An LED bulb operating efficiency optimization system

Abstract

This invention belongs to the field of LED bulb operating efficiency management technology, specifically involving an LED bulb operating efficiency optimization system. By integrating lighting time periods and energy consumption data from lighting control records and lighting energy consumption records, it can identify key periods for energy efficiency optimization. Based on the lighting brightness data of these key periods, it performs correlation analysis between lighting brightness and energy consumption, thereby predicting energy consumption trends. This data-driven method not only enables targeted and rational lighting energy efficiency optimization but also promptly detects aging lamps or power supply system problems, ensuring the stability and efficient operation of the lighting system. Furthermore, when the predicted energy consumption trend during the energy efficiency optimization period indicates excessive lighting brightness, it achieves localized control of the light source within the lamp by locating the user's position in real time and adjusting the brightness of the corresponding light source during the optimization period. This reduces energy consumption while maximizing the satisfaction of the user's lighting brightness preferences.

Description

Technical Field

[0001] This invention belongs to the field of LED bulb operating efficiency management technology, and specifically relates to an LED bulb operating efficiency optimization system. Background Technology

[0002] In recent years, with the widespread application of LED bulbs in architectural lighting, their advantages over traditional incandescent bulbs, such as higher luminous efficacy and longer lifespan, have significantly reduced building lighting energy consumption, thereby reducing electricity bills and maintenance costs. However, despite the energy-saving characteristics of LED bulbs, in practical applications, especially during certain periods (such as office hours), users have higher brightness demands, resulting in relatively high actual energy consumption for LED bulbs. Therefore, to further improve energy efficiency, it is necessary to optimize lighting systems during these periods to ensure that energy consumption is minimized while meeting user needs.

[0003] Current energy efficiency optimization measures for LED bulbs in buildings often focus too much on brightness as the primary cause of high energy consumption, neglecting other potential factors. In reality, not all high energy consumption is caused by high brightness levels. For example, when LED luminaires themselves show signs of aging or the power supply system is unstable, even at lower brightness levels, energy consumption may increase abnormally. Therefore, energy efficiency optimization based solely on brightness has significant limitations, easily resulting in limited effectiveness and failing to meet expected needs. Furthermore, failure to promptly identify luminaire aging or power supply problems can lead to delayed detection and handling of equipment malfunctions, increasing maintenance costs and downtime. In the long run, this not only affects the stability of the lighting system but may also shorten the lifespan of luminaires, increasing replacement frequency.

[0004] Furthermore, current energy efficiency optimization measures for high lighting brightness mainly focus on reducing brightness and adjusting color temperature. However, these measures have certain limitations in practical applications. Specifically, regarding brightness, in scenarios requiring high brightness, such as office environments, simply reducing brightness may lead to insufficient lighting, affecting user visual comfort and work efficiency. As for color temperature, different users have significantly different needs and preferences. For example, some users may prefer cool-toned light, believing it helps improve concentration; while others prefer warm-toned light, finding it more comfortable and relaxing. Forcibly adjusting color temperature may go against users' personal preferences, affecting their comfort and satisfaction. Summary of the Invention

[0005] In view of this, the present invention aims to propose an LED bulb operating efficiency optimization system, which optimizes energy efficiency in a targeted manner by adding guidance prediction of high energy consumption of LED bulbs, and effectively solves the problems mentioned in the background art.

[0006] The objective of this invention can be achieved through the following technical solution: an LED bulb operating efficiency optimization system, comprising the following modules: a historical lighting control information extraction module, used to retrieve lighting control records from the building lighting control center within a selected historical period, and extract the lighting period and lighting brightness from them.

[0007] The lighting preference analysis module is used to construct lighting control record sets corresponding to each lighting control record according to the same lighting time period, and then analyze the brightness preference of different lighting time periods based on the lighting brightness corresponding to each lighting control record in the lighting control record set.

[0008] The lighting period energy consumption analysis module is used to retrieve lighting energy consumption records from the building lighting control center for selected historical periods and construct a lighting energy consumption record set corresponding to each lighting period, thereby analyzing the trend unit lighting energy consumption corresponding to each lighting period.

[0009] The energy efficiency optimization period identification module is used to identify energy efficiency optimization periods based on the trend of unit lighting energy consumption during different lighting periods.

[0010] The energy consumption orientation prediction module is used to compare the preferred lighting brightness during energy efficiency optimization periods to predict energy consumption orientation.

