Garage light energy-saving management system and device

Abstract

The invention provides a garage light energy-saving management system and device, and the method comprises the steps: obtaining the environment illuminance and traffic flow parameters of a garage based on a sensor network, and building a garage light energy-saving control model according to the environment illuminance and traffic flow parameters; obtaining a garage light energy-saving control strategy based on the garage light energy-saving control model parameters; generating an expected lighting strategy of the garage lighting system through a scene control and induction control algorithm, wherein the expected lighting strategy is dynamically adjusted along with the traffic flow parameters; constructing an intelligent control model of the garage lighting system; and receiving an illumination instruction of the central control system, updating the illumination instruction based on the intelligent control model, and performing illumination control based on the updated illumination instruction to realize optimal management of light brightness and energy consumption. Energy consumption can be reduced, and lighting efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent lighting management systems, and more specifically, to a garage lighting energy-saving management system and device. Background Art

[0002] Existing garage lighting management systems generally use simple timing or induction control methods. These systems are often unable to flexibly adjust lighting strategies according to changes in actual ambient light and vehicle flow, resulting in energy waste and unsatisfactory lighting effects. In terms of technical principles, these systems lack the ability to comprehensively analyze and dynamically respond to environmental parameters, and cannot achieve a balance between optimizing lighting effects and energy-saving effects based on real-time data.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: lack of an intelligent control system that can dynamically adjust the lighting strategy according to the real-time ambient light illumination and vehicle flow parameters, and inability to effectively combine scene control and sensing control algorithms to generate and update the desired lighting strategy, thereby failing to achieve optimal management of light brightness and energy consumption. Summary of the invention

[0004] The invention provides a garage lighting energy-saving management system and device.

[0005] In a first aspect of the present invention, a garage lighting energy-saving management system is provided, comprising: Based on the sensor network, the garage ambient light illumination and vehicle flow parameters are obtained, and the garage lighting energy-saving control model is constructed accordingly; Based on the garage lighting energy-saving control model parameters, the garage lighting energy-saving control strategy is obtained; Generate the desired lighting strategy of the garage lighting system through scene control and sensor control algorithms, where the desired lighting strategy is dynamically adjusted according to the vehicle flow parameters; Construct an intelligent control model for the garage lighting system; Receive lighting instructions from the central control system, update the lighting instructions based on the intelligent control model, and perform lighting control based on the updated lighting instructions to achieve optimized management of light brightness and energy consumption.

[0006] Further, updating the lighting instruction based on the intelligent control model includes: During each update, based on the measured actual light brightness and traffic flow parameters, the expected lighting strategy and the garage light energy-saving control model are used to obtain the lighting adjustment instructions. The lighting adjustment instructions obtained from the previous update and the lighting adjustment instructions obtained from the current update are brought into the intelligent control model to obtain the lighting adjustment instructions for the current update.

[0007] Furthermore, based on the measured actual light brightness and vehicle flow parameters, the lighting adjustment instructions are obtained using the expected lighting strategy and the garage lighting energy-saving control model, including: Obtaining expected lighting brightness corresponding to actual brightness and vehicle flow parameters from the expected lighting strategy; The desired lighting brightness is input into the garage lighting energy-saving control model to obtain the desired lighting adjustment instruction corresponding to the desired lighting brightness; A lighting adjustment instruction is obtained based on the expected lighting adjustment instruction and the actual lighting adjustment instruction.

[0008] Furthermore, the intelligent control model is shown in formula (1): (1): in, is the lighting adjustment instruction updated for k+1 times, is the lighting adjustment instruction updated for the kth time, is the proportional control gain, is the expected lighting brightness of the kth update, is the actual lighting brightness updated at the kth time, is the traffic flow control gain, is the expected traffic flow of the kth update, is the actual traffic flow parameter updated for the kth time.

