In subsequent analysis, these identifiers can be used to accurately refer to each illuminated area.

[0063] In the specific implementation process, step S2 includes:

[0064] S2.1. Based on historical trajectory data, statistics personnel calculate the transfer frequency, i.e., correlation, between different irradiation areas and construct a transfer probability matrix.

[0065] Specifically, from the acquired historical activity trajectory data of personnel, the transfer records of personnel between different irradiation areas are extracted. For example, the information of personnel moving from area A to area B, and from area B to area C, etc. is recorded and organized into an ordered sequence. For each pair of irradiation areas, the number of times personnel move from area i to area j is counted. A two-dimensional table can be used to record these statistical results. The rows and columns of the table represent different irradiation areas, and the elements in the table represent the number of times personnel move from the area corresponding to the row to the area corresponding to the column. In this embodiment, the calculation formula for calculating the transfer frequency of personnel between different irradiation areas is: Transfer to area Probability ,in, Indicates from the area Transfer to area The probability of Indicates from the area Transfer to area The number of times, Indicates from the area The total number of transfers to all other areas. Finally, all the calculated transfer probabilities are filled into a two-dimensional table according to the area number to form a transfer probability matrix. In the transfer probability matrix, the rows and columns correspond to different irradiation areas, and the elements in the matrix are Indicates the irradiation area Transfer to the irradiation area probability.

[0066] S2.2. Based on the transition probability matrix, the illumination area with the strongest correlation is selected as the illumination node, and the illumination area excluding the illumination node is set as the common illumination area.

[0067] Specifically, for each illuminated area, its correlation index is calculated based on the transition probability matrix. Specifically, the calculated correlation index is the sum of the transition probabilities of the determined area and all other areas. Next, the correlation indexes of all illuminated areas are compared, and the illuminated area with the largest correlation index is selected as the illuminated node. All other illuminated areas except the illuminated node are set as normal illuminated areas. It should be noted that if there are multiple illuminated areas with the same and largest correlation index, one of the illuminated areas can be selected as the illuminated node according to actual needs, or multiple illuminated areas can be selected as illuminated nodes.

[0068] In the specific implementation process, step S3 includes:

[0069] S3.1. With lighting nodes as vertices and the associations between lighting nodes as edges, a lighting association network graph with a directed weighted graph structure is constructed.

[0070] Specifically, based on the constructed transfer probability matrix, the transfer relationship between lighting nodes is analyzed. If there is a personnel transfer from lighting node i to lighting node j, a directed edge is drawn between the two vertices, with the direction from i to j. For example, if the transfer probability matrix shows that personnel often transfer from area A to area B, then a directed edge from A to B is drawn between vertex A and vertex B. All the determined vertices and edges are combined to form a directed weighted graph structure, namely the lighting association network graph.

[0071] In this embodiment, the constructed lighting association network diagram has vertices: Node 1 (Conference Room), Node 2 (Office Area), and Node 3 (Rest Area). Edges and directions: A directed edge is drawn from Node 1 to Node 2, indicating a person's movement from the conference room to the office area; a directed edge is drawn from Node 2 to Node 3, indicating a person's movement from the office area to the rest area. Node 3 has no edges pointing to other nodes because few people move from the rest area to other areas.

[0072] S3.2. Assign a transition probability value or normalized association strength, i.e., initial weight, to each edge based on historical data.

[0073] Specifically, the transition probability values ​​of the edges between lighting nodes are directly obtained from the transition probability matrix constructed in step S2.1. If the association strength is unified within a specific range (such as between 0 and 1), the transition probability values ​​can be normalized. Specifically, the transition probability values ​​of all edges are divided by the maximum value of all edge transfer probability values; the obtained transition probability value or the normalized association strength is used as the initial weight of each edge and assigned to the corresponding edge of the lighting association network diagram.

[0074] In the specific implementation process, step S4 includes:

[0075] S4.1. Use the deployed infrared sensor network to detect the current location, movement direction, and duration of stay of personnel in real time.

