Energy-saving intelligent lighting control system

Abstract

The invention provides an energy-saving intelligent illumination control system, and relates to the technical field of intelligent illumination, and the system comprises the steps: collecting the current ambient light data of a target illumination region through an ambient light sensor, and generating the target ambient illumination brightness; identifying personnel activity information in the target illumination area through a human body activity sensor, and determining target activity illumination brightness; determining target adjustment illumination brightness based on the target environment illumination brightness and the target activity illumination brightness, and generating a self-adaptive illumination brightness control strategy in combination with the actual illumination brightness; adjusting the brightness of the target lighting equipment in the target lighting area based on a self-adaptive brightness control strategy, and monitoring the current lighting energy consumption data of the target lighting equipment through a lighting energy consumption sensor; the illumination feedback information of the user side is obtained, and the adaptive illumination brightness control strategy is optimized based on the illumination feedback information and the current illumination energy consumption data, so that intelligent adjustment of the illumination brightness can be realized, the energy consumption is effectively reduced, and the illumination efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent lighting, and in particular to an energy-saving intelligent lighting control system. Background Art

[0002] Smart lighting can achieve lighting control through a variety of intelligent technologies to provide people with a comfortable lighting environment. Among them, intelligent technologies include sensors, communication protocols, cloud platforms, artificial intelligence, etc., which can realize functions such as remote control of lights, voice control, scene mode switching, energy-saving optimization, etc. In recent years, smart lighting technology has developed rapidly, especially in the increasingly widespread application of home and commercial fields.

[0003] In the prior art, traditional lighting systems often lack intelligent management and cannot achieve intelligent adjustment of lighting brightness. The lighting efficiency is relatively low, which may lead to serious energy waste. In addition, they are also insufficient in user-friendliness and system stability, making it difficult to ensure the safety of the lighting system.

[0004] Therefore, it is necessary to provide an energy-saving intelligent lighting control system to solve the above technical problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an energy-saving intelligent lighting control system, which is used to solve the problems that traditional lighting systems lack intelligent management, cannot realize intelligent adjustment of lighting brightness, have relatively low lighting efficiency, and cause serious energy waste. In addition, they are also insufficient in user-friendliness and system stability, and it is difficult to ensure the safety of the lighting system.

[0006] The present invention provides an energy-saving intelligent lighting control system, the control system comprising:

[0007] An ambient brightness acquisition module is used to acquire current ambient light data of a target lighting area through an ambient light sensor based on the target lighting area, and generate corresponding target ambient lighting brightness according to the current ambient light data;

[0008] A human activity recognition module, used to recognize human activity information in the target lighting area through a human activity sensor, and determine the corresponding target activity lighting brightness according to the human activity information;

[0009] A control strategy generation module, used to determine a target adjusted lighting brightness based on the target environment lighting brightness and the target activity lighting brightness, and generate a corresponding adaptive lighting brightness control strategy in combination with the actual lighting brightness;

[0010] A real-time adjustment monitoring module, used to adjust the brightness of the target lighting device in the target lighting area based on the adaptive brightness control strategy, and monitor the current lighting energy consumption data of the target lighting device through a lighting energy consumption sensor;

[0011] The strategy optimization and adjustment module is used to obtain lighting feedback information from the user end, and optimize the adaptive lighting brightness control strategy based on the lighting feedback information and the current lighting energy consumption data.

[0012] Preferably, the ambient brightness acquisition module is used to collect current ambient light data of the target lighting area through an ambient light sensor based on the target lighting area, and generate corresponding target ambient lighting brightness according to the current ambient light data, specifically including:

[0013] A deployment unit, configured to deploy a plurality of the ambient light sensors according to the distribution of the target lighting devices in the target lighting area;

[0014] A collection unit, used for collecting current ambient light data of the target lighting area in real time through the ambient light sensor;

[0015] A determination unit is used to determine the target ambient lighting brightness of the target lighting area if the current ambient light data meets the preset lighting requirements.

[0016] Preferably, determining the corresponding target activity lighting brightness according to the personnel activity information specifically includes:

[0017] The average frequency of personnel activities in the target lighting area is calculated according to the personnel activity information. The calculation formula of the average frequency of personnel activities is as follows:

[0018]

[0019] In the formula, HDPL t represents the moving average value at the current time t, that is, the average frequency of personnel activities in the target lighting area at the current time t; n represents the number of moving average cycles; JR i Indicates the number of people entering the target lighting area at time i; LK i represents the number of people leaving the target lighting area at time i;

[0020] The target activity lighting brightness is determined according to the average frequency of human activities in the target lighting area.

