Public facility illumination control method and system

Abstract

The invention discloses a public facility lighting control method and system, relates to the technical field of public facility lighting control, and aims to solve the technical problems that an existing public facility lighting control method cannot effectively recognize false low-light signals caused by local environment shielding, and consequently resource waste and user experience reduction are caused. The method comprises the following steps: acquiring real-time brightness data of a plurality of illumination sensors distributed in a public facility area; in response to the situation that the brightness data reported by the first target illumination sensor is lower than a first illumination turn-on threshold value, obtaining real-time brightness data of one or more reference illumination sensors; and when it is judged that the low-brightness data is caused by the local shielding event, an illumination starting instruction triggered based on the brightness data of the first target illumination sensor is inhibited, and an illumination control decision is made for illumination equipment associated with the first target illumination sensor based on the brightness data of the reference illumination sensor or preset astronomical clock information.

Description

Technical Field

[0001] This application relates to the field of public facility lighting control technology, and in particular to a public facility lighting control method and system. Background Technology

[0002] In modern urban environments, public lighting systems are a crucial component of urban infrastructure, and their operational efficiency and level of intelligence directly impact urban energy consumption and residents' quality of life. Currently, public lighting control methods generally rely on on-site light sensors to detect ambient brightness and trigger the activation or adjustment of lighting equipment based on preset lighting thresholds. However, rapid urban development, particularly the continuous emergence of new high-rise buildings, presents new challenges to these traditional control methods. These new buildings cast localized and constantly shifting shadows on some light sensors during specific times, such as before sunrise and after sunset, causing discrepancies between the brightness data reported by the sensors and the actual ambient brightness. Summary of the Invention

[0003] This application provides a method and system for controlling public facility lighting, aiming to solve the technical problem that existing public facility lighting control methods cannot effectively identify false low light signals caused by local environmental shading, thereby causing resource waste and a decline in user experience.

[0004] In a first aspect, to address the aforementioned technical problems, the present invention provides a public facility lighting control method, comprising: acquiring real-time brightness data from multiple light sensors distributed within a public facility area; in response to a brightness data reported by a first target light sensor being lower than a first lighting activation threshold, acquiring real-time brightness data from one or more reference light sensors; wherein the reference light sensors are light sensors within a preset neighborhood of the first target light sensor, and the first target light sensor is any one of the multiple light sensors; determining a first illumination spatial difference measure based on the brightness data of the first target light sensor and the brightness data of the reference light sensors; determining whether the low brightness data of the first target light sensor is caused by a local occlusion event based on a comparison result of the first illumination spatial difference measure and a first preset difference threshold, and a comparison result of the brightness data of the reference light sensors and the first lighting activation threshold; and when it is determined that the low brightness data is caused by a local occlusion event, suppressing a lighting activation command triggered based on the brightness data of the first target light sensor, and making a lighting control decision for the lighting equipment associated with the first target light sensor based on the brightness data of the reference light sensors or preset astronomical clock information.

[0005] Secondly, this application provides a public facility lighting control system, the system comprising: a first acquisition unit, configured to acquire real-time brightness data from multiple light sensors distributed within a public facility area; a second acquisition unit, configured to acquire real-time brightness data from one or more reference light sensors in response to a brightness data reported by a first target light sensor being lower than a first lighting activation threshold; wherein the reference light sensors are light sensors within a preset neighborhood of the first target light sensor, and the first target light sensor is any one of the multiple light sensors; and a determination unit, configured to determine the brightness data based on the brightness data of the first target light sensor and the brightness data of the reference light sensors. The system comprises: a brightness data set to determine a first illumination spatial difference measure; a judgment unit, configured to determine whether the low brightness data of the first target illumination sensor is caused by a partial occlusion event based on a comparison between the first illumination spatial difference measure and a first preset difference threshold, and a comparison between the brightness data of the reference illumination sensor and a first illumination on-threshold; and a control unit, configured to, when it is determined that the low brightness data is caused by a partial occlusion event, suppress the illumination on-threshold command triggered based on the low brightness data of the first target illumination sensor, and make illumination control decisions for the lighting equipment associated with the first target illumination sensor based on the brightness data of the reference illumination sensor or preset astronomical clock information.

[0006] This application has at least the following beneficial effects: The public facility lighting control method disclosed in this application acquires real-time brightness data from multiple light sensors within a public facility area, and in response to a first target light sensor reporting brightness data lower than a first lighting on threshold, further acquires real-time brightness data from a reference light sensor within its preset neighborhood. Based on this, a first illumination spatial difference measure is determined according to the brightness data of the first target light sensor and the reference light sensor. Combining the comparison result of this difference measure with a preset difference threshold, and the comparison result of the brightness data of the reference light sensor with the first lighting on threshold, it is determined whether the low brightness data of the first target light sensor is caused by a local occlusion event. When it is determined that it is caused by a local occlusion event, the lighting on command triggered based on the brightness data of the first target light sensor is suppressed, and lighting control decisions are made for the lighting equipment associated with the first target light sensor based on the brightness data of the reference light sensor or preset astronomical clock information.

[0007] This method effectively solves the problem in existing technologies where local obstructions such as high-rise buildings in cities cause "localized false nights" to light sensors, leading to misjudgments and premature activation of lighting by the lighting system, resulting in energy waste and light pollution. By introducing a spatial difference measurement of illumination and referencing light sensor data, this application can accurately distinguish between genuine natural light reduction and false low-brightness signals caused by local obstruction, avoiding ineffective maintenance and resource waste due to sensor reading deviations in traditional methods. This technical solution enables refined and intelligent control of public facility lighting, significantly reducing unnecessary energy consumption, improving the operational efficiency and management level of lighting systems, and enhancing residents' visual comfort, effectively quelling public complaints about light pollution and resource waste. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating a public facility lighting control method provided in this application. Detailed Implementation

[0009] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0010] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0011] Traditional public facility lighting control methods generally rely on on-site light sensors to detect ambient brightness and trigger lighting equipment to start or adjust brightness based on preset lighting thresholds. However, rapid urban development, especially the continuous emergence of new high-rise buildings, presents new challenges to these traditional control methods. These new buildings cast localized and constantly shifting shadows on some light sensors during specific times, such as before sunrise and after sunset, causing the brightness data reported by the sensors to differ from the actual ambient brightness. This phenomenon leads to the lighting system starting prematurely at unnecessary times, resulting in energy waste and light pollution. Furthermore, traditional maintenance methods cannot fundamentally solve the problem, leaving the system with insufficient adaptability to environmental changes.