[0009] Furthermore, the garage lighting energy-saving control model is shown in formula (2): (2):

[0010] In the formula, is the power consumption of the lamp, is the actual lighting brightness, is the reference lighting brightness, is the proportionality constant, is the actual traffic flow parameter, is the reference traffic flow parameter, is the traffic flow impact coefficient.

[0011] Furthermore, the desired lighting strategy is shown in formula (3): (3):

[0012] In the formula, For the moment The expected lighting brightness, is the initial illumination brightness, and is the time strategy coefficient, For time, and is the traffic flow strategy coefficient, is the traffic flow parameter.

[0013] Furthermore, the strategy coefficient of the desired lighting strategy is shown in formula (4): : : : (4):

[0014] in, is the maximum lighting brightness, is the time period, is the maximum traffic flow parameter.

[0015] Furthermore, the lighting adjustment instruction of the garage lighting energy-saving control model is shown in formula (5): (5):

[0016] in, is the lighting adjustment instruction at time t, To control the gain for lighting, is the reference lighting brightness, is the actual lighting brightness, is the traffic flow control gain, is the reference traffic flow parameter, is the actual traffic flow parameter.

[0017] In a second aspect of the present invention, a garage lighting energy-saving management device is provided, comprising: Energy-saving control model building module, used to obtain garage ambient light illumination and vehicle flow parameters based on the sensor network, and build a garage lighting energy-saving control model based on them; An energy-saving control strategy building module is used to obtain a garage light energy-saving control strategy based on garage light energy-saving control model parameters; An expected lighting strategy module is used to generate an expected lighting strategy for the garage lighting system through scene control and induction control algorithms, wherein the expected lighting strategy is dynamically adjusted according to vehicle flow parameters; Intelligent control model building module, used to build the intelligent control model of the garage lighting system; The lighting control module is used to receive lighting instructions from the central control system, update the lighting instructions based on the intelligent control model, and perform lighting control based on the updated lighting instructions to achieve optimized management of light brightness and energy consumption.

[0018] Furthermore, the lighting control module includes: an updating unit, which is used to obtain lighting adjustment instructions based on the measured actual light brightness and vehicle flow parameters, using the expected lighting strategy and the garage light energy-saving control model at each update, and bringing the lighting adjustment instructions obtained from the previous update and the lighting adjustment instructions obtained from the current update into the intelligent control model to obtain the lighting adjustment instructions for the current update.

[0019] The above-mentioned embodiments of the present invention have at least the following beneficial effects: by implementing the garage lighting energy-saving management system of the present invention, the lighting strategy can be adjusted according to the real-time ambient light illumination and vehicle flow parameters to achieve optimal management of lighting brightness and energy consumption. The system acquires data through a sensor network, builds an energy-saving control model and strategy, and generates a dynamically adjusted desired lighting strategy, which can accurately control the lighting brightness, reduce unnecessary energy consumption, and ensure that the lighting in the garage meets safety and usage requirements.

[0020] In addition, the system receives and updates lighting instructions through an intelligent control model, which can further improve the response speed and accuracy of lighting control. This intelligent control method can reduce errors caused by human operation and improve the reliability and stability of the system. At the same time, the system design allows for flexible adjustment of lighting strategies based on actual lighting needs and changes in traffic flow, making the lighting system more adaptable to different usage scenarios and times, thereby achieving higher energy efficiency and cost savings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which: Figure 1 A schematic diagram of a process flow of a garage lighting energy-saving management system provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a garage lighting energy-saving management device provided by an embodiment of the present invention; Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0023] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0024] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0025] Reference below Figure 1 , Figure 1 This is a flow chart of a garage lighting energy-saving management system provided by an embodiment of the present invention. Figure 1 As shown, a garage lighting energy-saving management system 100 includes: Step 101, obtaining garage ambient light illumination and vehicle flow parameters based on a sensor network, and building a garage lighting energy-saving control model based on the obtained parameters; Step 102, obtaining a garage light energy-saving control strategy based on the garage light energy-saving control model parameters; Step 103, generating a desired lighting strategy for the garage lighting system through scene control and induction control algorithms, wherein the desired lighting strategy is dynamically adjusted according to vehicle flow parameters; Step 104, constructing an intelligent control model for the garage lighting system; Step 105, receiving lighting instructions from the central control system, updating the lighting instructions based on the intelligent control model, and performing lighting control based on the updated lighting instructions to achieve optimized management of light brightness and energy consumption.

