Device and method for generating Internet of Things lamp deployment map

Abstract

The invention belongs to the field of intelligent lighting systems, and discloses an Internet of Things lamp deployment map generation device and method.The Internet of Things lamp deployment map generation device comprises a positioning tool, a gateway and a system platform, and the system platform is in communication connection with Internet of Things lamps through the gateway; the control module is used for sending a control command containing light emitting parameters to control each Internet of Things lamp to emit light and flicker according to different rules; the positioning tool continuously collects light emitting images of a plurality of Internet of Things lamps through a camera, extracts physical positions and light emitting parameters of the Internet of Things lamps through a positioning processor and sends the physical positions and the light emitting parameters to the system platform; and the system platform generates a lamp deployment map according to the physical position of each Internet of Things lamp, and obtains the network address of the corresponding Internet of Things lamp according to the light emitting parameter, thereby realizing the state and control of each Internet of Things lamp. According to the invention, the generation efficiency of the Internet of Things lamp deployment map and the management efficiency of the lamps are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent lighting technology, and in particular to a device and method for generating an Internet of Things lamp deployment map. Background Art

[0002] The smart lighting market is growing rapidly, with applications expanding from commercial to residential and urban lighting. To improve the management efficiency of IoT lighting fixtures, generating deployment maps after installation can be an effective method, enhancing the management and maintenance efficiency of IoT lighting equipment. To accelerate the adoption and adoption of smart lighting systems, it is necessary to improve system usability and ease of installation, simplify the device installation and commissioning process, and streamline the lighting network access process to enable the rapid generation of IoT lighting deployment maps.

[0003] There are several common ways to connect IoT lamps to the network: 1. Attach a label to the device, scan the QR code with a mobile phone to obtain the device's address, password, and other information, and then add the device to the gateway. This method is reliable, but requires on-site installation and commissioning personnel to be trained and familiar with the device addition and installation process. It also requires sufficient space in the housing for attaching the label, and installation will be impossible if the label is damaged. 2. Attach a label to the device, and the mobile phone uses NFC communication to obtain the device's address, password, and other information. This solution is easy to debug, but requires sufficient space in the device, and the NFC antenna needs to be separated from the device's internal circuitry, increasing costs. 3. Attach a label to the device to actively report information such as the device's address and password to the gateway, and then the corresponding address and device are determined by turning the light on and off. For a large number of devices, the device instructions can be very complex.

[0004] Since IoT lighting fixtures are typically installed on rooftops, the aforementioned network access solutions require close proximity and are complex to implement. Therefore, a new network access solution is needed to enable IoT lighting deployment map generation. Summary of the Invention

[0005] In order to simplify the installation and debugging process and network access process of IoT lamps, and thereby improve the efficiency of generating IoT lamp deployment maps, the present invention provides an IoT lamp deployment map generation device and method.

[0006] To solve the above technical problems, the present invention adopts the following technical solution: a device for generating a deployment map of IoT lamps, comprising a positioning tool, a gateway, and a system platform. The system platform is connected to the IoT lamps via the gateway and is used to send control commands containing lighting parameters to control each IoT lamp to flash with different patterns.

[0007] The positioning tooling includes: a direction sensor, a camera, a positioning processor, and a communication module; the camera is used to continuously capture luminous images of multiple IoT lamps, and the positioning processor is used to extract the coordinates of each IoT lamp in the luminous image and, combined with the direction information captured by the direction sensor, determine the physical position of each IoT lamp in the image; the positioning tooling is also used to identify the on / off state of each IoT lamp in multiple consecutive luminous images, and obtain the luminous parameters of each IoT lamp based on the on / off state of each IoT lamp within a time sequence, and then package the physical position and luminous parameters of each IoT lamp to generate first data and send it to the system platform;

[0008] The system platform is used to generate a lamp deployment map based on the physical location of each IoT lamp, obtain the network address of the corresponding IoT lamp based on the luminous parameters, and associate the status and control of each IoT lamp in the lamp deployment map.

[0009] The positioning processor uses a convolutional neural network to extract the coordinates of each lamp in the luminous image and identify the switch status of each IoT lamp in each luminous image.