[0011] The energy efficiency optimization implementation module is used to optimize energy efficiency based on the energy consumption guidelines of the energy efficiency optimization period.

[0012] Compared to existing technologies, the beneficial effects of this invention are as follows: 1. By integrating lighting time periods and energy consumption data from lighting control records and lighting energy consumption records, this invention can identify key periods for energy efficiency optimization. Based on the lighting brightness data of these key periods, it performs correlation analysis between lighting brightness and energy consumption, thereby predicting energy consumption trends. This data-driven method not only enables targeted and rational optimization of lighting energy efficiency but also allows for timely detection of aging lamps or power supply system problems, ensuring the stability and efficient operation of the lighting system.

[0013] 2. When the predicted energy consumption trend during the energy efficiency optimization period indicates excessive lighting brightness, this invention achieves localized control of the light source within the luminaire by locating the user's position in real time and adjusting the brightness of the corresponding light source during the energy efficiency optimization period. This reduces energy consumption while maximizing the satisfaction of the user's lighting brightness preferences and without altering the lighting color temperature. This method not only significantly improves the effectiveness of energy efficiency optimization but also enhances user comfort while maintaining a stable visual environment. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram showing the connections of the various modules in the system of the present invention.

[0016] Figure 2 This is a schematic diagram illustrating the use of lighting control recording and lighting energy consumption recording in this invention.

[0017] Figure 3 This is a schematic diagram of the energy consumption-oriented prediction results in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 As shown, this invention proposes an LED bulb operating efficiency optimization system, including a historical lighting control information extraction module, a lighting preference analysis module, a lighting period energy consumption analysis module, an energy efficiency optimization period identification module, an energy consumption guidance prediction module, and an energy efficiency optimization implementation module. The historical lighting control information extraction module is connected to the lighting preference analysis module, the lighting period energy consumption analysis module is connected to the energy efficiency optimization period identification module, both the lighting preference analysis module and the energy efficiency optimization period identification module are connected to the energy consumption guidance prediction module, and the energy consumption guidance prediction module is connected to the energy efficiency optimization implementation module.

[0020] The historical lighting control information extraction module is used to retrieve lighting control records from the building lighting control center within a selected historical period and extract the lighting period and lighting brightness from them.

[0021] It's important to note that the purpose of selecting historical time periods is to accurately reflect users' lighting brightness preferences by analyzing historical lighting control records. To ensure accuracy and efficiency, the historical time period should be appropriate—neither too short nor too long. If the selected period is too short, the number of historical lighting control records retrieved will be limited, potentially failing to comprehensively capture users' lighting needs across different scenarios and time periods. This would result in unrepresentative analysis results, making it difficult to accurately reflect users' actual lighting brightness preferences. Furthermore, short-term lighting settings may be affected by unforeseen factors (such as temporary activities or special events), leading to significant data fluctuations and failing to reflect users' long-term stable preferences. Conversely, if the selected period is too long, retrieving too many historical lighting control records may introduce a large amount of unnecessary data, increasing the complexity and computational burden of the analysis, reducing efficiency. Moreover, users' lighting brightness preferences may change over time, and an excessively long historical period may render earlier data irrelevant, affecting the timeliness and accuracy of the analysis.

[0022] In the supplementary example above, the selected historical period must be limited to the current time and have a duration of 6 months.

[0023] Furthermore, it's worth noting that the aforementioned lighting control center is a key component of the building's intelligent management system. It is responsible for real-time monitoring and management of the operational status of all lighting equipment within the building, including the on / off status of the lights, brightness adjustment, and color temperature settings. Through integration with various intelligent sensors (such as light sensors and motion sensors), the lighting control center can automatically adjust lighting settings based on user behavior patterns, ensuring a suitable lighting environment for different time periods and scenarios. For example, when a motion sensor detects a user entering the room, the system can automatically adjust the light brightness according to the user's activity type (such as working, entertaining, or resting) to meet the corresponding lighting needs. Simultaneously, the lighting control center also supports manual adjustment of light brightness by users, providing flexible and personalized control. Each time the lighting control center adjusts the lighting settings, the system automatically generates detailed lighting control records and lighting energy consumption records. The lighting control records include the following information: Lighting Period: Recording the specific time of each lighting adjustment, including the start and end times of lighting, constituting the lighting period. Lighting brightness: Records the brightness setting for each luminaire; Color temperature setting: Records the color temperature setting for each luminaire (e.g., cool white light, warm white light, etc.) to reflect the user's color temperature preference; Control method: Records the source of the lighting setting, i.e., whether the adjustment was executed automatically by the system (based on sensor data and preset logic) or manually set by the user. Lighting energy consumption records record the electrical energy consumed during the lighting period. These lighting control records and lighting energy consumption records not only reflect the process of each lighting adjustment in detail but also provide valuable data support for subsequent lighting preference analysis and lighting energy consumption analysis. The generated lighting control records and lighting energy consumption records form a mapping and are stored in the cloud storage of the lighting control center to ensure data security and convenience. Managers can access and view these records at any time for data analysis.