[0012] In view of the above problems, this application provides a public facility lighting control method. By introducing distortion pre-compensation processing, it effectively addresses the image distortion problem caused by oil film on the lens surface, significantly improving the accuracy and reliability of image recognition. Simultaneously, by combining mechanical state information for multimodal fusion decision-making, the robustness of the system under complex operating conditions is further enhanced, thereby reducing manual intervention and improving automated production efficiency.

[0013] The public facility lighting control method and system provided in this application will be described and explained in detail below through specific embodiments.

[0014] Reference Figure 1 This application provides a method for controlling public facility lighting, which may include the following steps: S1. Obtain real-time brightness data from multiple light sensors distributed within the public facility area.

[0015] Among them, light sensors are devices that can sense the intensity of ambient light and convert it into electrical signals for output. They are typically used to monitor the real-time brightness in public facility areas.

[0016] Specifically, this method first requires acquiring real-time brightness data from multiple light sensors distributed throughout the public facility area. These light sensors can be deployed at various locations within the public facility area, such as streetlights, building facades, or dedicated monitoring points. Each sensor periodically measures the ambient brightness at its location and transmits this real-time brightness data to a central control system. For example, the sensors can send the data to a gateway via wireless communication modules (such as LoRa, NB-IoT, or Wi-Fi), which then aggregates the data and uploads it to a cloud platform or local server for processing.

[0017] S2. In response to the brightness data reported by the first target illumination sensor being lower than the first illumination activation threshold, acquire real-time brightness data from one or more reference illumination sensors.

[0018] The reference illumination sensor refers to other illumination sensors located within a preset neighborhood of the first target illumination sensor. Their brightness data is compared with the data from the first target illumination sensor to assess the uniformity of the local illumination environment. The first target illumination sensor can be any one of multiple illumination sensors. For example, the first target illumination sensor could be the sensor selected as the current object of analysis among multiple illumination sensors.

[0019] As one possible implementation, when the central control system responds to a brightness data reported by a first target illumination sensor being lower than a first lighting activation threshold, the system further acquires real-time brightness data from one or more reference illumination sensors. The first target illumination sensor can be any sensor within the public facility area that reports low brightness. Reference illumination sensors refer to other illumination sensors within a preset neighborhood of the first target illumination sensor. For example, the preset neighborhood could be a circular area with a radius of 50 or 100 meters centered on the first target illumination sensor, or a logical area containing several adjacent sensors. The system can determine these reference illumination sensors by querying a Geographic Information System (GIS) or a preset sensor topology.

[0020] S3. Determine the first illumination spatial difference measure based on the brightness data of the first target illumination sensor and the brightness data of the reference illumination sensor.

[0021] In some possible implementations, this metric is used to quantify the degree of difference between the first target illumination sensor and the ambient illumination. For example, it can be a simple calculation of the difference between the brightness data of the first target illumination sensor and the average brightness data of the reference illumination sensor, or the difference between the maximum or minimum value of the brightness data of the first target illumination sensor and the brightness data of the reference illumination sensor.

[0022] S4. Based on the comparison result of the first illumination space difference measurement and the first preset difference threshold, and the comparison result of the brightness data of the reference illumination sensor and the first illumination turn-on threshold, determine whether the low brightness data of the first target illumination sensor is caused by a local occlusion event.

[0023] The first illumination activation threshold refers to a preset brightness value. When the brightness reported by the light sensor is lower than this threshold, the lighting equipment is usually triggered to turn on. A partial occlusion event refers to the phenomenon where tall buildings, trees, or other obstacles cause partial shadows to the light sensor at a specific time and location, resulting in the sensor reporting brightness data lower than the actual ambient brightness. The first illumination spatial difference measure is an index used to quantify the brightness difference between the first target light sensor and the reference light sensor; its magnitude reflects the degree of non-uniformity in the local illumination environment. The first preset difference threshold is a preset value used to determine whether there is a significant illumination spatial difference.

[0024] For example, if the first illumination spatial difference measure is greater than the first preset difference threshold (indicating that there is a significant brightness difference between the first target illumination sensor and the surrounding environment), and the brightness data of the reference illumination sensor is higher than the first illumination on threshold (indicating that the surrounding environment is well illuminated), it can be determined that the low brightness data of the first target illumination sensor is caused by a local occlusion event.

[0025] S5. When it is determined that the low brightness data is caused by a partial occlusion event, the lighting turn-on command triggered based on the brightness data of the first target light sensor is suppressed, and the lighting control decision is made for the lighting device associated with the first target light sensor based on the brightness data of the reference light sensor or the preset astronomical clock information.

[0026] Among these, astronomical clock information refers to astronomical data such as sunrise and sunset times calculated based on geographical location and date, which can be used to assist in lighting control decisions. Lighting equipment refers to various public lighting facilities associated with light sensors, such as streetlights and courtyard lights.

[0027] Specifically, the system suppresses lighting activation commands triggered by brightness data from the first target illumination sensor. This means that even if the first target illumination sensor reports low brightness, its associated lighting equipment will not be immediately activated. Simultaneously, the system makes lighting control decisions for the lighting equipment associated with the first target illumination sensor based on brightness data from a reference illumination sensor or preset astronomical clock information. For example, if the brightness data from the reference illumination sensor is still higher than the first lighting activation threshold, the lighting equipment can remain off; if the astronomical clock information indicates that it is still daytime, the lighting equipment can also remain off. This approach avoids misjudgments and unnecessary lighting activation caused by partial occlusion.