[0076] Specifically, when an infrared sensor detects a person, the signal processing circuit converts the detection signal into a digital signal. Based on the detection results of each infrared sensor and the pre-set illumination area division information, the person's current illumination area, i.e., the person's current location, is determined. The direction of movement is then determined by analyzing the changes in the person's position at consecutive time points. For example, if a person moves from the illumination area covered by infrared sensor A to the illumination area covered by infrared sensor B within a very short time interval, it can be determined that the person's movement direction is from A to B, thus determining the person's movement direction. When the person enters a certain illumination area, a timer begins, and the timer continues to accumulate time as long as the person remains detected by the infrared sensor within the illumination area. When the person leaves the illumination area, the timer stops, and the length of time the person has remained in the illumination area is determined.

[0077] For example, in an office lighting space, multiple infrared sensors are deployed, covering different office areas and corridors. At a certain moment, infrared sensor 1 detects a person entering office area A, which it covers, and records the person's current location as office area A. Subsequently, at a later point in time, infrared sensor 2 detects the person entering office area B, while sensor 1 no longer detects the person. Based on the detection results of these two sensors, the person's movement direction is determined to be from office area A to office area B. Furthermore, the time interval from the person entering office area A to leaving the illuminated area is the person's duration of stay in office area A.

[0078] S4.2. Based on the distance between the lighting node and the personnel's current location and the length of stay, the initial lighting weight is dynamically adjusted through an adjusted calculation formula to obtain an adjusted priority weight, where the adjusted calculation formula is: priority weight = α*(1 / distance) + β*length of stay.

[0079] Specifically, based on the person's current location determined in step S4.1 and the lighting node locations determined in step S2, the distance between the person's current location and each lighting node is calculated using the Euclidean distance formula (in two-dimensional space). For example, if the lighting space is a rectangular area, a rectangular coordinate system is established, with the person's current location and the lighting node locations represented as coordinate points, and the straight-line distance between the two points is calculated. In this embodiment, the calculation formula for priority weight = α * (1 / distance) + β * dwell time is set to 0.5 and β = 0.3.

[0080] In the specific implementation process, step S5 includes:

[0081] S5.1. Based on the adjusted priority weights, linkage control is initiated for the nodes whose priority weights are higher than a preset threshold and their associated illumination areas, including lighting up the LEDs of the current illumination nodes and illumination areas.

[0082] Specifically, in areas with frequent human activity, the threshold can be set relatively low to more sensitively respond to human activity; in areas with less human activity, the threshold can be set higher to avoid unnecessary lighting activation. In this embodiment, a priority weight threshold of 5 is preset. When the priority weight of a lighting node exceeds the preset threshold, the lighting node is determined as a node requiring linkage control. Simultaneously, based on the lighting association network diagram constructed in step S3, the illumination area associated with the lighting node is determined. The associated illumination area generally refers to an area that is directly or indirectly associated with the lighting node along the path of human activity transfer. A control signal is sent to the LED control module of the lighting node and its associated illumination area determined to require linkage control, illuminating the LEDs in these areas. Specifically, the control signal is transmitted to the corresponding LED control element of the prior art via wired or wireless communication (such as ZigBee or Wi-Fi), thereby achieving on / off control of the lighting equipment.

[0083] S5.2. Based on the result of starting the linkage control, the brightness of each node LED is dynamically adjusted according to the priority weight ratio, that is, the higher the priority weight, the stronger the brightness.

[0084] Specifically, the adjustable range of LED brightness is determined based on the characteristics of the lighting equipment and lighting requirements. For each lighting node that has activated linkage control, the proportion of its priority weight in the total priority weight of all activated linkage control nodes is calculated. The formula is: , where is the serial number of the current lighting node, and n is the total number of lighting nodes that have started linkage control. Then, according to the calculated brightness adjustment ratio, the LED brightness of each lighting node is adjusted to the corresponding value. , where and They represent the minimum brightness and maximum brightness of the LED brightness respectively. In this way, the higher the priority weight of the lighting node, the stronger its LED brightness.

