LED bulb operation efficiency optimization system

Abstract

The invention belongs to the technical field of LED bulb operation efficiency management, and particularly relates to an LED bulb operation efficiency optimization system, which can identify key time periods of energy efficiency optimization by integrating illumination time periods and energy consumption data in illumination control records and illumination energy consumption records, and can optimize energy efficiency based on illumination brightness data of the key time periods. And performing correlation analysis on the illumination brightness and the energy consumption so as to predict the energy consumption guidance. The method based on data driving not only can realize pertinence and rationality of illumination energy efficiency optimization, but also can find out lamp aging or power supply system problems in time, ensure stability and efficient operation of an illumination system, and can predict the energy consumption of the energy efficiency optimization time period to guide the illumination brightness to be too high. The user position is positioned in real time in the energy efficiency optimization period, the brightness of the corresponding light source is adjusted, local control over the light source in the lamp is achieved, and energy consumption can be reduced under the condition that the illumination brightness preference of the user is met to the maximum extent.

Description

Technical Field

[0001] The present invention belongs to the technical field of LED bulb operation efficiency management, and particularly relates to an LED bulb operation efficiency optimization system. Background Art

[0002] In recent years, with the wide application of LED bulbs in building lighting, their advantages such as high luminous efficiency and long lifespan compared to traditional incandescent bulbs have significantly reduced the lighting energy consumption of buildings, thereby reducing electricity bills and maintenance costs. However, although LED bulbs themselves have energy-saving characteristics, in actual applications, especially during certain specific periods (such as office hours), users have a high demand for brightness, resulting in a still relatively high actual energy consumption of LED bulbs. Therefore, in order to further improve energy efficiency, it is necessary to optimize the lighting system during these periods to ensure that while meeting user needs, energy consumption is minimized to the greatest extent.

[0003] Currently, the energy efficiency optimization measures for LED bulbs in buildings often focus too much on lighting brightness as the main cause of high energy consumption, while ignoring other potential factors. In fact, not all high-energy consumption situations are caused by a relatively high lighting brightness. For example, when the LED lighting fixture itself shows signs of aging or the power supply system is unstable, even at a relatively low brightness, the energy consumption may increase abnormally. Therefore, simply optimizing energy efficiency based on lighting brightness has obvious limitations in applicability, easily resulting in limited energy efficiency optimization effects and being difficult to meet the expected requirements; in addition, due to the failure to promptly identify fixture aging or power supply problems, it may lead to delayed discovery and handling of equipment failures, thereby increasing maintenance costs and downtime. In the long run, this will not only affect the stability of the lighting system but may also shorten the service life of the fixtures and increase the replacement frequency.

[0004] Furthermore, the current energy efficiency optimization measures for high lighting brightness mainly focus on reducing lighting brightness and adjusting lighting color temperature. However, these measures have certain limitations in actual applications. Specifically, for lighting brightness, in scenarios where high brightness is required, such as office scenarios, simply reducing lighting brightness may result in insufficient lighting, affecting the visual comfort and work efficiency of users. For lighting color temperature, different users have significant differences in their needs and preferences for color temperature. For example, some users may prefer cold-toned light, believing that it helps to improve concentration; while others may be more inclined to warm-toned light, believing that it is more comfortable and relaxing. If the color temperature is forcibly adjusted, it may go against the personal preferences of users, affecting their comfort and satisfaction. Summary of the Invention

[0005] In view of this, the present invention aims to propose an LED bulb operation efficiency optimization system, which targets energy efficiency optimization by increasing the guiding prediction of high energy consumption of LED bulbs, effectively solving the problems mentioned in the background art.

[0006] The object of the present invention can be achieved by the following technical solutions: An LED bulb operation efficiency optimization system, comprising the following modules: A historical lighting control information extraction module, which is used to retrieve lighting control records from a building lighting control center within a selected historical period and extract the lighting period and lighting brightness therefrom.

[0007] A lighting preference analysis module, which is used to group the lighting periods of each lighting control record according to the same lighting period to form a lighting control record set corresponding to each lighting period, and then analyze the brightness preference of different lighting periods based on the lighting brightness corresponding to each lighting control record in the lighting control record set.

