An underground garage positioning method and system based on a lamp composite controller

Abstract

This invention discloses a method and system for locating users in underground parking garages based on a lighting fixture composite controller. The method includes: a mobile terminal device sending a location request to the composite controller of the smart lighting fixtures; the composite controller receiving the request and obtaining the mobile terminal device ID and wireless signal strength value, and uploading the relevant information to a server via a wireless router gateway; the server constructing a parking garage spatial network topology using the composite controller as a graph node, the spacing between lighting fixtures as edges, and the wireless signal strength value as node features, and aggregating the signal features through a graph neural network to obtain the mobile terminal's location information; after the mobile terminal sends a search request, the server combining current and historical location information to formulate a guidance route and sending brightness control signals to designated smart lighting fixtures to provide lighting guidance. This invention integrates lighting fixtures with positioning functions, eliminating the need for dedicated positioning equipment, providing intuitive search instructions, and effectively improving the user experience of locating users in underground parking garages.

Description

Technical Field

[0001] This invention relates to the field of smart city related technologies, specifically to a method and system for locating underground parking garages based on a lighting fixture composite controller. Background Technology

[0002] Underground parking garages cannot receive GPS signals, necessitating the deployment of separate indoor positioning systems. However, existing systems generally lack intuitive audio-visual and optical location guidance, causing inconvenience for users. Furthermore, underground parking garages require lighting fixtures due to insufficient illumination, but these fixtures lack positioning capabilities. Even those lighting fixtures with integrated positioning functions still cannot achieve network-based positioning and lack location guidance.

[0003] Therefore, the present invention proposes a solution that can realize indoor positioning and search indication functions while adjusting the lighting brightness, which can effectively meet the positioning and search needs of users in underground parking garages. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention adopts the following solution:

[0005] A method for locating underground parking garages based on a lighting fixture composite controller, comprising:

[0006] S1. The mobile terminal device sends a location request to the composite controller of one or more smart lighting fixtures;

[0007] S2. The intelligent lighting composite controller accepts the connection request and obtains the mobile terminal device ID and wireless signal strength value;

[0008] S3, the intelligent lighting composite controller sends the mobile terminal device ID, wireless signal strength value and composite controller ID information to the server through the wireless router gateway;

[0009] S4. The server constructs a garage space network topology with the composite controller as a graph node, the lamp spacing as an edge, and the wireless signal strength value as a node feature. It then aggregates the signal features through the PosGNN graph neural network to obtain the location information corresponding to the mobile terminal device ID.

[0010] S5. The mobile terminal device sends a search request to the server.

[0011] S6. The server formulates a guidance route based on the current location information and historical location information of the mobile terminal device ID, and sends a brightness control signal to the designated smart lamps through the wireless router gateway to guide the lighting.

[0012] Furthermore, the intelligent lighting composite controller acquires positioning requirement information through S2 and S3, and simultaneously senses the movement status of vehicles or people and controls the lighting brightness of the lights through PWM.

[0013] Furthermore, the location requirement information includes the mobile terminal device ID, wireless signal strength value, and composite controller ID information.

[0014] Furthermore, S4 includes:

[0015] S41. Preset the correspondence between the ID of the intelligent lighting composite controller and the location information, and record the spacing parameters between the composite controllers.

[0016] S42. The server receives the wireless signal strength value corresponding to the mobile terminal device ID uploaded by the composite controller and uses the wireless signal strength value as the characteristic parameter of the corresponding composite controller.

[0017] S43. Construct a garage space network topology using the composite controller as a graph node, the lamp spacing parameter as an edge, and the wireless signal strength value as a node feature.

[0018] S44. The PosGNN graph neural network is used to perform weighted aggregation operations on the neighborhood signal features of each graph node. Combined with the distance weight between nodes, the core graph nodes that contribute the most to the localization calculation are selected.

[0019] S45. Based on the location information of the core graph nodes, obtain the location information corresponding to the mobile terminal device ID.

[0020] Furthermore, the brightness control signal in S6 includes a constant-on mode, a fixed-time-interval flashing mode, and an emergency escape mode. Specifically, the emergency escape mode is a dynamic guidance path from the current location of the mobile terminal device ID to the nearest safe exit location, and controls the smart lights corresponding to the guidance path to be in a constant-on state.