[0010] The convolutional neural network used by the positioning processor is a trained YOLO model; the training method of the YOLO model is:

[0011] Get the YOLO model pre-trained on general data;

[0012] Collecting luminous images of IoT lamps when all are in the on state and luminous images of IoT lamps when all are in the off state;

[0013] Mark the frames and status of all IoT lamps in the captured image as training data for the YOLO model;

[0014] The YOLO model is trained using labeled training data to obtain a trained YOLO model.

[0015] The positioning processor determines the physical location of each IoT lamp in the luminous image in the following manner:

[0016] (1) Obtain the two-dimensional coordinates of the pixel points corresponding to each IoT lamp in the luminous image;

[0017] (2) Establishing a camera coordinate system with the camera location as the coordinate origin, and determining the three-dimensional coordinates of each IoT lamp in the camera coordinate system;

[0018] (3) Determine the rotation matrix of the camera coordinate system relative to the physical space coordinate system based on the direction information collected by the direction sensor;

[0019] (4) Based on the rotation matrix and the three-dimensional coordinates of each IoT lamp in the camera coordinate system, calculate the three-dimensional coordinates of the pixel points corresponding to each IoT lamp in the luminous image in the physical space coordinate system; the calculation formula is:

[0020]

[0021] Where (x′, y′, z′) represents the three-dimensional coordinates of the pixel in the physical space coordinate system, R represents the rotation matrix, and (x, y, z) represents the three-dimensional coordinates of the pixel in the camera coordinate system.

[0022] (5) According to the equation of the plane of the IoT lamp corresponding to the luminous image in the physical coordinate system, the coordinates of each IoT lamp in the physical space coordinate system are determined.

[0023] The specific method for the system platform to send control commands to control each IoT lamp to emit light and flash in different patterns is as follows:

[0024] The system platform obtains the network address of each IoT lamp;

[0025] The system platform configures different luminous parameters according to the network address of each IoT lamp;

[0026] The system platform sends control commands including lighting parameters to each corresponding IoT lamp according to the network address;

[0027] The IoT lamp parses the luminous parameters and flashes according to the binary coded data bits of the luminous parameters. When the coded data bit is 1, the light continues to be on for T time, and when the coded data bit is 0, the light continues to be off for T time. The T time is 3 to 10 times the camera sampling interval.

[0028] In the lamp deployment map, a lamp status display module, a lamp information display module, and a lamp control module are set on the device icon of each IoT lamp; the lamp status display module is used to obtain the on and off status of the IoT lamp and display it; the lamp information display module is used to display detailed information of the IoT lamp; the lamp control module is used to generate control commands based on input commands and send them to the corresponding IoT lamp to control the switching and dimming of the corresponding IoT lamp.

[0029] The IoT lighting fixture deployment map generating device further includes a mobile terminal, the mobile terminal being used to send an automatic search command to the system platform; receiving the first data sent by the positioning tool and sending the data to the system platform via a mobile network; obtaining the lighting fixture deployment map generated by the system platform, and sending a lighting fixture deployment map adjustment command to the platform based on manually input information;

[0030] The system platform is also used to send control commands to IoT lamps through the gateway based on the received automatic search commands to control each IoT lamp to emit light and flash, and has been used to adjust the lamp deployment map based on the adjustment commands.

[0031] The mobile terminal is a mobile phone or a tablet, and the mobile terminal is connected to the positioning tool via a USB;

[0032] The gateway includes a power module, a processor, a carrier communication circuit, and an Ethernet communication module. The gateway is connected to the IoT lamp through the carrier communication circuit and is connected to the system platform through the Ethernet communication module.

[0033] In addition, the present invention also provides a method for generating an IoT lamp deployment map, which is implemented based on the IoT lamp deployment map generation device, and includes the following steps:

[0034] Step 1: The system platform obtains the network address of each IoT lamp and configures different lighting parameters according to the network address of each IoT lamp; then sends the control command including the lighting parameters to each corresponding IoT lamp according to the network address;

[0035] Step 2: The IoT lamp analyzes the luminous parameters and flashes according to the binary coded data bits of the luminous parameters;

[0036] Step 3: Place the positioning tool under the IoT lamp, continuously capture the light images of multiple IoT lamps through the camera, and obtain the light parameters and physical location of each IoT lamp;

[0037] Step 4: Move the positioning tool to a new location and repeat step 3 until the luminous parameters and physical locations of all IoT lamps are obtained;

[0038] Step 5: Send the lighting parameters and physical locations of all IoT lamps to the system platform. The system platform is used to generate a lamp deployment map based on the physical location of each IoT lamp, obtain the network address of the corresponding IoT lamp based on the lighting parameters, and associate the status and control of each IoT lamp in the lamp deployment map;

[0039] Step 6: The system platform controls all lamps to stop flashing through the gateway according to the stop search command.

