Lamp control method, device, equipment and storage medium
By comparing historical illumination data from the photosensitive sensor with preset thresholds, the problems of delayed and asynchronous cabinet light activation were solved, enabling timely lighting and synchronized control of the cabinet lights, thus improving the user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU EZVALO TECH CO LTD
- Filing Date
- 2026-04-08
- Publication Date
- 2026-07-14
Smart Images
Figure CN122395783A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of smart home technology, and in particular to a lighting control method, device, equipment and storage medium. Background Technology
[0002] As smart cabinet lights, wardrobe lights, and other similar applications continue to evolve towards automation and enhanced user experience, lighting control methods based on infrared detection and photosensitive judgment have become an important technological means to improve user convenience.
[0003] Existing technology uses real-time data from a photosensor to collect current illumination values and combines this with human body detection results to determine whether cabinet lights should illuminate, thus avoiding unnecessary activation even in well-lit environments. However, in practical applications, cabinet lights often fail to turn on promptly or not at all when a user opens the cabinet door, impacting the user experience. Furthermore, in scenarios where multiple lights are used simultaneously, their activation times often differ, resulting in some lights being on while others are off, leading to a lack of synchronization and consistency in the overall lighting effect. Summary of the Invention
[0004] This application provides a lighting control method, device, equipment, and storage medium, which solves the problems in the prior art where cabinet lights experience delayed or no lighting due to excessively fast response of the photosensitive sensor when the cabinet door is opened, as well as asynchronous lighting in multi-light scenarios. This solution can effectively avoid illumination disturbances at the moment the cabinet door is opened, and achieve lighting determination based on the actual ambient brightness before the cabinet is opened.
[0005] In a first aspect, this application provides a lighting control method, including: When human activity is confirmed to exist within the target area, historical illumination data collected by a photosensor is acquired. Extract the historical illumination intensity corresponding to the first time interval before the current moment from the historical illumination data; When the historical light intensity is less than a preset light intensity threshold, the lamp corresponding to the photosensitive sensor is turned on.
[0006] Secondly, this application provides a lighting control device, comprising: The human detection module is configured to acquire historical illumination data collected by the photosensitive sensor when human activity is detected in the target area. The historical illumination module is configured to extract the historical illumination intensity corresponding to a first time interval prior to the current moment from the historical illumination data; The lighting control module is configured to control the lighting fixture corresponding to the photosensitive sensor to turn on when the historical light intensity is less than a preset light intensity threshold.
[0007] Thirdly, this application provides a lighting control device, comprising: One or more processors; A memory that stores one or more programs that, when executed by one or more processors, cause the one or more processors to implement the lighting control method as described in the first aspect.
[0008] Fourthly, this application provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the lighting control method as described in the first aspect.
[0009] In this application, a lighting control processing mechanism based on historical illumination data analysis and comparison of environmental brightness temporal characteristics is constructed to accurately identify current lighting needs and precisely determine when to turn on the lights. After confirming human activity in the target area at the detection end, historical illumination data recorded by the photosensitive sensor within a preset duration is acquired, and the target illumination intensity corresponding to the first time interval before the current moment is parsed out as a benchmark information reflecting the actual ambient brightness before the user approaches. After extracting the historical illumination intensity, the illumination intensity is compared with a preset illumination intensity threshold to determine whether the ambient illumination is insufficient before the user enters the target area. When the comparison result shows that the historical illumination intensity is lower than the illumination intensity threshold, the light fixture corresponding to the photosensitive sensor is controlled to perform a lighting operation, thereby enabling the light fixture to respond promptly when there is an actual lighting need. Through the above lighting determination process, an environmental brightness recognition mechanism based on historical illumination characteristics rather than the current instantaneous illumination is realized, which can effectively avoid misjudgment problems caused by cabinet door opening or changes in external light, and ensure that the light fixture lights up quickly when triggered by the user. This solution, based on the collaborative design of illumination timing parameters and threshold rules, improves the reliability and consistency of lighting in scenarios such as cabinet lights and wardrobe lights. It is suitable for lighting control in enclosed spaces, delay-sensitive supplemental lighting scenarios, and synchronous lighting requirements in multi-light layouts. Attached Figure Description
[0010] Figure 1 This is a flowchart of a lighting control method provided in an embodiment of this application; Figure 2 This is a flowchart of a human activity detection method provided in an embodiment of this application; Figure 3 This is a flowchart of an infrared data extraction method provided in an embodiment of this application; Figure 4This is a flowchart of a target activity value calculation method provided in an embodiment of this application; Figure 5 This is a flowchart of the first time interval adjustment method provided in the embodiments of this application; Figure 6 This is a flowchart of the illumination data acquisition frequency adjustment method provided in the embodiments of this application; Figure 7 This is a flowchart illustrating the steps of a lighting control method provided in an embodiment of this application; Figure 8 This is a structural block diagram of a lighting control device provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of a lighting control device provided in an embodiment of this application. Detailed Implementation
[0011] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as being processed sequentially, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. A process can be terminated when its operation is completed, but it may also have additional steps not included in the drawings. A process can correspond to a method, function, procedure, subroutine, subroutine, etc.