[0021] A positioning system for underground parking garages based on a lighting fixture composite controller is characterized by comprising: intelligent lighting fixtures, a wireless routing gateway, a mobile terminal device, and a server.

[0022] Furthermore, the intelligent lighting fixture includes: a driver power supply, a composite controller, a wireless module, a radar module, and a light-emitting component.

[0023] Furthermore, the radar module senses the movement of vehicles or people, and the composite controller receives the sensing signals from the radar module to control the illumination brightness of the light-emitting components.

[0024] Furthermore, the wireless module is a Bluetooth module.

[0025] The beneficial effects of this invention are as follows:

[0026] This invention effectively solves the positioning problem caused by the lack of GPS signals in underground parking garages. It constructs an indoor positioning system through a composite controller for intelligent lighting fixtures, eliminating the need for additional dedicated positioning equipment. This invention integrates positioning functionality with lighting control for coordinated control. A server sends brightness control signals (such as flashing or constant-on modes) to designated lighting fixtures, providing users with intuitive audio-visual and optical search instructions. This solves the problem of existing systems lacking visual guidance and significantly improves the user's positioning experience. In summary, this invention integrates underground parking garage lighting fixtures with positioning functionality, enabling indoor positioning without the need for additional dedicated positioning equipment. Furthermore, it provides intuitive search instructions through lighting brightness control, effectively enhancing the positioning experience in underground parking garages. Attached Figure Description

[0027] Figure 1 This is a flowchart of the method steps in Embodiment 1 of the present invention;

[0028] Figure 2 This is a functional block diagram of the composite controller in Embodiment 1 of the present invention;

[0029] Figure 3 This is a flowchart of step S4 in Embodiment 1 of the present invention;

[0030] Figure 4 This is a system composition diagram of Embodiment 2 of the present invention;

[0031] Figure 5 This is a diagram of the intelligent lighting fixture according to Embodiment 2 of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1

[0034] Figure 1 This is a flowchart of the method steps in Embodiment 1 of the present invention.

[0035] This embodiment provides a method for locating underground parking garages based on a lighting fixture composite controller, such as... Figure 1 As shown, it includes the following steps:

[0036] S1. The mobile terminal device sends a location request to the composite controller of one or more smart lighting fixtures;

[0037] S2. The intelligent lighting composite controller accepts the connection request and obtains the mobile terminal device ID and wireless signal strength value;

[0038] S3, the intelligent lighting composite controller sends the mobile terminal device ID, wireless signal strength value and composite controller ID information to the server through the wireless router gateway;

[0039] S4. The server constructs a garage space network topology with the composite controller as a graph node, the lamp spacing as an edge, and the wireless signal strength value as a node feature. It then aggregates the signal features through the PosGNN graph neural network to obtain the location information corresponding to the mobile terminal device ID.

[0040] S5. The mobile terminal device sends a search request to the server.

[0041] S6. The server formulates a guidance route based on the current location information and historical location information of the mobile terminal device ID, and sends a brightness control signal to the designated smart lamps through the wireless router gateway to guide the lighting.

[0042] The intelligent lighting composite controller obtains positioning requirement information through steps S2 and S3, and simultaneously senses the movement status of vehicles or people and controls the lighting brightness of the lights through PWM.

[0043] The location requirement information includes the mobile terminal device ID, wireless signal strength value, and composite controller ID information.

[0044] Figure 2 This is a functional block diagram of the composite controller in Embodiment 1 of the present invention.

[0045] like Figure 2 As shown, on the one hand, the composite controller senses the movement of vehicles or people and controls the lighting brightness of the lamps through PWM; on the other hand, the composite controller receives positioning requests from mobile terminal devices, sends positioning request information to the server, and receives positioning information from the server.

[0046] Figure 3 This is a flowchart of step S4 in Embodiment 1 of the present invention.

[0047] In step S4, the server constructs a garage space network topology using the composite controller as a graph node, the spacing between lamps as edges, and the wireless signal strength value as a node feature. It then aggregates the signal features using a PosGNN graph neural network to obtain the location information corresponding to the mobile terminal device ID. Figure 3 As shown, it includes the following steps:

[0048] S41. Preset the correspondence between the ID of the intelligent lighting composite controller and the location information, and record the spacing parameters between the composite controllers.

[0049] S42. The server receives the wireless signal strength value corresponding to the mobile terminal device ID uploaded by the composite controller and uses the wireless signal strength value as the characteristic parameter of the corresponding composite controller.