[0026] It should be noted that the system obtains the garage ambient light intensity and vehicle flow parameters through the sensor network to build a garage lighting energy-saving control model. The sensor network here refers to a series of sensors arranged in the garage, which can monitor and collect the light intensity and vehicle flow in the garage in real time. The garage lighting energy-saving control model refers to a mathematical model based on these parameters to optimize lighting energy consumption.

[0027] Specifically, the sensor network includes light sensors and vehicle flow counters, which are responsible for measuring the light intensity in the garage and counting the number of vehicles entering and exiting. The data collected by these sensors will be transmitted to the central processing unit, which uses this data to build and adjust the energy-saving control model. The model parameters include but are not limited to light thresholds, vehicle flow thresholds, and expected energy consumption reduction targets.

[0028] Preferably, the system will also take into account changes in traffic flow and light intensity at different time periods to achieve more refined control. For example, during peak traffic periods, the system will automatically increase the lighting brightness to ensure safety, while during low traffic periods, the brightness will be reduced to save energy. In addition, the system can also adjust the light intensity parameters according to seasonal changes and weather conditions to adapt to different environmental conditions.

[0029] In some embodiments, updating the lighting instructions based on the intelligent control model includes: During each update, based on the measured actual light brightness and traffic flow parameters, the expected lighting strategy and the garage light energy-saving control model are used to obtain the lighting adjustment instructions. The lighting adjustment instructions obtained from the previous update and the lighting adjustment instructions obtained from the current update are brought into the intelligent control model to obtain the lighting adjustment instructions for the current update.

[0030] It should be noted that the process of updating the lighting instructions based on the intelligent control model includes obtaining the lighting adjustment instructions based on the measured actual light brightness and vehicle flow parameters, using the expected lighting strategy and the garage light energy-saving control model at each update. The intelligent control model here refers to an algorithm model that can dynamically adjust the lighting strategy according to real-time data, and the lighting adjustment instruction refers to the specific command calculated by the system based on the model for adjusting the light brightness.

[0031] Specifically, the intelligent control model receives the actual lighting brightness and traffic flow parameters from the sensor network, and then calculates the lighting adjustment instructions at the current moment in combination with the preset expected lighting strategy and energy-saving control model. These parameters include but are not limited to the actual lighting brightness, traffic flow, expected lighting brightness, and expected traffic flow. The model will calculate new lighting adjustment instructions through algorithms based on the changes in these parameters to optimize the lighting system.

[0032] Preferably, the intelligent control model can use advanced control algorithms, such as PID control algorithms, to improve the accuracy and response speed of lighting adjustment. At each update, the system will not only consider the current lighting and traffic flow parameters, but also refer to the lighting adjustment instructions obtained in the previous update to achieve a smoother and more continuous lighting adjustment process. In addition, the system can also predict future lighting needs based on historical data and trend analysis, so as to adjust the lighting strategy in advance to further improve energy efficiency and response efficiency.

[0033] In some embodiments, based on the measured actual light brightness and vehicle flow parameters, the lighting adjustment instruction is obtained using the expected lighting strategy and the garage lighting energy-saving control model, including: Obtaining expected lighting brightness corresponding to actual brightness and vehicle flow parameters from the expected lighting strategy; The desired lighting brightness is input into the garage lighting energy-saving control model to obtain the desired lighting adjustment instruction corresponding to the desired lighting brightness; A lighting adjustment instruction is obtained based on the expected lighting adjustment instruction and the actual lighting adjustment instruction.

