System for controlling lighting device, prediction device, controller, and method thereof
By capturing photos inside the train carriage and using artificial intelligence to detect passenger tendencies and control the brightness of lighting equipment, it solves the problem in the existing technology that lights are difficult to predict when passengers will get off, improves passenger safety and comfort, and reduces costs.
Patent Information
- Application Number
- CN202280096865.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2022-06-09
- Publication Date
- 2025-01-21
AI Technical Summary
In the prior art, the lights in the blank area of the subway station or the train station are difficult to predict the time for passengers to get off the train, causing the lights to not light up before the train arrives, affecting passenger safety and comfort.
By capturing photos from the inside of the train carriage, an artificial intelligence pattern recognition algorithm is used to detect passengers who are inclined to get off the train carriage and generate control signals that control the driver to adjust the brightness of the lighting equipment before the train reaches the station.
It is possible to increase the brightness of lighting equipment before the train arrives at the station, which improves passenger safety and comfort, saves the need for PIR sensors on each train, and reduces modification costs.
Smart Images
Figure CN119343288A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present disclosure relate generally to the field of lighting, and more particularly to systems, prediction devices, controllers, and methods thereof for controlling lighting equipment. Background Art
[0002] This section introduces aspects that may be helpful for a better understanding of the present disclosure. Therefore, the statements in this section should be read in this light, and should not be understood as an admission that what is in the prior art is or is not in the prior art.
[0003] Subway platform lights are always on. These lights are set to the same brightness because it is inconvenient to manually adjust the brightness based on the presence of people. Current solutions are set to change the brightness of a specific area, for example, a DALI (Digital Addressable Lighting Interface) dimming command can be sent to a group of lights to adjust the brightness of that area.
[0004] In a subway station or a train station, for a blank area (eg, a platform of a suburban station), the light needs to be turned on all the time, regardless of when passengers get off the train.
[0005] It is possible to control the light in an empty area by using a PIR (pyroelectric infrared) sensor. For example, when the door of a train opens and a passenger gets off the train, the PIR sensor can detect the passenger and send a signal to the controller, which can send a DALI dimming command to the driver to control the brightness of the light.
[0006] In one example, the lights in the blank area can operate in an energy-saving mode in which the brightness of the lights is about 40% of the normal brightness, and when the PIR sensor detects that a passenger gets off the train, the controller can control the driver to drive the lights to 100% of the normal brightness. Summary of the invention
[0007] The inventors found that in the above mentioned possible approach, when the train arrives, the empty area is dark, and the lights will only become brighter after the passengers get off the train; that is, the tendency of passengers to get off the train will not be predicted, so the lights will not become brighter before the train arrives.
[0008] Generally speaking, embodiments of the present disclosure provide a system, a prediction device, a controller and a method thereof for controlling lighting equipment. In an embodiment, passengers who are inclined to get off a train car can be detected based on photos captured inside the train car, and the detected results will be used to control the lights at the station, so that the lights will become brighter before the train arrives.
[0009] In a first aspect, a system for controlling a lighting device is provided, the system comprising:
[0010] a prediction device configured to detect passengers who are inclined to get off the train compartment based on a photograph captured inside the train compartment; and
[0011] a controller configured to generate a control signal according to the detection result of the prediction device, the control signal being used to control a driver to drive a lighting device in a predetermined area of the site,
[0012] When it is detected that there is at least one passenger who has a tendency to get off the train car, the control signal controls the driver to dim the brightness of the lighting device to a predetermined level before the train arrives at the station.
[0013] In at least one embodiment, the predictive device comprises:
[0014] A prediction unit is configured to detect passengers who have a tendency to get off the train compartment by using an artificial intelligence (AI) pattern recognition algorithm, and to generate a first signal according to the detection result.
[0015] In at least one embodiment, the prediction unit detects passengers with a propensity to exit the train car based on passengers in a first area near a door of the train car.
[0016] In at least one embodiment, the prediction unit determines that the passenger has a tendency to get off the train car while the passenger is standing in the first area; or
[0017] When the passenger is standing in the first area and performs a predetermined action, the prediction unit determines that the passenger has a tendency to get off the train car.