[0024] The lighting preference analysis module is used to construct a lighting control record set corresponding to each lighting control record according to the same lighting time period, and then analyze the brightness preference of different lighting time periods based on the lighting brightness corresponding to each lighting control record in the lighting control record set.

[0025] To apply the above scheme, the lighting control records of each lighting control record are organized into lighting control record sets corresponding to the same lighting period, as described in the following process: the start time and end time of lighting are extracted from the lighting period of each lighting control record, and then the start time of each lighting control record is compared for similarity, and lighting control records with similar start times are extracted as candidate lighting control records.

[0026] In the specific implementation of the above operation, the similarity comparison of the starting lighting time of each lighting control record can be achieved by calculating the difference between the starting lighting time of each lighting control record and other lighting control records, and comparing it with the system's preset critical time difference. For example, the critical time difference is 20 minutes. The purpose of the preset critical time difference is to provide a standard for judging similarity. If the difference between the starting lighting time of a certain lighting control record and other lighting control records is less than or equal to the critical time difference, then that lighting control record and other lighting control records are regarded as candidate lighting control records.

[0027] In the example above, assume that the starting lighting time for lighting control record a is 14:00, for lighting control record b it is 14:05, for lighting control record c it is 14:15, and for lighting control record d it is 14:20. The difference in starting lighting times between a and b is 5 minutes, which is less than 20 minutes, therefore they are marked as candidate lighting control records. The difference in starting lighting times between a and c is 15 minutes, which is less than 20 minutes, therefore they are considered candidate lighting control records. The difference in starting lighting times between b and c is 10 minutes, which is less than 20 minutes, therefore they are marked as candidate lighting control records. The difference in starting lighting times between b and d is 15 minutes, which is less than 20 minutes, therefore they are also marked as candidate lighting control records. The difference in starting lighting times between c and d is 5 minutes, which is less than 20 minutes, therefore they are also candidate lighting control records. Therefore, the difference in starting lighting times between a, b, c, and d is less than the critical time difference, and thus a, b, c, and d are all considered candidate lighting control records.

[0028] By comparing the end times of lighting in each candidate lighting control record during the lighting period, and similarly comparing the start times, lighting control records with similar end times are extracted to form a lighting control record set corresponding to the lighting period.

[0029] It's important to understand that in the lighting control record set constituting a lighting period, since there are multiple similar start and end times, the start and end times of that lighting period can be determined according to the following rules: For all lighting control records with similar start times (i.e., records with a start time difference less than or equal to a preset critical time difference), the earliest start time is selected as the start time of that lighting period. This ensures that the start time of the lighting period is the moment when lighting is first triggered. Similarly, for all lighting control records with similar end times (i.e., records with an end time difference less than or equal to a preset critical time difference), the latest end time is selected as the end time of that lighting period. This ensures that the end time of the lighting period is the moment when the lighting is last turned off.

[0030] In the example described above, assuming the identified start times for lighting are 14:00, 14:05, 14:15, and 14:20, and the end times are 16:30, 16:27, 16:20, and 16:15, the earliest start time (14:00) is selected as the start time for this lighting period, and the latest end time (16:30) is selected as the end time. Therefore, the final lighting period is from 14:00 to 16:30. The lighting control record system defining the lighting period determines its boundaries by selecting the earliest start time and the latest end time. This method ensures that the definition of the lighting period covers all similar lighting events.

[0031] It's important to understand that users' time spent in a room tends to follow a certain pattern because their daily activities usually adhere to a fixed schedule. For example, office workers enter the office every morning to start work and leave in the afternoon. These regular behavioral patterns lead users to frequently enter and exit the room within the same time period each day, triggering multiple lighting control events. In addition, some tasks or activities are repeated regularly, such as studying or exercising at fixed times. These repetitive tasks cause users to generate similar lighting control records within the same time period.