[0028] In summary, this application, by introducing a "first illumination spatial difference measurement" and a "partial occlusion event judgment" mechanism, can accurately distinguish between a genuine overall decrease in ambient illumination and a false low brightness caused by partial occlusion. Specifically, when the first target illumination sensor reports low brightness, the system does not immediately trigger illumination, but instead further acquires the brightness data of a "reference illumination sensor" within its preset neighborhood. By comparing the brightness difference between the first target illumination sensor and the reference illumination sensor (i.e., the first illumination spatial difference measurement), and combining this with whether the brightness of the reference illumination sensor is higher than a first illumination activation threshold, the system can intelligently determine whether the current low brightness is caused by partial occlusion.

[0029] For example, at dusk, the shadow of a newly built high-rise building falls on a first target illumination sensor, causing it to report a brightness data below a first illumination activation threshold. However, surrounding reference illumination sensors, which are not obscured, still report higher brightness data, and the first illumination spatial difference metric is significant. In this case, the method of this application can determine that the low brightness is caused by a local occlusion event, thereby suppressing the illumination activation command based on the first target illumination sensor. Instead, the system makes illumination control decisions based on the actual brightness data of surrounding reference illumination sensors or astronomical clock information, ensuring that the lighting equipment is only turned on when truly needed.

[0030] In some embodiments, before acquiring real-time brightness data from multiple light sensors distributed within a public facility area, the method further includes: dividing the multiple light sensors into one or more logical regions based on their geographical location information; the reference light sensor is a light sensor belonging to the same logical region as the first target light sensor and within a preset neighborhood of the first target light sensor, or a light sensor whose physical distance from the first target light sensor is within a preset distance range and within a preset neighborhood of the first target light sensor.

[0031] Specifically, before acquiring real-time brightness data from multiple light sensors distributed within a public facility area, these sensors can be preprocessed. This preprocessing step includes dividing the multiple light sensors into one or more logical regions based on their geographical location information. The division of logical regions can be based on various criteria, such as the functional zoning of public facilities (e.g., different areas of a park, different functional zones of a plaza, different sections of a road), the distribution of physical obstacles (e.g., buildings, tall trees), or administrative areas. This division ensures that light sensors within the same logical region have similar environmental characteristics and lighting patterns.

[0032] Furthermore, the selection criteria for the reference illumination sensor are refined. As a preferred implementation, the reference illumination sensor can be defined as an illumination sensor belonging to the same logical region as the first target illumination sensor and within a preset neighborhood of the first target illumination sensor. This means that when selecting the reference illumination sensor, not only its geographical proximity to the first target illumination sensor must be considered, but also that they are in the same logical environment. As another optional implementation, the reference illumination sensor can also be defined as an illumination sensor whose physical distance to the first target illumination sensor is within a preset distance range and within a preset neighborhood of the first target illumination sensor. This approach provides a supplementary or alternative option based on pure physical distance in certain scenarios, such as when the logical region division is unclear or inapplicable, but the condition of a preset neighborhood range must still be met.

[0033] This application's solution effectively addresses the issue of potentially unrepresentative reference sensor selection in basic solutions by introducing a logical region division step before data acquisition and thereby optimizing the reference illumination sensor selection mechanism. Specifically, dividing the illumination sensor into logical regions ensures that sensors within the same logical region are more likely to be affected by similar environmental factors, such as weather, sunlight angle, and obstruction from surrounding buildings. When the first target illumination sensor reports low brightness data, the system prioritizes selecting a reference illumination sensor from sensors within the same logical region and a preset neighborhood. This selection method ensures that the reference illumination sensor more accurately reflects the true illumination conditions of the local environment where the first target illumination sensor is located, thereby improving the accuracy of judging local occlusion events. For example, if the first target illumination sensor is located in a tree-lined avenue area of ​​a park, its reference illumination sensor will also be selected from the same tree-lined avenue area, rather than from an open lawn area, avoiding misjudgments caused by environmental differences.

[0034] In some embodiments, determining the first illumination spatial difference measure based on the brightness data of the first target illumination sensor and the brightness data of the reference illumination sensor may include the following steps: calculating the arithmetic mean of the brightness data of the reference illumination sensor as a reference average brightness; calculating the difference between the reference average brightness and the brightness data of the first target illumination sensor, and determining the difference as the first illumination spatial difference measure.

[0035] The reference average brightness refers to the arithmetic mean of the real-time brightness data reported by all reference lighting sensors within a preset neighborhood of the first target lighting sensor. This arithmetic mean is calculated to obtain the overall illumination level of the area surrounding the first target lighting sensor, serving as a benchmark for judging the ambient lighting conditions of the first target lighting sensor. Specifically, it can be obtained by summing the brightness data of all reference lighting sensors at a certain point in time or over a certain period of time, and then dividing by the number of reference lighting sensors.

[0036] Furthermore, the difference refers to the numerical difference between the reference average brightness and the real-time brightness data of the first target illumination sensor. This difference directly reflects the degree of deviation between the illumination intensity at the location of the first target illumination sensor and the average illumination intensity of the surrounding area. When the brightness data of the first target illumination sensor is significantly lower than the reference average brightness, this difference will show a large positive value (if defined as the reference average brightness minus the target brightness), indicating that the first target illumination sensor may be affected by local occlusion.

[0037] The proposed solution calculates the arithmetic mean of the brightness data from a reference illumination sensor as a reference average brightness and compares it with the brightness data from a first target illumination sensor, thereby quantifying the difference between the location of the first target illumination sensor and the surrounding ambient light. This method can effectively capture local illumination anomalies because local occlusion events typically cause a significant drop in the brightness data of one or a few illumination sensors, while the brightness data of the surrounding reference illumination sensors remain relatively normal. By calculating the difference as a measure of the first illumination spatial difference, an intuitive and quantitative indicator can be provided for subsequent determination of whether low brightness data is caused by a local occlusion event.