[0021] Preferably, the process of determining the target lighting brightness adjustment is as follows:

[0022] Set the target ambient lighting brightness at the current time t to HJLD t, and the target activity lighting brightness is HDLD t ;

[0023] Obtain the device brightness adaptation factor of the target lighting device in the target environment area, and determine the target adjusted lighting brightness by balancing the target environment lighting brightness, the target activity lighting brightness, and the influence of the device brightness adaptation factor on the target adjusted lighting brightness. The calculation formula of the target adjusted lighting brightness is as follows:

[0024] TJLD t =α HJ *HJLD t +α HD *HDLD t +α SB *τ

[0025] α HJ +α HD +α SB =1

[0026] Where, TJLD t represents the target adjustment lighting brightness at the current time t; α HJ Indicates the weight coefficient corresponding to the target environment lighting brightness, that is, the degree of influence of the target environment lighting brightness on the target adjustment lighting brightness; HJLD t Represents the target environment lighting brightness at the current time t; α HD Indicates the weight coefficient corresponding to the target activity lighting brightness, that is, the degree of influence of the target activity lighting brightness on the target adjustment lighting brightness; HJLD t represents the target activity lighting brightness at the current time t; α SB It represents the weight coefficient corresponding to the device brightness adaptation factor, that is, the influence of the device brightness adaptation factor on the target adjusted lighting brightness; τ represents the device brightness adaptation factor of the target lighting device in the target environment area.

[0027] Preferably, the adaptive lighting brightness control strategy is obtained based on the target lighting brightness adjustment and combined with the actual lighting brightness. The adaptive lighting brightness control strategy is expressed as follows:

[0028]

[0029] Where KZCL t represents the adaptive lighting brightness control strategy at the current time t; TJLD t Indicates the target lighting brightness at the current time t; SJLD t Represents the actual lighting brightness at the current time t.

[0030] Preferably, after obtaining the current lighting energy consumption data, extracting the current lighting energy consumption features corresponding to the current lighting energy consumption data;

[0031] Based on the support vector machine, the current lighting energy consumption characteristics are classified, and the corresponding optimization problem is as follows:

[0032]

[0033] In the formula, w u represents the weight vector of the u-th support vector machine, which is used to determine the direction of the classification hyperplane; b u represents the bias term of the u-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; ξ uv represents the slack variable when the vth current lighting energy consumption feature is linearly inseparable in the uth support vector machine; C represents the regularization parameter, which is used to control the trade-off between classification interval and classification error; R represents the total number of current lighting energy consumption features; min represents the minimum value operation.

[0034] Preferably, the constraints corresponding to the optimization problem are as follows:

[0035] LBBQ v (w u *NHTZ v +b u )≥1-ξ uv ,LBBQ v =u

[0036] LBBQ v (w u *NHTZ v +b u )≤ξ uv -1,LBBQ v ≠u

[0037]

[0038] Where, LBBQ v NHTZ represents the feature category label corresponding to the vth current lighting energy consumption feature; v represents the vth current lighting energy consumption characteristic; w u represents the weight vector of the u-th support vector machine, which is used to determine the direction of the classification hyperplane; b u represents the bias term of the u-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; ξ uv represents the slack variable when the vth current lighting energy consumption feature is linearly inseparable in the uth support vector machine; R represents the total number of current lighting energy consumption features;

[0039] The feature category label corresponding to the current lighting energy consumption feature is identified, the abnormal lighting energy consumption feature and the corresponding abnormal lighting energy consumption data are determined, and the abnormal lighting energy consumption data is sent to the user end.