[0032] Further applied to the above scheme, the brightness preference for different lighting periods is analyzed as follows: The lighting brightness of each lighting control record in the set corresponding to each lighting period is clustered using a clustering algorithm to obtain several lighting brightness classification clusters corresponding to each lighting period.

[0033] In the supplementary operations of the above scheme, the clustering algorithm can be DBSCAN, which can automatically discover clusters of different densities without needing to pre-specify the number of clusters.

[0034] The intra-cluster distance and silhouette coefficient of each illumination brightness category cluster during the same illumination period are detected separately, and the evaluation formula is then used. The quality coefficient corresponding to each illumination brightness cluster in the same illumination period was calculated. In the formula Indicates intra-cluster distance, Represents the profile coefficient. , These represent the weighting factors corresponding to intra-cluster distance and silhouette coefficient, respectively. .

[0035] It's important to note that when obtaining lighting brightness classification clusters using clustering algorithms, effective clusters are first selected by detecting intra-cluster distance and silhouette coefficients. This ensures the quality and validity of the clustering results, preventing invalid or unreasonable clusters from affecting subsequent analysis. Intra-cluster distance refers to the average distance between all data points within the same cluster. It reflects the compactness of the data points within the cluster; a smaller intra-cluster distance indicates that the data points within the cluster are more similar and have higher internal consistency. If the intra-cluster distance is too large, it indicates that the data points within the cluster are highly diverse, potentially indicating an ineffective clustering result. Therefore, by detecting intra-cluster distance, clusters with high internal consistency can be selected, ensuring that the lighting brightness records within each cluster have similar characteristics. The silhouette coefficient is used to evaluate the degree of matching between each data point within a cluster and its own cluster, as well as its separation from other clusters. A higher silhouette coefficient indicates that the clustering result has good internal consistency and external separation, effectively distinguishing different lighting brightness patterns. By detecting the silhouette coefficient, clusters that are both internally compact and externally separated can be selected, ensuring the validity of the clustering results.

[0036] It should be noted that there are existing detection methods for intra-cluster distance and contour coefficient, which will not be elaborated upon in this invention.

[0037] It should also be noted that when evaluating the quality coefficient of a cluster based on intra-cluster distance and contour coefficient, if the intra-cluster distance detection has physical units, the intra-cluster distance needs to be normalized to eliminate dimensions before being substituted into the quality coefficient evaluation formula. If the intra-cluster distance detection does not have physical units, it can be directly substituted into the quality coefficient evaluation formula.

[0038] In the example of the above operation, the weight factors corresponding to intra-cluster distance and silhouette coefficient in the quality coefficient evaluation formula of the classification cluster can be determined to be 0.4 and 0.6, respectively, in order to emphasize the silhouette coefficient. This is because the main goal of clustering is to identify the lighting behavior patterns of users in different time periods. This not only requires that the lighting control records within the cluster are similar, but also requires that the lighting patterns between different clusters can be clearly distinguished in order to formulate personalized energy efficiency optimization.

[0039] In innovative implementations of the above scheme, if there are no effective classification clusters for a certain lighting period, adjacent clusters can be merged into one cluster, and their quality coefficients can be recalculated for effective classification cluster screening.

[0040] The quality coefficient corresponding to each lighting brightness category cluster in the same lighting period is compared with the quality coefficient threshold set by the system. For example, the quality coefficient threshold is 0.6, thereby selecting the lighting brightness category clusters that reach the quality coefficient threshold as effective lighting brightness category clusters.

[0041] The number of lighting control records in the effective lighting brightness category cluster corresponding to the same lighting period is counted, and the lighting brightness category cluster corresponding to the largest number of lighting control records is taken as the main lighting brightness category cluster corresponding to the lighting period.

[0042] The average lighting brightness of each lighting control record in the main lighting brightness category cluster corresponding to each lighting period is calculated, and the calculation result is used as the preferred lighting brightness for each lighting period.

[0043] The lighting period energy consumption analysis module is used to retrieve lighting energy consumption records from the building lighting control center during selected historical periods and construct a lighting energy consumption record set corresponding to each lighting period, thereby analyzing the trend unit lighting energy consumption corresponding to each lighting period.

[0044] It is important to know that since lighting energy consumption records and lighting control records form a mapping, the lighting control record set corresponding to each lighting period, which is constructed through lighting control records, is also applicable to lighting energy consumption records.