[0018] In at least one embodiment, the prediction unit begins detecting passengers who are inclined to exit the train car at a predetermined time before the train arrives at the station.
[0019] In at least one embodiment, the predictive device further comprises:
[0020] A communication unit is configured to transmit the first signal to the controller via wireless communication.
[0021] In at least one embodiment, when the lighting devices in a predetermined area of the site are in energy-saving mode, the controller generates a control signal according to the detection result to control the driver to dim the brightness of the lighting devices to a predetermined level.
[0022] In a second aspect, a prediction device is provided, the prediction device comprising:
[0023] a prediction unit configured to detect passengers who are inclined to get off the train compartment based on a photograph captured inside the train compartment, and to generate a first signal based on a detection result,
[0024] When the presence of at least one passenger with a propensity to get off the train car is detected, a first signal is generated.
[0025] In at least one embodiment, the prediction unit detects passengers who are inclined to exit the train car by using an artificial intelligence (AI) pattern recognition algorithm.
[0026] In at least one embodiment, the prediction unit determines that the passenger has a tendency to get off the train car while the passenger is standing in the first area; or
[0027] When the passenger is standing in the first area and performs a predetermined action, the prediction unit determines that the passenger has a tendency to get off the train car.
[0028] In at least one embodiment, the prediction unit begins detecting passengers who are inclined to exit the train car at a predetermined time before the train arrives at the station.
[0029] In at least one embodiment, the predictive device further comprises:
[0030] A communication unit is configured to transmit the first signal to the controller via wireless communication.
[0031] In a third aspect, there is provided a controller configured to:
[0032] Based on the pictures captured inside the train car, a control signal is generated based on the detection result of passengers who are inclined to get off the train car.
[0033] The control signal is used to control the driver to drive the lighting device in the predetermined area of the site,
[0034] When it is detected that there is at least one passenger who has a tendency to get off the train car, the control signal controls the driver to dim the brightness of the lighting device to a predetermined level before the train arrives at the station.
[0035] In at least one embodiment, when lighting devices in a predetermined area of the site are in energy saving mode, the controller generates a control signal to control the driver to dim the brightness of the lighting devices to a predetermined level.
[0036] In a fourth aspect, a method for controlling a system for a lighting device is provided, the method comprising:
[0037] detecting passengers who are inclined to get off the train car based on photos captured inside the train car; and
[0038] generating a control signal according to the detection result, the control signal being used to control a driver to drive a lighting device in a predetermined area of the site,
[0039] When it is detected that there is at least one passenger who has a tendency to get off the train car, the control signal controls the driver to dim the brightness of the lighting device to a predetermined level before the train arrives at the station.
[0040] In a fifth aspect, a method for predicting a device is provided, the method comprising:
[0041] detecting passengers who are inclined to get off the train compartment based on a photograph captured inside the train compartment, and generating a first signal based on the detection result,
[0042] When the presence of at least one passenger with a propensity to get off the train car is detected, a first signal is generated.
[0043] In a sixth aspect, a controller method is provided, the method comprising:
[0044] Based on the pictures captured inside the train car, a control signal is generated based on the detection result of passengers who are inclined to get off the train car.
[0045] The control signal is used to control the driver to drive the lighting device in the predetermined area of the site,
[0046] When it is detected that there is at least one passenger who has a tendency to get off the train car, the control signal controls the driver to dim the brightness of the lighting device to a predetermined level before the train arrives at the station.
[0047] In the seventh aspect, a non-transitory computer-readable storage medium is provided, which stores multiple programs for execution by an electronic device having one or more processing units, wherein when the multiple programs are executed by the one or more processing units, the multiple programs cause the electronic device to execute the method of any one of the fourth to sixth aspects.
[0048] In the seventh aspect, a computer program product is provided, which includes one or more programs capable of being executed by a computer device having one or more processors, wherein when the one or more programs are executed by the one or more processors, the one or more programs cause the computer device to perform the method of any one of the fourth to sixth aspects.