[0085] In the specific implementation process, step S6 includes:

[0086] S6.1. Receive user instructions via a terminal connected to a wireless network, use the wireless network to feed back the user instructions to a control element of the lighting node, adjust physical parameters of the lighting node, and obtain interactive control adjustment results.

[0087] Specifically, users can use wireless network-enabled devices such as mobile phones and tablets. These devices connect to the lighting system's control components via wireless network technologies such as Wi-Fi and Bluetooth. Users can then input specific values ​​to change parameters related to the LED coverage of a lighting node, such as the angle and distance of the LED lamp. After adjusting the physical parameters, the lighting node control components provide feedback and, based on the lighting node's identification information, locate the lighting node in the lighting association network diagram constructed in step S3.

[0088] S6.2. Feedback the interactive control adjustment results to the associated network graph to synchronously update the initial weights.

[0089] According to the interactive control adjustment result, that is, the parameters after the physical parameters of the lighting node control element are adjusted, the correlation is re-evaluated and the initial weight of the lighting node in the lighting association network diagram is updated.

[0090] Example 2

[0091] like Figure 2 As shown, the LED induction control system based on human-computer interaction includes:

[0092] The sequence identification module is used to divide the lighting space into multiple lighting areas, obtain the historical activity trajectory data of people in the lighting space, perform sequence identification on each lighting area, and obtain the sequence identification results of the lighting area;

[0093] A data analysis module is used to analyze the lighting correlation between the illumination areas and identify the illumination area with the correlation center from all illumination areas as the lighting node;

[0094] An association construction module is used to construct a lighting association network diagram between each illumination area based on the sequence identification result and the lighting nodes, and set the initial weight of each lighting node based on the lighting association network diagram;

[0095] The dynamic adjustment module is used to collect the current activity trajectory data of personnel in real time through the infrared sensor network, and dynamically adjust the initial weights in the lighting association network diagram according to the current activity trajectory data of personnel to obtain the adjusted priority weights;

[0096] A linkage control module, for performing linkage control on lighting nodes based on the adjusted priority weights;

[0097] The interactive adjustment module is used to adjust the coverage of the lighting nodes through the human-computer interaction terminal based on the linkage control results of the lighting nodes.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0100] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. The LED induction control method based on human-computer interaction is characterized in that: The control method comprises the following steps: Divide the lighting space into multiple lighting areas, obtain historical activity trajectory data of people in the lighting space, perform sequence identification on each lighting area, and obtain the sequence identification results of the lighting area; Analyze the lighting correlation between each illumination area and select the illumination area with the strongest correlation among all illumination areas as the illumination node. The steps include: based on historical trajectory data, calculate the transfer frequency, i.e., correlation, between different illumination areas and construct a transition probability matrix; based on the transition probability matrix, select the illumination area with the strongest correlation as the illumination node, and set the illumination areas excluding the illumination node as the common illumination area; Based on the sequence identification results and the lighting nodes, a lighting association network diagram between each illumination area is constructed, and an initial weight of each lighting node is set based on the lighting association network diagram. The steps include: constructing a lighting association network diagram with a directed weighted graph structure, with the lighting nodes as vertices and the associations between the lighting nodes as edges; assigning a transition probability value or a normalized association strength, i.e., an initial weight, to each edge based on historical data; Through the infrared sensor network, the current activity trajectory data of personnel is collected in real time, and the initial weights in the lighting association network diagram are dynamically adjusted according to the current activity trajectory data of personnel to obtain the adjusted priority weights; Based on the adjusted priority weights, the lighting nodes are controlled in a linked manner; Based on the linkage control results of the lighting nodes, the coverage of the lighting nodes is adjusted through the human-computer interaction terminal.