[0045] In the preferred implementation of the above scheme, the trend unit lighting energy consumption corresponding to each lighting period is analyzed as follows: extract the unit lighting energy consumption and the recording date from each lighting energy consumption record corresponding to the lighting energy consumption record set.

[0046] The unit lighting energy consumption mentioned above is obtained by extracting the lighting power consumption from the lighting energy consumption record and dividing it by the duration of the lighting period in the corresponding lighting energy consumption record.

[0047] It should be added that unit lighting energy consumption is selected because the actual lighting duration corresponding to each lighting control record in the lighting control records for each lighting period is similar but not completely consistent. Selecting unit lighting energy consumption can avoid unfair comparisons caused by differences in lighting duration.

[0048] A coordinate axis is constructed with the recorded date as the horizontal axis and the unit lighting energy consumption as the vertical axis. This generates a curve showing the change in unit lighting energy consumption for each lighting energy consumption record in each lighting period.

[0049] The rate of change of lighting energy consumption is extracted from the unit lighting energy consumption change curve of each lighting period.

[0050] It is important to know that the lighting energy consumption change rate mentioned above is the overall change rate of the unit lighting energy consumption change curve. It reflects the change trend of unit lighting energy consumption throughout the entire lighting period. If the overall change rate is positive, it means that the unit lighting energy consumption shows an upward trend during the corresponding lighting period. If the overall change rate is negative, it means that the unit lighting energy consumption shows a downward trend during the corresponding lighting period. If the overall change rate is zero, it means that the unit lighting energy consumption remains unchanged during the corresponding lighting period.

[0051] The average unit lighting energy consumption of each lighting energy consumption record in each lighting period is taken, and then combined with the lighting energy consumption change rate to calculate the trend unit lighting energy consumption for each lighting period. The specific statistical formula is as follows: In the formula This indicates a trend towards unit lighting energy consumption. This represents the average energy consumption per unit of lighting. This indicates the rate of change in lighting energy consumption.

[0052] For the use of lighting control records and lighting energy consumption records mentioned above, please refer to [link / reference]. Figure 2 As shown.

[0053] The energy efficiency optimization period identification module is used to identify energy efficiency optimization periods based on the trend unit lighting energy consumption of different lighting periods. Specifically, the identification is as follows: the average trend unit lighting energy consumption corresponding to each lighting period is calculated by averaging the average trend unit lighting energy consumption.

[0054] The energy consumption per unit of lighting for each lighting period is compared with the average energy consumption per unit of lighting, and the lighting periods with energy consumption per unit of lighting that are higher than the average energy consumption per unit of lighting are selected as the energy efficiency optimization periods.

[0055] It is important to know that the average trend unit lighting energy consumption calculated above reflects the overall energy efficiency level of all lighting periods and provides a standard for measuring the energy efficiency performance of each period. Lighting periods with higher than the average trend unit lighting energy consumption usually mean that the lighting system consumes more electricity during these periods and can be regarded as energy efficiency optimization periods because they have greater energy-saving potential.

[0056] See Figure 3 As shown, the energy consumption guidance prediction module is used to compare the preferred lighting brightness during the energy efficiency optimization period, thereby analyzing the energy consumption guidance. The specific analysis is as follows: the average preferred lighting brightness is obtained by averaging the preferred lighting brightness corresponding to each lighting period.

[0057] The preferred lighting brightness for each lighting period is compared with the average preferred lighting brightness, and the lighting periods with brightness higher than the average preferred lighting brightness are selected as high-brightness lighting periods.

[0058] The system compares the energy efficiency optimization period with the high-brightness lighting period, calculates the percentage of overlapping energy efficiency optimization periods, and compares it with the effective percentage set by the system. For example, the effective percentage is 0.7. If the percentage of overlapping energy efficiency optimization periods reaches the effective percentage, the predicted energy consumption is directed towards excessive lighting brightness; otherwise, the predicted energy consumption is directed towards excessive non-lighting brightness.

[0059] The energy efficiency optimization implementation module is used to optimize energy efficiency based on the energy consumption orientation during the energy efficiency optimization period. In one specific optimization, when the energy consumption orientation is analyzed to be that the lighting brightness is too high, the human body sensor is used to locate the user's position in the room during the energy efficiency optimization period.

[0060] Obtain the illumination range of different light sources in the room's lighting fixtures.