[0049] In a seventh aspect, an electronic device is provided, the electronic device comprising:
[0050] one or more processing units;
[0051] a memory coupled to the one or more processing units; and
[0052] A plurality of programs are stored in a memory, and when the plurality of programs are executed by the one or more processing units, the plurality of programs enable the electronic device to execute the method of any one of the fourth aspect to the sixth aspect.
[0053] According to various embodiments of the present disclosure, passengers who are inclined to get off the train car can be detected based on photos captured inside the train car, and the detected results will be used to control the lights at the station, so that the lights will become brighter before the train arrives. BRIEF DESCRIPTION OF THE DRAWINGS
[0054] By way of example, the above and other aspects, features and benefits of various embodiments of the present disclosure will become more apparent through the following detailed description with reference to the accompanying drawings, in which like reference numerals or letters are used to represent similar or equivalent elements. The accompanying drawings are shown to facilitate a better understanding of the embodiments of the present disclosure and are not necessarily drawn to scale, wherein:
[0055] Figure 1 is a diagram of a system for controlling a lighting device according to an embodiment of the present disclosure.
[0056] Figure 2 FIG. 1 is a flow chart of a working process of the system 100 for controlling a lighting device.
[0057] Figure 3 A flow chart of a method of a system 100 for controlling a lighting device is shown.
[0058] Figure 4 A flow chart of the method of the prediction device 10 is shown.
[0059] Figure 5 A flow chart of the method of controller 50 is shown.
[0060] Figure 6 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION
[0061] The present disclosure will now be discussed with reference to several exemplary embodiments. It should be understood that the purpose of discussing these embodiments is only to enable those skilled in the art to better understand the present disclosure and thus implement the present disclosure, rather than to propose any limitation on the scope of the present disclosure.
[0062] As used herein, the terms "first" and "second" refer to different elements. Unless the context clearly indicates otherwise, the singular forms "one" and "a kind of" are intended to also include plural forms. As used herein, the terms "include", "comprise", "have" and / or "contain" specify the presence of the features, elements and / or components, etc., but do not exclude the presence or addition of one or more other features, elements, components and / or combinations thereof. The term "based on" should be understood as "based at least in part". The terms "one embodiment" and "embodiment" should be understood as "at least one embodiment". The term "another embodiment" should be understood as "at least one other embodiment". Other explicit and implicit definitions may be included below.
[0063] First aspect of the implementation plan
[0064] In a first aspect of the embodiment, a system for controlling a lighting device is provided.
[0065] Figure 1 is a diagram of a system for controlling a lighting device according to an embodiment of the present disclosure.
[0066] like Figure 1 As shown, a system 100 for controlling a lighting device includes a prediction device 10 and a controller 20 .
[0067] The prediction device 10 is configured to detect the passenger Po who has a tendency to get off the train compartment 201 based on a photo captured inside the train compartment 201. For example, the photo may be captured by a camera 202 inside the train compartment 201.
[0068] The controller 20 is configured to generate a control signal Cs according to the detection result of the prediction device 10. In at least one embodiment, the controller 20 may be a cloud server.
[0069] The control signal Cs is used to control the driver 30 to drive the lighting device 40 in the predetermined area 50 of the station. The predetermined area 50 may be an area near the door 2012 of the train compartment 201 when the train 200 arrives at the station.
[0070] When the presence of at least one passenger Po who has a tendency to get off the train car 201 is detected, the control signal Cs sent by the controller 20 controls the driver 30 to dim the brightness of the lighting device 40 to a predetermined level before the train 200 arrives at the station.
[0071] According to the first aspect of the embodiment, passengers who tend to get off the train car can be detected based on photos captured inside the train car, and the detected results will be used to control the lighting equipment at the station, so that the lighting equipment will become brighter before the train arrives at the station.
[0072] like Figure 1 As shown, the prediction device 10 includes a prediction unit 101. The prediction unit 101 is configured to detect a passenger Po who has a tendency to get off the train car 201 by using an artificial intelligence (AI) graphic recognition algorithm, and generate a first signal S1 according to the detection result. In one example, the artificial intelligence (AI) graphic recognition algorithm can be a skeleton-based action recognition algorithm, by which a person with a specific action or gesture in a photo will be identified. Regarding the working principle of the prediction device 10, reference can be made to the relevant field for more details.