[0038] In some embodiments, the operation of determining whether the low brightness data of the first target illumination sensor is caused by a partial occlusion event can be performed in the following manner.

[0039] If the first illumination spatial difference metric is greater than the first preset difference threshold, and the brightness data of the reference illumination sensor is higher than the first illumination activation threshold, then it is determined that the low brightness data of the first target illumination sensor is caused by a partial occlusion event.

[0040] Specifically, when the brightness data reported by the first target illumination sensor is lower than the first illumination activation threshold, the system further analyzes whether the low brightness data is caused by a local occlusion event. Here, the first illumination spatial difference metric is used to quantify the brightness difference between the first target illumination sensor and one or more surrounding reference illumination sensors. The first preset difference threshold is a preset reference value used to determine whether a significant brightness difference exists. The brightness data of the reference illumination sensors reflects the overall illumination level of the environment surrounding the first target illumination sensor. The first illumination activation threshold is the brightness benchmark used to trigger the illumination activation command. The core of this judgment logic is that if the illumination difference between the first target illumination sensor and the surrounding environment is significant (i.e., the first illumination spatial difference metric is greater than the first preset difference threshold), and the overall illumination level of the surrounding environment is still high (i.e., the brightness data of the reference illumination sensor is higher than the first illumination activation threshold), then it can be reasonably inferred that the low brightness data of the first target illumination sensor is not caused by the overall darkening of the environment, but rather by local occlusion, such as being blocked by trees, buildings, or temporary obstacles. Conversely, if the difference is not significant or the surrounding environment is generally dark, it may not be a local occlusion event.

[0041] This application's solution, by introducing a comparison between a first illumination spatial difference metric and a first preset difference threshold, and combining this with a comparison between the brightness data of a reference illumination sensor and a first lighting on threshold, can effectively identify whether the low brightness data reported by the first target illumination sensor is caused by a partial occlusion event. When the brightness data of the first target illumination sensor is low due to partial occlusion, the brightness difference between it and the surrounding unoccluded reference illumination sensor will significantly increase, meaning the first illumination spatial difference metric will exceed the first preset difference threshold. Simultaneously, since it is partial occlusion, the overall illumination level of the surrounding environment (reflected by the reference illumination sensor) should still be higher than the threshold for normal lighting on. By combining these two conditions, partial occlusion events can be accurately distinguished from a decrease in overall ambient illumination, avoiding unnecessary lighting on activation due to misjudgment.

[0042] In some embodiments, the above-mentioned suppression of lighting turn-on commands triggered by low brightness data from a first target illumination sensor, and the making of lighting control decisions for the lighting device associated with the first target illumination sensor based on brightness data from a reference illumination sensor or preset astronomical clock information, specifically includes: when it is determined that the low brightness data is caused by a partial occlusion event, blocking the brightness data reported by the first target illumination sensor; adjusting the effective lighting turn-on threshold of the lighting device associated with the first target illumination sensor to a second lighting turn-on threshold, wherein the second lighting turn-on threshold is lower than the first lighting turn-on threshold; and switching the control of the lighting device associated with the first target illumination sensor to be controlled by the brightness data or astronomical clock information from the reference illumination sensor.

[0043] Specifically, shielding the brightness data reported by the first target illumination sensor means that when the system determines that the low brightness data is caused by a local shading event, the brightness data reported by the sensor will be temporarily or permanently removed from the input of the lighting control decision and will no longer be used as the basis for triggering the lighting turn-on command. The purpose is to prevent the erroneous activation of lighting equipment due to localized, non-global insufficient illumination, thereby avoiding energy waste. Adjusting the effective lighting turn-on threshold of the lighting equipment associated with the first target illumination sensor to a second lighting turn-on threshold, where the second threshold is lower than the first, can be understood as follows: during a local shading event, the lighting equipment associated with the sensor will no longer use the conventional first lighting turn-on threshold as the turn-on standard. Instead, its turn-on standard is relaxed, requiring a lower ambient brightness to trigger illumination. This further ensures that even after the local shading is removed, if the overall ambient light is still sufficient, the lighting equipment will not immediately turn on, thus avoiding unnecessary frequent switching. In practical applications, switching the control of the lighting equipment associated with the first target illumination sensor to be controlled by the brightness data of the reference illumination sensor or astronomical clock information means that the control logic of the lighting equipment no longer directly relies on the data of the shielded first target illumination sensor, but instead uses the data of its neighboring, unaffected reference illumination sensor, or preset astronomical clock information (such as sunrise and sunset times) to determine its on / off state. The purpose is to enable accurate and stable control of the lighting equipment based on more reliable global or regional illumination information, or based on time patterns, even when the data from the first target illumination sensor is distorted.

[0044] The solution presented in this application effectively addresses the detailed issues of suppression and control decisions in the basic solution through the aforementioned specific mechanisms. When the system determines that the low brightness data from the first target illumination sensor is caused by a local shading event, it first blocks the brightness data from that sensor, fundamentally cutting off the triggering path of the erroneous data to the lighting turn-on command, thus avoiding misjudgments and unnecessary lighting activation caused by local shadows. Furthermore, by adjusting the effective lighting turn-on threshold of the associated lighting equipment to a lower second lighting turn-on threshold, the lighting equipment will not immediately turn on due to slight insufficient light after the local shading is removed, thereby improving the system's energy-saving effect and response stability. Simultaneously, by switching the control of the lighting equipment to the reference illumination sensor or astronomical clock information, it ensures that even when the data from the first target illumination sensor is unavailable, the lighting equipment can still be intelligently controlled based on more representative ambient light data or reliable time information, maintaining the overall lighting strategy of the public facility area and avoiding lighting malfunctions caused by single sensor failures or local anomalies.