[0040] In step 4, when the positioning tool is moved to a new position, if the light-emitting image at the new position includes an IoT lamp whose physical location information has been previously obtained, the physical locations of other lamps in the new light-emitting image are calculated based on the physical location of the IoT lamp;

[0041] If the luminous image at the new location does not contain any IoT lamps whose physical location information has been obtained, the relative position information of the IoT lamps in the two sets of images is manually adjusted in the lamp deployment map.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention provides a device and method for generating an IoT lamp deployment map. By controlling IoT lamps to flash and illuminate, and using cameras in positioning fixtures to capture illuminated images, the device's network address information is obtained through image recognition using a neural network. This simplifies the process of connecting lamps to the network in a lighting system, making them easier for on-site construction, commissioning, and maintenance personnel, while avoiding the drawbacks of traditional methods such as code scanning. Furthermore, the device's network address information is simultaneously determined while the device's relative physical location is obtained. By coordinating the generation of a lamp deployment map with the system platform, lamp management can be upgraded from name-based to graphical management. The location of each lamp can be intuitively viewed on the lamp deployment map, facilitating maintenance and daily use. For example, when grouping devices, previously, it was necessary to add each lamp to the group individually, which was very cumbersome. However, with graphical management, the desired device can be selected and added to the newly created group, enabling faster device grouping. Furthermore, the present invention requires no hardware modifications to the IoT lamp structure, requiring only software support, significantly improving its applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a structural diagram of a device for generating an IoT lighting fixture deployment map proposed in the first embodiment of the present invention;

[0045] Figure 2 This is a circuit structure block diagram of the positioning tool in an embodiment of the present invention;

[0046] Figure 3 Schematic diagram of the mechanical structure of the positioning tool in an embodiment of the present invention;

[0047] Figure 4 1 is a circuit structure block diagram of a gateway in an embodiment of the present invention;

[0048] Figure 5 This is a circuit structure block diagram of an IoT lamp according to an embodiment of the present invention;

[0049] Figure 6 Schematic diagram of coding in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments and drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] Example 1

[0052] like Figure 1 As shown, the first embodiment of the present invention provides an IoT lamp deployment map generation device, including a positioning tool, a gateway, and a system platform.

[0053] In this embodiment, the system platform is connected to the IoT lamps through a gateway for sending control commands containing lighting parameters to control each IoT lamp to flash with different patterns.

[0054] like Figures 2-3 As shown, the positioning tooling in this embodiment includes: a direction sensor, a camera, a positioning processor, and a communication module; the camera is used to continuously collect luminous images of multiple Internet of Things lamps, and the positioning processor is used to extract the coordinates of each Internet of Things lamp in the luminous image, and determine the physical position of each Internet of Things lamp in the image in combination with the direction information collected by the direction sensor; it is also used to identify the switching state of each Internet of Things lamp in multiple consecutive luminous images, and obtain the luminous parameters of each Internet of Things lamp according to the switching state of each Internet of Things lamp in the time sequence, and package the physical position and luminous parameters of each Internet of Things lamp to generate first data and send it to the system platform.

[0055] In this embodiment, the system platform is used to generate a lamp deployment map based on the physical location of each IoT lamp, obtain the network address of the corresponding IoT lamp based on the luminous parameters, and associate the status and control of each IoT lamp in the lamp deployment map.

[0056] Specifically, in this embodiment, the positioning processor uses a convolutional neural network to extract the coordinates of each lamp in the luminous image and identify the switch status of each IoT lamp in each luminous image.