[0012] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.
[0013] Currently, with the widespread application of smart cabinet lights, wardrobe lights, and other spatial lighting devices, automatic lighting based on human body sensing and ambient light detection is becoming an important technological direction for improving the user experience in homes and businesses. In these products, automatically controlling the light fixtures' on / off states by detecting ambient brightness using photosensors and combining this with human activity signals has become one of the fundamental capabilities of smart lighting systems. Whether in cabinet spaces, enclosed storage areas, or multi-light coordinated indoor scenarios, ensuring timely and consistent lighting when a user approaches has become a key factor influencing the smart lighting experience.
[0014] In existing technologies, lighting decisions typically rely on real-time data collected by photosensors to determine the current illumination level. The system compares the current brightness to a threshold to decide whether to turn on the light. This method can meet basic automatic lighting needs in some scenarios and is therefore widely used in various cabinet lights, wardrobe lights, and closet lighting products. However, with the increasing complexity of application scenarios, this decision-making method, which relies solely on the current illumination level, has gradually revealed significant limitations in practical use, failing to cover the lighting needs of users during actions such as opening doors, approaching, or quickly passing by. On the one hand, at the moment a user opens a cabinet door or initiates an action, the lighting often experiences a delay in lighting, fails to illuminate, or exhibits unstable response. Users expect the lights to illuminate immediately upon approaching or opening a door, but in reality, the lights often fail to illuminate even after the action is completed, resulting in a significant decrease in the lighting experience. This phenomenon is particularly common in enclosed or semi-enclosed cabinets. On the other hand, when multiple lights are deployed in the same space, the timing of their illumination often becomes asynchronous, meaning that some lights are already on while others remain off, resulting in inconsistent overall lighting. This can easily lead users to mistakenly believe that there are problems with the performance or quality of the equipment.
[0015] To address issues such as delayed lighting, failure to light up, and asynchronous lighting of multiple lights in existing cabinet lights, this embodiment provides a lighting control method. By introducing a lighting determination mechanism based on historical illumination data analysis and comparison of environmental brightness temporal characteristics, an automatic lighting control system with temporal illumination feature recognition capabilities is constructed, enabling accurate judgment of current lighting needs and precise control of the lighting timing. This method uses historical illumination data from a photosensitive sensor during operation as the analysis object. During the lighting determination process, if human activity is confirmed within the target area, historical illumination data collected by the photosensitive sensor is acquired. The illumination record prior to the first time interval corresponding to the current moment is located, thereby extracting the historical illumination intensity reflecting the actual environmental brightness before the user's approach. This historical illumination intensity serves as the basis for the lighting decision stage. Through these steps, a correlation is established between human activity triggering events and historical illumination characteristics. After extracting the historical illumination intensity, it is further compared with a preset illumination intensity threshold to determine whether the environment was in a low-light state before the user entered the target area. Subsequently, the lighting control operation is executed based on the comparison results: when the historical light intensity is lower than the light intensity threshold, the lighting fixture corresponding to the photosensitive sensor is triggered to turn on, thus forming a complete control closed loop from activity detection, data backtracking, light intensity comparison to lighting execution.
[0016] Figure 1 This is a flowchart of a lighting control method provided in an embodiment of this application. (Reference) Figure 1 The lighting control method specifically includes: S110. If human activity is confirmed to exist within the target area, acquire historical illumination data collected by the photosensitive sensor.
[0017] In some embodiments, the presence of human activity within the target area can be determined by an infrared sensor, motion detection module, or other sensing methods. When the system detects an activity event, it can proceed to the illumination data analysis stage. The photosensitive sensor can be a photoelectric element used to detect ambient light intensity, such as a photodiode, photoresistor, or digital illumination sensor, and its output signal can be illumination data reflecting changes in ambient brightness.