[0073] In at least one embodiment, the prediction unit 101 detects the passengers Po who have a tendency to get off the train car 201 based on the passengers Po in the first area 2011 near the door 2012 of the train car 201 .
[0074] For example, when the passenger Po is standing in the first area 2011 , the prediction unit 101 determines that the passenger Po has a tendency to get off the train car 201 .
[0075] For another example, when the passenger Po is standing in the first area 2011 and performs a predetermined action, the prediction unit 101 determines that the passenger Po has a tendency to get off the train car 201. The predetermined action may be carrying a bag, and / or carrying a suitcase, and / or facing the door 2012, and / or moving toward the door 2012, and the like.
[0076] In at least one embodiment, the size of the first area 2011 may be fixed. The position of the first area 2011 may be changed according to information indicating which door will be opened after the train 20 arrives at the station. For example, when the information indicates that the right door will be opened at the station, the prediction unit 101 changes the first area 2011 to a position near the right door of the train car 201; when the information indicates that the left door will be opened at the station, the prediction unit 101 changes the first area 2011 to a position near the left door of the train car 201.
[0077] In at least one embodiment, the prediction unit 101 may start detection at a predetermined time before the train 200 arrives at the station. The predetermined time may be several seconds (e.g., 3 seconds) before the train 200 arrives at the station. For example, the train 200 or a server (not shown) may broadcast a signal to notify that the train 200 will arrive at the station; the prediction unit 101 may start detection upon receiving the broadcast signal, or the prediction unit 101 may calculate the time to start detection based on the received broadcast signal.
[0078] Information indicating which door will be opened after the train 20 arrives at the station will be included in the broadcast signal.
[0079] according to Figure 1 The prediction device 10 further includes a communication unit 102. The communication unit 102 is configured to send the first signal S1 generated by the prediction unit 101 to the controller 20 via wireless communication. In an example, the wireless communication may be Bluetooth communication.
[0080] In at least one embodiment, the communication unit 102 may further receive a broadcast signal.
[0081] When or after receiving the first signal S1 , the controller 20 may generate a control signal Cs according to the operation mode of the lighting device 40 in the predetermined area 50 of the site.
[0082] For example, when the lighting device 40 in a predetermined area 50 of the site operates in energy-saving mode (for example, the brightness of the lighting device 40 is approximately 40% of the normal brightness), the controller 20 generates a control signal Cs to control the driver 30 to dim the brightness of the lighting device 40 to a predetermined level (for example, the brightness of the lighting device 40 is dimmed to 100% of the normal brightness).
[0083] Figure 2 FIG. 1 is a flow chart of a working process of a system 100 for controlling a lighting device. Figure 2 As shown, the workflow 2000 includes the following blocks:
[0084] Box 21: The prediction device 10 receives a broadcast signal indicating that the train 200 will arrive at the station within a few seconds (eg, 3 seconds).
[0085] Frame 22: Based on the photo taken by the camera 202 on the inside of the train car 201, the prediction device 10 starts to analyze whether there is a passenger Po who tends to get off the train car 201; if so, there is at least one passenger Po who tends to get off the train car 201, and the process goes to frame 23; if not, there is no passenger Po who tends to get off the train car 201, and the prediction device 10 stands by.
[0086] Box 23 : The prediction device 10 generates a first signal S1 , and sends the first signal S1 to the controller 20 via wireless communication.
[0087] Box 24: The controller 20 determines whether the lighting devices 40 in the predetermined area 50 of the site are operating in the energy-saving mode. If yes, the controller 20 goes to box 25. If not, the controller 20 is on standby.
[0088] Box 25: The controller 200 generates a control signal Cs and sends the control signal Cs to the driver 30. The control signal Cs will control the driver 30 to dim the brightness of the lighting device 40 to a predetermined level before the train 200 arrives at the station.