[0057] Furthermore, in this embodiment, the convolutional neural network used by the positioning processor is a trained YOLO model; the training method of the YOLO model is:

[0058] Get the YOLO model pre-trained on general data;

[0059] Collecting luminous images of IoT lamps when all are in the on state and luminous images of IoT lamps when all are in the off state;

[0060] Mark the frames and status of all IoT lamps in the captured image as training data for the YOLO model;

[0061] The YOLO model is trained using labeled training data to obtain a trained YOLO model.

[0062] In this embodiment, a general image detection model, such as the YOLO model, is used to simultaneously predict the position and category of the lamp (on or off status). Initialization training of the model: A general pre-trained YOLO model is used, and the parameters are fine-tuned on this basis. Select a position and orientation to place the positioning tool, turn on all lamps, and use the tool to obtain images; turn off all lamps and use the tool to obtain images. Mark the outer frame and status (on or off) of the lamps in the two images. Adjust the position and orientation of the tool and repeat the above operations to obtain the neural network training data.

[0063] Furthermore, an IoT lamp deployment map generating device of this embodiment also includes a mobile terminal, which is used to send an automatic search command to the system platform; is also used to receive the first data sent by the positioning tool and send it to the system platform via the mobile network; is also used to obtain the lamp deployment map generated by the system platform, and is used to send a lamp deployment map adjustment command to the platform based on manually input information.

[0064] The tooling can capture images and perform recognition functions. The mobile app displays the image along with the frame and on / off status of the lamps in the image. During system operation, all lamps can be commanded to turn on or off, and the user can view the image recognition results. If a lamp in the image is found to have an incorrect frame or on / off status, on-site personnel can correct the result by marking the correct frame and on / off status on the image and uploading the image and markings to the platform via the mobile app. By adjusting the position and orientation of the tooling and repeating these steps, sufficient neural network training data can be obtained. Fine-tuning the model can then be initiated to continuously improve the adaptability and accuracy of the neural network model.

[0065] Furthermore, in this embodiment, the positioning processor determines the physical position of each IoT lamp in the luminous image by:

[0066] (1) Obtain the two-dimensional coordinates of the pixel points corresponding to each IoT lamp in the luminous image;

[0067] (2) Establishing a camera coordinate system with the camera location as the coordinate origin, and determining the three-dimensional coordinates of each IoT lamp in the camera coordinate system;

[0068] (3) Determine the rotation matrix of the camera coordinate system relative to the physical space coordinate system based on the direction information collected by the direction sensor;

[0069] (4) Based on the rotation matrix and the three-dimensional coordinates of each IoT lamp in the camera coordinate system, calculate the three-dimensional coordinates of the pixel points corresponding to each IoT lamp in the luminous image in the physical space coordinate system; the calculation formula is:

[0070]

[0071] Where (x′, y′, z′) represents the three-dimensional coordinates of the pixel in the physical space coordinate system, R represents the rotation matrix, and (x, y, z) represents the three-dimensional coordinates of the pixel in the camera coordinate system.

[0072] (5) According to the equation of the plane of the IoT lamp corresponding to the luminous image in the physical coordinate system, the coordinates of each IoT lamp in the physical space coordinate system are determined.

[0073] The coordinate calibration principle of the present invention is introduced below.

[0074] 1. Mapping image coordinates to camera coordinates;

[0075] Each pixel has a coordinate in the image coordinate system, which is a two-dimensional coordinate (Xp, Yp). A three-dimensional coordinate system is established with the camera as the coordinate origin. The camera's central axis is the Z axis, and the image plane is perpendicular to the Z axis. In the camera coordinate system, the Z coordinate of all pixels is Zconst (a constant), and the coordinates of each pixel are (Xp, Yp, Zconst).

[0076] 2. Mapping camera coordinates to physical space coordinates;

[0077] Establish a physical space coordinate system (Xw, Yw, Zw) (the two horizontal coordinate axes are Xw and Zw. To obtain the absolute orientation, the Xw axis is defined as the north-south direction, the Zw axis is the east-west direction, and the Yw axis is the direction perpendicular to the horizontal plane and upward). Initially, assume that the camera coordinate system coincides with the physical space coordinate system, and the camera is at the origin. When the camera is in use, the camera's height change is not considered. Only the camera's horizontal rotation angle and pitch angle are adjusted. In the physical space coordinate system, this corresponds to the camera rotating around Yw and Xw. For a coordinate point P (X, Y, Z) in the camera space, if the camera is rotated around the Yw and Xw axes of the physical space coordinate system, the coordinates of the point can be calculated using the rotation matrix. The rotation matrix is ​​represented as R, and the coordinates of the point after rotation are P′ (X′, Y′, Z′). The coordinate transformation relationship can be given by the above formula (1).