[0034] It should be noted that the system obtains lighting adjustment instructions by measuring the actual lighting brightness and traffic flow parameters, using the expected lighting strategy and the garage lighting energy-saving control model. The expected lighting strategy here refers to the lighting brightness target preset by the system, which is dynamically adjusted according to the traffic flow parameters to ensure that appropriate lighting can be provided under different traffic flow conditions. The garage lighting energy-saving control model is a mathematical model that calculates the expected lighting adjustment instructions based on the actual lighting brightness and traffic flow parameters.

[0035] Specifically, the system first obtains the expected lighting brightness corresponding to the actual brightness and traffic flow parameters from the expected lighting strategy. Then, the expected lighting brightness is input into the garage light energy-saving control model, and the model calculates the corresponding expected lighting adjustment instructions based on the input parameters. Then, the system will comprehensively derive the final lighting adjustment instructions based on the expected lighting adjustment instructions and the actual lighting adjustment instructions. These parameters include but are not limited to actual lighting brightness, traffic flow, expected lighting brightness, and expected traffic flow.

[0036] Preferably, the system can adopt a more refined control strategy to further optimize the generation process of the lighting adjustment instruction. For example, the system can set different weight parameters to adjust the difference between the expected lighting brightness and the actual lighting brightness, and the degree of influence of the traffic flow on the lighting adjustment instruction.

[0037] Furthermore, the system can also temporarily adjust the desired lighting strategy according to environmental changes, such as weather conditions or special events, to adapt to different lighting needs. These adjustments can be manually set through the user interface or automatically optimized through intelligent learning algorithms to achieve more intelligent and automated lighting control.

[0038] In some embodiments, the intelligent control model is shown in formula (1): (1):

[0039] in, is the lighting adjustment instruction updated for k+1 times, is the lighting adjustment instruction updated for the kth time, is the proportional control gain, is the expected lighting brightness of the kth update, is the actual lighting brightness updated at the kth time, is the traffic flow control gain, is the expected traffic flow of the kth update, is the actual traffic flow parameter updated for the kth time.

[0040] It should be noted that the calculation process of the intelligent control model involves the formula for updating the lighting adjustment command, which includes parameters such as proportional control gain and traffic flow control gain. This formula is used to calculate the new lighting adjustment command based on the actual lighting brightness and traffic flow parameters, as well as the expected lighting brightness and expected traffic flow parameters at each update. The proportional control gain here refers to the coefficient for adjusting the control strength according to the lighting brightness deviation, and the traffic flow control gain refers to the coefficient for adjusting the control strength according to the traffic flow deviation.

[0041] Preferably, the intelligent control model can adjust the values ​​of the proportional control gain and the traffic flow control gain according to the actual application scenario to achieve the best control effect. For example, during periods of heavy traffic, the value of the traffic flow control gain needs to be increased to respond more quickly to changes in traffic flow.

[0042] Furthermore, the system can dynamically adjust these gain values ​​based on historical data and prediction models to adapt to different environmental conditions and usage requirements. Alternative solutions include using fuzzy logic control or neural network models to handle nonlinear and complex control scenarios to improve the adaptability and robustness of the system.

[0043] In some embodiments, the garage lighting energy-saving control model is as shown in formula (2): (2):

[0044] In the formula, is the power consumption of the lamp, is the actual lighting brightness, is the reference lighting brightness, is the proportionality constant, is the actual traffic flow parameter, is the reference traffic flow parameter, is the traffic flow impact coefficient.

[0045] It should be noted that the system determines the power consumption of the lamp through a specific calculation formula, which takes into account factors such as actual lighting brightness, reference lighting brightness, actual traffic flow parameters and reference traffic flow parameters. The power consumption here refers to the actual energy consumption of the lamp at a specific moment, while the reference lighting brightness and reference traffic flow parameters are preset standard values ​​used to compare with the actual measured values ​​to calculate the adjustment instructions.