[0089] According to the first aspect of the embodiment, passengers who tend to get off the train car can be detected based on photos captured inside the train car, and the detected results will be used to control the lighting equipment at the station, so that the lighting equipment will become brighter before the train arrives at the station.
[0090] The following advantages are achieved by the first aspect of the implementation scheme:
[0091] 1. For sites (especially those in the suburbs), large-scale energy saving is environmentally friendly;
[0092] 2. There is no need to set up PIR sensors for each train, which saves 60 sensors per train, which will be more acceptable to customers who do not need to modify the trains and greatly saves modification costs;
[0093] 3. It will only require software installation, where all cameras are connected to the system 100, and the driver 30 can also be connected to the system 100 through an API (Application Programming Interface);
[0094] 4. These implementations will also not bring any changes to passengers as they will not feel any difference, thanks to accurate dimming time control.
[0095] Second aspect of the implementation plan
[0096] In a second aspect of the embodiment, a method for controlling a system of a lighting device is provided. In a first aspect of the embodiment, a system for controlling a lighting device is provided. The same contents as those in the first aspect of the embodiment are omitted.
[0097] Figure 3 A flow chart of a method of a system 100 for controlling a lighting device is shown.
[0098] like Figure 3 As shown, the method 300 includes:
[0099] Box 31: The prediction device detects passengers who are inclined to get off the train compartment based on the photos captured inside the train compartment; and
[0100] Box 32: The controller generates a control signal according to the detection result; the control signal is used to control the driver to drive the lighting device in the predetermined area of the site.
[0101] In block 32 , when it is detected that there is at least one passenger who has a tendency to get off the train car, the control signal controls the driver to dim the brightness of the lighting device to a predetermined level before the train arrives at the station.
[0102] The detailed description of the blocks 31 and 32 will refer to the description of the prediction device 10 and the controller 20, respectively.
[0103] In the second aspect of the embodiment, a method of predicting an apparatus is further provided. In the first aspect of the embodiment, a predicting apparatus 10 is provided. The same contents as those in the first aspect of the embodiment are omitted.
[0104] Figure 4 A flow chart of the method of the prediction device 10 is shown.
[0105] like Figure 4 As shown, the method 400 includes:
[0106] Box 41: Detecting passengers who are inclined to get off the train compartment based on photos captured inside the train compartment, and generating a first signal based on the detection result.
[0107] In block 41 , a first signal is generated when the presence of at least one passenger with a propensity to get off the train car is detected.
[0108] The detailed description of block 41 will refer to the description of the prediction device 10 .
[0109] In the second aspect of the embodiment, a method of a controller is also provided. In the first aspect of the embodiment, a controller 20 is provided. The same contents as those in the first aspect of the embodiment are omitted.
[0110] Figure 5 A flow chart of the method of controller 50 is shown.
[0111] like Figure 5 As shown, the method 500 includes:
[0112] Box 51: Based on the pictures captured inside the train car 201, a control signal is generated based on the detection of passengers who have a tendency to get off the train car.
[0113] In block 51, the control signal is used to control the driver to drive the lighting device in the predetermined area of the station. When it is detected that there is at least one passenger who has the tendency to get off the train car, the control signal controls the driver to dim the brightness of the lighting device to a predetermined level before the train arrives at the station.
[0114] The detailed description of the block 51 will refer to the description of the controller 50 .
[0115] According to the first aspect of the embodiment, passengers who tend to get off the train car can be detected based on photos captured inside the train car, and the detected results will be used to control the lighting equipment at the station, so that the lighting equipment will become brighter before the train arrives at the station.
[0116] The third aspect of the implementation plan
[0117] In a third aspect of the embodiments, an electronic device is provided.
[0118] Figure 6 Schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Figure 6 As shown, the electronic device 600 may include one or more processors 610 and a memory 620 coupled to the processor 610. The memory 620 may store various data, and further, it may store a program 630 for data processing and execute the program 630 under the control of the processor 610.
[0119] In one embodiment, the functions of the prediction device 10 and / or the controller 20 may be integrated into the processor 610. The processor 610 may be configured to implement the method 300, 400 or 500 described in the second aspect of the embodiment of the present disclosure.