[0008] A lighting period energy consumption analysis module, which is used to retrieve lighting energy consumption records from a building lighting control center within a selected historical period and form a lighting energy consumption record set corresponding to each lighting period, thereby analyzing the trend of unit lighting energy consumption corresponding to each lighting period.

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

[0010] An energy consumption guiding prediction module, which is used to compare the preferred lighting brightness of the energy efficiency optimization period, thereby predicting the energy consumption guiding direction.

[0011] An energy efficiency optimization implementation module, which is used to perform energy efficiency optimization according to the energy consumption guiding direction of the energy efficiency optimization period.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By integrating the lighting periods and energy consumption data in the lighting control records and lighting energy consumption records, the present invention can identify the key periods for energy efficiency optimization, conduct correlation analysis of lighting brightness and energy consumption based on the lighting brightness data of these key periods, and then predict the energy consumption guiding direction. This data-driven method can not only achieve the pertinence and rationality of lighting energy efficiency optimization, but also timely detect problems such as lamp aging or power supply system problems, ensuring the stability and efficient operation of the lighting system.

[0013] 2. When the predicted energy consumption guiding direction of the energy efficiency optimization period is too high lighting brightness, the present invention can achieve local control of the light sources in the lamp by real-time positioning the user's location and adjusting the brightness of the corresponding light source within the energy efficiency optimization period, which can reduce energy consumption while maximizing the satisfaction of the user's lighting brightness preference without changing the lighting color temperature. This method can not only significantly improve the effect of energy efficiency optimization, but also enhance the user's comfort level while maintaining a stable visual environment. Description of the Drawings

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0015] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0016] Figure 2 It is a schematic diagram of the use of lighting control records and lighting energy consumption records in the present invention.

[0017] Figure 3 It is a schematic diagram of the energy consumption-oriented prediction result in the present invention. Specific embodiments

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] See Figure 1 As shown, the present invention proposes an LED bulb operation 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-oriented prediction module, and an energy efficiency optimization implementation module. Among them, 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-oriented prediction module, and the energy consumption-oriented 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 therefrom.

[0021] It should be added that the purpose of selecting the historical period is to accurately reflect the user's lighting brightness preference by analyzing the historical lighting control records. To ensure the accuracy and efficiency of the analysis, the selection of the historical period should be appropriate, neither too short nor too long. If the selected historical period is too short, the number of historical lighting control records retrieved is limited, and it may not be possible to comprehensively capture the user's lighting needs in different scenarios and time periods, which will lead to the lack of representativeness of the analysis results and make it difficult to accurately reflect the user's actual lighting brightness preference. In addition, the lighting settings in the short term may be affected by accidental factors (such as temporary activities, special events, etc.), resulting in large data fluctuations and unable to reflect the user's long-term stable preference. If the selected historical period is too long, too many historical lighting control records will be retrieved, which may introduce a large amount of unnecessary data, increase the complexity and computational burden of the analysis, and reduce the analysis efficiency. Moreover, the user's lighting brightness preference may change over time, and the early data in a too long historical period may no longer be of reference value, affecting the timeliness and accuracy of the analysis.

[0022] In the above supplementary example, the selected historical period is limited by the current time and has a duration of 6 months.

[0023] It should be further supplemented that the above-mentioned lighting control center is a key component in the building intelligent management system, responsible for real-time monitoring and management of the operating status of all lighting devices in the building, including the on / off status of lamps, brightness adjustment, color temperature setting, etc. Through integration with a variety of intelligent sensors (such as light sensors, human sensors, etc.), the lighting control center can automatically adjust lighting settings according to the user's behavior pattern to ensure a suitable lighting environment in different time periods and scenarios. For example, when the human sensor detects that a user enters the room, the system can automatically adjust the light brightness according to the user's activity type (such as office work, entertainment, rest, etc.) to meet the corresponding lighting requirements. At the same time, the lighting control center also supports users to manually set the lamp brightness to provide flexible personalized control. Each time the lighting control center adjusts the lighting settings of the lamps, the system will automatically generate detailed lighting control records and lighting energy consumption records. The lighting control records contain the following information: Lighting period: Records the specific time of each lighting adjustment, including the start lighting time and the end lighting time, which constitute the lighting period. Lighting brightness: Records the brightness settings of each lamp; Color temperature setting: Records the color temperature settings of each lamp (such as cool white light, warm white light, etc.) to reflect the user's color temperature preference; Control method: Records the source of the lighting settings, that is, whether this adjustment is automatically executed by the system (based on sensor data and preset logic) or manually set by the user. The lighting energy consumption record records the lighting power consumption 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 will be stored in the cloud storage of the lighting control center to ensure data security and convenience. Managers can retrieve and view these records at any time for data analysis.