[0061] It's important to note that in modern room lighting design, luminaires typically incorporate multiple light sources (such as multiple LED beads). This design choice primarily addresses the issue of uneven light distribution from a single light source within a space. Specifically, a single light source often struggles to achieve uniform illumination in a large room, easily leading to some areas being too bright while others are too dark. Introducing multiple light sources effectively improves this situation, ensuring more even light coverage across the entire room and avoiding localized overexposure or underexposure. Multiple light sources can illuminate objects from different angles, significantly reducing shadows, and this design provides clearer and more comfortable lighting conditions. Each light source can be independently controlled based on its installation location, allowing for precise adjustment of its brightness and illumination range.

[0062] Each light source can be used to determine its illumination area within the room based on its installation location. For example, data from the lighting manufacturer can be referenced to obtain a light distribution map, revealing the light intensity distribution and illumination range of each sub-light fixture. Alternatively, building information modeling (BIM) or lighting design software can be used to create a 3D model of the room, simulating the lighting effect of each sub-light fixture and determining its illumination area.

[0063] By comparing the user's real-time location in the room with the illumination areas of different light sources in the lamps, the light source corresponding to the user's location is identified and recorded as the required light source.

[0064] As an optimized implementation of the above scheme, when determining the required light source, the number of light sources corresponding to the user's location is statistically identified. If there is only one light source, it is determined as the required light source; if there are multiple light sources, these light sources are used as alternative light sources.

[0065] A three-dimensional coordinate system is constructed in the room, thereby extracting the center point of the ground from the lighting area of ​​each candidate light source in the room, and then obtaining the coordinates of the center point of the ground and the coordinates of the user's location in the constructed three-dimensional coordinate system.

[0066] The above-mentioned construction of a three-dimensional coordinate system can create a three-dimensional rectangular coordinate system (x, y, z) within the room, where the x and y axes define the horizontal plane (such as the floor), the z axis defines the vertical direction (such as the ceiling height), and the origin of the coordinate system is located at a fixed reference point within the room (such as a corner or center of the room).

[0067] The illumination proximity distance of each candidate light source is obtained by comparing the coordinates of the center point of the ground within the room's lighting area with the coordinates of the user's location.

[0068] Compare the illumination proximity distances of each candidate light source, and select the candidate light source with the smallest illumination proximity distance as the required light source.

[0069] The lighting control center adjusts the brightness of the required light sources within the luminaires to meet the preferred brightness during energy-optimized periods, while simultaneously adjusting other light sources to maintain low brightness to reduce unnecessary energy consumption.

[0070] In another specific optimization, when the energy consumption analysis indicates that the non-lighting brightness is too high, the power supply voltage and current during the energy efficiency optimization period in the historical period are retrieved to identify power supply fluctuations. Specifically, the identification is as follows: the power supply voltage waveform and power supply current waveform during the energy efficiency optimization period are retrieved from the power supply end of the building, and the peak-to-peak voltage and peak-to-peak current are extracted from them. The peak-to-peak voltage refers to the difference between the maximum positive value and the minimum negative value of the voltage waveform, which represents the maximum amplitude difference of the voltage waveform in one cycle. The peak-to-peak current refers to the difference between the maximum positive value and the minimum negative value of the current waveform, which represents the maximum amplitude difference of the current waveform in one cycle. The peak-to-peak voltage and peak-to-peak current are compared with the corresponding effective voltage and effective current, respectively, and then the comparison results are added together and divided by 2 to obtain the power supply fluctuation.

[0071] In the above discussion of effective voltage and effective current, effective voltage refers to the equivalent DC voltage value generated by the AC voltage waveform within one complete cycle. In the case of a sinusoidal waveform, the effective voltage is equal to its peak voltage divided by the square root of 2.

[0072] Effective current refers to the equivalent direct current value generated by an alternating current waveform within one complete cycle. Similar to effective voltage, in the case of a sinusoidal waveform, effective current is equal to its peak current divided by the square root of 2.

[0073] The power supply fluctuation during energy efficiency optimization periods in historical time periods is compared with the warning power supply fluctuation. For example, the warning power supply fluctuation is 0.3. The percentage of energy efficiency optimization periods with power supply fluctuation higher than the warning power supply fluctuation is counted and compared with the effective percentage set by the system. If the effective percentage is reached, the power supply fluctuation during the energy efficiency optimization period is identified.