[0120] In addition, if Figure 6 As shown, the electronic device 600 may include a transceiver 640 and an antenna 650, etc. The functions of the above components are similar to those in the related art and will not be further described herein. It should be noted that the electronic device 600 does not necessarily include Figure 6 In addition, the electronic device 600 may include Figure 6 For parts not shown in the figure, reference may be made to the related art.
[0121] In the present disclosure, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium stores a plurality of programs, the plurality of programs are used to be executed by an electronic device having one or more processing units, and when the plurality of programs are executed by the one or more processing units, the plurality of programs cause the electronic device to execute method 300, 400, or 500.
[0122] In the present disclosure, a computer program product is provided. The computer program product includes one or more programs that can be executed by a computer device having one or more processors. When the one or more programs are executed by the one or more processors, the one or more programs cause the computer device to perform method 300, 400, or 500.
[0123] The above-mentioned apparatus and method of the present disclosure can be implemented by hardware, or by hardware combined with software. The present disclosure relates to such computer-readable programs, and when the programs are executed by logic devices, the logic devices can implement the above-mentioned apparatus or components, or can implement the above-mentioned methods or steps. The present disclosure also relates to storage media for storing the above-mentioned programs, such as hard disks, floppy disks, CDs, DVDs, and flash memory.
[0124] The method / device described with reference to the embodiments of the present disclosure may be directly embodied as hardware, software modules, or combinations thereof executed by a processor. For example, one or more functional block diagrams and / or one or more combinations of functional block diagrams shown in the accompanying drawings may correspond to software modules of a computer program process, or to hardware modules. Such software modules may correspond to the steps shown in the accompanying drawings, respectively. And the hardware modules may be implemented, for example, by using a field programmable gate array (FPGA) fixed soft module.
[0125] The soft module may be located in RAM, flash memory, ROM, EPROM and EEPROM, registers, hard disk, floppy disk, CD-ROM or any other form of storage medium known in the art. The storage medium may be coupled to the processor so that the processor may be able to read information from the storage medium and write information to the storage medium; or the storage medium may be a component of the processor. The processor and the storage medium may be located in an ASIC. The soft module may be stored in the memory of the mobile terminal and may also be stored in a memory card of a pluggable mobile terminal. For example, if a device (such as a mobile terminal) uses a relatively large-capacity MEGA-SIM card or a large-capacity flash memory device, the soft module may be stored in a large-capacity MEGA-SIM card or a flash memory device.
[0126] One or more functional blocks and / or one or more combinations of functional blocks in the accompanying drawings may be implemented as a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or any suitable combination thereof that implements the functions described in the present application. And the one or more functional block diagrams and / or one or more combinations of functional block diagrams in the accompanying drawings may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple processors, one or more microprocessors in communication with a DSP, or any other such configuration.
[0127] The present disclosure is described above with reference to specific embodiments. However, it should be understood by those skilled in the art that such description is only illustrative and is not intended to limit the scope of protection of the present disclosure. According to the principles of the present disclosure, those skilled in the art can make various variations and modifications, and such variations and modifications fall within the scope of the present disclosure.
Claims
1. A system for controlling a lighting device, the system comprising: a prediction device configured to detect passengers who are inclined to get off the train car based on a photograph captured inside the train car; and a controller configured to generate a control signal according to the detection result of the prediction device, wherein the control signal is used to control a driver to drive a lighting device in a predetermined area of the site, When it is detected that there is at least one passenger who has a tendency to get off the train carriage, the control signal controls the driver to dim the brightness of the lighting device to a predetermined level before the train arrives at the station.
2. The system for controlling a lighting device according to claim 1, wherein: The prediction device comprises: A prediction unit is configured to detect the passenger who has a tendency to get off the train car by using an artificial intelligence (AI) pattern recognition algorithm, and generate a first signal according to the detection result.
3. The system for controlling a lighting device according to claim 2, wherein: The prediction unit detects the passenger who is likely to get off the train car based on the passenger in a first area near a door of the train car.