[0045] In some embodiments of this application, the method further includes: when it is determined that the low brightness data is not caused by a local shading event, a hierarchical analysis of the causes of the low brightness data is initiated; the hierarchical analysis includes a combination of one or more of the following analyses: assessing the uniformity of illumination over a wide area within the public facility area; querying non-illumination environmental sensor data related to the first target illumination sensor; matching the geographical location of the first target illumination sensor with a predicted shadow area calculated based on pre-stored building information and real-time astronomical data; checking the self-diagnostic health status information of the first target illumination sensor; classifying the causes of the low brightness data into one of the following based on the results of the hierarchical analysis: overall ambient light reduction, high-rise building shadow shading, local atmospheric disturbance, or sensor malfunction; and executing corresponding lighting control strategies according to different classification causes.

[0046] Specifically, when the system determines that the low brightness data reported by the first target illumination sensor is not caused by a local occlusion event, a hierarchical analysis process will be initiated to avoid misjudgment and improper control. This hierarchical analysis aims to delve into the true cause of the low brightness. The hierarchical analysis may include a combination of one or more of the following analyses: First, the uniformity of illumination over a wide area within a public facility can be assessed. This is typically achieved by analyzing brightness data from multiple illumination sensors over a large geographical area to determine whether there is a general decrease in illumination, rather than a problem with a single sensor. The aim is to distinguish between localized phenomena and overall environmental changes.

[0047] Secondly, data from non-light-related environmental sensors related to the primary target light sensor can be queried. For example, air quality concentration data, ambient humidity data, and temperature data can be obtained. This data may indicate local atmospheric disturbances such as fog, rain, or snow, which can affect light intensity. The aim is to utilize multi-source environmental information to aid in judgment.

[0048] Furthermore, the geographical location of the first target illumination sensor can be matched with predicted shadow areas calculated based on pre-stored building information and real-time astronomical data. Using pre-established building models and real-time solar position information, it is possible to predict whether a specific area will be under the shadow of tall buildings at a specific time. The aim is to identify low brightness caused by building shadows.

[0049] In addition, the self-diagnostic health status information of the primary target illumination sensor can be checked. Modern sensors typically have self-diagnostic capabilities, which can report their operating status, calibration status, or the presence of internal faults. The purpose is to rule out the possibility of sensor malfunction.

[0050] By combining one or more of the above analyses, the system can classify the causes of low brightness data into one of the following categories based on the hierarchical analysis results: overall decrease in ambient light, shadow shading from tall buildings, local atmospheric disturbances, or sensor malfunction. This classification forms the basis for subsequent execution of precise lighting control strategies. Ultimately, the system will execute corresponding lighting control strategies based on different classification causes to ensure that the lighting system's response matches the actual environmental requirements.

[0051] This application's solution effectively addresses the limitation of the aforementioned methods in determining that low brightness data is not caused by local occlusion events by introducing a hierarchical analysis mechanism. Specifically, when the system initially determines that low brightness is not caused by local occlusion, it no longer simply ignores or processes it according to conventional logic, but instead initiates multi-dimensional hierarchical analysis. For example, by assessing the uniformity of illumination over a wide area, it can distinguish between insufficient local illumination and a decrease in overall ambient illumination; by querying non-illumination-related environmental sensor data, it can identify the impact of local atmospheric disturbances such as fog, haze, rain, and snow on illumination; by matching and predicting shadow areas, it can accurately determine whether it is occlusion caused by tall building shadows; and by checking the sensor's self-diagnostic health status information, it can rule out sensor malfunctions. These analytical steps work together, enabling the system to cross-validate from multiple perspectives, thereby more accurately identifying the true cause of low brightness data. Once the cause is accurately classified, the system can execute customized lighting control strategies based on that classification, avoiding a "one-size-fits-all" approach and ensuring the accuracy and rationality of lighting response.

[0052] In some embodiments, this application further proposes the above-mentioned execution of corresponding lighting control strategies based on different classification causes, including: if the classification cause is a decrease in overall ambient light, then a lighting turn-on command is generated; if the classification cause is shading by tall buildings, then an operation is performed to suppress the lighting turn-on command triggered by low brightness data from the first target light sensor; if the classification cause is local atmospheric disturbance, then the lighting turn-on threshold of the lighting equipment associated with the first target light sensor is dynamically adjusted according to the degree of local atmospheric disturbance; if the classification cause is sensor failure, then a fault alarm is sent to the maintenance system, and the control of the lighting equipment associated with the first target light sensor is switched to the reference light sensor or astronomical clock information.

[0053] Specifically, when hierarchical analysis indicates that low brightness data is caused by a decrease in overall ambient light, it means that the light level in the entire public facility area or most areas is generally reduced, requiring a general increase in lighting. Therefore, the system will generate a lighting activation command to start or enhance the lighting equipment in the relevant areas.

[0054] When low-brightness data is classified as being caused by the shadows of tall buildings, it indicates that the area where the first target illumination sensor is located is only experiencing insufficient lighting due to the shadows cast by tall buildings during a specific time period, rather than a true lack of ambient light. In this case, directly turning on the lighting based on the low-brightness data from the first target illumination sensor would result in unnecessary energy waste. Therefore, the solution in this application performs an operation to suppress lighting turn-on commands triggered by the low-brightness data from the first target illumination sensor, avoiding misjudgment and over-illumination.

[0055] In practical applications, if the cause of low brightness data is identified as local atmospheric disturbance, such as localized haze, smoke, or short-term rainfall, these disturbances may cause light scattering or absorption, thereby reducing local brightness. Since the degree and duration of such disturbances are uncertain, the solution in this application dynamically adjusts the illumination activation threshold of the lighting device associated with the first target illumination sensor based on the degree of local atmospheric disturbance. For example, when the disturbance is minor, the activation threshold can be appropriately lowered to turn on the lighting earlier; when the disturbance is severe, the threshold can be further lowered to ensure sufficient illumination, or the normal threshold can be restored after the disturbance ends.