[0040] Preferably, the strategy optimization and adjustment module is used to obtain lighting feedback information from the user end, and optimize the adaptive lighting brightness control strategy based on the lighting feedback information and the current lighting energy consumption data, specifically including:

[0041] The user satisfaction data of the user terminal is obtained according to the lighting feedback information, and the first brightness optimization target is set to minimize the current lighting energy consumption data, and the second brightness optimization target is set to maximize the user satisfaction data; based on the first brightness optimization target and the second brightness optimization target, a multi-objective brightness optimization problem corresponding to the adaptive brightness control strategy is determined, and the expression corresponding to the multi-objective brightness optimization problem is as follows:

[0042]

[0043] ZMNH(t)=HJLD t *P t *Δt

[0044] ε ZMNH +ε MYD =1

[0045] Where, HJLD t represents the target environment lighting brightness at the current time t; ε ZMNH Indicates the weight coefficient corresponding to the current lighting energy consumption data; ε MYD represents the weight coefficient corresponding to the user satisfaction data; ZMNH(t) represents the current lighting energy consumption data at the current time t; MYD(HJLD t ) represents the target environment lighting brightness HJLD at the user end t User satisfaction data of P t represents the power of the target lighting device; Δt represents the preset time interval;

[0046] By solving the multi-objective brightness optimization problem, the weight coefficients corresponding to the current lighting energy consumption data and the user satisfaction data are determined respectively, and the adaptive lighting brightness control strategy is optimized and adjusted.

[0047] Compared with the related art, the energy-saving intelligent lighting control system provided by the present invention has the following advantages:

[0048] Beneficial effects:

[0049] The present invention can collect the current ambient light data of the target lighting area through the ambient light sensor based on the target lighting area through the ambient light sensor, and generate the corresponding target ambient lighting brightness according to the current ambient light data through the human activity sensor; identify the human activity information in the target lighting area through the human activity sensor, and determine the corresponding target activity lighting brightness according to the human activity information; determine the target adjustment lighting brightness based on the target ambient lighting brightness and the target activity lighting brightness, and generate the corresponding adaptive lighting brightness control strategy in combination with the actual lighting brightness; adjust the brightness of the target lighting equipment in the target lighting area based on the adaptive brightness control strategy, and monitor the current lighting energy consumption data of the target lighting equipment through the lighting energy consumption sensor; obtain the lighting feedback information of the user end, and optimize the adaptive lighting brightness control strategy based on the lighting feedback information and the current lighting energy consumption data, so as to realize the intelligent adjustment of the lighting brightness, effectively reduce the energy consumption, improve the lighting efficiency, and ensure the safety and stability of the lighting system.

[0050] The system of the present invention can monitor the ambient light, human activities and other information in real time through the built-in multiple sensors, formulate an adaptive lighting brightness control strategy, and thus automatically adjust the lighting brightness; the present invention can collect and analyze the energy consumption data of the lighting system, combine it with user feedback information, further optimize the lighting strategy, and effectively reduce energy consumption; at the same time, the lighting energy consumption data can be monitored in real time, abnormal conditions in the system operation can be discovered in time and fed back to the user, to ensure the safe and stable operation of the lighting system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A system block diagram of an energy-saving intelligent lighting control system of the present invention;

[0052] Figure 2 It is a structural block diagram of an ambient brightness acquisition module of an energy-saving intelligent lighting control system of the present invention;

[0053] Figure 3 This is a schematic diagram of sending abnormal lighting energy consumption data to a user end according to the present invention. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0055] Embodiment 1

[0056] like Figure 1 As shown, an energy-saving intelligent lighting control system, the control system comprises:

[0057] An ambient brightness acquisition module is used to acquire current ambient light data of a target lighting area through an ambient light sensor based on the target lighting area, and generate corresponding target ambient lighting brightness according to the current ambient light data;

[0058] Among them, the ambient brightness acquisition module can use a high-precision ambient light sensor to collect real-time data on the lighting area. These data specifically include key parameters such as ambient light intensity and color temperature in the current lighting area, and then determine the target ambient lighting brightness.

[0059] Through the above method, the natural light changes in the lighting area can be simulated, thereby improving the user experience and reducing unnecessary energy consumption.

[0060] A human activity recognition module, used to recognize human activity information in the target lighting area through a human activity sensor, and determine the corresponding target activity lighting brightness according to the human activity information;

[0061] It is understandable that the personnel activity recognition module can perform non-contact monitoring of personnel activities in the lighting area by deploying human activity sensors, such as infrared sensors, radar sensors, etc.

[0062] Specifically, the module can intelligently identify the number of people, movement trajectories and activity intensity, and dynamically adjust the lighting brightness of the target activity accordingly to ensure that the lighting needs are highly matched with the actual activity status of the people, further promoting the efficient use of energy.