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

[0025] Applied to the above solution, the process of forming a lighting control record set corresponding to each lighting period by grouping the lighting periods of each lighting control record according to the same lighting period is as follows: Extract the start lighting time and the end lighting time from the lighting periods of each lighting control record, and then compare the start lighting times of each lighting control record for similarity, and extract the lighting control records with similar start lighting times as alternative lighting control records.

[0026] In the specific implementation of the above operations, comparing the start lighting times of each lighting control record for similarity can be achieved by calculating the difference in start lighting times between each lighting control record and other lighting control records, and comparing it with the critical time difference preset by the system. Exemplarily, the critical time difference is 20 minutes. The purpose of presetting the critical time difference is to provide a criterion for judging similarity. If the difference in start lighting times between a certain lighting control record and other lighting control records is less than or equal to the critical time difference, then this lighting control record and the other lighting control records are regarded as alternative lighting control records.

[0027] In the example of the above implementation, assume that the start lighting time of lighting control record a is 14:00, the start lighting time of lighting control record b is 14:05, the start lighting time of lighting control record c is 14:15, and the start lighting time of lighting control record d is 14:20. At this time, the difference in start lighting times between a and b is 5 minutes, which is less than 20 minutes. Therefore, they are marked as alternative lighting control records. The difference in start lighting times between a and c is 15 minutes, which is less than 20 minutes. Therefore, they are regarded as alternative lighting control records. The difference in start lighting times between b and c is 10 minutes, which is less than 20 minutes. Therefore, they are marked as alternative lighting control records. The difference in start lighting times between b and d is 15 minutes, which is less than 20 minutes. Therefore, they are also marked as alternative lighting control records. The difference in start lighting times between c and d is 5 minutes, which is less than 20 minutes. Therefore, they are also alternative lighting control records. It can be seen that the differences in start lighting times between a, b, c, and d are all less than the critical time difference. Then, a, b, c, and d are all regarded as alternative lighting control records.

[0028] Compare the end lighting times in the lighting periods of each alternative lighting control record for similarity. Similarly, referring to the method of comparing the start lighting times for similarity, extract the lighting control records with similar end setting times to form the lighting control record set corresponding to the lighting period.

[0029] It should be noted that in the lighting control record set that constitutes the lighting period, since there are multiple similar start lighting times and end lighting times, the start lighting time and end lighting time of this lighting period can be determined according to the following rules. Among all the lighting control records with similar start lighting times (i.e., records with a start time difference less than or equal to the preset critical time difference), select the earliest start time as the start lighting time of this lighting period. This ensures that the start time of the lighting period is the moment when the lighting is first triggered. Similarly, among all the lighting control records with similar end lighting times (i.e., records with an end time difference less than or equal to the preset critical time difference), select the latest end time as the end lighting time of this lighting period. This ensures that the end time of the lighting period is the moment when the lighting is finally turned off.

[0030] In the above-known example description, assume that the recognized start lighting times are 14:00, 14:05, 14:15, 14:20, and the end lighting times are 16:30, 16:27, 16:20, 16:15. At this time, according to the above rules, the earliest start time 14:00 is selected as the start lighting time of this lighting period, and the latest end time 16:30 is selected as the end lighting time of this lighting period. Therefore, the finally formed lighting period is from 14:00 to 16:30. In the lighting control record set for forming the lighting period, the system determines the boundary of this lighting period by selecting the earliest start lighting time and the latest end lighting time. This method ensures that the definition of the lighting period covers all similar lighting events.

[0031] It should be understood that the stay period of the user in the room has a certain regularity because the user's daily activities usually follow a fixed schedule. For example, office workers enter the office to start work every morning and leave after finishing work in the afternoon. These regular behavior patterns will cause the user to enter and leave the room frequently at the same time period every day, triggering multiple lighting control events. In addition, some tasks or activities are repeated regularly, such as learning or exercising at fixed times, etc. These repetitive tasks cause the user to generate similar lighting control records in the same time period.