[0018] In one embodiment, historical illumination data can be obtained by: after detecting an activity event, reading illumination intensity data over a continuous period of time from a preset illumination data cache structure, and using this data as historical illumination data for subsequent judgment.
[0019] In one embodiment, the illumination data cache structure can be a circular queue, a sliding window, or a time-series buffer, used to continuously update illumination sampling values, enabling the system to immediately acquire a complete historical illumination data range after detecting activity.
[0020] Through the above steps, historical illumination data collected by the photosensitive sensor can be obtained when human activity is confirmed in the target area. This allows the system to consider both activity information and ambient lighting conditions when determining whether to turn on home appliances, thereby improving the accuracy of automatic control logic.
[0021] Optionally, Figure 2 This is a flowchart of a human activity detection method provided in an embodiment of this application. (Reference) Figure 2 The method for detecting human activity specifically includes: S1101. Obtain infrared data of the target area collected by the infrared sensor, and determine environmental data and detection data based on the infrared data.
[0022] For example, infrared data can be thermal radiation intensity data acquired by an infrared sensor during continuous sampling of a target area, used to reflect the background thermal radiation of the target area and dynamic changes caused by human activity. Environmental data can be low-frequency data segments extracted from infrared data to represent stable changes in the environmental background, while detection data can be high-frequency or abrupt data segments extracted from infrared data to represent dynamic changes in thermal radiation.
[0023] In one embodiment, infrared data can be acquired by driving an infrared sensor to continuously collect thermal radiation amplitude according to a preset sampling period, writing the data obtained from each sampling into a cache structure, and directly acquiring infrared data from the cache structure.
[0024] In one embodiment, the environmental data can be determined by filtering or truncating the infrared data into a time window, and selecting a range of gradual changes as the environmental data.
[0025] In one embodiment, the detection data can be determined by identifying time periods of abrupt amplitude changes, rapid shifts, or significant dynamic fluctuations in the infrared data and using these as the detection data.
[0026] Through the above steps, after acquiring infrared data of the target area collected by the infrared sensor, it is possible to effectively divide it into environmental data and detection data, so that the system can use background information and dynamic activity information respectively in subsequent processing, thereby improving the accuracy and stability of infrared detection.
[0027] Optionally, Figure 3 This is a flowchart of an infrared data extraction method provided in an embodiment of this application. (Reference) Figure 3 The infrared data extraction method specifically includes: S11011. Extract the low-frequency component from the infrared data to obtain environmental data.
[0028] For example, the low-frequency component can be a part of the infrared data that changes gradually and fluctuates less, used to represent the background thermal radiation of the target area.
[0029] In one embodiment, environmental data can be extracted by performing low-pass filtering on infrared data to filter out high-frequency dynamic changes and retain the relatively stable signal portion, using the low-frequency signal as environmental data.
[0030] In one embodiment, low-pass filtering can be implemented by means of moving average, weighted average, finite impulse response filtering or infinite impulse response filtering, to smooth instantaneous changes and highlight the trend of background thermal radiation.
[0031] Through the above steps, low-frequency components can be extracted from infrared data to obtain environmental data, enabling the system to perform subsequent activity analysis and judgment based on stable background thermal radiation information, thereby improving the anti-interference capability and stability of the detection process.
[0032] S11012. Extract the high-frequency change components within the second time interval from the current time in the infrared data to obtain the detection data.
[0033] For example, the second time interval can be a short window for capturing dynamic changes, such as 1 second, 2 seconds, or other time lengths suitable for activity detection.
[0034] In one embodiment, the method for extracting detection data may be as follows: based on the current system time, backtrack to the second time interval, extract the data sequence of that time interval from the infrared data, and then perform high-pass filtering or change mutation detection operation on the sequence, using the high-frequency change component obtained by filtering as the detection data.
[0035] In one embodiment, the high-frequency variation component can be extracted by performing differential calculation, sliding change amplitude calculation, or high-pass filtering on the infrared data within the second time interval, so that the signal portion with abrupt changes, rapid fluctuations, or large gradient changes can be extracted.
[0036] Through the above steps, the high-frequency change components within the second time interval from the current moment in the infrared data can be extracted to obtain detection data, enabling the system to distinguish between static background information and dynamic activity changes, thereby improving the sensitivity and accuracy of the human activity recognition process.
[0037] S1102. Determine the target activity value based on the environmental data and the detection data.
[0038] For example, in some embodiments, the target activity value may be an activity intensity value calculated based on the difference characteristics between environmental data and detection data.