[0056] Furthermore, when the hierarchical analysis results indicate a sensor malfunction, it means that the first target illumination sensor itself may have inaccurate readings or be completely faulty. In this case, its reported brightness data is unreliable. To avoid lighting control based on erroneous data, the solution in this application sends a fault alarm to the maintenance system for timely repair. Simultaneously, to ensure the continuity and accuracy of lighting services, control of the lighting equipment associated with the first target illumination sensor is switched to the aforementioned reference illumination sensor or preset astronomical clock information, thereby achieving redundant control and fault tolerance.

[0057] This application's solution addresses the ambiguity of the aforementioned technical basis's statement, "execute corresponding lighting control strategies based on different classifications of causes," by providing specific and targeted lighting control strategies for each type of low brightness caused by non-local occlusion events. It is precisely because of the refined differentiation of different causes and the matching of corresponding control logic that the lighting system can respond more intelligently and accurately to changes in the actual environment.

[0058] Specifically, when a decrease in overall ambient light is detected, generating a lighting activation command is direct and necessary, ensuring the overall safety and comfort of public facility areas. When the issue is determined to be shading from high-rise buildings, suppressing the lighting activation command avoids energy waste caused by localized, temporary shading, reflecting the principle of energy conservation. For localized atmospheric disturbances, dynamically adjusting the lighting activation threshold allows the lighting response to flexibly adapt to environmental changes, avoiding both over- and under-illumination, achieving refined management. Furthermore, when a sensor malfunction occurs, sending a fault alarm and switching control ensures the system's robustness and reliability, preventing lighting outages due to a single sensor failure, and providing maintenance personnel with a basis for timely intervention.

[0059] In some embodiments, the method further includes: continuously acquiring multi-source environmental data within the public facility area, including real-time brightness data, air quality concentration data, ambient humidity data, and geographic environmental feature data from multiple light sensors distributed within the public facility area; standardizing the acquired multi-source environmental data to obtain standardized values ​​corresponding to each environmental parameter; weighting the standardized values ​​corresponding to each environmental parameter according to preset weighting coefficients to generate a comprehensive visibility safety index, which is used to quantify the degree of influence of the current environment on human visual perception clarity; comparing the visibility safety index with a preset safety threshold, and triggering or enhancing the lighting in the corresponding area when the visibility safety index is lower than the safety threshold.

[0060] Specifically, multi-source environmental data refers to various types of data that comprehensively reflect the environmental conditions within public facility areas. Among these, real-time brightness data from light sensors is used to assess the basic light level of the environment; air quality concentration data, such as the concentrations of pollutants like PM2.5, PM10, and O3, reflects atmospheric transparency, and high concentrations of pollutants typically lead to reduced visibility; environmental humidity data, such as relative or absolute humidity, is closely related to weather phenomena like fog, haze, and rain, which significantly affect light propagation and visual clarity; and geographic environmental feature data can include static or semi-static information such as topography, building density, vegetation cover, and water distribution within the area. These features may affect light reflection, scattering, and shadow formation, thus having a long-term or periodic impact on visibility. This data can be continuously acquired through various sensors deployed within public facility areas (such as light sensors, air quality sensors, and temperature and humidity sensors) and Geographic Information Systems (GIS).

[0061] Furthermore, the acquired multi-source environmental data undergoes standardization processing. The purpose is to eliminate dimensional and numerical range differences between different types of data, enabling them to be compared and weighted on a uniform scale. For example, data can be transformed into a uniform range of 0 to 1 or -1 to 1 through methods such as linear normalization and Z-score standardization, thereby obtaining standardized values ​​for each environmental parameter.

[0062] Based on this, a comprehensive visibility safety index is generated by weighting the standardized values ​​of each environmental parameter according to preset weighting coefficients. The weighting coefficients can be set according to the importance of different environmental parameters to human visual perception clarity and public safety. For example, in smoggy weather, air quality concentration and ambient humidity may be assigned higher weights. These weighting coefficients can be optimized and adjusted through expert experience, historical data analysis, or machine learning algorithms. The visibility safety index is a quantitative indicator used to intuitively represent the degree of impact of the current environment on human visual perception clarity; the lower the index value, the worse the visibility and the higher the potential safety risk.

[0063] Finally, the visibility safety index is compared with a preset safety threshold. The safety threshold is a critical value set based on safety standards, design specifications, and actual needs for public facility areas. When the visibility safety index falls below the safety threshold, it indicates that the current visibility is insufficient to guarantee public safety. In this case, the system will trigger or enhance the lighting in the corresponding area. Triggering lighting can mean turning on lighting equipment that was originally off, while enhancing lighting can mean increasing the brightness of existing lighting equipment, extending the lighting time, or expanding the lighting range to ensure that public facility areas can provide sufficient lighting to protect pedestrians and vehicles in poor visibility conditions.

[0064] This application's solution effectively overcomes the limitations of relying solely on light sensor data for lighting control by introducing multi-source environmental data and constructing a visibility safety index. Traditional solutions, while avoiding accidental activation due to localized shadows when judging partial occlusion events, have limited responsiveness to overall visibility degradation caused by large-scale environmental factors (such as fog, haze, and dust storms). These environmental factors can lead to severely impaired visual clarity even when light sensor readings are acceptable, posing potential safety hazards. This application constructs a more comprehensive environmental perception system by continuously acquiring multi-dimensional data such as light intensity, air quality, ambient humidity, and geographical features. After standardization, these multi-source data eliminate differences between various physical quantities, allowing for unified weighted calculations. Through preset weighting coefficients, the system can comprehensively evaluate the actual importance of different environmental factors on visibility, thereby generating a visibility safety index that accurately reflects the degree of impact of the current environment on human visual perception clarity. When the index falls below a preset safety threshold, it indicates that the ambient visibility has reached a level requiring additional lighting intervention. At this point, the system will proactively trigger or enhance the lighting to ensure that the lighting level in public facility areas always meets safety requirements under various complex environmental conditions. This mechanism allows lighting control to move beyond simply passively responding to changes in light intensity and instead proactively adapt to environmental changes, thereby improving the intelligence and safety of the lighting system.

