LED induction control system and method based on man-machine interaction

Abstract

The invention provides an LED induction control system and method based on man-machine interaction, and belongs to the technical field of LED control, the energy efficiency, experience and intelligence are improved through data-driven dynamic illumination control and user feedback closed-loop optimization, on one hand, personnel activities are accurately matched through real-time trajectory analysis and weight dynamic adjustment, and on the other hand, the energy efficiency, experience and intellectualization are improved; only a necessary area is activated and the brightness is adaptively adjusted by using a wireless network, so that the invalid energy consumption is remarkably reduced; on the other hand, in combination with pre-judgment type illumination and gradient dimming, the method actively adapts to a personnel behavior mode and reduces visual discomfort, and the use comfort is improved; meanwhile, the system automatically identifies key lighting nodes based on historical data, improves the track precision through multi-sensor fusion, supports a user to remotely customize a coverage range, gives consideration to flexibility, privacy protection and self-learning ability, and finally forms a sustainable lighting solution with energy efficiency optimization, experience upgrading and intelligent management cooperation.

Description

Technical Field

[0001] The present invention relates to the technical field of LED control, and particularly 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, severely restricting the further improvement of lighting systems in terms of energy efficiency, comfort, and management effectiveness, which are specifically reflected in the following aspects: Currently, most lighting systems adopt zonal unified control or timed switching modes, and cannot dynamically adjust the lighting area and brightness according to actual human activities. For example, in large office buildings, even if only a few areas on a floor are occupied by people, the lights on the entire floor are still fully turned on, and the lighting in a large number of idle areas continues to consume energy; in the public areas of shopping malls during non-business hours, even if there is no one walking, the lights in the corridors remain fully lit, and the proportion of ineffective energy consumption is extremely high.

[0003] Traditional brightness adjustment relies on manual presetting or simple light sensors, and cannot adapt the brightness in real time according to the intensity and position changes of human activities. As shown in the reading area of a library, the lighting brightness requirements are different when readers walk between bookshelves and when they read at their seats, but the existing systems are difficult to accurately identify and dynamically adjust, resulting in excessive or insufficient brightness in some areas, causing energy waste.

[0004] Existing lighting systems are mostly passive responses and cannot predict human behavior and provide active lighting services. For example, when people enter the corridor at night, the lights need to be manually triggered or delayed to turn on, and the sudden change in brightness is likely to cause visual discomfort; when the meeting room scene changes, manual operation is required to switch the lighting mode, and it cannot be automatically adjusted according to the meeting process, affecting the meeting efficiency and experience.

[0005] Therefore, it is necessary to provide an LED induction control system and method based on human-computer interaction to solve the above technical problems. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides an LED induction control system and method based on human-computer interaction to solve the problems of extensive regional lighting control, lack of accuracy in brightness adjustment, and disconnection between lighting response and behavior patterns in existing lighting control technologies.

[0007] The LED induction control method based on human-computer interaction provided by the present invention includes the following steps: Divide the lighting space into multiple light irradiation areas, obtain the historical activity trajectory data of people in the lighting space, and perform sequence identification for each irradiation area to obtain the sequence identification result of the irradiation area; Analyze the lighting correlation between each irradiation area, and identify the irradiation area with the correlation center among all irradiation areas as the lighting node; Based on the sequence identification result and the lighting node, construct a lighting correlation network diagram between each irradiation area, and set the initial weight of each lighting node based on the lighting correlation network diagram; Through the infrared sensor network, collect the current activity trajectory data of personnel in real time, and dynamically adjust the initial weight in the lighting correlation network diagram according to the current activity trajectory data of personnel to obtain the adjusted priority weight; Based on the adjusted priority weight, perform linkage control on the lighting nodes; Based on the linkage control result of the lighting node, adjust the coverage range of the lighting node through the human-computer interaction terminal.