[0063] A control strategy generation module, used to determine a target adjusted lighting brightness based on the target environment lighting brightness and the target activity lighting brightness, and generate a corresponding adaptive lighting brightness control strategy in combination with the actual lighting brightness;

[0064] It should be noted that the control strategy generation module can integrate the output information of the environment brightness acquisition module and the personnel activity recognition module to calculate the optimal target adjustment lighting brightness.

[0065] At the same time, the module can obtain the actual lighting brightness of the lighting area in real time, and generate an adaptive lighting brightness control strategy through comparative analysis, thereby achieving dual optimization of lighting comfort and energy-saving effects.

[0066] A real-time adjustment monitoring module, used to adjust the brightness of the target lighting device in the target lighting area based on the adaptive brightness control strategy, and monitor the current lighting energy consumption data of the target lighting device through a lighting energy consumption sensor;

[0067] Among them, the real-time adjustment monitoring module can accurately adjust the brightness of the intelligent lighting equipment in the lighting area according to the adaptive lighting brightness control strategy output by the control strategy generation module.

[0068] In addition, the module is also integrated with lighting energy consumption sensors, which can monitor and record the energy consumption data of each lighting device in real time, which is helpful for subsequent energy efficiency analysis and strategy optimization.

[0069] The strategy optimization and adjustment module is used to obtain lighting feedback information from the user end, and optimize the adaptive lighting brightness control strategy based on the lighting feedback information and the current lighting energy consumption data.

[0070] It is understandable that the strategy optimization and adjustment module can collect user feedback on lighting effects through the user-side interface, and continuously optimize the adaptive lighting brightness control strategy in combination with the lighting energy consumption data monitored in real time.

[0071] Through the above methods, the adaptive ability of the lighting system can be improved, and it can be ensured that the adaptive lighting brightness control strategy can continue to meet the diverse needs of users and achieve maximum benefits in energy use.

[0072] In the specific implementation process, Figure 2 As shown, the ambient brightness acquisition module 200 is used to collect the current ambient light data of the target lighting area through the ambient light sensor based on the target lighting area, and generate the corresponding target ambient lighting brightness according to the current ambient light data, specifically including:

[0073] A deployment unit 201 is used to deploy a plurality of the ambient light sensors according to the distribution of the target lighting devices in the target lighting area;

[0074] A collection unit 202 is used to collect current ambient light data of the target lighting area in real time through the ambient light sensor;

[0075] The determination unit 203 is configured to determine the target ambient lighting brightness of the target lighting area if the current ambient light data meets the preset lighting requirement.

[0076] Among them, the ambient brightness acquisition module can efficiently collect the current ambient light data in the lighting area through the ambient light sensor, so as to quantitatively measure the light intensity in the lighting area and comprehensively evaluate multi-dimensional information such as light quality and color temperature.

[0077] Specifically, the deployment unit can reasonably deploy multiple ambient light sensors based on the spatial distribution characteristics of the lighting equipment in the lighting area, combined with the type, power and positional relationship of the lighting equipment, thereby ensuring that the ambient light sensors can fully cover and accurately reflect the light environment conditions of the entire lighting area.

[0078] The collection unit can collect the current ambient light data of the lighting area in real time and continuously through the deployed ambient light sensors. The unit can use the high sensitivity of the sensor to quickly capture the slight changes in the light intensity in the lighting area and the light fluctuations in different time periods.

[0079] After obtaining the current ambient light data, the determination unit can perform in-depth analysis and comparison of these current ambient light data according to preset lighting requirements, such as light intensity range, energy-saving indicators, comfort considerations, etc.

[0080] Specifically, if the current ambient light data meets the preset lighting requirements, the determination unit will determine the target ambient lighting brightness of the lighting area accordingly, thereby ensuring the intelligent operation of the lighting system and effectively improving lighting efficiency and user experience.

[0081] The determining the corresponding target activity lighting brightness according to the personnel activity information specifically includes:

[0082] The average frequency of personnel activities in the target lighting area is calculated according to the personnel activity information. The calculation formula of the average frequency of personnel activities is as follows:

[0083]

[0084] In the formula, HDPL t represents the moving average value at the current time t, that is, the average frequency of personnel activities in the target lighting area at the current time t; n represents the number of moving average cycles; JR i Indicates the number of people entering the target lighting area at time i; LK i represents the number of people leaving the target lighting area at time i;

[0085] The target activity lighting brightness is determined according to the average frequency of human activities in the target lighting area.