[0065] In some of the embodiments described above in this application, in order to accurately assess the impact of the environment on the clarity of human visual perception, it is necessary to standardize the acquired multi-source environmental data. Specifically, the standardization of the acquired multi-source environmental data to obtain standardized values ​​for each environmental parameter can be performed in the following manner.

[0066] The above-mentioned standardization processing of the acquired multi-source environmental data yields standardized values ​​for each environmental parameter, including: dividing real-time brightness data by a preset maximum illuminance reference value to obtain normalized illuminance; dividing real-time air quality concentration data by a preset maximum safe concentration reference value to obtain normalized air quality concentration; dividing real-time ambient humidity data by a preset ambient humidity reference value to obtain normalized ambient humidity; and mapping normalized geographic safety requirement values ​​based on predefined geographic environmental feature data.

[0067] The process of dividing real-time brightness data by a preset maximum illuminance reference value to obtain normalized illuminance intensity involves calculating the ratio of the currently measured illuminance data to a pre-set reference value representing ideal or maximum illuminance conditions, thereby transforming the brightness data into a unified, dimensionless range. This maximum illuminance reference value can be empirically set based on factors such as the geographical location of the public facility area, seasonal variations, and typical midday illuminance on a sunny day, or obtained through historical data statistics. Its purpose is to eliminate dimensional differences in brightness data from different sensors or under different environments, making them comparable.

[0068] Furthermore, the normalized air quality concentration is obtained by dividing the real-time air quality concentration data by a preset maximum safe concentration reference value. This involves calculating the ratio of the real-time monitored air quality concentration data (such as the concentrations of pollutants like PM2.5, PM10, and O3) to a pre-set reference value that represents the highest safe or acceptable concentration. This maximum safe concentration reference value can be set based on national or local environmental air quality standards, health guidelines, etc., with the aim of standardizing the concentration data of different air pollutants and reflecting their degree relative to safety standards, thus facilitating subsequent comprehensive assessment.

[0069] Furthermore, dividing real-time ambient humidity data by a preset ambient humidity reference value yields normalized ambient humidity. This involves calculating the ratio between the real-time measured ambient humidity data and a pre-set humidity reference value representing normal or specific environmental conditions. This ambient humidity reference value can be set based on local climate characteristics, seasonal average humidity, or a humidity threshold that significantly affects visibility. The purpose is to standardize the humidity data, enabling it to be compared and analyzed on the same scale as other environmental parameters.

[0070] Specifically, mapping normalized geographic safety requirement values ​​based on predefined geographic environmental feature data refers to converting predefined and stored geographic environmental feature information such as the geographical location, topography, distribution of surrounding buildings, traffic flow, and historical accident rate of a public facility area into a standardized value reflecting the area's lighting safety requirements through preset mapping rules or models. For example, the normalized geographic safety requirement value may be set higher for busy intersections, densely populated pedestrian areas, or areas with blind spots to reflect their higher lighting requirements. The purpose is to incorporate static geographic environmental factors into a dynamic visibility assessment system, ensuring that lighting control strategies fully consider the inherent safety needs of the area.

[0071] The solution presented in this application, through the aforementioned specific standardization steps, uniformly transforms multi-source environmental data from different types of sensors and different dimensions into comparable standardized values. It is precisely because real-time brightness data, air quality concentration data, ambient humidity data, and geographical environmental feature data are normalized separately that these heterogeneous data can be fairly and effectively integrated in subsequent weighted calculations. This processing method eliminates the biases that may be introduced by differences in units, ranges, or physical meanings of the original data, ensuring that the contribution of each environmental parameter to the comprehensive visibility safety index is based on its relative importance and actual impact, rather than the magnitude of its original value. Therefore, it lays a solid foundation for the accurate calculation of the subsequent visibility safety index, thereby improving the decision-making accuracy and reliability of the entire lighting control system.

[0072] This application also discloses a public facility lighting control system, comprising: a first acquisition unit for acquiring real-time brightness data from multiple light sensors distributed within a public facility area; a second acquisition unit for acquiring real-time brightness data from one or more reference light sensors in response to a brightness data reported by a first target light sensor being lower than a first lighting activation threshold; the reference light sensors being light sensors within a preset neighborhood of the first target light sensor, and the first target light sensor being any one of the multiple light sensors; and a determination unit for determining the brightness data based on the brightness data from the first target light sensor and the reference light sensors. The system uses the brightness data to determine a first illumination spatial difference measure; a judgment unit is used to determine whether the low brightness data of the first target illumination sensor is caused by a partial occlusion event based on the comparison result of the first illumination spatial difference measure and a first preset difference threshold, and the comparison result of the brightness data of the reference illumination sensor and a first illumination on-threshold; and a control unit is used to suppress the illumination on-threshold command triggered based on the low brightness data of the first target illumination sensor when it is determined that the low brightness data is caused by a partial occlusion event, and to make illumination control decisions for the lighting equipment associated with the first target illumination sensor based on the brightness data of the reference illumination sensor or preset astronomical clock information.

[0073] The core of the public facility lighting control system proposed in this application lies in the intelligent analysis of illumination data through the collaborative work of various functional units, accurately identifying and responding to local shading events, thereby optimizing the lighting control strategy.

[0074] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A public facility lighting control method characterized by, The method comprises: acquiring real-time brightness data of a plurality of light sensors distributed in a public facility area; in response to the brightness data reported by a first target light sensor being lower than a first lighting-on threshold, acquiring real-time brightness data of one or more reference light sensors; the reference light sensor is a light sensor within a preset neighborhood range of the first target light sensor, and the first target light sensor is any one of the plurality of light sensors; determining a first light space difference metric according to the brightness data of the first target light sensor and the brightness data of the reference light sensor; judging whether the low brightness data of the first target light sensor is caused by a local shading event according to a comparison result of the first light space difference metric and a first preset difference threshold, and a comparison result of the brightness data of the reference light sensor and the first lighting-on threshold; when it is judged that the low brightness data is caused by a local shading event, then suppressing a lighting-on instruction triggered based on the brightness data of the first target light sensor, and making lighting control decisions for a lighting device associated with the first target light sensor based on the brightness data of the reference light sensor or preset astronomical clock information.