[0008] Preferably, the steps of dividing the lighting space into multiple lighting irradiation areas, obtaining the historical activity trajectory data of personnel in the lighting space, performing sequence identification for each irradiation area, and obtaining the sequence identification result of the irradiation area include: Divide the target lighting space into multiple independent lighting irradiation areas according to the physical layout or functional requirements; Obtain the historical activity trajectory data of personnel in the target lighting space through infrared sensors or cameras, including the moving path, staying position and duration; Assign a unique sequence identification to each irradiation area to form a regional identification set.

[0009] Preferably, the steps of analyzing the lighting correlation between each irradiation area, and identifying the irradiation area with the correlation center among all irradiation areas as the lighting node include: Based on the historical trajectory data, count the transfer frequency between different irradiation areas, that is, the correlation, and construct a transfer probability matrix; Based on the transfer probability matrix, select the irradiation area with the strongest correlation as the lighting node, and set the irradiation areas other than the lighting node as ordinary irradiation areas.

[0010] Preferably, the steps of constructing a lighting correlation network diagram between each irradiation area based on the sequence identification result and the lighting node, and setting the initial weight of each lighting node based on the lighting correlation network diagram include: Construct a lighting correlation network diagram with a directed weighted graph structure with the lighting node as the vertex and the correlation between lighting nodes as the edge; Assign a transfer probability value or a normalized correlation intensity, that is, the initial weight, to each edge according to the historical data.

[0011] Preferably, the method for collecting the current activity trajectory data of personnel in real time through the infrared sensor network and dynamically adjusting the initial weights in the lighting association network diagram according to the current activity trajectory data of personnel to obtain the adjusted priority weights includes the following steps: Detect the current position, moving direction and staying duration of personnel in real time through the deployed infrared sensor network; Dynamically adjust the initial lighting weights according to the distance between the lighting nodes and the current position of personnel and the staying duration through an adjusted calculation formula to obtain the adjusted priority weights. The adjusted calculation formula is: Priority weight = α * (1 / distance) + β * staying duration.

[0012] Preferably, the method for performing linkage control on lighting nodes based on the adjusted priority weights includes the following steps: Perform linkage control on the nodes whose priority weights are higher than the preset threshold and their associated irradiation areas according to the adjusted priority weights, including turning on the LEDs of the current lighting nodes and irradiation areas; Based on the result of starting the linkage control, dynamically adjust the brightness of the LEDs of each node according to the priority weight ratio, that is, the higher the priority weight, the stronger the brightness.

[0013] Preferably, the method for adjusting the coverage range of lighting nodes through a human-machine interaction terminal based on the linkage control result of lighting nodes includes the following steps: The terminal connected through the wireless network receives the user instruction, and feeds back the user instruction to the control element of the lighting node through the wireless network to adjust the physical parameters of the lighting node to obtain the interactive control adjustment result; Feed back the interactive control adjustment result to the association network diagram to synchronously update the initial weights.

[0014] The LED induction control system based on human-machine interaction, the control system includes: A sequence identification module, which is used to divide the lighting space into multiple lighting irradiation areas, obtain the historical activity trajectory data of personnel in the lighting space, and perform sequence identification on each irradiation area to obtain the sequence identification result of the irradiation area; A data analysis module, which is used to analyze the lighting correlation between each irradiation area, and identify the irradiation area with the correlation center from all irradiation areas as the lighting node; An association construction module, which is used to construct a lighting association network diagram between each irradiation area based on the sequence identification result and the lighting node, and set the initial weights of each lighting node based on the lighting association network diagram; A dynamic adjustment module, which 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; The linkage control module is used to perform linkage control on lighting nodes based on the adjusted priority weights; The interaction adjustment module is used to adjust the coverage range of lighting nodes through a human-machine interaction terminal based on the linkage control results of lighting nodes.