[0039] In one embodiment, the target activity value can be determined by: calculating the mean or integral average of environmental data to obtain an environmental baseline value, calculating the mean or integral average of detection data to obtain a detection activity value, and performing a difference or ratio calculation between the detection activity value and the environmental baseline value, and using the calculation result as the target activity value.
[0040] In one embodiment, the target activity value can be a basic parameter used for subsequent activity judgment. When the target activity value is large, it indicates that the dynamic thermal radiation changes significantly, and when the target activity value is small, it indicates that the target area is in a stable state.
[0041] Through the above steps, the target activity value can be determined based on environmental and detection data, enabling the system to comprehensively consider background stability and dynamic change amplitude, forming an effective basis for judging actual human activity and improving the accuracy and robustness of infrared detection.
[0042] Optionally, calculating the target activity value based on the environmental baseline value and the detected activity value includes: The target activity value is obtained by calculating the ratio of the detected activity value to the environmental baseline value.
[0043] For example, the target activity value can be a normalized activity index calculated by the ratio of the detected activity value to the environmental baseline value.
[0044] In one embodiment, the target activity value can be calculated by performing a division operation with the detected activity value as the numerator and the environmental baseline value as the denominator, and using the result of the division operation as the target activity value.
[0045] Through the above steps, the target activity value can be calculated by the ratio of the detected activity value to the environmental reference value. This enables the system to make a uniform scale judgment on dynamic activities under different background brightness, different ambient temperature and different static conditions, thereby improving the stability and applicability of activity detection.
[0046] Optionally, Figure 4 This is a flowchart illustrating a method for calculating a target activity value provided in an embodiment of this application. (Reference) Figure 4 The specific method for calculating the target activity value includes: S11021. Calculate the integral average of the environmental data and the detection data respectively to obtain the environmental baseline value and the detection activity value.
[0047] For example, environmental baseline values and detection activity values can be obtained by integrating and averaging environmental data and detection data respectively, and used for subsequent activity judgment.
[0048] In one embodiment, the environmental baseline value can be calculated by performing an integral operation on the environmental data sequence to obtain a cumulative value, dividing the cumulative value by the number of sampling points of the environmental data, and using the result as the environmental baseline value.
[0049] In one embodiment, the detection activity value can be calculated by performing an integral operation on the detection data sequence to obtain a cumulative value, dividing the cumulative value by the number of sampling points of the detection data, and using the calculated result as the detection activity value.
[0050] Through the above steps, the integral average of environmental data and detection data can be calculated separately, thereby obtaining the environmental baseline value and the detection activity value. This enables the system to construct subsequent activity judgment logic based on background information and dynamic change information, improving the stability and accuracy of human activity detection.
[0051] S11022. Calculate the target activity value based on the environmental baseline value and the detection activity value.
[0052] For example, the target activity value can be an activity intensity parameter used to determine human activity, calculated based on the numerical relationship between environmental baseline values and detected activity values.
[0053] In one embodiment, the target activity value can be calculated by comparing the detected activity value with an environmental baseline value, and using the ratio result as the target activity value.
[0054] In one embodiment, the target activity value can also be calculated by subtracting the environmental baseline value from the detected activity value and using the difference as the target activity value.
[0055] Through the above steps, the target activity value can be calculated based on the environmental baseline value and the detected activity value, enabling the system to simultaneously consider the background stability and the dynamic change amplitude, thereby accurately judging the activity intensity in the current target area and improving the human activity recognition effect.
[0056] S1103. If the target activity value is greater than a preset activity value threshold, it is determined that there is human activity in the target area.
[0057] For example, the activity value threshold can be a reference threshold preset based on sensor sensitivity, background noise characteristics, or usage scenario, used to distinguish between normal static states and significant dynamic changes.
[0058] In one embodiment, the method for determining the presence of human activity may be: comparing the calculated target activity value with an activity value threshold, and when the target activity value exceeds the threshold, marking the current detection result as indicating the presence of human activity.
[0059] In one embodiment, the activity threshold can be a fixed value or an adaptive threshold that is dynamically adjusted based on ambient temperature, sensor noise level, or historical activity data, so that activity recognition remains stable under different environmental conditions.
[0060] By following the steps above, it is possible to determine the presence of human activity within the target area when the target activity value is greater than the preset activity value threshold. This enables the system to accurately filter out real activity events, avoid false triggering caused by background noise, and improve the overall detection accuracy and reliability.
[0061] Optionally, Figure 5 This is a flowchart of the first time interval adjustment method provided in an embodiment of this application. (See reference...) Figure 5 The first time interval adjustment method specifically includes: S111. Calculate the variance of the historical illumination data to obtain the illumination stability.