[0050] S43. Construct a garage space network topology using the composite controller as a graph node, the lamp spacing parameter as an edge, and the wireless signal strength value as a node feature.

[0051] S44. The PosGNN graph neural network is used to perform weighted aggregation operations on the neighborhood signal features of each graph node. Combined with the distance weight between nodes, the core graph nodes that contribute the most to the localization calculation are selected.

[0052] S45. Based on the location information of the core graph nodes, obtain the location information corresponding to the mobile terminal device ID.

[0053] Step S44 employs a PosGNN graph neural network to perform weighted aggregation operations on the neighborhood signal features of each graph node: First, with the current composite controller node as the center, a spatial distance weight matrix is ​​constructed based on the node spacing parameter, with closer neighboring nodes assigned higher initial weights; the wireless signal strength value is used as the node input feature and input to the multi-layer attention aggregation layer of the PosGNN, adaptively learning the feature contribution of neighboring nodes through a multi-head graph attention mechanism, fusing spatial distance weights and signal feature weights to obtain a weighted aggregated node feature vector; the positioning contribution score of each node is calculated through a fully connected layer, and the core graph nodes with the highest contribution to the positioning calculation are selected based on the score ranking, while weak signal and long-distance interference nodes are eliminated to ensure the accuracy of the positioning results.

[0054] PosGNN employs a two-layer graph attention convolutional structure. The first layer takes wireless signal strength values ​​as input node features, with the number of nodes equal to the number of deployed lights, and edge weights defined by an initial spatial distance weight matrix. This layer uses a 4-head attention mechanism, where each head independently calculates the attention coefficients of its neighboring nodes, and the outputs of each head are concatenated to obtain 128-dimensional intermediate features. The second layer maps these intermediate features to single-value node scores, using single-head attention and introducing residual connections to prevent gradient vanishing. Both layers use the LeakyReLU activation function and Dropout (scale 0.2) regularization. Wireless signal strength data is input into PosGNN, and the output is the score for each node. A threshold of 0.65 is set to filter core graph nodes.

[0055] The brightness control signal in S6 includes a constant-on mode and a fixed-time-interval flashing mode. The fixed-time-interval flashing mode can be set to intervals of 1 second, 2 seconds, 3 seconds, 4 seconds, or 5 seconds. For example, a 1-second interval means the lighting fixture is on for 1 second and then off for 1 second, cycling in this manner. Setting different brightness control signals can differentiate the positioning needs of different users.

[0056] The brightness control signal in S6 also includes an emergency escape mode. In the event of an emergency, the user can request to trigger the emergency escape mode, which specifically provides a dynamic guidance path from the current location of the mobile terminal device ID to the nearest safe exit, and controls the smart lights corresponding to the guidance path to remain constantly on.

[0057] Example 2

[0058] Figure 4 This is a system composition diagram of Embodiment 2 of the present invention.

[0059] This embodiment provides a positioning system for underground parking garages based on a lighting fixture composite controller, such as... Figure 4 As shown, it includes the following modules: smart lighting fixtures, wireless router gateway, mobile terminal devices, and server.

[0060] The smart lighting fixtures, wireless router gateways, and mobile terminal devices are connected wirelessly using the same method, while the wireless router gateways are connected to the server via wired network cables.

[0061] Figure 5 This is a diagram of the intelligent lighting fixture according to Embodiment 2 of the present invention.

[0062] The intelligent lighting fixture includes: a driver, a composite controller, a wireless module, a radar module, and a light-emitting component.

[0063] The driving power supply powers the composite controller and the light-emitting components, while the composite controller powers the wireless module and the radar module. The wireless module sends wireless signals back to the composite controller, and the radar module sends radar signals back to the composite controller. The composite controller controls the driving power supply via PWM, thereby controlling the brightness of the light-emitting components.

[0064] The radar module senses the movement of vehicles or people, and the composite controller receives the sensing signals from the radar module to control the illumination brightness of the light-emitting components.

[0065] The wireless module is a Bluetooth module, specifically the nRF51822 module. The nRF51822 module integrates a 2.4GHz multi-protocol radio transceiver, supporting Bluetooth® Low Energy and the 2.4GHz proprietary protocol.