[0086] In practical applications, firstly, the average frequency of personnel activities in the lighting area can be calculated using the moving average method based on the personnel activity information.

[0087] Then, the target activity lighting brightness can be further determined based on the average frequency of human activities in the lighting area, thereby ensuring that the lighting system achieves efficient use of energy while meeting the actual needs of human activities.

[0088] The process of determining the target lighting brightness adjustment is as follows:

[0089] Set the target ambient lighting brightness at the current time t to HJLD t , and the target activity lighting brightness is HDLD t ;

[0090] Obtain the device brightness adaptation factor of the target lighting device in the target environment area, and determine the target adjusted lighting brightness by balancing the target environment lighting brightness, the target activity lighting brightness, and the influence of the device brightness adaptation factor on the target adjusted lighting brightness. The calculation formula of the target adjusted lighting brightness is as follows:

[0091] TJLD t =α HJ *HJLD t +α HD *HDLD t +α SB *τ

[0092] α HJ +α HD +α SB =1

[0093] Where, TJLD t represents the target adjustment lighting brightness at the current time t; α HJ Indicates the weight coefficient corresponding to the target environment lighting brightness, that is, the degree of influence of the target environment lighting brightness on the target adjustment lighting brightness; HJLD t Represents the target environment lighting brightness at the current time t; α HD Indicates the weight coefficient corresponding to the target activity lighting brightness, that is, the degree of influence of the target activity lighting brightness on the target adjustment lighting brightness; HJLD t represents the target activity lighting brightness at the current time t; α SB It represents the weight coefficient corresponding to the device brightness adaptation factor, that is, the influence of the device brightness adaptation factor on the target adjusted lighting brightness; τ represents the device brightness adaptation factor of the target lighting device in the target environment area.

[0094] It should be noted that, first, the target environment lighting brightness and the target activity lighting brightness at the current moment can be clearly set, and then the brightness adaptation factors of all lighting devices in the environment area can be collected and analyzed.

[0095] Furthermore, the target adjusted lighting brightness may be determined by balancing and integrating the influence of the target environment lighting brightness, the target activity lighting brightness, and the device brightness adaptation factor on the target adjusted lighting brightness.

[0096] Specifically, the weight coefficient directly reflects the relative importance of each factor to the final lighting brightness. The weight coefficient corresponding to the target ambient lighting brightness measures the direct impact of ambient light conditions on the adjustment result; the weight coefficient corresponding to the target activity lighting brightness reflects the user's activity demand for the lighting level; and the weight coefficient corresponding to the device brightness adaptation factor ensures that the lighting equipment can be flexibly adjusted according to the actual operating conditions.

[0097] Based on the target lighting brightness adjustment, combined with the actual lighting brightness, the adaptive lighting brightness control strategy is obtained. The expression of the adaptive lighting brightness control strategy is as follows:

[0098]

[0099] Where KZCL t represents the adaptive lighting brightness control strategy at the current time t; TJLD t Indicates the target lighting brightness at the current time t; SJLD t Represents the actual lighting brightness at the current time t.

[0100] The target lighting brightness is determined based on multiple factors such as lighting environment, personnel activities and equipment characteristics, which reflects the optimal brightness level that the lighting system should achieve. The actual lighting brightness reflects the actual brightness output of the lighting system under the current operating state, which is a direct reflection of the system performance and effect.

[0101] Furthermore, by comparing the difference between the target adjusted lighting brightness and the actual lighting brightness, the adaptive lighting brightness control strategy can identify the direction and amplitude of the lighting system that needs to be adjusted, and then trigger the corresponding regulation mechanism to achieve accurate matching and continuous optimization of brightness output, thereby improving the comfort and energy efficiency of the lighting environment, effectively reducing energy consumption, and extending the service life of lighting equipment.

[0102] After obtaining the current lighting energy consumption data, extracting current lighting energy consumption features corresponding to the current lighting energy consumption data;

[0103] Based on the support vector machine, the current lighting energy consumption characteristics are classified, and the corresponding optimization problem is as follows:

[0104]

[0105] In the formula, w u represents the weight vector of the u-th support vector machine, which is used to determine the direction of the classification hyperplane; b u represents the bias term of the u-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; ξ uvrepresents the slack variable when the vth current lighting energy consumption feature is linearly inseparable in the uth support vector machine; C represents the regularization parameter, which is used to control the trade-off between classification interval and classification error; R represents the total number of current lighting energy consumption features; min represents the minimum value operation.