[0062] For example, illumination stability can be a value calculated based on the variance of historical illumination data, used to represent the degree of fluctuation of illumination within a certain time interval.
[0063] In one embodiment, the method for calculating illumination stability can be as follows: perform mean calculation on historical illumination data, sum the squared differences between each sampling point and the mean, divide by the number of sampling points or the number of sampling points minus one, and use the calculation result as the illumination stability.
[0064] In one embodiment, a smaller light stability indicates a more stable light change and a more stable lighting environment; a larger light stability indicates a more drastic light change, which may be affected by rapid changes in natural light, shadow changes, or interference from other light sources.
[0065] Through the above steps, the light stability can be calculated based on the variance of historical light data, enabling the system to quantify changes in light and provide basic data support for subsequent brightness judgment, lamp control, or adaptive light adjustment.
[0066] S112. If the illumination stability is lower than the stability threshold, reduce the first time interval.
[0067] For example, the stability threshold can be a value preset by the system based on the characteristics of light fluctuations, used to determine whether the current lighting environment is in a stable state.
[0068] In one embodiment, the first time interval can be reduced by adjusting it to a shorter duration when the system detects that the illumination stability is less than a stability threshold, so that the brightness determination is based on illumination data closer to the current moment. For example, a preset amount of time can be subtracted from the original first time interval, such as reducing 3 seconds to 1 second.
[0069] Through the above steps, the first time interval can be automatically reduced when the illumination stability is lower than the stability threshold, making subsequent illumination judgments more sensitive and closer to the current illumination environment, thereby improving the response speed and accuracy of the light control logic.
[0070] S120. Extract the historical illumination intensity corresponding to the first time interval before the current moment from the historical illumination data.
[0071] In some embodiments, historical illumination data can be a sequence of illumination intensity recorded by a photosensor during continuous sampling, used to reflect the brightness changes of the target area over a period of time. The first time interval can be a preset time length, such as 1 second, 3 seconds, 4 seconds, or other time periods used to determine ambient brightness.
[0072] In one embodiment, the method for extracting historical light intensity can be: after detecting human activity and entering the light judgment process, controlling the current system time to backtrack the length of the first time interval, and obtaining the historical light intensity at that time point from the historical light data sequence.
[0073] Through the above steps, the light intensity of the first time interval before the current moment can be extracted from historical lighting data, enabling the system to perform environmental brightness analysis based on local continuous brightness information and improve the reliability of light control decisions.
[0074] S130. When the historical light intensity is less than the preset light intensity threshold, control the lamp corresponding to the photosensitive sensor to turn on.
[0075] In some embodiments, the illuminance threshold may be a brightness limit preset by the system according to the usage scenario, used to determine whether the light fixture needs to be turned on. The light fixture may be a cabinet light, drawer light, wall-mounted lighting unit, or embedded storage space lighting module.
[0076] In one embodiment, the method of controlling the lamp to turn on may be as follows: after extracting the historical light intensity, the value is compared with the light intensity threshold; when the historical light intensity is lower than the threshold, the control module sends an on command to the lamp to switch the lamp from the off state to the on state.
[0077] In one embodiment, the light intensity threshold can be a fixed value set according to different environments, or an adaptive value dynamically adjusted according to the sampling characteristics of the photosensitive sensor, in order to ensure the stability of the lamp triggering.
[0078] In one embodiment, turning on the luminaire can be either directly illuminating the lighting equipment or activating a multi-level brightness adjustment mode, enabling the luminaire to quickly provide the required illumination under low light conditions.
[0079] Through the above steps, the system can promptly control the lights to turn on when the historical light intensity is less than the preset light intensity threshold, enabling the system to automatically provide lighting when human activity occurs and ambient light is insufficient, thereby improving user experience and the reliability of automated control.
[0080] Optionally, Figure 6 This is a flowchart of the illumination data acquisition frequency adjustment method provided in an embodiment of this application. (Reference) Figure 6 The method for adjusting the light data acquisition frequency specifically includes: S140. When it is determined that there is human activity in the target area, the photosensitive sensor is controlled to collect light data at a first frequency.
[0081] For example, the presence of human activity within the target area can be determined by an infrared sensor or activity detection module, which triggers the illumination acquisition logic. The first frequency can be a sampling frequency used to quickly sense changes in illumination after activity detection is established.