[0046] Preferably, the system can adjust the values ​​of the proportional constant and the traffic flow impact coefficient according to the actual environment and usage requirements. For example, during the day when the illumination is high, the proportional constant needs to be reduced. Similarly, if the traffic volume increases significantly during a specific period of time, the traffic volume will affect the coefficient Adjustments are needed to respond more effectively to changes in traffic volume.

[0047] Furthermore, the system can also automatically optimize these parameters through learning algorithms to adapt to different usage patterns and environmental changes. Alternative solutions include using adaptive control strategies to dynamically adjust parameters based on historical data and real-time feedback to achieve more refined energy consumption management.

[0048] In some embodiments, the desired lighting strategy is as shown in formula (3): (3):

[0049] In the formula, For the moment The expected lighting brightness, is the initial illumination brightness, and is the time strategy coefficient, For time, and is the traffic flow strategy coefficient, is the traffic flow parameter.

[0050] It should be noted that the system determines the expected lighting brightness at a given moment through a specific calculation formula, which takes into account factors such as initial lighting brightness, time strategy coefficient, time, traffic flow strategy coefficient and traffic flow parameters. The expected lighting brightness here refers to the target brightness set by the system at a specific moment, while the time strategy coefficient and traffic flow strategy coefficient are parameters used to adjust the expected lighting brightness.

[0051] Preferably, the system can adjust the values ​​of the time strategy coefficient and the traffic flow strategy coefficient according to the actual environment and usage requirements. For example, during the day or during peak hours, the time strategy coefficient needs to be increased. and to increase the lighting intensity, and reduce these values ​​at night or during off-peak hours to save energy. Similarly, if the traffic volume increases significantly during a specific period, the traffic flow strategy coefficient and Adjustments are needed to respond more effectively to changes in traffic volume.

[0052] Furthermore, the system can also automatically optimize these parameters through learning algorithms to adapt to different usage patterns and environmental changes. Alternative solutions include using adaptive control strategies to dynamically adjust parameters based on historical data and real-time feedback to achieve more refined lighting control.

[0053] In some embodiments, the strategy coefficient of the desired lighting strategy is as shown in formula (4): ; ; ; (4);

[0054] in, is the maximum lighting brightness, is the time period, is the maximum traffic flow parameter.

[0055] It should be noted that the system determines the strategy coefficients of the desired lighting strategy through a specific calculation formula. These coefficients include the time strategy coefficient and the traffic flow strategy coefficient, which are calculated based on the maximum lighting brightness, time cycle and maximum traffic flow parameters. The strategy coefficients here refer to the parameters used to adjust the desired lighting brightness, which determine the degree of response of the lighting brightness to changes in time and traffic flow.

[0056] Specifically, the system uses the following calculation formula to determine the strategy coefficient (5): ; ; ; (5);

[0057] in, is the maximum illumination brightness, is the initial illumination brightness, is the time cycle, is the maximum traffic flow parameter, is the initial traffic flow parameter.

[0058] Preferably, the system can adjust the calculation method of these strategy coefficients according to the actual environment and usage requirements. For example, the values ​​of the maximum lighting brightness and maximum vehicle flow parameters can be adjusted according to seasonal changes or special events to adapt to different lighting and vehicle flow requirements.

[0059] Furthermore, the system can also automatically optimize these parameters through learning algorithms to adapt to different usage patterns and environmental changes. Alternative solutions include using adaptive control strategies to dynamically adjust parameters based on historical data and real-time feedback to achieve more refined lighting control.

[0060] In some embodiments, the lighting adjustment instruction of the garage lighting energy-saving control model is as shown in formula (5): (5);

[0061] in, is the lighting adjustment instruction at time t, To control the gain for lighting, is the reference lighting brightness, is the actual lighting brightness, is the traffic flow control gain, is the reference traffic flow parameter, is the actual traffic flow parameter.