[0074] When power supply fluctuations are detected during the energy efficiency optimization period, voltage stabilization control is implemented during the energy efficiency optimization period; otherwise, lighting maintenance is performed.

[0075] It's important to understand that power supply fluctuations during energy efficiency optimization periods can lead to decreased luminaire efficiency and increased energy consumption. For example, voltage fluctuations can increase the current in the luminaires, increasing energy consumption. Fluctuations in the power supply current (such as harmonic interference and current distortion) can affect the stability of the luminaires, resulting in additional energy loss. For instance, harmonic components in the current can increase the system's reactive power, reducing overall energy efficiency. Voltage regulation can effectively reduce voltage and current fluctuations, ensuring that lighting equipment operates under stable power conditions, thereby reducing energy consumption. When there are no power supply fluctuations during energy efficiency optimization periods, increased luminaire energy consumption is likely due to aging or malfunction of the luminaires. Regular maintenance of the luminaires can ensure their light output efficiency. Maintenance can include cleaning the luminaires and replacing aging parts to prevent increased energy consumption caused by aging or malfunction.

[0076] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. An LED bulb operating efficiency optimization system, characterized in that... It includes the following modules: The historical lighting control information extraction module is used to retrieve lighting control records from the building lighting control center within a selected historical period and extract the lighting period and lighting brightness from them. The lighting preference analysis module is used to construct lighting control record sets corresponding to each lighting control record according to the same lighting time period, and then analyze the brightness preference of different lighting time periods based on the lighting brightness corresponding to each lighting control record in the lighting control record set. The lighting period energy consumption analysis module is used to retrieve lighting energy consumption records from the building lighting control center for selected historical periods and construct a lighting energy consumption record set corresponding to each lighting period, thereby analyzing the trend unit lighting energy consumption corresponding to each lighting period. The analysis of the trend of unit lighting energy consumption for each lighting period is implemented as follows: extract the unit lighting energy consumption and the recording date from each lighting energy consumption record in the lighting energy consumption record set; construct a coordinate axis with the recording date as the horizontal axis and the unit lighting energy consumption as the vertical axis, thereby generating a unit lighting energy consumption change curve for each lighting period based on the unit lighting energy consumption of each lighting energy consumption record in each lighting period on the constructed coordinate axis; extract the lighting energy consumption change rate from the unit lighting energy consumption change curve for each lighting period; The average unit lighting energy consumption of each lighting energy consumption record in each lighting period is taken, and then combined with the lighting energy consumption change rate to calculate the trend unit lighting energy consumption for each lighting period. The specific statistical formula is as follows: In the formula This indicates a trend towards unit lighting energy consumption. This represents the average energy consumption per unit of lighting. Indicates the rate of change in lighting energy consumption; An energy efficiency optimization period identification module is used to identify energy efficiency optimization periods based on the trend of unit lighting energy consumption during different lighting periods; The energy consumption orientation prediction module is used to compare the preferred lighting brightness during energy efficiency optimization periods to predict energy consumption orientation. The predicted energy consumption guidance refers to the following process: the average preferred lighting brightness is calculated by averaging the preferred lighting brightness corresponding to each lighting period; Compare the preferred lighting brightness for each lighting period with the average preferred lighting brightness, and select the lighting periods with higher than the average preferred lighting brightness as high-brightness lighting periods. Compare the energy efficiency optimization period with the high brightness lighting period, calculate the percentage of overlapping energy efficiency optimization periods, and compare it with the effective percentage set by the system. If the percentage of overlapping energy efficiency optimization periods reaches the effective percentage, the predicted energy consumption direction is that the lighting brightness is too high; otherwise, the predicted energy consumption direction is that the non-lighting brightness is too high. The energy efficiency optimization implementation module is used to optimize energy efficiency based on the energy consumption guidelines of the energy efficiency optimization period. The energy efficiency optimization based on the energy consumption orientation during the energy efficiency optimization period is as follows: When the predicted energy consumption orientation is that the lighting brightness is too high, the location of the user in the room is located using a human body sensor during the energy efficiency optimization period; the lighting area range of different light sources in the room is obtained respectively; the real-time location of the user in the room is compared with the lighting area of ​​different light sources in the light fixtures, and the light source corresponding to the user's location is identified and recorded as the required light source; the lighting brightness of the required light source in the light fixtures is adjusted by the lighting control center to meet the preferred lighting brightness during the energy efficiency optimization period, while other light sources other than the required light source are adjusted to maintain low lighting brightness; When the predicted energy consumption is due to excessive non-lighting brightness, the power supply voltage and current during the energy efficiency optimization period in the historical time period are retrieved to identify power supply fluctuations; if power supply fluctuations are identified during the energy efficiency optimization period, voltage stabilization control is performed during the energy efficiency optimization period, otherwise, lamp maintenance is performed.