2. The LED induction control method based on human-computer interaction according to claim 1, characterized in that: The steps of dividing the lighting space into a plurality of lighting areas, obtaining historical activity trajectory data of people in the lighting space, performing sequence identification on each lighting area, and obtaining sequence identification results of the lighting areas include: Divide the target lighting space into multiple independent lighting areas according to physical layout or functional requirements; Use infrared sensors or cameras to obtain historical activity trajectory data of people in the target lighting space, including movement paths, stay locations, and duration; A unique sequence identifier is assigned to each irradiation area to form an area identifier set.

3. The LED induction control method based on human-computer interaction according to claim 2, characterized in that: The method collects the current activity trajectory data of personnel in real time through the infrared sensor network, and dynamically adjusts the initial weights in the lighting association network diagram according to the current activity trajectory data of personnel to obtain the adjusted priority weights, and the steps include: Through the deployed infrared sensor network, the current location, movement direction and duration of stay of personnel are detected in real time; According to the distance between the lighting node and the personnel's current location and the length of stay, the initial lighting weight is dynamically adjusted through the adjusted calculation formula to obtain the adjusted priority weight, where the adjusted calculation formula is: priority weight = α*(1 / distance)+β*length of stay.

4. The LED induction control method based on human-computer interaction according to claim 3, characterized in that: The steps of performing linkage control on the lighting nodes based on the adjusted priority weights include: According to the adjusted priority weight, the nodes with priority weight higher than the preset threshold and their associated illumination areas are activated for linkage control, including lighting up the LEDs of the current lighting nodes and illumination areas; Based on the result of starting linkage control, the brightness of each node LED is dynamically adjusted according to the priority weight ratio, that is, the higher the priority weight, the stronger the brightness.

5. The LED induction control method based on human-computer interaction according to claim 4, characterized in that: The steps of adjusting the coverage of the lighting nodes through the human-computer interaction terminal based on the linkage control results of the lighting nodes include: The terminal connected via the wireless network receives user instructions, and uses the wireless network to feed back the user instructions to the control element of the lighting node, adjusts the physical parameters of the lighting node, and obtains the interactive control adjustment result; The interactive control adjustment results are fed back to the associated network graph to synchronously update the initial weights.

6. An LED induction control system based on human-computer interaction, applying the LED induction control method based on human-computer interaction according to any one of claims 1 to 5, characterized in that: The control system includes: The sequence identification module is used to divide the lighting space into multiple lighting areas, obtain the historical activity trajectory data of people in the lighting space, perform sequence identification on each lighting area, and obtain the sequence identification results of the lighting area; The data analysis module is used to analyze the lighting correlation between each illumination area and select the illumination area with the strongest correlation among all illumination areas as the illumination node. The steps include: based on historical trajectory data, counting the transfer frequency, i.e., correlation, between different illumination areas, and constructing a transition probability matrix; based on the transition probability matrix, selecting the illumination area with the strongest correlation as the illumination node, and setting the illumination areas excluding the illumination node as the common illumination area; An association construction module is used to construct a lighting association network diagram between each illumination area based on the sequence identification results and the lighting nodes, and set the initial weight of each lighting node based on the lighting association network diagram. The steps include: constructing a lighting association network diagram with a directed weighted graph structure using the lighting nodes as vertices and the associations between the lighting nodes as edges; assigning a transition probability value or normalized association strength, i.e., an initial weight, to each edge based on historical data; The dynamic adjustment module is used to collect the current activity trajectory data of personnel in real time through the infrared sensor network, and dynamically adjust the initial weights in the lighting association network diagram according to the current activity trajectory data of personnel to obtain the adjusted priority weights; A linkage control module, for performing linkage control on lighting nodes based on the adjusted priority weights; The interactive adjustment module is used to adjust the coverage of the lighting nodes through the human-computer interaction terminal based on the linkage control results of the lighting nodes.

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