[0082] In one embodiment, the method of controlling the photosensitive sensor to collect light data at a first frequency can be: when the system determines that human activity exists, the sampling timer of the photosensitive sensor is updated to the time interval corresponding to the first frequency, so that the photosensitive sensor continuously acquires light data at a faster sampling speed.
[0083] In one embodiment, the first frequency can be a sampling frequency higher than the conventional standby sampling frequency, such as increasing it from once per second to five or ten times per second, enabling the system to capture changes in illumination more promptly and execute subsequent illumination judgment logic.
[0084] By following the steps above, when human activity is confirmed within the target area, the photosensitive sensor can be controlled to collect illumination data at a first frequency, thereby improving the system's response speed to changes in illumination and ensuring that the lighting control process is more timely and accurate.
[0085] S150. If it is determined that there is no human activity in the target area, control the photosensitive sensor to collect light data at a second frequency lower than the first frequency.
[0086] For example, the second frequency may be a sampling frequency lower than the first frequency, used to reduce power consumption and reduce unnecessary data processing burden in the inactive state.
[0087] In one embodiment, the method of controlling the photosensitive sensor to collect illumination data at a second frequency can be: when the system determines that there is currently no activity, the sampling timer of the photosensitive sensor is adjusted to the time interval corresponding to the second frequency, and the photosensitive sensor is controlled to continuously collect illumination data at a lower rate.
[0088] In one embodiment, the second frequency can be a fixed low-frequency sampling rate, such as reducing from 5 samples per second to 1 sample per second, or using an even lower sampling frequency depending on the scenario, in order to reduce energy consumption and extend system uptime.
[0089] Through the above steps, even when there is no human activity in the target area, the photosensitive sensor can be controlled to collect light data at a second frequency lower than the first frequency, thereby reducing the overall power consumption of the system and maintaining the basic monitoring capability of changes in ambient light.
[0090] Optionally, Figure 7 This is a flowchart illustrating the steps of a lighting control method provided in an embodiment of this application. (Reference) Figure 7 The lighting control method specifically includes: S201. Collect historical illumination data.
[0091] For example, the photosensitive sensor is driven to continuously acquire light intensity values according to a preset sampling period, and the data of each sample is written into a buffer or circular queue so that the buffer structure always stores the light data of the most recent period.
[0092] In one embodiment, the acquisition time of historical illumination data can be a fixed interval set by the system according to the illumination judgment requirements, such as 1 second, 3 seconds or 5 seconds, to ensure that the acquired data can represent the changing trend of the current ambient illumination.
[0093] In one embodiment, the collected historical illumination data can be used for subsequent calculations of average illumination intensity, illumination stability, illumination fluctuation amplitude, or as basic data for window extraction, enabling the light control logic to make decisions based on real illumination changes.
[0094] S202. Perform human activity detection on the target area.
[0095] For example, an infrared sensor is driven to acquire infrared data according to a preset sampling period, and dynamic change analysis is performed on the acquired data to determine whether there is human activity at present.
[0096] In one embodiment, human activity detection can be achieved based on the high-frequency change characteristics of infrared data. When the infrared signal shows a sudden change in amplitude, rapid fluctuation, or continuous fluctuation in a short period of time, it is judged that human activity exists.
[0097] In one embodiment, human activity detection can also be implemented based on the difference between dynamic values and background values: the background data and the changing data are separated from the collected infrared data, the target activity value is calculated and compared with a preset activity threshold, and when the target activity value exceeds the threshold, it is determined that there is human activity.
[0098] S203. If human activity is confirmed within the target area, obtain historical light intensity.
[0099] For example, light intensity data for a specified time period prior to the current moment is read from the light data cache structure of the photosensitive sensor and used as historical light intensity.
[0100] In one embodiment, the illumination data cache structure can be a circular queue, a sliding window, a fixed-length cache array, or a time-series buffer, used to continuously record the sampled values of the photosensor, enabling the system to immediately acquire the complete historical illumination intensity range when needed.
[0101] In one embodiment, historical light intensity can be the basic data for subsequent brightness judgment. When human activity is triggered and the light is insufficient, the system can decide whether to turn on the lights or perform other light control operations based on the data.
[0102] S204. Determine whether to control the lights based on historical light intensity.
[0103] For example, the extracted historical light intensity is compared with a preset light intensity threshold. When the historical light intensity is lower than the light intensity threshold, it is determined that the ambient light is insufficient, and the system can trigger the lamp turning-on process.
[0104] In one embodiment, after determining that the lighting is insufficient, the control module sends an on command to the lamp to turn it on in order to compensate for the current ambient lighting.