[0015] Compared with related technologies, the LED induction control system and method based on human-machine interaction provided by the present invention have the following beneficial effects: Through data-driven dynamic lighting control and user feedback closed-loop optimization, the present invention realizes triple improvements in energy efficiency, experience, and intelligence. On the one hand, by analyzing real-time trajectories and dynamically adjusting weights to accurately match personnel activities, only necessary areas are activated using a wireless network and the brightness is adaptively adjusted, significantly reducing ineffective energy consumption. On the other hand, combined with predictive lighting and gradual dimming, it actively adapts to personnel behavior patterns and reduces visual discomfort, improving the usage 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 the coverage range, taking into account flexibility, privacy protection, and self-learning ability, and finally forms a sustainable lighting solution that synergizes energy efficiency optimization, experience upgrade, and intelligent management. Description of the Drawings

[0016] Figure 1 It is a schematic flowchart of the LED induction control method based on human-machine interaction of the present invention; Figure 2 It is a system block diagram of the LED induction control system based on human-machine interaction of the present invention. Detailed Embodiments

[0017] The present invention will be further described below with reference to the drawings and embodiments.

[0018] Embodiment 1 As Figure 1 shown, the LED induction control method based on human-machine interaction includes the following steps: S1. Divide the lighting space into multiple light irradiation areas, obtain the historical activity trajectory data of personnel in the lighting space, perform sequence identification on each irradiation area, and obtain the sequence identification result of the irradiation area; S2. Analyze the lighting correlation between each irradiation area, and identify the irradiation area with the associated center from all irradiation areas as the lighting node; S3. Based on the sequence identification result and the lighting node, construct a lighting correlation network diagram between each irradiation area, and set the initial weight of each lighting node based on the lighting correlation network diagram; S4. Through the infrared sensor network, collect the current activity trajectory data of personnel in real time, and dynamically adjust the initial weight in the lighting correlation network diagram according to the current activity trajectory data of personnel to obtain the adjusted priority weight; S5. Based on the adjusted priority weights, perform linkage control on the lighting nodes; S6. Based on the linkage control results of the lighting nodes, adjust the coverage range of the lighting nodes through the human-machine interaction terminal.

[0019] In the specific implementation process, step S1 includes: S1.1. Divide the target lighting space into multiple independent lighting irradiation areas according to the physical layout or functional requirements.

[0020] Specifically, obtain the physical layout data of the target lighting space, including the type of the room and the partition situation, and determine the principle for dividing the lighting irradiation areas, that is, determined according to the type of the room and the partition situation. Exemplarily, if the type of the room is an office, then divide the lighting irradiation areas according to the distribution of the desks, the partition situation of the meeting room, etc.; if the type of the room is a corridor, then divide the lighting irradiation areas according to the length, the turning points, etc.

[0021] In this embodiment, the type of the room is a large office. The large office is rectangular, with multiple partitions in the middle forming different office areas, and there is also a small meeting room. Then, according to the arrangement of the desks, divide the area where each desk is located into an independent lighting irradiation area, and a total of 10 office areas are divided; at the same time, divide the meeting room into a separate area; in addition, near the entrance and exit of the office, since people come in and out frequently, also divide out an area respectively, and a total of 12 lighting irradiation areas are divided.

[0022] S1.2. Obtain the historical activity trajectory data of the personnel in the target lighting space through an infrared sensor or a camera, including the movement path, the staying position and the duration.

[0023] Specifically, access the infrared sensing network and the camera network, and start collecting the activity data of the personnel in the lighting space in the previous period (the period is half a month or one month) by starting the infrared sensor or the camera. The infrared sensor or the camera will record the movement path of the personnel in real time, that is, the trajectory of the personnel moving from one position to another; at the same time, record the staying position and the staying duration of the personnel at different positions, and store the collected historical activity trajectory data in a database or a storage device for subsequent analysis and processing.

[0024] S1.3. Assign a unique sequence identifier to each irradiation area to form a set of area identifiers.