[0032] Further applied to the above scheme, the process of analyzing the brightness preferences of different lighting periods is as follows: The lighting brightness of each lighting control record in the set of settings 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 operation of the above scheme, the DBSCAN clustering algorithm can be used, which can automatically discover clusters of different densities and does not require specifying the number of clusters in advance.

[0034] The within-cluster distance and silhouette coefficient corresponding to each lighting brightness classification cluster in the same lighting period are respectively detected, and thus the evaluation formula is used to calculate the quality coefficient corresponding to each lighting brightness classification cluster in the same lighting period , where represents the within-cluster distance, represents the silhouette coefficient, , respectively represent the weight factors corresponding to the within-cluster distance and the silhouette coefficient, and .

[0035] It should be noted that when obtaining the lighting brightness classification clusters through the clustering algorithm, effective clusters are first screened out by detecting the within-cluster distance and silhouette coefficient. This is to ensure the quality and effectiveness of the clustering results and avoid invalid or unreasonable clusters from affecting subsequent analysis. The within-cluster distance refers to the average distance between all data points within the same cluster, which reflects the tightness of the data points within the cluster. A smaller within-cluster distance means that the data points within the cluster are more similar to each other and have a higher internal consistency. If the within-cluster distance is too large, it indicates that the data points within the cluster have large differences and may not be a valid clustering result. Therefore, by detecting the within-cluster distance, clusters with higher internal consistency can be screened out to ensure that the lighting brightness records within each cluster have similar characteristics. The silhouette coefficient is used to evaluate the matching degree of each data point within the cluster with its own cluster and the separation degree from other clusters. A higher silhouette coefficient means that the clustering result has good internal consistency and external separation, and can effectively distinguish different lighting brightness patterns. By detecting the silhouette coefficient, clusters that are both internally compact and externally separated can be screened out to ensure the effectiveness of the clustering result.

[0036] It should be pointed out that there are corresponding detection methods for the within-cluster distance and silhouette coefficient in the prior art, and they will not be elaborated in this invention.

[0037] It should also be noted that when evaluating the quality coefficient of the cluster based on the within-cluster distance and silhouette coefficient, if the detection of the within-cluster distance has physical units, the within-cluster distance needs to be normalized to eliminate the dimension before substituting it into the evaluation formula of the quality coefficient. If the detection of the within-cluster distance has no physical units, it can be directly substituted into the evaluation formula of the quality coefficient.

[0038] Furthermore, in the example of the above operation, the weight factors corresponding to the within-cluster distance and silhouette coefficient in the evaluation formula of the quality coefficient of the classification cluster can be determined as 0.4 and 0.6 to achieve the purpose of emphasizing the silhouette coefficient. This is because the main goal of clustering is to identify the lighting behavior patterns of users in different time periods, which not only requires the lighting control records within the cluster to have similarity, but also requires the lighting patterns between different clusters to be clearly distinguishable in order to formulate personalized energy efficiency optimization.

[0039] Applied to the innovative implementation of the above scheme, if there are no effective classification clusters in a certain lighting period, it can be considered to merge adjacent clusters into one cluster and recalculate its quality coefficient for screening effective classification clusters.

[0040] Compare the quality coefficient corresponding to each lighting brightness classification cluster in the same lighting period with the quality coefficient threshold set by the system. Exemplarily, the quality coefficient threshold is 0.6, and thus the lighting brightness classification clusters that reach the quality coefficient threshold are screened out as effective lighting brightness classification clusters.

[0041] Count the number of lighting control records existing in the lighting brightness classification clusters corresponding to the same lighting period, and take the lighting brightness classification cluster with the largest number of lighting control records as the main lighting brightness classification cluster corresponding to the lighting period.

[0042] Calculate the average value of the lighting brightness corresponding to each lighting control record existing in the main lighting brightness classification cluster corresponding to each lighting period, and take the calculation result as the preferred lighting brightness corresponding to 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 in the selected historical period and form a lighting energy consumption record set corresponding to each lighting period, thereby analyzing the trend of unit lighting energy consumption corresponding to each lighting period.

[0044] It should be noted that since the lighting energy consumption records and lighting control records form a mapping, the lighting control record sets corresponding to each lighting period constituted by the lighting control records are also applicable to the lighting energy consumption records.