[0106] The constraints corresponding to the optimization problem are as follows:

[0107] LBBQ v (w u *NHTZ v +b u )≥1-ξ uv ,LBBQ v =u

[0108] LBBQ v (w u *NHTZ v +b u )≤ξ uv -1,LBBQ v ≠u

[0109]

[0110] Where, LBBQ v NHTZ represents the feature category label corresponding to the vth current lighting energy consumption feature; v represents the vth current lighting energy consumption characteristic; w u represents the weight vector of the u-th support vector machine, which is used to determine the direction of the classification hyperplane; b u represents the bias term of the u-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; ξ uv represents the slack variable when the vth current lighting energy consumption feature is linearly inseparable in the uth support vector machine; R represents the total number of current lighting energy consumption features;

[0111] The feature category label corresponding to the current lighting energy consumption feature is identified, the abnormal lighting energy consumption feature and the corresponding abnormal lighting energy consumption data are determined, and the abnormal lighting energy consumption data is sent to the user end.

[0112] After obtaining the current lighting energy consumption data, the feature extraction step can be further performed to accurately capture the key information in the current lighting energy consumption data, that is, the current lighting energy consumption features. Then, the support vector machine (SVM) can be used to perform feature classification on the current lighting energy consumption features. And according to the feature classification results, the feature category labels corresponding to the current lighting energy consumption features can be discriminated to identify abnormal lighting energy consumption features and their corresponding abnormal lighting energy consumption data.

[0113] like Figure 3As shown, these abnormal data can be sent to the user end in a timely manner through the terminal device, so that the user can take quick measures to optimize the energy consumption performance of the lighting system.

[0114] The strategy optimization and adjustment module is used to obtain the lighting feedback information of the user end, and optimize the adaptive lighting brightness control strategy based on the lighting feedback information and the current lighting energy consumption data, specifically including:

[0115] The user satisfaction data of the user terminal is obtained according to the lighting feedback information, and the first brightness optimization target is set to minimize the current lighting energy consumption data, and the second brightness optimization target is set to maximize the user satisfaction data; based on the first brightness optimization target and the second brightness optimization target, a multi-objective brightness optimization problem corresponding to the adaptive brightness control strategy is determined, and the expression corresponding to the multi-objective brightness optimization problem is as follows:

[0116]

[0117] ZMNH(t)=HJLD t *P t *Δt

[0118] ε ZMNH +ε MYD =1

[0119] Where, HJLD t represents the target environment lighting brightness at the current time t; ε ZMNH Indicates the weight coefficient corresponding to the current lighting energy consumption data; ε MYD represents the weight coefficient corresponding to the user satisfaction data; ZMNH(t) represents the current lighting energy consumption data at the current time t; MYD(HJLD t ) represents the target environment lighting brightness HJLD at the user end t User satisfaction data of P t represents the power of the target lighting device; Δt represents the preset time interval;

[0120] By solving the multi-objective brightness optimization problem, the weight coefficients corresponding to the current lighting energy consumption data and the user satisfaction data are determined respectively, and the adaptive lighting brightness control strategy is optimized and adjusted.

[0121] In practical applications, user satisfaction data can be determined based on lighting feedback information from the user end, which is a key indicator for evaluating the degree of match between lighting effects and user expectations.

[0122] Then, the brightness optimization goals of the multi-objective brightness optimization problem can be set. Specifically, the first brightness optimization goal is to minimize the energy consumption data of the current lighting system; the second brightness optimization goal is to maximize the user satisfaction data, thereby ensuring that the lighting effect can fully meet the actual needs of users.

[0123] Finally, by solving the multi-objective brightness optimization problem, the weight coefficients corresponding to the current lighting energy consumption data and user satisfaction data can be accurately determined, and then the adaptive lighting brightness control strategy can be optimized and adjusted in a targeted manner to ensure that the lighting system can minimize energy consumption while ensuring user satisfaction, thus achieving the best balance between lighting effects and economic benefits.