[0105] Based on the above embodiments, Figure 8 This is a structural block diagram of a lighting control device provided in an embodiment of this application. (Reference) Figure 8 The lighting control device provided in this embodiment specifically includes: a human body detection module 11, a historical illumination module 12, and a lighting control module 13.
[0106] The human detection module 11 is configured to acquire historical illumination data collected by a photosensitive sensor when human activity is detected in the target area; the historical illumination module 12 is configured to extract historical illumination intensity corresponding to a first time interval before the current moment from the historical illumination data; and the lighting control module 13 is configured to control the lighting fixture corresponding to the photosensitive sensor to turn on when the historical illumination intensity is less than a preset illumination intensity threshold.
[0107] Based on the above embodiments, the human body detection module 11 includes: an infrared data unit configured to acquire infrared data of a target area collected by an infrared sensor, and determine environmental data and detection data based on the infrared data; a target activity value unit configured to determine a target activity value based on the environmental data and the detection data; and an activity determination unit configured to determine that human activity exists in the target area when the target activity value is greater than a preset activity value threshold.
[0108] Based on the above embodiments, the infrared data unit includes: an environmental data subunit configured to extract low-frequency components from the infrared data to obtain environmental data; and a detection data subunit configured to extract high-frequency change components from the infrared data within a second time interval from the current time to obtain detection data.
[0109] Based on the above embodiments, the target activity value unit includes: an integral averaging subunit configured to calculate the integral average of the environmental data and the detection data respectively to obtain an environmental baseline value and a detection activity value; and a target activity value subunit configured to calculate a target activity value based on the environmental baseline value and the detection activity value.
[0110] Based on the above embodiments, the target activity value subunit includes: a ratio calculation component configured to calculate the ratio of the detected activity value to the environmental reference value to obtain the target activity value.
[0111] Based on the above embodiments, the lighting control device further includes: a first frequency module configured to control the photosensitive sensor to collect illumination data at a first frequency when human activity is determined to exist in the target area; and a second frequency module configured to control the photosensitive sensor to collect illumination data at a second frequency lower than the first frequency when human activity is determined to not exist in the target area.
[0112] Based on the above embodiments, the lighting control device further includes: a light stability module configured to calculate the variance of the historical light data to obtain light stability; and a time interval adjustment module configured to reduce the first time interval when the light stability is lower than the stability threshold.
[0113] The lighting control device provided in this application embodiment, by constructing a hierarchical perception and decision-making processing architecture consisting of a human body detection module 11, a historical illumination analysis module 12, and a lighting control module 13, realizes an end-to-end linkage control process from human activity recognition and illumination intensity extraction to lighting start / stop decision-making. This device can jointly determine the human activity status within the target area and the data collected by the photosensitive sensor, and comprehensively identify the lighting start-up conditions based on the environmental brightness evolution characteristics, thereby improving the intelligence and energy efficiency of the lighting control process while ensuring timely lighting. The human detection module 11 has the ability to identify target area activities and trigger events. When human activity is detected in the target area, it reads the historical illumination data collected by the photosensitive sensor to provide real-time behavior triggering basis for subsequent time period illumination analysis. The historical illumination analysis module 12 is responsible for the tasks of illumination sequence extraction and intensity feature analysis. By performing a first time interval window interception operation on the historical illumination data, it extracts the historical illumination intensity corresponding to the target interval before the current moment to characterize the short-term trend change of ambient brightness. The lighting control module 13 is used to implement lighting start-stop decisions based on the correlation characteristics between ambient brightness and human activity. By comparing the historical illumination intensity with the preset illumination intensity threshold, when the historical illumination intensity is detected to be lower than the illumination intensity threshold, it outputs an on command to the lighting fixture corresponding to the photosensitive sensor to realize the automatic lighting of the lighting fixture in low brightness and human activity scenarios. Through the hierarchical and collaborative processing of the above modules, the lighting brightness trigger control device constructs a closed-loop control link from human activity recognition and light intensity analysis to lighting activation decision. It can complete the joint perception and judgment of ambient light changes and user activities without additional sensors or external intervention, significantly improving the intelligence level, response speed and user experience of lighting control.
[0114] Figure 9 This is a schematic diagram of the structure of a lighting control device provided in an embodiment of this application, with reference to... Figure 9 The lighting control device includes a processor 21, a memory 22, a communication device 23, an input device 24, and an output device 25. The number of processors 21 and the number of memories 22 in the lighting control device can be one or more. The processor 21, memory 22, communication device 23, input device 24, and output device 25 of the lighting control device can be connected via a bus or other means.