[0025] Specifically, the areas are numbered according to their division order and functional characteristics, and a unique serial identifier is assigned to each irradiation area. The identifier can be a number, a letter, or a combination of numbers and letters. To ensure the uniqueness and readability of the identifier, the serial identifiers of all irradiation areas are aggregated to form a regional identifier set.

[0026] In this embodiment, among the 12 lighting areas of a large office, according to the order of area division, the area near the entrance is marked as "1", and then the 10 office areas are marked as "2-11" and the conference room is marked as "12". Thus, a set of area identifiers {1,2,3,...,12} is formed. In subsequent analysis, these identifiers can be used to accurately refer to each lighting area.

[0027] In the specific implementation process, step S2 includes: S2.1. Based on the historical trajectory data, the statistical personnel calculate the transfer frequency, i.e., correlation, between different irradiation areas and construct a transfer probability matrix.

[0028] 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 sorted 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, rows and columns correspond to different irradiation areas, and the elements in the matrix are Indicates the irradiation area Transfer to irradiation area probability.

[0029] S2.2. Select the irradiation area with the strongest correlation as the lighting node based on the transition probability matrix, and set the irradiation areas other than the lighting node as ordinary irradiation areas.

[0030] Specifically, for each irradiation area, calculate its correlation index according to the transition probability matrix, specifically, calculate the sum of the transition probabilities between the determined area and all other areas. Then, compare the correlation indexes of all irradiation areas, and select the irradiation area with the largest correlation index as the lighting node, and set the other irradiation areas except the lighting node as ordinary irradiation areas. It should be noted that if there are multiple irradiation areas with the same and largest correlation index, one of the irradiation areas can be selected as the lighting node according to actual needs, or multiple irradiation areas can be selected as the lighting nodes.

[0031] In the specific implementation process, step S3 includes: S3.1. Construct a lighting correlation network diagram with a directed weighted graph structure, with the lighting nodes as vertices and the correlation between the lighting nodes as edges.

[0032] Specifically, based on the constructed transition probability matrix, analyze the transfer relationship between the lighting nodes. If there is a personnel transfer from lighting node i to lighting node j, draw a directed edge between the two vertices, with the direction from i to j. For example, if the transition probability matrix shows that personnel often transfer from area A to area B, then draw a directed edge from A to B between vertex A and vertex B. Combine all the determined vertices and edges to form a directed weighted graph structure, that is, the lighting correlation network diagram.

[0033] In this embodiment, in the constructed lighting correlation network diagram, vertices: Node 1 (meeting room), Node 2 (office area), Node 3 (rest area); edges and directions: draw a directed edge from Node 1 to Node 2, indicating that personnel transfer from the meeting room to the office area; draw a directed edge from Node 2 to Node 3, indicating that personnel transfer from the office area to the rest area. There is no edge pointing from Node 3 to other nodes because few personnel transfer from the rest area to other areas.

[0034] S3.2. Assign a transition probability value or a normalized correlation strength, that is, an initial weight, to each edge according to historical data.

[0035] Specifically, directly obtain the transition probability values of the edges between the lighting nodes from the transition probability matrix constructed in step S2.1. If the correlation strength is unified to a specific range (such as between 0 and 1), the transition probability values can be normalized. Specifically, divide the transition probability values of all edges by the maximum value of the transition probability values of all edges; use the obtained transition probability values or the normalized correlation strength as the initial weight of each edge and assign it to the corresponding edge of the lighting correlation network diagram.

[0036] In the specific implementation process, step S4 includes: S4.1. Real-time detect the current position, moving direction and staying duration of the person through the deployed infrared sensor network.