2. The LED bulb operating efficiency optimization system as described in claim 1, characterized in that: The process of assembling lighting control record sets corresponding to each lighting control record according to the same lighting time period is described below: The start and end times of lighting are extracted from the lighting periods of each lighting control record. Then, the start times of each lighting control record are compared for similarity, and lighting control records with similar start times are extracted as candidate lighting control records. By comparing the end times of lighting in each candidate lighting control record during the lighting period, lighting control records with similar end times are extracted to form a lighting control record set corresponding to the lighting period.

3. The LED bulb operating efficiency optimization system as described in claim 1, characterized in that: The analysis of brightness preferences during different lighting periods is as follows: Cluster the lighting brightness of each lighting control record in the lighting control record set corresponding to each lighting period to obtain several lighting brightness classification clusters corresponding to each lighting period; The intra-cluster distance and silhouette coefficient of each illumination brightness category cluster during the same illumination period are detected separately, and the evaluation formula is then used. The quality coefficient corresponding to each illumination brightness cluster in the same illumination period was calculated. In the formula Indicates intra-cluster distance, Represents the profile coefficient. , These represent the weighting factors corresponding to intra-cluster distance and silhouette coefficient, respectively. ; The quality coefficient of each lighting brightness category cluster in the same lighting period is compared with the quality coefficient threshold set by the system, thereby selecting the lighting brightness category clusters that reach the quality coefficient threshold as effective lighting brightness category clusters. The number of lighting control records in the effective lighting brightness category cluster corresponding to the same lighting period is counted, and the lighting brightness category cluster corresponding to the largest number of lighting control records is taken as the main lighting brightness category cluster corresponding to the lighting period. The average lighting brightness of each lighting control record in the main lighting brightness category cluster corresponding to each lighting period is calculated, and the calculation result is used as the preferred lighting brightness for each lighting period.

4. The LED bulb operating efficiency optimization system as described in claim 1, characterized in that: The process for identifying the energy efficiency optimization period is as follows: The average unit lighting energy consumption is calculated by averaging the trend unit lighting energy consumption corresponding to each lighting period. The energy consumption per unit of lighting for each lighting period is compared with the average energy consumption per unit of lighting, and the lighting periods with energy consumption per unit of lighting that are higher than the average energy consumption per unit of lighting are selected as the energy efficiency optimization periods.

5. The LED bulb operating efficiency optimization system as described in claim 1, characterized in that: The process of identifying the light source corresponding to the user's location, referred to as the required light source, also includes the following steps: The system counts and identifies the number of light sources corresponding to the user's location. If there is only one light source, it is identified as the required light source. If there are multiple light sources, they are selected as alternative light sources. A three-dimensional coordinate system is constructed in the room, thereby extracting the center point of the ground from the lighting area of ​​each candidate light source in the room, and then obtaining the coordinates of the center point of the ground and the coordinates of the user's location in the constructed three-dimensional coordinate system; The illumination proximity distance of each candidate light source is obtained based on the coordinates of the center point of the ground within the room's lighting area and the coordinates of the user's location. Compare the illumination proximity distances of each candidate light source, and select the candidate light source with the smallest illumination proximity distance as the required light source.

6. The LED bulb operating efficiency optimization system as described in claim 1, characterized in that: The power supply fluctuation identification process is as follows: The power supply voltage and current waveforms during the energy efficiency optimization period are retrieved from the power supply end of the building, and the peak-to-peak voltage and peak-to-peak current are extracted from them. The peak-to-peak voltage and peak-to-peak current are then compared with the corresponding effective voltage and effective current to calculate the power supply fluctuation during the energy efficiency optimization period. Compare the power supply fluctuation during energy efficiency optimization periods with the warning power supply fluctuation during historical periods, and count the percentage of energy efficiency optimization periods where the power supply fluctuation is higher than the warning power supply fluctuation. Compare this percentage with the effective percentage set by the system. If the effective percentage is reached, then power supply fluctuation is identified during the energy efficiency optimization period.

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