[0062] It should be noted that the system determines the lighting adjustment command through a specific calculation formula, which involves the lighting control gain and the traffic flow control gain, which are used to adjust the deviation between the reference lighting brightness and the reference traffic flow parameters and the actual values. The lighting adjustment command here refers to the specific command for adjusting the light brightness calculated by the system based on the deviation, while the lighting control gain and the traffic flow control gain are coefficients used to quantify the intensity of this adjustment.

[0063] Preferably, the system can adjust the values ​​of the lighting control gain and the traffic flow control gain according to the actual environment and usage requirements. For example, during periods of heavy traffic, the traffic flow control gain needs to be increased. Similarly, if the ambient light intensity changes greatly, the lighting control gain Adjustments are needed to respond more effectively to changes in light levels.

[0064] Furthermore, the system can also automatically optimize these parameters through learning algorithms to adapt to different usage patterns and environmental changes. Alternative solutions include using adaptive control strategies to dynamically adjust parameters based on historical data and real-time feedback to achieve more refined lighting control.

[0065] The above-mentioned embodiments of the present invention have the following beneficial effects: The garage lighting energy-saving management system described in the present invention can improve the energy efficiency and intelligence level of garage lighting. By acquiring ambient light illumination and vehicle flow parameters in real time through the sensor network, the system can build an accurate energy-saving control model and strategy, and generate a dynamically adjusted desired lighting strategy. This control method based on real-time data can ensure that the lighting system minimizes energy consumption while meeting safety and visual requirements. In addition, the system updates lighting instructions through an intelligent control model, which can quickly respond to environmental changes and achieve more sophisticated and flexible lighting control, thereby optimizing light brightness and energy consumption management.

[0066] Furthermore, the system can improve the adaptability and response speed of the garage lighting system. Through the intelligent control model, the system can not only dynamically adjust the lighting strategy according to the measured actual light brightness and traffic flow parameters, but also accurately calculate the lighting adjustment instructions through the proportional control gain and traffic flow control gain. It can ensure that the lighting system can provide appropriate lighting brightness under different traffic flow and lighting conditions, while reducing energy waste and improving the economic and environmental benefits of the system.

[0067] Example 1 The present invention is described below with reference to specific examples. A scenario in a medium-sized underground garage is taken as an example. The garage has an area of ​​5,000 square meters and is equipped with 500 lamps evenly distributed in various areas. Traffic flow counters are installed at the entrance and exit of the garage, and 10 photosensitive sensors are evenly distributed in the garage to monitor the ambient light intensity.

[0068] During the rush hour from 8 to 9 in the morning, set the time period T to 1 hour (i.e. 60 minutes), maximum lighting brightness, initial lighting brightness, reference lighting brightness, maximum traffic flow parameter vehicles / hour, and reference traffic flow parameter vehicles / hour. Maximum lighting brightness Initial lighting brightness , reference lighting brightness , maximum traffic flow parameters , refer to the traffic flow parameters .

[0069] The strategy coefficient calculation formula according to the desired lighting strategy is: ; ; ; ; The expected lighting strategy formula is During this period it becomes: ; Assume that at 8:15, the actual measured traffic flow parameters are , the expected lighting brightness , the garage lighting energy-saving control model is , set the proportional constant k = 0.5, and the traffic flow coefficient m = 0.3. Substitute the expected lighting brightness E = 500lux and calculate the power consumption P of the lamp. At this time, the actual lighting brightness is assumed to be 400lux, then .

[0070] The calculation formula for lighting adjustment command is: , assuming that the lighting control gain K = 0.8 and the traffic flow control gain L = 0.6. Then the lighting adjustment command , which means that the light brightness needs to be reduced.

[0071] The intelligent control model is , assuming that the lighting adjustment instructions last updated (at 8 o'clock) , proportional control gain , vehicle flow control gain , the actual measured traffic volume at 8:15 Expected traffic volume , actual lighting brightness, expected lighting brightness The lighting adjustment instructions updated this time (8:15) are: .