[0124] Through the introduction of the above embodiments, the present invention can collect the current ambient light data of the target lighting area through the ambient light sensor based on the target lighting area through the energy-saving intelligent lighting control system, through the ambient brightness acquisition module, the personnel activity recognition module, the control strategy generation module, the real-time adjustment monitoring module and the strategy optimization and adjustment module, and generate the corresponding target ambient lighting brightness according to the current ambient light data; identify the personnel activity information in the target lighting area through the human activity sensor, and determine the corresponding target activity lighting brightness according to the personnel activity information; determine the target adjustment lighting brightness based on the target ambient lighting brightness and the target activity lighting brightness, and generate the corresponding adaptive lighting brightness control strategy in combination with the actual lighting brightness; adjust the brightness of the target lighting equipment in the target lighting area based on the adaptive brightness control strategy, and monitor the current lighting energy consumption data of the target lighting equipment through the lighting energy consumption sensor; obtain the lighting feedback information of the user end, and optimize the adaptive lighting brightness control strategy based on the lighting feedback information and the current lighting energy consumption data, so as to realize the intelligent adjustment of the lighting brightness, effectively reduce energy consumption, improve lighting efficiency, and ensure the safety and stability of the lighting system.

[0125] The system of the present invention can monitor the ambient light, human activities and other information in real time through the built-in multiple sensors, formulate an adaptive lighting brightness control strategy, and thus automatically adjust the lighting brightness; the present invention can collect and analyze the energy consumption data of the lighting system, combine it with user feedback information, further optimize the lighting strategy, and effectively reduce energy consumption; at the same time, the lighting energy consumption data can be monitored in real time, abnormal conditions in the system operation can be discovered in time and fed back to the user, to ensure the safe and stable operation of the lighting system.

[0126] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0127] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0128] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. An energy-saving intelligent lighting control system, characterized in that: The control system comprises: An ambient brightness acquisition module is used to acquire current ambient light data of a target lighting area through an ambient light sensor based on the target lighting area, and generate corresponding target ambient lighting brightness according to the current ambient light data; A human activity recognition module, used to recognize human activity information in the target lighting area through a human activity sensor, and determine the corresponding target activity lighting brightness according to the human activity information; A control strategy generation module, used to determine a target adjusted lighting brightness based on the target environment lighting brightness and the target activity lighting brightness, and generate a corresponding adaptive lighting brightness control strategy in combination with the actual lighting brightness; A real-time adjustment monitoring module, used to adjust the brightness of the target lighting device in the target lighting area based on the adaptive brightness control strategy, and monitor the current lighting energy consumption data of the target lighting device through a lighting energy consumption sensor; The strategy optimization and adjustment module is used to obtain lighting feedback information from the user end, and optimize the adaptive lighting brightness control strategy based on the lighting feedback information and the current lighting energy consumption data.

2. The energy-saving intelligent lighting control system according to claim 1, characterized in that: The ambient brightness acquisition module is used to collect the current ambient light data of the target lighting area through the ambient light sensor based on the target lighting area, and generate the corresponding target ambient lighting brightness according to the current ambient light data, specifically including: A deployment unit, configured to deploy a plurality of the ambient light sensors according to the distribution of the target lighting devices in the target lighting area; A collection unit, used for collecting current ambient light data of the target lighting area in real time through the ambient light sensor; A determination unit is used to determine the target ambient lighting brightness of the target lighting area if the current ambient light data meets the preset lighting requirements.

3. The energy-saving intelligent lighting control system according to claim 1, characterized in that: The determining the corresponding target activity lighting brightness according to the personnel activity information specifically includes: The average frequency of personnel activities in the target lighting area is calculated according to the personnel activity information. The calculation formula of the average frequency of personnel activities is as follows: In the formula, HDPL t represents the moving average value at the current time t, that is, the average frequency of personnel activities in the target lighting area at the current time t; n represents the number of moving average cycles; JR i Indicates the number of people entering the target lighting area at time i; LK i represents the number of people leaving the target lighting area at time i; The target activity lighting brightness is determined according to the average frequency of human activities in the target lighting area.