[0045] In the preferred implementation of the above solution, the analysis of the trend of unit lighting energy consumption corresponding to each lighting period is carried out 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 above-mentioned unit lighting energy consumption is obtained by extracting the lighting electrical energy consumption from the lighting energy consumption record and dividing it by the duration of the lighting period in the corresponding lighting energy consumption record to obtain the unit lighting energy consumption.

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

[0048] Taking the recording date as the horizontal axis and the unit lighting energy consumption as the vertical axis, construct a coordinate axis, and thereby generate a unit lighting energy consumption change curve for each lighting period for the unit lighting energy consumption of each lighting energy consumption record in each lighting period on the constructed coordinate axis.

[0049] Extract the lighting energy consumption change rate from the unit lighting energy consumption change curves of each lighting period.

[0050] It should be noted that the above-mentioned lighting energy consumption change rate is the overall change rate of the unit lighting energy consumption change curve, which reflects the change trend of the unit lighting energy consumption during the entire lighting period. If the overall change rate is positive, it indicates that the unit lighting energy consumption shows an upward trend during the corresponding lighting period. If the overall change rate is negative, it indicates that the unit lighting energy consumption shows a downward trend during the corresponding lighting period. If the overall change rate is zero, it indicates that the unit lighting energy consumption remains unchanged during the corresponding lighting period.

[0051] Take the average of the unit lighting energy consumption of each lighting energy consumption record in each lighting period, and combine it with the lighting energy consumption change rate to statistically calculate the trend unit lighting energy consumption corresponding to each lighting period. The specific statistical formula is , where represents the trend unit lighting energy consumption, represents the average value of the unit lighting energy consumption, represents the lighting energy consumption change rate.

[0052] For the use of the above lighting control records and lighting energy consumption records, please refer to Figure 2 as shown.

[0053] The above-mentioned energy efficiency optimization period identification module is used to identify the energy efficiency optimization period based on the trend unit lighting energy consumption of different lighting periods. The specific identification method is as follows: Calculate the average trend unit lighting energy consumption by taking the average of the trend unit lighting energy consumption corresponding to each lighting period.

[0054] Compare the trend unit lighting energy consumption corresponding to each lighting period with the average trend unit lighting energy consumption, and select the lighting periods with a trend unit lighting energy consumption higher than the average trend unit lighting energy consumption as the energy efficiency optimization periods.

[0055] It should be noted that the average trend unit lighting energy consumption calculated above reflects the overall energy efficiency level of all lighting periods, provides a standard for measuring the energy efficiency performance of each period, and the lighting periods with a trend unit lighting energy consumption higher than the average trend unit lighting energy consumption usually mean that the lighting system consumes more electric energy during these periods. These periods can be used as energy efficiency optimization periods because they have great energy-saving potential.

[0056] Refer to Figure 3 as shown. The above-mentioned energy consumption-oriented prediction module is used to compare the preferred lighting brightness of the energy efficiency optimization periods, and thus analyze the energy consumption orientation. The specific analysis method is as follows: Calculate the average preferred lighting brightness by taking the average of the preferred lighting brightness corresponding to each lighting period.

[0057] Compare the preferred lighting brightness corresponding to each lighting period with the average preferred lighting brightness, and select the lighting periods with a preferred lighting brightness higher than the average preferred lighting brightness as the high-brightness lighting periods.

[0058] Compare the energy efficiency optimization periods with the high-brightness lighting periods, statistically calculate the ratio of the overlapping energy efficiency optimization periods, and compare it with the effective ratio set by the system. Exemplarily, the effective ratio is 0.7. If the ratio of the overlapping energy efficiency optimization periods reaches the effective ratio, it is predicted that the energy consumption orientation is too high lighting brightness; otherwise, it is predicted that the energy consumption orientation is not too high lighting brightness.

[0059] The energy efficiency optimization implementation module is used to perform energy efficiency optimization according to the energy consumption orientation during the energy efficiency optimization period. In a specific optimization aspect, when analyzing that the energy consumption orientation is too high lighting brightness, a human body sensor is used to locate the user's position in the room during the energy efficiency optimization period.

[0060] Respectively obtain the lighting area ranges of different light sources in the room within the lamps.