[0072] The system adjusts the light brightness according to the updated lighting adjustment instructions, dynamically adjusting the lighting strategy according to real-time traffic flow and light intensity, thereby achieving the goal of optimizing light brightness and energy consumption management.

[0073] Example 2

[0074] like Figure 2 As shown, a garage lighting energy-saving management device 200 in some embodiments includes: The energy-saving control model building module 201 is used to obtain the garage ambient light illumination and vehicle flow parameters based on the sensor network, and build a garage lighting energy-saving control model accordingly; An energy-saving control strategy building module 202 is used to obtain a garage light energy-saving control strategy based on garage light energy-saving control model parameters; The expected lighting strategy module 203 is used to generate an expected lighting strategy for the garage lighting system through scene control and induction control algorithms, wherein the expected lighting strategy is dynamically adjusted according to vehicle flow parameters; An intelligent control model building module 204 is used to build an intelligent control model for a garage lighting system; The lighting control module 205 is used to receive lighting instructions from the central control system, update the lighting instructions based on the intelligent control model, and perform lighting control based on the updated lighting instructions to achieve optimal management of light brightness and energy consumption.

[0075] It is understandable that the modules recorded in the garage lighting energy saving management device 200 are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the garage light energy-saving management system are also applicable to the garage light energy-saving management device 200 and the modules contained therein, and will not be repeated here.

[0076] In some embodiments, the lighting control module includes: an update unit, which is used to obtain lighting adjustment instructions based on the measured actual light brightness and vehicle flow parameters, using the expected lighting strategy and the garage light energy-saving control model at each update, and bringing the lighting adjustment instructions obtained from the previous update and the lighting adjustment instructions obtained from the current update into the intelligent control model to obtain the lighting adjustment instructions for the current update.

[0077] It should be noted that the lighting control module in the device includes an update unit, which is responsible for obtaining lighting adjustment instructions in each update cycle based on the measured actual light brightness and vehicle flow parameters, using the expected lighting strategy and the garage light energy-saving control model. The update unit here refers to a functional component in the device, which is responsible for executing the update process of the lighting adjustment instructions.

[0078] Specifically, the update unit receives actual lighting brightness and traffic flow parameters from the sensor network each time it updates, and then calculates new lighting adjustment instructions based on these parameters and the preset desired lighting strategy and energy-saving control model. In this process, the update unit brings the lighting adjustment instructions obtained from the previous update and the lighting adjustment instructions obtained from the current update into the intelligent control model to obtain the lighting adjustment instructions for the current update.

[0079] Preferably, the operation of the update unit can be further refined or alternative solutions can be provided to improve the response speed and adjustment accuracy of the system. For example, the update unit can set a feedback mechanism to monitor the effect of the lighting adjustment instruction in real time and adjust subsequent instructions based on the feedback.

[0080] Further, the update unit can adopt more advanced algorithms, such as adaptive control algorithms, to dynamically adjust the parameters of the desired lighting strategy and energy-saving control model to adapt to environmental changes and improve energy efficiency. Alternative solutions include using cloud computing platforms to process large amounts of sensor data, or developing a mobile application that allows users to remotely monitor and adjust lighting control settings.

[0081] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0082] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0083] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0084] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0085] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A garage lighting energy-saving management system, characterized in that: The system performs the following steps: Based on the sensor network, the garage ambient light illumination and vehicle flow parameters are obtained, and the garage lighting energy-saving control model is constructed accordingly; Based on the garage lighting energy-saving control model parameters, the garage lighting energy-saving control strategy is obtained; Generate the desired lighting strategy of the garage lighting system through scene control and sensor control algorithms, where the desired lighting strategy is dynamically adjusted according to the vehicle flow parameters; Construct an intelligent control model for the garage lighting system; Receive lighting instructions from the central control system, update the lighting instructions based on the intelligent control model, and perform lighting control based on the updated lighting instructions to achieve optimized management of light brightness and energy consumption.