[0037] Specifically, when the infrared sensor detects a person, the detection signal is converted into a digital signal through the signal processing circuit. According to the detection results of each infrared sensor and in combination with the pre-set irradiation area division information, the irradiation area where the person is currently located, that is, the current position of the person, is determined. Then, the moving direction is judged by analyzing the changes in the position of the person at consecutive time points. For example, within a very short time interval, if the person moves from the irradiation area covered by infrared sensor A to the irradiation area covered by infrared sensor B, then it can be judged that the moving direction of the person is from A to B, thus determining the moving direction of the person. When the person enters a certain irradiation area, timing starts. As long as the person is continuously detected by the infrared sensor within this irradiation area, the timer will continue to accumulate time. When the person leaves this irradiation area, the timer stops timing, and the staying duration of the person in this irradiation area is obtained.

[0038] Exemplarily, in an office lighting space, multiple infrared sensors are deployed, covering different office areas and corridor areas. At a certain moment, infrared sensor 1 detects that a person has entered the office area A covered by it, and records the current position of the person as office area A. Subsequently, at the next time point, infrared sensor 2 detects that the person has entered the office area B covered by it, and sensor 1 no longer detects the person. According to the detection results of these two sensors, it is judged that the moving direction of the person is from office area A to office area B. At the same time, the time interval from when the person enters office area A to when the person leaves this irradiation area is the staying duration of the person in office area A.

[0039] S4.2. Dynamically adjust the initial lighting weight according to the distance between the lighting node and the current position of the person and the staying duration, and obtain the adjusted priority weight. The adjusted calculation formula is: Priority weight = α * (1 / distance) + β * staying duration.

[0040] Specifically, according to the current position of the person determined in step S4.1 and the positions of the lighting nodes determined in step S2, calculate the distances between the current position of the person and each lighting node. Use the Euclidean distance formula (in a two-dimensional space). Exemplarily, if the lighting space is a rectangular area, establish a rectangular coordinate system, represent the current position of the person and the positions of the lighting nodes as coordinate points, and then calculate the straight-line distance between the two points. In this embodiment, in the calculation formula of Priority weight = α * (1 / distance) + β * staying duration, the set parameters are α = 0.5 and β = 0.3.

[0041] In the specific implementation process, step S5 includes: S5.1. Based on the adjusted priority weights, initiate linkage control for the nodes whose priority weights are higher than the preset threshold and their associated illumination areas, including turning on the LEDs of the current lighting nodes and illumination areas.

[0042] Specifically, in areas where personnel activities are relatively frequent, the threshold can be set relatively low to more sensitively respond to personnel activities; while in areas with less personnel activities, the threshold can be set higher to avoid unnecessary lighting activation. In this embodiment, a preset threshold for the priority weight is 5. When the priority weight of a certain lighting node is higher than the preset threshold, this lighting node is determined as the node that needs to initiate linkage control. At the same time, according to the lighting association network diagram constructed in step S3, determine the illumination areas associated with this lighting node. The associated illumination area generally refers to the area that has a direct or indirect association with this lighting node on the personnel activity transfer path. Send a control signal to the LED control modules of the lighting nodes and their associated illumination areas that are determined to require linkage control, and turn on the LEDs in these areas. Specifically, transmit the control signal to the corresponding LED control elements of the existing technology through wired or wireless communication methods (such as ZigBee, Wi-Fi) to achieve the on-off control of the lighting equipment.

[0043] S5.2. Based on the result of initiating the linkage control, dynamically adjust the brightness of the LEDs of each node according to the priority weight ratio, that is, the higher the priority weight, the stronger the brightness.

[0044] Specifically, according to the characteristics of the lighting equipment and lighting requirements, determine the adjustable range of the LED brightness. For each lighting node that has initiated the linkage control, calculate the proportion of its priority weight in the total priority weights of all lighting nodes that have initiated the linkage control. The formula is: , where is the serial number of the current lighting node, and n is the total number of lighting nodes that have initiated the linkage control. Then, according to the calculated brightness adjustment ratio, adjust the LED brightness of each lighting node to the corresponding value, , where and respectively represent the minimum brightness and maximum brightness of the LED brightness. In this way, the higher the priority weight of the lighting node, the stronger the brightness of its LED.