[0078] The rotation matrix R is related to the rotation angle of the camera: the camera rotates around the Xw axis, that is, the pitch angle. Here, the optimal choice is 45 degrees, such as Figure 3 As shown, camera 1 is positioned at a 45-degree angle to the bottom surface 2 of the positioning fixture; other angles are also possible. The camera rotates about the Y-axis, i.e., the azimuth angle, which is determined by the fixture's built-in orientation sensor. As described above, point P in the camera coordinate system can be transformed into coordinate P' in physical space by obtaining the camera's absolute pitch and azimuth angles.

[0079] 3. Calculation of the coordinates of the imaged “object” in the physical space coordinate system

[0080] According to imaging principles, the object, image, and camera are aligned. In the camera coordinate system, connecting the origin and the pixel yields the equation of a straight line. This line must pass through the object, and can be considered the ray of light that formed the image at that pixel. In the physical coordinate system, this line also passes through the origin and must pass through the object.

[0081] For any pixel in the image, its coordinates Pc in the camera coordinate system can be obtained, and then its coordinates Pw in the physical space coordinate system can be obtained. By connecting Pw and the origin, the equation of the straight line in the physical space coordinate system can be listed. The equation of the plane where the lamp is located (assuming that the lamps are all on the same plane) in the physical space coordinate system can also be obtained (if the height of the camera from the plane where the lamp is located is H, then the plane equation is Yw = H). Solving the intersection of the above straight line and the plane, the coordinates of the "object" corresponding to the pixel in the physical space coordinate system can be known. The position of the lamp in the image coordinate system can be obtained through image recognition, and then the physical coordinates of the lamp in the real space can be solved through the above-mentioned calculation algorithm. Optimally, dual cameras can be used, so that even if the LED lamp is not on the same plane, its spatial physical coordinates can be given.

[0082] Furthermore, in this embodiment, the specific method for the system platform to send control commands to control each IoT lamp to emit light and flash in different patterns is:

[0083] The system platform obtains the network address of each IoT lamp;

[0084] The system platform configures different luminous parameters according to the network address of each IoT lamp;

[0085] The system platform sends control commands including lighting parameters to each corresponding IoT lamp according to the network address;

[0086] The IoT lamp parses the luminous parameters and flashes according to the binary coded data bits of the luminous parameters. When the coded data bit is 1, the light continues to be on for T time, and when the coded data bit is 0, the light continues to be off for T time. The T time is 3 to 10 times the camera sampling interval.

[0087] Furthermore, in this embodiment, in the lamp deployment map, a lamp status display module, a lamp information display module and a lamp control module are set on the device icon of each IoT lamp; the lamp status display module is used to obtain the on and off status of the IoT lamp and display it; the lamp information display module is used to display detailed information of the IoT lamp, and the lamp control module is used to generate a control command based on the input command and send it to the corresponding IoT lamp to control the switching and dimming of the corresponding IoT lamp.

[0088] The system platform is also used to send control commands to IoT lamps through the gateway based on the received automatic search commands to control each IoT lamp to emit light and flash, and has been used to adjust the lamp deployment map based on the adjustment commands.

[0089] Furthermore, in this embodiment, the mobile terminal is a mobile phone or tablet, connected to the positioning tool via USB. The positioning tool can be powered by the mobile phone and achieve two-way communication with the mobile phone. The image captured by the camera is displayed in real time on the mobile phone screen. The direction sensor is a geomagnetic sensor. The system platform and mobile terminal app can realize the status display and user interface of the entire system.