2. The method of claim 1, wherein, Before acquiring the real-time brightness data of the plurality of light sensors distributed in the public facility area, the method further comprises: dividing the plurality of light sensors into one or more logical areas according to their geographical location information; the reference light sensor is a light sensor belonging to the same logical area as the first target light sensor and within a preset neighborhood range of the first target light sensor, or a light sensor within a preset distance range from the first target light sensor and within a preset neighborhood range of the first target light sensor.

3. The method of claim 1, wherein, The determination of the first light space difference metric according to the brightness data of the first target light sensor and the brightness data of the reference light sensor comprises: calculating an arithmetic mean of the brightness data of the reference light sensor as a reference average brightness; calculating a difference between the reference average brightness and the brightness data of the first target light sensor, and determining the difference as the first light space difference metric.

4. The method of claim 1, wherein, The judgment of whether the low brightness data of the first target light sensor is caused by a local shading event comprises: if the first light space difference metric is greater than the first preset difference threshold, and the brightness data of the reference light sensor is higher than the first lighting-on threshold, it is judged that the low brightness data of the first target light sensor is caused by a local shading event.

5. The method of claim 1, wherein, The suppression of the lighting-on instruction triggered based on the brightness data of the first target light sensor when it is judged that the low brightness data is caused by a local shading event, and the making of lighting control decisions for the lighting device associated with the first target light sensor based on the brightness data of the reference light sensor or the preset astronomical clock information, comprises: when it is judged that the low brightness data is caused by a local shading event, shielding the brightness data reported by the first target light sensor; adjusting an effective lighting-on threshold of the lighting device associated with the first target light sensor to a second lighting-on threshold, the second lighting-on threshold being lower than the first lighting-on threshold; and switching control of the lighting device associated with the first target light sensor to be controlled by the reference light sensor or the astronomical clock information.

6. The method of claim 1, wherein, The method further comprises: when determining that the low luminance data is not caused by a local shading event, initiating hierarchical analysis of the cause of the low luminance data; the hierarchical analysis comprises one or a combination of the following analyses: evaluating the uniformity of the light in a wide area within the public facility area; querying non-light environmental sensor data related to the first target light sensor; matching the geographic location of the first target light sensor with a predicted shadow area calculated based on pre-stored building information and real-time astronomical data; checking the self-diagnosis health status information of the first target light sensor; According to the result of the hierarchical analysis, the cause of the low luminance data is classified as one of overall environmental light decrease, high-rise building shadow shading, local atmospheric disturbance, or sensor failure; According to different classification causes, corresponding lighting control strategies are executed.

7. The method of claim 6, wherein, According to different classification causes, corresponding lighting control strategies are executed, including: If the classification cause is the overall environmental light decrease, a lighting-on instruction is generated; If the classification cause is the high-rise building shadow shading, an operation of suppressing the lighting-on instruction triggered based on the low luminance data of the first target light sensor is performed; If the classification cause is the local atmospheric disturbance, the lighting-on threshold of the lighting device associated with the first target light sensor is dynamically adjusted according to the degree of the local atmospheric disturbance; If the classification cause is the sensor failure, a failure alarm is sent to a maintenance system, and the control of the lighting device associated with the first target light sensor is switched to the reference light sensor or the astronomical clock information.

8. The method of claim 1, wherein, The method further comprises: continuously acquiring multi-source environmental data in the public facility area, the multi-source environmental data including real-time luminance data of multiple light sensors distributed in the public facility area, air quality concentration data, environmental humidity data, and geographic environmental feature data; standardizing the acquired multi-source environmental data to obtain standardized values corresponding to each environmental parameter; According to a preset weight coefficient, the standardized values corresponding to each environmental parameter are weighted calculated to generate a comprehensive visibility safety index, which is used to quantify the influence degree of the current environment on human visual perception clarity; The visibility safety index is compared with a preset safety threshold, and when the visibility safety index is lower than the safety threshold, lighting of the corresponding area is triggered or enhanced.

9. The method of claim 8, wherein, The standardization processing of the acquired multi-source environmental data to obtain standardized values corresponding to each environmental parameter comprises: dividing the real-time luminance data by a preset maximum light intensity reference value to obtain a normalized light intensity; Divide the real-time air quality concentration data by a preset maximum safe concentration reference value to obtain normalized air quality concentration; Divide the real-time environmental humidity data by a preset environmental humidity reference value to obtain normalized environmental humidity; Map the normalized geographical safety requirement value according to the predefined geographical environmental feature data.

10. A public facility lighting control system characterized by comprising: Comprise: A first acquisition unit configured to acquire real-time luminance data of a plurality of light sensors distributed in a public facility area; A second acquisition unit configured to acquire real-time luminance data of one or more reference light sensors in response to luminance data reported by a first target light sensor being lower than a first lighting-on threshold; The reference light sensor is a light sensor within a preset neighborhood range of the first target light sensor, and the first target light sensor is any one of the plurality of light sensors; A determination unit configured to determine a first light spatial difference measure according to the luminance data of the first target light sensor and the luminance data of the reference light sensor; A judgment unit configured to judge whether the low luminance data of the first target light sensor is caused by a local shading event according to a comparison result of the first light spatial difference measure and a first preset difference threshold, and a comparison result of the luminance data of the reference light sensor and the first lighting-on threshold; A control unit configured to, when judging that the low luminance data is caused by a local shading event, suppress a lighting-on instruction triggered based on the luminance data of the first target light sensor, and make lighting control decisions for a lighting device associated with the first target light sensor based on the luminance data of the reference light sensor or preset astronomical clock information.

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