[0045] In the specific implementation process, step S6 includes: S6.1. Receive user instructions through a terminal connected to the wireless network, and use the wireless network to feedback the user instructions to the control elements of the lighting nodes to adjust the physical parameters of the lighting nodes and obtain the interactive control adjustment result.

[0046] Specifically, users can use terminal devices such as mobile phones and tablet computers that have the function of wireless network connection. The terminal device is connected to the control element of the lighting system through wireless network technologies such as Wi-Fi and Bluetooth. The user inputs specific values through the terminal device to change the parameters of the LED coverage range in the lighting node, such as the irradiation angle and irradiation distance of the LED lamp. After the lighting node control element completes the physical parameter adjustment, it feeds back the adjustment result and locates the lighting node in the lighting association network diagram constructed in step S3 according to the identification information of the lighting node.

[0047] S6.2. Feed back the interactive control adjustment result to the association network diagram to synchronously update the initial weight.

[0048] According to the interactive control adjustment result, that is, each parameter after the lighting node control element completes the physical parameter adjustment, re-evaluate the relevance and update the initial weight of the lighting node in the lighting association network diagram.

[0049] Embodiment 2 As Figure 2 shown, the LED induction control system based on human-computer interaction includes: A sequence identification module, which is used to divide the lighting space into multiple light irradiation areas, obtain the historical activity trajectory data of people in the lighting space, perform sequence identification on each irradiation area, and obtain the sequence identification result of the irradiation area; A data analysis module, which is used to analyze the lighting relevance between each irradiation area, and identify the irradiation area of the association center from all irradiation areas as the lighting node; An association construction module, which is used to construct a lighting association network diagram between each irradiation area based on the sequence identification result and the lighting node, and set the initial weight of each lighting node based on the lighting association network diagram; A dynamic adjustment module, which is used to collect the current activity trajectory data of people in real time through the infrared sensor network, and dynamically adjust the initial weight in the lighting association network diagram according to the current activity trajectory data of people to obtain the adjusted priority weight; A linkage control module, which is used to perform linkage control on the lighting node based on the adjusted priority weight; An interactive adjustment module, which is used to adjust the coverage range of the lighting node through the human-computer interaction terminal based on the linkage control result of the lighting node.

[0050] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0051] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and this program can be stored in a computer-readable storage medium. The storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), or other optical disc memories, magnetic disk memories, tape memories, or any other medium that can be used to carry or store data and is computer-readable.

[0052] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of another identical element in the process, method, commodity or device comprising the element.

Claims

1. An LED induction control method based on human-computer interaction, characterized in that The control method includes the following steps: Divide the lighting space into multiple lighting areas, obtain the historical activity trajectory data of the personnel in the lighting space, assign sequence identifiers to each lighting area, and obtain the sequence identifier result of the lighting area; Analyze the lighting correlation between each lighting area, and identify the lighting area with the correlation center from all lighting areas as the lighting node; Based on the sequence identifier result and the lighting node, construct a lighting correlation network diagram between each lighting area, and set the initial weight of each lighting node based on the lighting correlation network diagram; Through the infrared sensor network, collect the current activity trajectory data of the personnel in real time, and dynamically adjust the initial weight in the lighting correlation network diagram according to the current activity trajectory data of the personnel to obtain the adjusted priority weight; Based on the adjusted priority weight, perform linkage control on the lighting nodes; Based on the linkage control result of the lighting node, adjust the coverage range of the lighting node through the human-computer interaction terminal.

2. The LED induction control method based on human-computer interaction according to claim 1, wherein The steps of dividing the lighting space into multiple lighting areas, obtaining the historical activity trajectory data of the personnel in the lighting space, assigning sequence identifiers to each lighting area, and obtaining the sequence identifier result of the lighting area include: Divide the target lighting space into multiple independent lighting areas according to the physical layout or functional requirements; Obtain the historical activity trajectory data of the personnel in the target lighting space through infrared sensors or cameras, including the movement path, stay position and duration; Assign a unique sequence identifier to each lighting area to form a set of area identifiers.