[0061] It should be noted that in modern room lighting design, lamps usually contain multiple light sources (such as multiple LED lamp beads). This design choice is mainly to solve the problem of uneven light distribution of a single light source in space. Specifically: It is often difficult for a single light source to achieve uniform light distribution in a room with a large area, which easily leads to some areas being too bright while other areas being darker. By introducing multiple light sources, this situation can be effectively improved, ensuring that the light covers the entire room more evenly and avoiding the phenomenon of local overbrightness or overdarkness. Multiple light sources can illuminate objects from different angles, significantly reducing the generation of shadows. This design can provide clearer and more comfortable lighting conditions. Each light source can be independently controlled according to its installation position, so as to precisely adjust the brightness and irradiation range of each light source.

[0062] For each light source, according to its installation position, the lighting area range of each light source in the room can be obtained. Exemplarily, the light distribution map provided by the lamp manufacturer can be referred to from the data provided by the lamp manufacturer to understand the light intensity distribution and irradiation range of each sub-lamp. As another example, a three-dimensional model of the room can be created using building information modeling or lighting design software to simulate the lighting effects of each sub-lamp and determine its lighting area range.

[0063] Compare the user's real-time position in the room with the lighting area ranges of different light sources in the lamp, and identify the light source corresponding to the user's position, denoted as the required light source.

[0064] As an optimized implementation of the above solution, when determining the required light source, count the number of light sources corresponding to the user's position. 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] Construct a three-dimensional coordinate system in the room. From this, extract the ground center points from the lighting area ranges of each alternative light source in the room, and then obtain the coordinates of the ground center points and the coordinates of the user's position under the constructed three-dimensional coordinate system.

[0066] In the above, constructing a three-dimensional coordinate system can construct a three-dimensional rectangular coordinate system (x, y, z) in 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 certain fixed reference point in the room (such as a corner or the center of the room).

[0067] Obtain the lighting proximity distance of each alternative light source based on the coordinates of the ground center point within the room lighting area range and the coordinates of the user's location.

[0068] Compare the lighting proximity distances of each alternative light source, and select the alternative light source corresponding to the minimum lighting proximity distance as the required light source.

[0069] Use the lighting control center to adjust the lighting brightness of the required light source in the lamp to meet the preferred lighting brightness during the energy efficiency optimization period, and at the same time adjust the other light sources except the required light source to maintain a low lighting brightness to reduce unnecessary energy consumption.

[0070] In a specific optimization on the other hand, when the analysis of the energy consumption orientation is that the non-lighting brightness is too high, retrieve the supply voltage and supply current during the energy efficiency optimization period in the historical period for power supply fluctuation identification. The specific identification is as follows: Retrieve the supply voltage waveform and supply current waveform during the energy efficiency optimization period from the power supply end of the building, and extract the voltage peak-to-peak value and current peak-to-peak value from them. The voltage peak-to-peak value 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 current peak-to-peak value 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. Then, divide the voltage peak-to-peak value and the current peak-to-peak value by the corresponding effective voltage and effective current respectively, and then add the ratio results and divide by 2 to obtain the power supply fluctuation degree.

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

[0073] Compare the power supply fluctuation degree during the energy efficiency optimization period in the historical period with the warning power supply fluctuation degree. Exemplarily, the warning power supply fluctuation degree is 0.3. Count the ratio of the energy efficiency optimization periods with a power supply fluctuation degree higher than the warning power supply fluctuation degree, and compare it with the effective ratio set by the system. If the effective ratio is reached, it is identified that there is a power supply fluctuation during the energy efficiency optimization period.

[0074] When it is identified that there is a power supply fluctuation during the energy efficiency optimization period, perform voltage stabilization control during the energy efficiency optimization period, otherwise perform lamp maintenance.

[0075] It should be understood that when there are power supply fluctuations during the energy efficiency optimization period, it may lead to a decrease in the working efficiency of the lamp and an increase in energy consumption. For example, power supply voltage fluctuations will cause an increase in the current of the lamp, increasing power consumption. Fluctuations in the power supply current (such as harmonic interference and current distortion) may affect the stability of the lamp, resulting in additional energy losses. For example, the harmonic components in the current will increase the reactive power of the system and reduce the overall energy efficiency. Through voltage stabilization control, the fluctuations of voltage and current can be effectively reduced, ensuring that the lighting equipment operates under stable power supply conditions, thereby reducing energy consumption. When there are no power supply fluctuations during the energy efficiency optimization period, it is very likely that the increase in lamp energy consumption is caused by lamp aging or failure. Regular maintenance of the lamp can ensure its light output efficiency. The maintenance can adopt measures such as cleaning the lamp and replacing aging components to avoid an increase in energy consumption caused by lamp aging or failure.