2. The system according to claim 1, characterized in that Updating the lighting instructions based on the intelligent control model includes: During each update, based on the measured actual light brightness and traffic flow parameters, the expected lighting strategy and the garage light energy-saving control model are used to obtain the lighting adjustment instructions. The lighting adjustment instructions obtained from the previous update and the lighting adjustment instructions obtained from the current update are brought into the intelligent control model to obtain the lighting adjustment instructions for the current update.

3. The system according to claim 2, characterized in that Based on the measured actual light brightness and vehicle flow parameters, the lighting adjustment instructions are obtained using the expected lighting strategy and the garage lighting energy-saving control model, including: Obtaining expected lighting brightness corresponding to actual brightness and vehicle flow parameters from the expected lighting strategy; The desired lighting brightness is input into the garage lighting energy-saving control model to obtain the desired lighting adjustment instruction corresponding to the desired lighting brightness; A lighting adjustment instruction is obtained based on the expected lighting adjustment instruction and the actual lighting adjustment instruction.

4. The system according to claim 3, characterized in that The intelligent control model is shown in formula (1): （1）； in, is the lighting adjustment instruction updated for k+1 times, is the lighting adjustment instruction updated for the kth time, is the proportional control gain, is the expected lighting brightness of the kth update, is the actual lighting brightness updated at the kth time, is the traffic flow control gain, is the expected traffic flow of the kth update, is the actual traffic flow parameter updated for the kth time.

5. The system according to claim 4, characterized in that The garage lighting energy-saving control model is shown in formula (2): （2）； In the formula, is the power consumption of the lamp, is the actual lighting brightness, is the reference lighting brightness, is the proportionality constant, is the actual traffic flow parameter, is the reference traffic flow parameter, is the traffic flow impact coefficient.

6. The system according to claim 5, characterized in that The desired lighting strategy is shown in formula (3): （3）； In the formula, For the moment The expected lighting brightness, is the initial illumination brightness, and is the time strategy coefficient, For time, and is the traffic flow strategy coefficient, is the traffic flow parameter.

7. The system according to claim 6, characterized in that The strategy coefficient of the expected lighting strategy is shown in formula (4): ； ； ； （4）； in, is the maximum lighting brightness, is the time period, is the maximum traffic flow parameter.

8. The system according to claim 7, characterized in that The lighting adjustment instruction of the garage lighting energy-saving control model is shown in formula (5): （5）； in, is the lighting adjustment instruction at time t, To control the gain for lighting, is the reference lighting brightness, is the actual lighting brightness, is the traffic flow control gain, is the reference traffic flow parameter, is the actual traffic flow parameter.

9. A garage lighting energy-saving management device, characterized in that: The device comprises: Energy-saving control model building module, used to obtain garage ambient light illumination and vehicle flow parameters based on the sensor network, and build a garage lighting energy-saving control model based on them; An energy-saving control strategy building module is used to obtain a garage light energy-saving control strategy based on garage light energy-saving control model parameters; An expected lighting strategy module is used to generate an expected lighting strategy for the garage lighting system through scene control and induction control algorithms, wherein the expected lighting strategy is dynamically adjusted according to vehicle flow parameters; Intelligent control model building module, used to build the intelligent control model of the garage lighting system; The lighting control module is used to receive lighting instructions from the central control system, update the lighting instructions based on the intelligent control model, and perform lighting control based on the updated lighting instructions to achieve optimized management of light brightness and energy consumption.

10. The device according to claim 9, characterized in that The lighting control module includes: The updating unit is used to obtain lighting adjustment instructions based on the measured actual light brightness and vehicle flow parameters at each update, using the expected lighting strategy and the garage light energy-saving control model, and bring the lighting adjustment instructions obtained in the previous update and the lighting adjustment instructions obtained in the current update into the intelligent control model to obtain the lighting adjustment instructions for the current update.

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