4. The system for controlling a lighting device according to claim 3, wherein: When the passenger is standing in the first area, the prediction unit determines that the passenger has a tendency to get off the train car; or When the passenger is standing in the first area and performs a predetermined action, the prediction unit determines that the passenger has a tendency to get off the train car.
5. The system for controlling a lighting device according to claim 2, wherein: The prediction unit starts detecting the passenger who is likely to get off the train car at a predetermined time before the train arrives at the station.
6. The system for controlling a lighting device according to claim 2, wherein: The prediction device also includes: A communication unit is configured to transmit the first signal to the controller via wireless communication.
7. The system for controlling a lighting device according to claim 1, wherein: When the lighting device in the predetermined area of the site is in energy-saving mode, the controller generates the control signal according to the detection result to control the driver to dim the brightness of the lighting device to the predetermined level.
8. A prediction device, comprising: a prediction unit configured to detect passengers who are inclined to get off the train compartment based on a photograph captured inside the train compartment, and to generate a first signal based on the detection result, The first signal is generated when the presence of at least one passenger who is inclined to get off the train car is detected.
9. The prediction device according to claim 8, wherein: The prediction unit detects the passenger who has a tendency to get off the train car by using an artificial intelligence (AI) pattern recognition algorithm.
10. The prediction device according to claim 8, wherein: When the passenger is standing in the first area, the prediction unit determines that the passenger has a tendency to get off the train car; or When the passenger is standing in the first area and performs a predetermined action, the prediction unit determines that the passenger has a tendency to get off the train car.
11. The prediction device according to claim 8, wherein: The prediction unit starts detecting the passenger who is likely to get off the train car at a predetermined time before the train arrives at the station.
12. The prediction device according to claim 8, wherein: The prediction device also includes: A communication unit is configured to transmit the first signal to a controller via wireless communication.
13. A controller, the controller being configured to: generating a control signal based on a photograph captured inside a train car and based on the detection of passengers who are inclined to get off the train car, The control signal is used to control the driver to drive the lighting device in a predetermined area of the site, in, When the presence of at least one passenger who has a tendency to get off the train car is detected, the control signal controls the driver to dim the brightness of the lighting device to a predetermined level before the train arrives at the station.
14. The controller according to claim 13, wherein: When the lighting devices in the predetermined area of the site are in energy-saving mode, the controller generates the control signal to control the driver to dim the brightness of the lighting devices to the predetermined level.
15. A method for controlling a system of a lighting device, the method comprising: detecting passengers who are inclined to get off the train car based on a photograph captured inside the train car; as well as generating a control signal according to the detection result, wherein the control signal is used to control a driver to drive a lighting device in a predetermined area of the site, When it is detected that there is at least one passenger who has a tendency to get off the train carriage, the control signal controls the driver to dim the brightness of the lighting device to a predetermined level before the train arrives at the station.
16. A method for predicting a device, the method comprising: detecting passengers who are inclined to get off the train compartment based on a photograph captured inside the train compartment, and generating a first signal based on the detection result, The first signal is generated when the presence of at least one passenger who is inclined to get off the train car is detected.
17. A method of a controller, the method comprising: generating a control signal based on a photograph captured inside a train car and based on the detection of passengers who are inclined to get off the train car, The control signal is used to control the driver to drive the lighting device in a predetermined area of the site, When it is detected that there is at least one passenger who has a tendency to get off the train carriage, the control signal controls the driver to dim the brightness of the lighting device to a predetermined level before the train arrives at the station.
18. A non-transitory computer-readable storage medium storing a plurality of programs for execution by an electronic device having one or more processing units, wherein when the plurality of programs are executed by the one or more processing units, the plurality of programs cause the electronic device to execute the method according to claims 15 to 17.
19. A computer program product, comprising one or more programs executable by a computer device having one or more processors, wherein when the one or more programs are executed by the one or more processors, the one or more programs cause the computer device to perform the method according to any one of claims 15 to 17.
20. An electronic device, comprising: one or more processing units; a memory coupled to the one or more processing units; and A plurality of programs are stored in the memory, and when the plurality of programs are executed by the one or more processing units, the plurality of programs cause the electronic device to perform the method according to claims 15 to 17.