[0090] Specifically, if Figure 4 As shown, the gateway includes a power module, a processor, a carrier communication circuit, and an Ethernet communication module. The gateway communicates with the IoT lighting fixtures via the carrier communication circuit and with the system platform via the Ethernet communication module. Furthermore, the gateway also includes a Bluetooth module and a Wi-Fi module to enable Bluetooth and Wi-Fi communication.

[0091] Example 2

[0092] A second embodiment of the present invention provides a method for generating an IoT lamp deployment map, which is implemented based on the IoT lamp deployment map generation device described in the first embodiment and includes the following steps:

[0093] Step 1: The system platform obtains the network address of each IoT lamp and configures different lighting parameters according to the network address of each IoT lamp; then sends the control command including the lighting parameters to each corresponding IoT lamp according to the network address.

[0094] In this embodiment, after the IoT lamps and gateways are powered on, the system platform can be notified through the mobile phone APP to start the automatic search function; the system platform sends a "start automatic network access" command to the gateway; after receiving the command, the gateway sends a control command including the lighting parameters to the IoT lamps through the communication interface.

[0095] Step 2: The IoT lamp analyzes the luminous parameters and flashes according to the binary coded data bits of the luminous parameters.

[0096] like Figure 5 As shown, after receiving the control command, the IoT lamp uses the dimming circuit to control the on and off of the LED lamp, and sends the luminous parameters of the IoT lamp in the form of light, and the sending method is as described above. Among them, the luminous parameters can be parameters configured by the system platform according to the device address, or they can be device information (including device address ID, device password, etc.), or they can be a summary and compression of the above information to shorten the sending time. In this embodiment, the IoT lamp does not require special hardware modifications, only software support is required, which greatly improves the debugging efficiency of the IoT lamp, solves the device network access and positioning functions, and has extremely high practical value.

[0097] Among them, Figure 6 As shown, IoT lamps flash by alternating between on and off to transmit information. Device information is logically represented as a string of binary data bits, which have two types: "0" and "1." Here, the lamp is off when coded as "0," and on when coded as "1." Here, a "1" is represented by the lamp being on for a certain time, T, and a "0" is represented by the lamp being off for a certain time, T. This time, T, can be changed to accommodate different lamp on / off speeds. The duration of a data "bit," T, is N times the sampling interval of the positioning tool's camera, where N can range from 3 to 10. Therefore, when the lamp transmits a "1" data bit, the camera will see the lamp as on in N consecutive images. Conversely, if the lamp is seen to be on in N consecutive images, it is considered to have transmitted a "1" data bit. The same applies to transmitting a "0" data bit. The tool's camera captures images at a specific frequency. The positioning processor uses a deep learning model to calculate the position and on / off status of each lamp in the image. By analyzing the on and off status of the lights in continuous images, the luminous parameters can be obtained. Since the luminous parameters are configured according to the IoT address, the correspondence between the physical location of the IoT lamp and the network address can be obtained.

[0098] Step 3: Place the positioning tool under the IoT lamp, continuously collect the luminous images of multiple IoT lamps through the camera, and obtain the luminous parameters and physical location of each IoT lamp.

[0099] During filming, the camera can capture as many unpositioned lamps as possible. The camera captures images at high speed and analyzes the on / off status and position of IoT lamps to obtain device information. Simultaneously, the physical position of each lamp is inferred using the orientation sensor and the position of each lamp in the image. This physical position is the relative position of each lamp. As previously described in calculating the physical spatial coordinates of lamps, the relative orientation and distance between lamps are absolute, and the lamps have absolute orientation to each other. If there are any deviations in the position of the lamps, the algorithm can adjust the offset lamps to align them.

[0100] In addition, in this embodiment, a bracket is provided at the bottom of the positioning tool to ensure that the direction sensor is in a horizontal state. At the same time, the camera can be kept at a fixed angle to the horizontal direction, preferably at a 45-degree angle.

[0101] Step 4: Move the positioning tool to a new location and repeat step 3 until the luminous parameters and physical locations of all IoT lamps are obtained.

[0102] In step 4, when the positioning tool is moved to a new position, if the light-emitting image at the new position includes an IoT lamp whose physical location information has been previously obtained, the physical locations of other lamps in the new light-emitting image are calculated based on the physical location of the IoT lamp;

[0103] If the luminous image at the new location does not contain any IoT lamps whose physical location information has been obtained, the relative position information of the IoT lamps in the two sets of images is manually adjusted in the lamp deployment map.