[0066] For the NRF51822 Bluetooth module, the RSSI (Radio Signal Strength Index) value can be obtained by calling a function. First, call the `sd_ble_gap_rssi_start` function, passing in the connection handle, a threshold, and the number of ignores. If the threshold parameter is set to `BLE_GAP_RSSI_THRESHOLD_INVALID` and the number of ignores is set to 0, then you can directly call the `sd_ble_gap_rssi_get` interface function, passing in the connection handle and the buffer, to obtain the received signal strength from the most recent connection event.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Based on the technical essence of the present invention, any simple modifications, equivalent substitutions, and improvements made to the above embodiments within the spirit and principles of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for locating underground parking garages based on a lighting fixture composite controller, characterized in that, include: S1. The mobile terminal device sends a location request to the composite controller of one or more smart lighting fixtures; S2. The intelligent lighting composite controller accepts the connection request and obtains the mobile terminal device ID and wireless signal strength value; S3, the intelligent lighting composite controller sends the mobile terminal device ID, wireless signal strength value and composite controller ID information to the server through the wireless router gateway; S4. The server constructs a garage space network topology with the composite controller as a graph node, the lamp spacing as an edge, and the wireless signal strength value as a node feature. It then aggregates the signal features through the PosGNN graph neural network to obtain the location information corresponding to the mobile terminal device ID. S5. The mobile terminal device sends a search request to the server. S6. The server formulates a guidance route based on the current location information and historical location information of the mobile terminal device ID, and sends a brightness control signal to the designated smart lamps through the wireless router gateway to guide the lighting.

2. The underground parking garage positioning method based on a lighting fixture composite controller according to claim 1, characterized in that, The intelligent lighting composite controller obtains positioning requirement information through S2 and S3, and simultaneously senses the movement status of vehicles or people and controls the lighting brightness of the lights through PWM.

3. The underground parking garage positioning method based on a lighting fixture composite controller according to claim 2, characterized in that, The location requirement information includes the mobile terminal device ID, wireless signal strength value, and composite controller ID information.

4. The underground parking garage positioning method based on a lighting fixture composite controller according to claim 1, characterized in that, S4 further includes: S41. Preset the correspondence between the ID of the intelligent lighting composite controller and the location information, and record the spacing parameters between the composite controllers. S42. The server receives the wireless signal strength value corresponding to the mobile terminal device ID uploaded by the composite controller and uses the wireless signal strength value as the characteristic parameter of the corresponding composite controller. S43. Construct a garage space network topology using the composite controller as a graph node, the lamp spacing parameter as an edge, and the wireless signal strength value as a node feature. S44. The PosGNN graph neural network is used to perform weighted aggregation operations on the neighborhood signal features of each graph node. Combined with the distance weight between nodes, the core graph nodes that contribute the most to the localization calculation are selected. S45. Based on the location information of the core graph nodes, obtain the location information corresponding to the mobile terminal device ID.

5. The underground parking garage positioning method based on a lighting fixture composite controller according to claim 1, characterized in that, The historical location information in S6 is the location information obtained in step S4 when the mobile terminal device sends a location request to the composite controller, and the current location information in S6 is the location information obtained in step S4 when the mobile terminal device sends a search request to the server.

6. The underground parking garage positioning method based on a lighting fixture composite controller according to claim 1, characterized in that, The brightness control signal in S6 includes a constant-on mode, a fixed-time-interval flashing mode, and an emergency escape mode. The emergency escape mode specifically refers to a dynamic guidance path from the current location of the mobile terminal device ID to the nearest safe exit location, and controls the smart lights corresponding to the guidance path to be in a constant-on state.

7. A positioning system for an underground parking garage based on a lighting fixture composite controller, characterized in that, include: Smart lighting fixtures, wireless routers and gateways, mobile terminal devices, and servers.

8. A positioning system for an underground parking garage based on a lighting fixture composite controller according to claim 7, characterized in that, The intelligent lighting fixture includes: a driver power supply, a composite controller, a wireless module, a radar module, and a light-emitting component.

9. A positioning system for an underground parking garage based on a lighting fixture composite controller according to claim 8, characterized in that, The radar module senses the movement of vehicles or people, and the composite controller receives the sensing signals from the radar module to control the illumination brightness of the light-emitting components.

10. A positioning system for an underground parking garage based on a lighting fixture composite controller according to claim 8, characterized in that, The wireless module is a Bluetooth module.

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