4. The energy-saving intelligent lighting control system according to claim 3, characterized in that: The process of determining the target lighting brightness adjustment is as follows: Set the target ambient lighting brightness at the current time t to HJLD t , and the target activity lighting brightness is HDLD t ; Obtain the device brightness adaptation factor of the target lighting device in the target environment area, and determine the target adjusted lighting brightness by balancing the target environment lighting brightness, the target activity lighting brightness, and the influence of the device brightness adaptation factor on the target adjusted lighting brightness. The calculation formula of the target adjusted lighting brightness is as follows: TJLD t =a HJ *HJLD t +a HD *HDLD t +a SB *t α HJ +α HD +α SB ＝1 Where, TJLD t represents the target lighting brightness at the current time t; α HJ Indicates the weight coefficient corresponding to the target environment lighting brightness, that is, the degree of influence of the target environment lighting brightness on the target adjustment lighting brightness; HJD t Represents the target environment lighting brightness at the current time t; α HD Indicates the weight coefficient corresponding to the target activity lighting brightness, that is, the degree of influence of the target activity lighting brightness on the target adjustment lighting brightness; HDLD t represents the target activity lighting brightness at the current time t; α SB It represents the weight coefficient corresponding to the device brightness adaptation factor, that is, the influence of the device brightness adaptation factor on the target adjusted lighting brightness; τ represents the device brightness adaptation factor of the target lighting device in the target environment area.

5. The energy-saving intelligent lighting control system according to claim 4, characterized in that: Based on the target lighting brightness adjustment, combined with the actual lighting brightness, the adaptive lighting brightness control strategy is obtained. The expression of the adaptive lighting brightness control strategy is as follows: Where KZCL t represents the adaptive lighting brightness control strategy at the current time t; TJLD t Indicates the target lighting brightness at the current time t; SJLD t Represents the actual lighting brightness at the current time t.

6. The energy-saving intelligent lighting control system according to claim 1, characterized in that: After obtaining the current lighting energy consumption data, extracting current lighting energy consumption features corresponding to the current lighting energy consumption data; Based on the support vector machine, the current lighting energy consumption characteristics are classified, and the corresponding optimization problem is as follows: In the formula, w u represents the weight vector of the u-th support vector machine, which is used to determine the direction of the classification hyperplane; b u represents the bias term of the u-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; ξ uv represents the slack variable when the vth current lighting energy consumption feature is linearly inseparable in the uth support vector machine; C represents the regularization parameter, which is used to control the trade-off between classification interval and classification error; R represents the total number of current lighting energy consumption features; min means taking the minimum value operation.

7. The energy-saving intelligent lighting control system according to claim 6, characterized in that: The constraints corresponding to the optimization problem are as follows: LBBQ v (W u *NHTZ v +b u )≥1-ξ uv ,LBBQ v =u LBBQ U (W u *NHTZ v +b u )≤ξ uv -1,LBBQ v ≠u Where, LBBQ v NHTZ represents the feature category label corresponding to the vth current lighting energy consumption feature; v represents the vth current lighting energy consumption characteristic; w u represents the weight vector of the u-th support vector machine, which is used to determine the direction of the classification hyperplane; b u represents the bias term of the u-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; ξ uv represents the slack variable when the vth current lighting energy consumption feature is linearly inseparable in the uth support vector machine; R represents the total number of current lighting energy consumption features; The feature category label corresponding to the current lighting energy consumption feature is identified, the abnormal lighting energy consumption feature and the corresponding abnormal lighting energy consumption data are determined, and the abnormal lighting energy consumption data is sent to the user end.

8. The energy-saving intelligent lighting control system according to claim 1, characterized in that: The strategy optimization and adjustment module is used to obtain the lighting feedback information of the user end, and optimize the adaptive lighting brightness control strategy based on the lighting feedback information and the current lighting energy consumption data, specifically including: The user satisfaction data of the user terminal is obtained according to the lighting feedback information, and the first brightness optimization target is set to minimize the current lighting energy consumption data, and the second brightness optimization target is set to maximize the user satisfaction data; based on the first brightness optimization target and the second brightness optimization target, a multi-objective brightness optimization problem corresponding to the adaptive brightness control strategy is determined, and the expression corresponding to the multi-objective brightness optimization problem is as follows: ZMNH(t)=HJLD t *P t *Δt e ZMNH +e MYD =1 Where, HJLD t represents the target environment lighting brightness at the current time t; ε ZMNH Indicates the weight coefficient corresponding to the current lighting energy consumption data; ε MYD represents the weight coefficient corresponding to the user satisfaction data; ZMNH(t) represents the current lighting energy consumption data at the current time t; MYD(HJLD t ) represents the target environment lighting brightness HJLD at the user end t User satisfaction data of P t represents the power of the target lighting device; Δt represents the preset time interval; By solving the multi-objective brightness optimization problem, the weight coefficients corresponding to the current lighting energy consumption data and the user satisfaction data are determined respectively, and the adaptive lighting brightness control strategy is optimized and adjusted.

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