[0104] Step 5: Send the luminous parameters and physical locations of all IoT lamps to the system platform. The system platform is used to generate a lamp deployment map based on the physical location of each IoT lamp, obtain the network address of the corresponding IoT lamp based on the luminous parameters, and associate the status and control of each IoT lamp in the lamp deployment map.

[0105] In this embodiment, a mobile terminal such as a mobile phone is connected to the positioning tool via a USB, and the luminous parameters and physical locations of all IoT lamps obtained in the positioning tool can be sent to the system platform through the mobile terminal.

[0106] In this embodiment, the system platform can generate a deployment map for each area. Each IoT lamp is distributed on the map according to its relative position information, with its position marked on the map and a simple door and window legend added. Each IoT lamp in the deployment map is specifically displayed as a device icon, and the device point reflects the on and off status of the lamp through different colors and icons. By clicking the icon, detailed information about the lamp can be displayed, and the switch and dimming of the lamp can be controlled. Multiple lamps can be selected at the same time by mouse or touch to perform unified operations, and the selected devices can also be flexibly grouped and set to scenes. When a lamp detects an abnormality, the system platform sends the lamp status to the deployment map. The specific physical location of the abnormal device can be directly seen from the map, which facilitates equipment maintenance and replacement.

[0107] Step 6: The system platform controls all lamps to stop flashing through the gateway according to the stop search command.

[0108] Specifically, after all lamps have been photographed and identified, a stop search command can be transmitted to the system platform via the mobile terminal, and the system platform controls all lamps to stop flashing via the gateway.

[0109] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

Claims

1. A device for generating an IoT lighting fixture deployment map, characterized in that: It includes positioning tooling, a gateway, and a system platform. The system platform is connected to the IoT lamps through the gateway and is used to send control commands containing lighting parameters to control each IoT lamp to flash with different patterns. The positioning tooling includes: a direction sensor, a camera, a positioning processor, and a communication module; the camera is used to continuously capture luminous images of multiple IoT lamps, and the positioning processor is used to extract the coordinates of each IoT lamp in the luminous image and, combined with the direction information captured by the direction sensor, determine the physical position of each IoT lamp in the image; the positioning tooling is also used to identify the on / off state of each IoT lamp in multiple consecutive luminous images, and obtain the luminous parameters of each IoT lamp based on the on / off state of each IoT lamp within a time sequence, and then package the physical position and luminous parameters of each IoT lamp to generate first data and send it to the system platform; The system platform is used to generate a lamp deployment map based on the physical location of each IoT lamp, obtain the network address of the corresponding IoT lamp based on the luminous parameters, and associate the status and control of each IoT lamp in the lamp deployment map.

2. The device for generating an IoT lighting fixture deployment map according to claim 1, characterized in that: The positioning processor uses a convolutional neural network to extract the coordinates of each lamp in the luminous image and identify the switch status of each IoT lamp in each luminous image.

3. The device for generating an IoT lighting fixture deployment map according to claim 1, characterized in that: The convolutional neural network used by the positioning processor is a trained YOLO model; the training method of the YOLO model is: Get the YOLO model pre-trained on general data; Collecting luminous images of IoT lamps when all are in the on state and luminous images of IoT lamps when all are in the off state; Mark the frames and status of all IoT lamps in the captured image as training data for the YOLO model; The YOLO model is trained using labeled training data to obtain a trained YOLO model.

4. The device for generating an IoT lighting fixture deployment map according to claim 1, characterized in that: The positioning processor determines the physical location of each IoT lamp in the luminous image in the following manner: (1) Obtain the two-dimensional coordinates of the pixel points corresponding to each IoT lamp in the luminous image; (2) Establishing a camera coordinate system with the camera location as the coordinate origin, and determining the three-dimensional coordinates of each IoT lamp in the camera coordinate system; (3) Determine the rotation matrix of the camera coordinate system relative to the physical space coordinate system based on the direction information collected by the direction sensor; (4) Based on the rotation matrix and the three-dimensional coordinates of each IoT lamp in the camera coordinate system, calculate the three-dimensional coordinates of the pixel points corresponding to each IoT lamp in the luminous image in the physical space coordinate system; the calculation formula is: Where (x′, y′, z′) represents the three-dimensional coordinates of the pixel in the physical space coordinate system, R represents the rotation matrix, and (x, y, z) represents the three-dimensional coordinates of the pixel in the camera coordinate system. (5) According to the equation of the plane of the IoT lamp corresponding to the luminous image in the physical coordinate system, the coordinates of each IoT lamp in the physical space coordinate system are determined.