3. The LED induction control method based on human-computer interaction according to claim 2, characterized in that, The steps of analyzing the lighting correlation between each lighting area, and identifying the lighting area with the correlation center from all lighting areas as the lighting node include: Based on the historical trajectory data, count the transfer frequency between different lighting areas, that is, the correlation, and construct a transfer probability matrix; Based on the transfer probability matrix, select the lighting area with the strongest correlation as the lighting node, and set the lighting areas other than the lighting node as ordinary lighting areas.

4. The LED induction control method based on human-computer interaction according to claim 3, wherein, The steps of constructing a lighting correlation network diagram between each lighting area based on the sequence identifier result and the lighting node, and setting the initial weight of each lighting node based on the lighting correlation network diagram include: Construct a lighting correlation network diagram with a directed weighted graph structure with the lighting node as the vertex and the correlation between the lighting nodes as the edge; Assign a transfer probability value or a normalized correlation intensity, that is, the initial weight, to each edge according to the historical data.

5. The LED induction control method based on human-computer interaction according to claim 4, wherein The steps of collecting the current activity trajectory data of the personnel in real time through the infrared sensor network, and dynamically adjusting the initial weight in the lighting correlation network diagram according to the current activity trajectory data of the personnel to obtain the adjusted priority weight include: Through the deployed infrared sensor network, detect the current position, movement direction and stay duration of the personnel in real time; According to the distance and stay duration between the lighting node and the current position of the personnel, dynamically adjust the lighting initial weight through the adjusted calculation formula to obtain the adjusted priority weight, where the adjusted calculation formula is: priority weight = α*(1 / distance)+β*stay duration.

6. The LED induction control method based on human-computer interaction according to claim 5, characterized in that, The steps of performing linkage control on the lighting nodes based on the adjusted priority weight include: According to the adjusted priority weights, start the linkage control for the nodes with priority weights higher than the preset threshold and their associated illumination areas, including lighting the LEDs of the current lighting nodes and illumination areas; Based on the results of the started linkage control, dynamically adjust the brightness of the LEDs of each node according to the proportion of the priority weights, that is, the higher the priority weight, the stronger the brightness.

7. The LED induction control method based on human-computer interaction according to claim 6, characterized in that, Based on the results of the linkage control of the lighting nodes, through the human-machine interaction terminal, adjust the coverage range of the lighting nodes. The steps include: The terminal connected through the wireless network receives the user instruction, and uses the wireless network to feedback the user instruction to the control element of the lighting node, adjust the physical parameters of the lighting node, and obtain the interactive control adjustment result; Feedback the interactive control adjustment result to the associated network diagram to synchronously update the initial weight.

8. An LED induction control system based on human-computer interaction, applied to the LED induction control method based on human-computer interaction according to any one of claims 1-7, characterized in that, The control system includes: A sequence identification module, which 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 result of the lighting area; A data analysis module, which is used to analyze the lighting correlation between each lighting area, and identify the lighting area of the correlation center from all lighting areas as the lighting node; An association construction module, which is used to construct a lighting association network diagram between each lighting area based on the sequence identification result and the lighting node, and set the initial weight of each lighting node based on the lighting association network diagram; A dynamic adjustment module, which is used to collect the current activity trajectory data of people in real time through the infrared sensor network, and dynamically adjust the initial weight in the lighting association network diagram according to the current activity trajectory data of people to obtain the adjusted priority weight; A linkage control module, which is used to perform linkage control on the lighting nodes based on the adjusted priority weights; An interactive adjustment module, which is used to adjust the coverage range of the lighting nodes through the human-machine interaction terminal based on the results of the linkage control of the lighting nodes.

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