[0076] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments 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, they should fall within the protection scope of the present invention.

Claims

1. An LED bulb operating efficiency optimization system, characterized in that , including the following modules: A historical lighting control information extraction module, which is used to retrieve lighting control records from a building lighting control center within a selected historical period and extract lighting periods and lighting brightness therefrom; A lighting preference analysis module, which is used to form lighting control record sets corresponding to each lighting period by grouping the lighting periods of each lighting control record according to the same lighting period, and then analyze the brightness preferences of different lighting periods based on the lighting brightness corresponding to each lighting control record in the lighting control record set; A lighting period energy consumption analysis module, which is used to retrieve lighting energy consumption records from a building lighting control center within a selected historical period and form lighting energy consumption record sets corresponding to each lighting period, and thereby analyze the trend of unit lighting energy consumption corresponding to each lighting period; An energy efficiency optimization period identification module, which is used to identify energy efficiency optimization periods based on the trend of unit lighting energy consumption in different lighting periods; An energy consumption oriented prediction module, which is used to compare the preferred lighting brightness of the energy efficiency optimization periods, and thereby predict the energy consumption orientation; An energy efficiency optimization implementation module, which is used to perform energy efficiency optimization according to the energy consumption orientation of the energy efficiency optimization periods.

2. The LED bulb operating efficiency optimization system according to claim 1, wherein: The process of forming lighting control record sets corresponding to each lighting period by grouping the lighting periods of each lighting control record according to the same lighting period is as follows: Extract the start lighting time and end lighting time from the lighting periods of each lighting control record, and then compare the start lighting times of each lighting control record for similarity, and extract the lighting control records with similar start lighting times as alternative lighting control records; Compare the end lighting times in the lighting periods of each alternative lighting control record for similarity, and extract the lighting control records with similar end setting times to form the lighting control record set corresponding to the lighting period.

3. The LED bulb operating efficiency optimization system according to claim 1, characterized in that: The process of analyzing the brightness preferences of 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; Respectively detect the within-cluster distance and silhouette coefficient corresponding to each illumination brightness classification cluster in the same illumination period, and thus use the evaluation formula to calculate the quality coefficient corresponding to each illumination brightness classification cluster in the same illumination period , where represents the within-cluster distance, represents the silhouette coefficient, , respectively represent the weight factors corresponding to the within-cluster distance and silhouette coefficient, and ; Compare the quality coefficient corresponding to each lighting brightness classification cluster in the same lighting period with the quality coefficient threshold set by the system, and thereby screen out the lighting brightness classification clusters that reach the quality coefficient threshold as effective lighting brightness classification clusters; Count the number of lighting control records existing in the effective lighting brightness classification clusters corresponding to the same lighting period, and use the lighting brightness classification cluster corresponding to the largest number of lighting control records as the main lighting brightness classification cluster corresponding to the lighting period; Calculate the average value of the lighting brightness corresponding to each lighting control record existing in the main lighting brightness classification clusters corresponding to each lighting period, and use the calculation result as the preferred lighting brightness corresponding to each lighting period.

4. The LED bulb operating efficiency optimization system according to claim 1, wherein: The implementation of analyzing the trend of unit lighting energy consumption corresponding to each lighting period is as follows: Extract the unit lighting energy consumption and recording date from each lighting energy consumption record corresponding to the lighting energy consumption record set; Use the recording date as the horizontal axis and the unit lighting energy consumption as the vertical axis to construct a coordinate axis, and thereby generate a unit lighting energy consumption change curve for each lighting energy consumption record of each lighting period on the constructed coordinate axis; Extract the lighting energy consumption change rate from the unit lighting energy consumption change curves of each lighting period; Take the average of the unit lighting energy consumption of each lighting energy consumption record in each lighting period, and combine it with the lighting energy consumption change rate to statistically calculate the trend unit lighting energy consumption corresponding to each lighting period.