5. The device for generating an IoT lighting fixture deployment map according to claim 1, characterized in that: The specific method for the system platform to send control commands to control each IoT lamp to emit light and flash in different patterns is as follows: The system platform obtains the network address of each IoT lamp; The system platform configures different luminous parameters according to the network address of each IoT lamp; The system platform sends control commands including lighting parameters to each corresponding IoT lamp according to the network address; The IoT lamp parses the luminous parameters and flashes according to the binary coded data bits of the luminous parameters. When the coded data bit is 1, the light continues to be on for T time, and when the coded data bit is 0, the light continues to be off for T time. The T time is 3 to 10 times the camera sampling interval.

6. The device for generating an IoT lighting fixture deployment map according to claim 1, characterized in that: In the lamp deployment map, a lamp status display module, a lamp information display module, and a lamp control module are set on the device icon of each IoT lamp; the lamp status display module is used to obtain the on and off status of the IoT lamp and display it; The lamp information display module is used to display detailed information of the IoT lamps, and the lamp control module is used to generate control commands based on input commands and send them to the corresponding IoT lamps to control the switching and dimming of the corresponding IoT lamps.

7. The device for generating an IoT lighting fixture deployment map according to claim 1, characterized in that: The system further includes a mobile terminal configured to send an automatic search command to the system platform; receive first data sent by the positioning tool and send the data to the system platform via a mobile network; obtain a lamp deployment map generated by the system platform, and send a lamp deployment map adjustment command to the platform based on manually input information; The system platform is also used to send control commands to IoT lamps through the gateway based on the received automatic search commands to control each IoT lamp to emit light and flash, and has been used to adjust the lamp deployment map based on the adjustment commands.

8. The device for generating an IoT lighting fixture deployment map according to claim 7, characterized in that: The mobile terminal is a mobile phone or a tablet, and the mobile terminal is connected to the positioning tool via a USB; The gateway includes a power module, a processor, a carrier communication circuit, and an Ethernet communication module. The gateway is connected to the IoT lamp through the carrier communication circuit and is connected to the system platform through the Ethernet communication module.

9. A method for generating an IoT lamp deployment map, implemented based on the IoT lamp deployment map generation device according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: The system platform obtains the network address of each IoT lamp and configures different lighting parameters according to the network address of each IoT lamp; Then, the control command including the lighting parameters is sent to each corresponding IoT lamp according to the network address; Step 2: The IoT lamp analyzes the luminous parameters and flashes according to the binary coded data bits of the luminous parameters; Step 3: Place the positioning tool under the IoT lamp, use the camera to continuously capture the light images of multiple IoT lamps, and obtain the light parameters and physical location of each IoT lamp; Step 4: Move the positioning tool to a new location and repeat step 3 until the luminous parameters and physical locations of all IoT lamps are obtained; Step 5: Send the lighting parameters and physical locations of all IoT lamps to the system platform. The system platform is used to generate a lamp deployment map based on the physical location of each IoT lamp, obtain the network address of the corresponding IoT lamp based on the lighting parameters, and associate the status and control of each IoT lamp in the lamp deployment map; Step 6: The system platform controls all lamps to stop flashing through the gateway according to the stop search command.

10. The method for generating an IoT lighting fixture deployment map according to claim 9, characterized in that: In step 4, when the positioning tool is moved to a new position, if the light-emitting image at the new position includes an IoT lamp whose physical location information has been previously obtained, the physical locations of other lamps in the new light-emitting image are calculated based on the physical location of the IoT lamp; If the luminous image at the new location does not contain any IoT lamps whose physical location information has been obtained, the relative position information of the IoT lamps in the two sets of images is manually adjusted in the lamp deployment map.

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