[0115] The memory 22, as a computer-readable storage medium, can be used to store software programs, computer executable files, and modules, such as program instructions / modules corresponding to the lighting control method in any embodiment of this application (e.g., human detection module 11, historical illumination module 12, and lighting control module 13 in the lighting control device). The memory 22 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the device, etc. Furthermore, the memory 22 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
[0116] The communication device 23 is used for data transmission.
[0117] The processor 21 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 22, thereby realizing the above-mentioned lighting control method.
[0118] Input device 24 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 25 may include display devices such as a display screen.
[0119] The lighting control device provided above can be used to execute the lighting control method provided in the above embodiments, and has corresponding functions and beneficial effects.
[0120] This application embodiment also provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to perform a lighting control method. The lighting control method includes: acquiring historical illumination data collected by a photosensitive sensor when it is determined that human activity exists in a target area; extracting historical illumination intensity corresponding to a first time interval before the current moment from the historical illumination data; and controlling the lamp corresponding to the photosensitive sensor to turn on when the historical illumination intensity is less than a preset illumination intensity threshold.
[0121] Storage medium—any type of memory device or storage device. The term "storage medium" is intended to include: mounting media, such as CD-ROM, floppy disk, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. Storage medium may also include other types of memory or combinations thereof. Furthermore, storage medium may reside in a first computer system in which a program is executed, or it may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). Storage medium may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.
[0122] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the lighting control method described above, but can also execute related operations in the lighting control method provided in any embodiment of this application.
[0123] The lighting control device, storage medium, and lighting control equipment provided in the above embodiments can execute the lighting control method provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the lighting control method provided in any embodiment of this application.
[0124] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application. The scope of this application is determined by the scope of the claims.
Claims
1. A lighting control method, characterized in that, include: When human activity is confirmed to exist within the target area, historical illumination data collected by a photosensor is acquired. Extract the historical illumination intensity corresponding to the first time interval before the current moment from the historical illumination data; When the historical light intensity is less than a preset light intensity threshold, the lamp corresponding to the photosensitive sensor is turned on.
2. The lighting control method according to claim 1, characterized in that, The determination that human activity exists within the target area includes: Acquire infrared data of the target area collected by an infrared sensor, and determine environmental data and detection data based on the infrared data; The target activity value is determined based on the environmental data and the detection data; If the target activity value is greater than a preset activity value threshold, it is determined that human activity exists within the target area.
3. The lighting control method according to claim 2, characterized in that, The step of determining environmental data and detection data based on the infrared data includes: The low-frequency components are extracted from the infrared data to obtain environmental data; The high-frequency change components within the second time interval from the current moment are extracted from the infrared data to obtain the detection data.
4. The lighting control method according to claim 2, characterized in that, Determining the target activity value based on the environmental data and the detection data includes: Calculate the integral average of the environmental data and the detection data respectively to obtain the environmental baseline value and the detection activity value; The target activity value is calculated based on the environmental baseline value and the detection activity value.
5. The lighting control method according to claim 4, characterized in that, The calculation of the target activity value based on the environmental baseline value and the detected activity value includes: The target activity value is obtained by calculating the ratio of the detected activity value to the environmental baseline value.
6. The lighting control method according to claim 1, characterized in that, The lighting control method further includes: When it is determined that there is human activity in the target area, the photosensitive sensor is controlled to collect light data at a first frequency. If it is determined that there is no human activity in the target area, the photosensitive sensor is controlled to collect light data at a second frequency lower than the first frequency.
7. The lighting control method according to claim 1, characterized in that, Before extracting the historical illumination intensity corresponding to the first time interval prior to the current moment from the historical illumination data, the method further includes: The variance of the historical illumination data is calculated to obtain the illumination stability. If the illumination stability is lower than the stability threshold, the first time interval is reduced.
8. A lighting control device, characterized in that, include: The human detection module is configured to acquire historical illumination data collected by the photosensitive sensor when human activity is detected in the target area. The historical illumination module is configured to extract the historical illumination intensity corresponding to a first time interval prior to the current moment from the historical illumination data; The lighting control module is configured to control the lighting fixture corresponding to the photosensitive sensor to turn on when the historical light intensity is less than a preset light intensity threshold.
9. A lighting control device, characterized in that, include: One or more processors; A memory that stores one or more programs that, when executed by one or more processors, cause the one or more processors to implement the lighting control method as described in any one of claims 1-7.
10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the lighting control method as described in any one of claims 1-7.