5. An LED bulb operating efficiency optimization system as described in claim 1, characterized in that: The identification of the energy efficiency optimization period is as follows: Calculate the average of the trend unit lighting energy consumption corresponding to each lighting period to obtain the average trend unit lighting energy consumption; Compare the trend unit lighting energy consumption corresponding to each lighting period with the average trend unit lighting energy consumption, and select the lighting periods with a trend unit lighting energy consumption higher than the average trend unit lighting energy consumption as the energy efficiency optimization periods.

6. The LED bulb operation efficiency optimization system according to claim 1, characterized in that: The prediction of the energy consumption orientation is as follows: Calculate the average of the preferred lighting brightness corresponding to each lighting period to obtain the average preferred lighting brightness; Compare the preferred lighting brightness corresponding to each lighting period with the average preferred lighting brightness, and select the lighting periods with a preferred lighting brightness higher than the average preferred lighting brightness as the high-brightness lighting periods; Compare the energy efficiency optimization periods with the high-brightness lighting periods, statistically calculate the ratio of the overlapping energy efficiency optimization periods, and compare it with the effective ratio set by the system. If the ratio of the overlapping energy efficiency optimization periods reaches the effective ratio, the predicted energy consumption orientation is that the lighting brightness is too high, otherwise the predicted energy consumption orientation is that the lighting brightness is not too high.

7. An LED bulb operating efficiency optimization system according to claim 6, characterized in that: The energy efficiency optimization based on the energy consumption orientation of the energy efficiency optimization period is as follows: When the predicted energy consumption orientation is that the lighting brightness is too high, use a human body sensor to locate the user's position in the room during the energy efficiency optimization period; Respectively obtain the lighting area ranges of different light sources in the room in the lamps; Compare the user's real-time position in the room with the lighting areas of different light sources in the lamp, and identify the light source corresponding to the user's position as the required light source; Use the lighting control center to adjust the lighting brightness of the required light source in the lamp to meet the preferred lighting brightness during the energy efficiency optimization period, and at the same time adjust the other light sources except the required light source to maintain a low lighting brightness.

8. The LED bulb operating efficiency optimization system according to claim 7, characterized in that: The identification of the light source corresponding to the user's position as the required light source also includes the following process: Statistically calculate the number of light sources corresponding to the user's identified position. If there is only one light source, determine it as the required light source. If there are multiple light sources, use these light sources as alternative light sources; Construct a three-dimensional coordinate system in the room, and extract the ground center points from the lighting area ranges of each alternative light source in the room. Then, obtain the coordinates of the ground center points and the coordinates of the user's position under the constructed three-dimensional coordinate system; Based on the coordinates of the ground center points of each alternative light source in the lighting area range of the room and the coordinates of the user's position, obtain the lighting proximity distances of each alternative light source; Compare the lighting proximity distances of each alternative light source, and select the alternative light source corresponding to the minimum lighting proximity distance as the required light source.

9. The LED bulb operation efficiency optimization system according to claim 6, characterized in that: The energy efficiency optimization based on the energy consumption orientation of the energy efficiency optimization period also includes the following process: When the predicted energy consumption orientation is not that the lighting brightness is too high, retrieve the supply voltage and supply current during the energy efficiency optimization period in the historical period for power supply fluctuation identification; When it is identified that there is a power supply fluctuation during the energy efficiency optimization period, perform voltage stabilization control during the energy efficiency optimization period, otherwise perform lamp maintenance.

10. The LED bulb operation efficiency optimization system according to claim 9, characterized in that: The power supply fluctuation identification is as follows: Retrieve the power supply voltage waveform and power supply current waveform during the energy efficiency optimization period from the power supply end of the building, extract the peak-to-peak voltage and peak-to-peak current therefrom, and compare the peak-to-peak voltage and peak-to-peak current with the corresponding effective voltage and effective current respectively to calculate the power supply fluctuation degree during the energy efficiency optimization period; Compare the power supply fluctuation degree during the energy efficiency optimization period in the historical period with the warning power supply fluctuation degree, count the ratio of the energy efficiency optimization periods in which the power supply fluctuation degree is higher than the warning power supply fluctuation degree, and compare it with the effective ratio set by the system. If the effective ratio is reached, it is identified that there is a power supply fluctuation during the energy efficiency optimization period.

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